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Fischer, G., A. Neurauter, L. Wieser, H. U. Strohmenger, and C. N. Nowak. "Prediction of Countershock Success." Methods of Information in Medicine 48, no. 05 (2009): 486–92. http://dx.doi.org/10.3414/me0580.
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Summary Objectives: Spectral analysis of the ventricular fibrillation (VF) ECG has been used for predicting countershock success, where the Fast Fourier Transformation (FFT) is the standard spectral estimator. Autoregressive (AR) spectral estimation should compute the spectrum with less computation time. This study compares the predictive power and computational performance of features obtained by the FFT and AR methods. Methods: In an animal model of VF cardiac arrest, 41 shocks were delivered in 25 swine. For feature parameter analysis, 2.5 s signal intervals directly before the shock and directly before the hands-off interval were used, respectively. Invasive recordings of the arterial pressure were used for assessing the outcome of each shock. For a proof of concept, a micro-controller program was implemented. Results: Calculating the area under the receiver operating characteristic (ROC) curve (AUC), the results of the AR-based features called spectral pole power (SPP) and spectral pole power with dominant frequency (DF) weighing (SPPDF) yield better outcome prediction results (85 %; 89 %) than common parameters based on FFT calculation method (centroid frequency (CF), amplitude spectrum area (AMSA)) (72%; 78%) during hands-off interval. Moreover, the predictive power of the feature parameters during ongoing CPR was not invalidated by closed-chest compressions. The calculation time of the AR-based parameters was nearly 2.5 times faster than the FFT-based features. Conclusion: Summing up, AR spectral estimators are an attractive option compared to FFT due to the reduced computational speed and the better outcome prediction. This might be of benefit when implementing AR prediction features on the microprocessor of a semi-automatic defibrillator.
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Sivaprasath, P., Raja Mookka Gounder, and B. Mythili. "Prediction of Shock by Peripheral Perfusion Index." Indian Journal of Pediatrics 86, no. 10 (June 13, 2019): 903–8. http://dx.doi.org/10.1007/s12098-019-02993-6.
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Thapa, Sameer, PN Prasad, and YM Shakya. "Serum Lactate Albumin Ratio as a Predictor of Mortality in Severe Sepsis and Septic Shock at Tribhuwan University Teaching Hospital, Kathmandu." Birat Journal of Health Sciences 2, no. 2 (November 2, 2017): 191–95. http://dx.doi.org/10.3126/bjhs.v2i2.18525.
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IntroductionSevere sepsis and septic shock, is a common cause of emergency room admission and is associated with high morbidity and mortality worldwide. This study inspects the prediction of mortality in severe sepsis and septic shock with increased lactate/albumin ratio.Objective The objective of the study was to predict the serum lactate albumin ratio as an indicator of mortality in severe sepsis and septic shock.MethodologyIt was a hospital based cross sectional study done at Tribhuvan University Teaching Hospital, Kathmandu from November 2015 to October 2016. The consent was taken from patients. Acute Physiology and Chronic Health Evaluation II score, serum lactate and serum albumin levels on first day of arrival in emergency room were calculated. Patients were classified as severe sepsis and septic shock and treated according to Surviving Sepsis Campaign 2012 guideline. The patient were follow up at 28 day, The associations of 28-day outcome with Acute Physiology and Chronic Health Evaluation II score, serum lactate value, serum albumin value and serum lactate albumin ratio value were derived.ResultsOut of total 240 severe sepsis and septic shock patients it is found that increased serum lactate/albumin ratio was an independent predictor of the mortality with cut off value of 0.07. Furthermore serum lactate albumin ratio shows strong correlation with APACHE 2 score in predicting mortality in severe sepsis and septic shock.ConclusionIncreased lactate/albumin ratio predicts mortality in patients with severe sepsis and septic shock. Birat Journal of Health Sciences Vol.2/No.1/Issue 2/ Jan - April 2017, Page: 191-195
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Ismail, Javed, and Jhuma Sankar. "Peripheral Perfusion Index – Magic Wand in Prediction of Shock?" Indian Journal of Pediatrics 86, no. 10 (July 13, 2019): 879–80. http://dx.doi.org/10.1007/s12098-019-03028-w.
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Miller, Robert J. H., Danielle Southern, Stephen B. Wilton, Matthew T. James, Bryan Har, Greg Schnell, Sean van Diepen, and Andrew D. M. Grant. "Comparative Prognostic Accuracy of Risk Prediction Models for Cardiogenic Shock." Journal of Intensive Care Medicine 35, no. 12 (October 14, 2019): 1513–19. http://dx.doi.org/10.1177/0885066619878125.
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Objectives: Despite advances in medical therapy, reperfusion, and mechanical support, cardiogenic shock remains associated with excess morbidity and mortality. Accurate risk stratification may improve patient management. We compared the accuracy of established risk scores for cardiogenic shock. Methods: Patients admitted to tertiary care center cardiac care units in the province of Alberta in 2015 were assessed for cardiogenic shock. The Acute Physiology and Chronic Health Evaluation-II (APACHE-II), CardShock, intra-aortic balloon pump (IABP) Shock II, and sepsis-related organ failure assessment (SOFA) risk scores were compared. Receiver operating characteristic curves were used to assess discrimination of in-hospital mortality and compared using DeLong’s method. Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Results: The study included 3021 patients, among whom 510 (16.9%) had cardiogenic shock. Patients with cardiogenic shock had longer median hospital stays (median 11.0 vs 4.1 days, P < .001) and were more likely to die (29.0% vs 2.5%, P < .001). All risk scores were adequately calibrated for predicting hospital morality except for the APACHE-II score (Hosmer-Lemeshow P < .001). Discrimination of in-hospital mortality with the APACHE-II (area under the curve [AUC]: 0.72, 95% confidence interval [CI]: 0.66-0.76) and IABP-Shock II (AUC: 0.73, 95% CI: 0.68-0.77) scores were similar, while the CardShock (AUC: 0.76, 95% CI: 0.72-0.81) and SOFA (AUC: 0.76, 95%CI: 0.72-0.81) scores had better discrimination for predicting in-hospital mortality. Conclusions: In a real-world population of patients with cardiogenic shock, existing risk scores had modest prognostic accuracy, with no clear superior score. Further investigation is required to improve the discriminative abilities of existing models or establish novel methods.
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Liberski, Piotr S., Michał Szewczyk, and Łukasz J. Krzych. "Haemogram-Derived Indices for Screening and Prognostication in Critically Ill Septic Shock Patients: A Case-Control Study." Diagnostics 10, no. 9 (August 27, 2020): 638. http://dx.doi.org/10.3390/diagnostics10090638.
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This study aimed (1) to assess the diagnostic accuracy of neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR) and platelet count-to-mean platelet volume (PLT/MPV) ratios in predicting septic shock in patients on admission to the intensive care unit (ICU) and (2) to compare it with the role of C-reactive protein (CRP), procalcitonin (PCT) and lactate level. We also sought (3) to verify whether the indices could be useful in ICU mortality prediction and (4) to compare them with Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II) and Sequential Organ Failure Assessment (SOFA) scores. This retrospective study covered 138 patients, including 61 subjects with multi-organ failure due to septic shock (study group) and 77 sex- and age-matched controls. Septic patients had significantly higher NLR (p < 0.01) and NLR predicted septic shock occurrence (area under the ROC curve, AUROC = 0.66; 95% CI 0.58–0.74). PLR, MLR and PLT/MPV were impractical in sepsis prediction. Combination of CRP with NLR improved septic shock prediction (AUROC = 0.88; 95% CI 0.81–0.93). All indices failed to predict ICU mortality. APACHE II and SAPS II predicted mortality with AUROC = 0.68; 95% CI 0.54–0.78 and AUROC = 0.7; 95% CI 0.57–0.81, respectively. High NLR may be useful to identify patients with multi-organ failure due to septic shock but should be interpreted along with CRP or PCT. The investigated indices are not related with mortality in this specific clinical setting.
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Thiel, Steven W., Jamie M. Rosini, William Shannon, Joshua A. Doherty, Scott T. Micek, and Marin H. Kollef. "Early prediction of septic shock in hospitalized patients." Journal of Hospital Medicine 5, no. 1 (January 2010): 19–25. http://dx.doi.org/10.1002/jhm.530.
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Vedy, Eric, and Frits van der Eerden. "Prediction of shock waves over a sound-absorbing area." Noise Control Engineering Journal 53, no. 3 (2005): 81. http://dx.doi.org/10.3397/1.2839247.
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Stanojevic, Sanja, Jenna Sykes, Anne L. Stephenson, Shawn D. Aaron, and George A. Whitmore. "Development and external validation of 1- and 2-year mortality prediction models in cystic fibrosis." European Respiratory Journal 54, no. 3 (May 16, 2019): 1900224. http://dx.doi.org/10.1183/13993003.00224-2019.
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IntroductionWe aimed to develop a clinical tool for predicting 1- and 2-year risk of death for patients with cystic fibrosis (CF). The model considers patients' overall health status as well as risk of intermittent shock events in calculating the risk of death.MethodsCanadian CF Registry data from 1982 to 2015 were used to develop a predictive risk model using threshold regression. A 2-year risk of death estimated conditional probability of surviving the second year given survival for the first year. UK CF Registry data from 2007 to 2013 were used to externally validate the model.ResultsThe combined effect of CF chronic health status and CF intermittent shock risk provided a simple clinical scoring tool for assessing 1-year and 2-year risk of death for an individual CF patient. At a threshold risk of death of ≥20%, the 1-year model had a sensitivity of 74% and specificity of 96%. The area under the receiver operating curve (AUC) for the 2-year mortality model was significantly greater than the AUC for a model that predicted survival based on forced expiratory volume in 1 s <30% predicted (AUC 0.95 versus 0.68 respectively, p<0.001). The Canadian-derived model validated well with the UK data and correctly identified 79% of deaths and 95% of survivors in a single year in the UK.ConclusionsThe prediction models provide an accurate risk of death over a 1- and 2-year time horizon. The models performed equally well when validated in an independent UK CF population.
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Sharma, Shruti, Abhishek Gupta, Sunil Kumar Virmani, and Ritesh Lal. "Assessment and comparison of 3 mortality prediction models SAPS II, APACHE II and SOFA for prediction of mortality in patients of sepsis." International Journal of Advances in Medicine 4, no. 3 (May 23, 2017): 623. http://dx.doi.org/10.18203/2349-3933.ijam20171476.
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Background: Little is known about outcomes of patients admitted to the ICU with severe sepsis and septic shock, despite the seriousness of sepsis as a public health problem in developing countries. Understanding sepsis outcome studies is hampered by lack of an agreed severity of illness scoring system for sepsis patients. The objective of the present study is to assess and compare the validity of 3 mortality prediction models SAPS 2, APACHE II and SOFA for prediction of mortality in patients of sepsis.Methods: One hundred patients of Sepsis were selected after applying the inclusion and exclusion criteria. Informed consent was taken from the patients or their relatives A careful and detailed history was recorded to assess the onset and duration of clinical events and the probable risk factors for the same; a detailed general physical examination was performed. Blood sampling for CBC, RFT, LFT and arterial blood gas analysis was done. SAPS 2, APACHE II and SOFA scores were calculated on the day of admission.Results: The ROC analysis shows that the best discrimination was provided by SAPS 2 score (AUROC=0.981), followed by APACHE II (AUROC=0.978) and SOFA (AUROC=0.911).Conclusions: SAPS 2 score was superior to the APACHE II and SOFA scores for predicting survival in patients with septic shock but a combination of factors must be taken in consideration to estimate the prognosis in the ICU.
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Misra, Debdipto, Venkatesh Avula, Donna M. Wolk, Hosam A. Farag, Jiang Li, Yatin B. Mehta, Ranjeet Sandhu, et al. "Early Detection of Septic Shock Onset Using Interpretable Machine Learners." Journal of Clinical Medicine 10, no. 2 (January 15, 2021): 301. http://dx.doi.org/10.3390/jcm10020301.
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Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.
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Keshavarz, Mohammad Hossein, Hadi Motamedoshariati, Hamid Reza Pouretedal, Masoud Kavosh Tehrani, and Abolfazl Semnani. "Prediction of shock sensitivity of explosives based on small-scale gap test." Journal of Hazardous Materials 145, no. 1-2 (June 2007): 109–12. http://dx.doi.org/10.1016/j.jhazmat.2006.10.091.
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Njim, Tsi, and Bayee Swiri Tanyitiku. "Prognostic models for the clinical management of malaria and its complications: a systematic review." BMJ Open 9, no. 11 (November 2019): e030793. http://dx.doi.org/10.1136/bmjopen-2019-030793.
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ObjectiveMalaria infection could result in severe disease with high mortality. Prognostic models and scores predicting severity of infection, complications and mortality could help clinicians prioritise patients. We conducted a systematic review to assess the various models that have been produced to predict disease severity and mortality in patients infected with malaria.DesignA systematic review.Data sourcesMedline, Global health and CINAHL were searched up to 4 September 2019.Eligibility criteria for selecting studiesPublished articles on models which used at least two points (or variables) of patient data to predict disease severity; potential development of complications (including coma or cerebral malaria; shock; acidosis; severe anaemia; acute kidney injury; hypoglycaemia; respiratory failure and sepsis) and mortality in patients with malaria infection.Data extraction and synthesisTwo independent reviewers extracted the data and assessed risk of bias using the Prediction model Risk Of Bias Assessment Tool.ResultsA total of 564 articles were screened and 24 articles were retained which described 27 models/scores of interests. Two of the articles described models predicting complications of malaria (severe anaemia in children and development of sepsis); 15 articles described original models predicting mortality in severe malaria; 3 articles described models predicting mortality in different contexts but adapted and validated to predict mortality in malaria; and 4 articles described models predicting severity of the disease. For the models predicting mortality, all the models had neurological dysfunction as a predictor; in children, half of the models contained hypoglycaemia and respiratory failure as a predictor meanwhile, six out of the nine models in adults had respiratory failure as a clinical predictor. Acidosis, renal failure and shock were also common predictors of mortality. Eighteen of the articles described models that could be applicable in real-life settings and all the articles had a high risk of bias due to lack of use of consistent and up-to-date methods of internal validation.ConclusionEvidence is lacking on the generalisability of most of these models due lack of external validation. Emphasis should be placed on external validation of existing models and publication of the findings of their use in clinical settings to guide clinicians on management options depending on the priorities of their patients.PROSPERO registration numberCRD42019130673.
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EL-Nawawy, Ahmed Ahmed, Omneya Magdy Omar, and Hadir Mohamed Hassouna. "Role of Inferior Vena Cava Parameters as Predictors of Fluid Responsiveness in Pediatric Septic Shock: A Prospective Study." Journal of Child Science 11, no. 01 (January 2021): e49-e54. http://dx.doi.org/10.1055/s-0041-1724034.
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AbstractFluid resuscitation is the initial therapy for septic shock worldwide. Prediction of fluid responsiveness is essential for optimizing fluid administration. Only few pediatric studies have evaluated the role of inferior vena cava (IVC) as a reliable predictor of fluid responsiveness. The aim of this study was to evaluate the role of IVC parameters as predictors of fluid responsiveness in children (under the age of 5 years) having septic shock at different times from admission. A prospective observational study included 51 children having septic shock. It was conducted in the nine-bedded pediatric intensive care unit of a university hospital from January 1, 2018, to the August 31, 2018. Echocardiography was used to assess minimal and maximal IVC diameters and its distensibility index with simultaneous assessment of stroke volume (SV), at 1, 6, and 24 hours from admission. The decision to give fluid in these children was thereby based on the presence of at least one sign of inadequate tissue perfusion. SV was reassessed directly after administration of a fluid bolus of 10 mL/kg over 10 minutes. Fluid responsiveness was considered adequate when there was ≥ 10% increase in SV after fluid bolus. Minimal IVC diameter indexed to body surface area and its distensibility index can be predictors of fluid responsiveness at all times: 1 hour (area under curve [AUC] = 0.88; 95% confidence interval [CI] = 0.77–0.96), 6 hours (AUC = 0.86; 95% CI = 0.67–0.97), and 24 hours (AUC = 0.77; 95% CI = 0.6–0.95). IVC distensibility index can also predict fluid responsiveness at 1 hour (AUC= 0.87; 95% CI = 0.74–0.95), 6 hours (AUC = 0.86; 95% CI = 0.73–0.94), and 24 hours (AUC = 1; 95% CI = 0.77–1). The cutoff points of each parameter differed from time to time (contradicts with previous statement that says it is predictor at all times). The maximum IVC diameter could not predict fluid responsiveness at any time from admission. Minimal IVC diameter and its distensibility index were feasible noninvasive surrogates of fluid responsiveness in pediatric septic shock at different times from admission.
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Chatzis, Georgios, Birgit Markus, Styliani Syntila, Christian Waechter, Ulrich Luesebrink, Holger Ahrens, Dimitar Divchev, Bernhard Schieffer, and Konstantinos Karatolios. "Comparison of Mortality Risk Models in Patients with Postcardiac Arrest Cardiogenic Shock and Percutaneous Mechanical Circulatory Support." Journal of Interventional Cardiology 2021 (January 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/8843935.
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Background. Although scoring systems are widely used to predict outcomes in postcardiac arrest cardiogenic shock (CS) after out-of-hospital cardiac arrest (OHCA) complicating acute myocardial infarction (AMI), data concerning the accuracy of these scores to predict mortality of patients treated with Impella in this setting are lacking. Thus, we aimed to evaluate as well as to compare the prognostic accuracy of acute physiology and chronic health II (APACHE II), simplified acute physiology score II (SAPS II), sepsis-related organ failure assessment (SOFA), the intra-aortic balloon pump (IABP), CardShock, the prediction of cardiogenic shock outcome for AMI patients salvaged by VA-ECMO (ENCOURAGE), and the survival after venoarterial extracorporeal membrane oxygenation (SAVE) score in patients with OHCA refractory CS due to an AMI treated with Impella 2.5 or CP. Methods. Retrospective study of 65 consecutive Impella 2.5 and 32 CP patients treated in our cardiac arrest center from September 2015 until June 2020. Results. Overall survival to discharge was 44.3%. The expected mortality according to scores was SOFA 70%, SAPS II 90%, IABP shock 55%, CardShock 80%, APACHE II 85%, ENCOURAGE 50%, and SAVE score 70% in the 2.5 group; SOFA 70%, SAPS II 85%, IABP shock 55%, CardShock 80%, APACHE II 85%, ENCOURAGE 75%, and SAVE score 70% in the CP group. The ENCOURAGE score was the most effective predictive model of mortality outcome presenting a moderate area under the curve (AUC) of 0.79, followed by the CardShock, APACHE II, IABP, and SAPS score. These derived an AUC between 0.71 and 0.78. The SOFA and the SAVE scores failed to predict the outcome in this particular setting of refractory CS after OHCA due to an AMI. Conclusion. The available intensive care and newly developed CS scores offered only a moderate prognostic accuracy for outcomes in OHCA patients with refractory CS due to an AMI treated with Impella. A new score is needed in order to guide the therapy in these patients.
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Lauritsen, Simon Meyer, Mads Ellersgaard Kalør, Emil Lund Kongsgaard, and Bo Thiesson. "Early Sepsis Detection with Deep Learning on EHR Event Sequences." Dansk Tidsskrift for Akutmedicin 2, no. 3 (April 30, 2019): 39. http://dx.doi.org/10.7146/akut.v2i3.112949.
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Background: Sepsis is a clinical condition involving an extreme inflammatory response to an infection, and is associated with high morbidity and mortality. Without intervention, this response can progress to septic shock, organ failure and death. Every hour that treatment is delayed mortality increases. Early identification of sepsis is therefore important for a positive outcome.
Methods: We constructed predictive models for sepsis detection and performed a register-based cohort study on patients from four Danish municipalities. We used event-sequences of raw electronic health record (EHR) data from 2013 to 2017, where each event consists of three elements: a timestamp, an event category (e.g. medication code), and a value. In total, we consider 25.622 positive (SIRS criteria) sequences and 25.622 negative sequences with a total of 112 million events distributed across 64 different hospital units. The number of potential predictor variables in raw EHR data easily exceeds 10.000 and can be challenging for predictive modeling due to this large volume of sparse, heterogeneous events. Traditional approaches have dealt with this complexity by curating a limited number of variables of importance; a labor-intensive process that may discard a vast majority of information. In contrast, we consider a deep learning system constructed as a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network. Importantly, our system learns representations of the key factors and interactions from the raw event sequence data itself.
Results: Our model predicts sepsis with an AUROC score of 0.8678, at 11 hours before actual treatment was started, outperforming all currently deployed approaches. At other prediction times, the model yields following AUROC scores. 15 min: 0.9058, 3 hours: 0.8803, 24 hours: 0.8073.
Conclusion: We have presented a novel approach for early detection of sepsis that has more true positives and fewer false negatives than existing alarm systems without introducing domain knowledge into the model. Importantly, the model does not require changes in the daily workflow of healthcare professionals at hospitals, as the model is based on data that is routinely captured in the EHR. This also enables real-time prediction, as healthcare professionals enters the raw events in the EHR.
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Umoh, Uduak, Imo Eyoh, Vadivel S. Murugesan, Abdultaofeek Abayomi, and Samuel Udoh. "Hybrid intelligent telemedical monitoring and predictive systems." International Journal of Hybrid Intelligent Systems 17, no. 1-2 (July 13, 2021): 43–57. http://dx.doi.org/10.3233/his-210005.
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Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work develops a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique is employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.
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Chen, Chien-Chih, Ing-Kit Lee, Jien-Wei Liu, Shi-Yu Huang, and Lin Wang. "Utility of C-Reactive Protein Levels for Early Prediction of Dengue Severity in Adults." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/936062.
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Dengue has broad clinical presentation with unpredictable clinical evolution and outcome. We aimed to evaluate the utility of C-reactive protein (CRP) levels for distinguishing between mild and severe cases in the early phase of the dengue illness. We retrospectively evaluated adults with dengue from 2006 to 2014, according to 1997 and 2009 World Health Organization (WHO) criteria for severity. Of 191 included patients, 32.9% had nonshock dengue hemorrhagic fever (DHF), 3.1% dengue shock syndrome (DSS), and 7.9% severe dengue. The risk of DHF/DSS and severe dengue is significantly related to the increasing levels of CRP. Of 191 patients, 97 had CRP levels measured during the febrile (days 1–3); 85 during the critical (days 4–6); and 9 during the convalescent (days 7–10) illness phases. During the febrile phase, there was significant higher CRP level for DSS versus DF/nonshock DHF and severe dengue versus nonsevere dengue, with CRP cutoff level 30.1 mg/L (area under the receiver operating characteristic curve (AUC), 0.938; 100% sensitivity, 76.3% specificity) and 24.2 mg/L (AUC, 0.717; 70% sensitivity, 71.3% specificity), respectively. Our study highlights the utility of the CRP levels in early prediction of DSS and severe dengue in adult patients.
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Wulff, Antje, Sara Montag, Michael Marschollek, and Thomas Jack. "Clinical Decision-Support Systems for Detection of Systemic Inflammatory Response Syndrome, Sepsis, and Septic Shock in Critically Ill Patients: A Systematic Review." Methods of Information in Medicine 58, S 02 (September 9, 2019): e43-e57. http://dx.doi.org/10.1055/s-0039-1695717.
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Abstract Background The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research. Objectives To provide an overview about current approaches for the design of clinical decision-support systems (CDSS) in the context of SIRS, sepsis, and septic shock, and to categorize and compare existing approaches. Methods A systematic literature review was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Searches for eligible articles were conducted on five electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Embase, Scopus, and ScienceDirect. Initial results were screened independently by two reviewers based on clearly defined eligibility criteria. A backward as well as an updated search enriched the initial results. Data were extracted from included articles and presented in a standardized way. Articles were classified into predefined categories according to characteristics extracted previously. The classification was performed according to the following categories: clinical setting including patient population and mono- or multicentric study, support type of the system such as prediction or detection, systems characteristics such as knowledge- or data-driven algorithms used, evaluation of methodology, and results including ground truth definition, sensitivity, and specificity. All results were assessed qualitatively by two reviewers. Results The search resulted in 2,373 articles out of which 55 results were identified as eligible. Over 80% of the articles describe monocentric studies. More than 50% include adult patients, and only four articles explicitly report the inclusion of pediatric patients. Patient recruitment often is very selective, which can be observed from highly varying inclusion and exclusion criteria. The task of disease detection is covered in 62% of the articles; prediction of upcoming conditions in 33%. Sepsis is covered in 67% of the articles, SIRS as sole entity in only 4%, whereas 27% focus on severe sepsis and/or septic shock. The most common combinations of categories “algorithm used” and “support type” are knowledge-based detection of sepsis and data-driven prediction of sepsis. In evaluations, manual chart review (38%) and diagnosis coding (29%) represent the most frequently used ground truth definitions; most studies present a sample size between 10,001 and 100,000 cases (31%) and performances highly differ with only five articles presenting sensitivities and specificities above 90%; four of them using knowledge-based rather than machine learning algorithms. The presentations of holistic CDSS approaches, including technical implementation details, system interfaces, and data and interoperability aspects enabling the use of CDSS in routine settings are missing in nearly all articles. Conclusions The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.
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Zhu, Yao, Mohammed Gagaoua, Anne Maria Mullen, Alan L. Kelly, Torres Sweeney, Jamie Cafferky, Didier Viala, and Ruth M. Hamill. "A Proteomic Study for the Discovery of Beef Tenderness Biomarkers and Prediction of Warner–Bratzler Shear Force Measured on Longissimus thoracis Muscles of Young Limousin-Sired Bulls." Foods 10, no. 5 (April 27, 2021): 952. http://dx.doi.org/10.3390/foods10050952.
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Beef tenderness is of central importance in determining consumers’ overall liking. To better understand the underlying mechanisms of tenderness and be able to predict it, this study aimed to apply a proteomics approach on the Longissimus thoracis (LT) muscle of young Limousin-sired bulls to identify candidate protein biomarkers. A total of 34 proteins showed differential abundance between the tender and tough groups. These proteins belong to biological pathways related to muscle structure, energy metabolism, heat shock proteins, response to oxidative stress, and apoptosis. Twenty-three putative protein biomarkers or their isoforms had previously been identified as beef tenderness biomarkers, while eleven were novel. Using regression analysis to predict shear force values, MYOZ3 (Myozenin 3), BIN1 (Bridging Integrator-1), and OGN (Mimecan) were the major proteins retained in the regression model, together explaining 79% of the variability. The results of this study confirmed the existing knowledge but also offered new insights enriching the previous biomarkers of tenderness proposed for Longissimus muscle.
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Rashedi, Navid, Yifei Sun, Vikrant Vaze, Parikshit Shah, Ryan Halter, Jonathan T. Elliott, and Norman A. Paradis. "Early Detection of Hypotension Using a Multivariate Machine Learning Approach." Military Medicine 186, Supplement_1 (January 1, 2021): 440–44. http://dx.doi.org/10.1093/milmed/usaa323.
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ABSTRACT Introduction The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension. Materials and Methods Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized. Results The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals. Conclusion In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task.
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Dauhan, Aileen Clarissa, Aridamuriany Dwiputri Lubis, and Munar Lubis. "Vasoactive-inotropic Score for Early Detection and Mortality Prediction of Sepsis in Children." Indonesian Biomedical Journal 13, no. 1 (March 1, 2021): 34–9. http://dx.doi.org/10.18585/inabj.v13i1.1323.
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BACKGROUND: Early detection and treatment of sepsis can prevent septic shock and reduce mortality rate. Troponin can become a prognostic factor in sepsis. However, not all health facilities are equipped to assess troponin levels. Vasoactive-inotropic score (VIS) is a simpler and more accessible method to describe hemodynamic status. The aim of this study was to assess the suitability of VIS score as early prognosis and mortality predictor of sepsisMETHODS: A retrospective study was conducted to determine the correlation between VIS and troponin levels for sepsis cases in Pediatric Intensive Care Unity (PICU) Haji Adam Malik Hospital, Medan from January 2018 to December 2019. VIS score at 48 hours, maximum VIS score, pediatric logistic organ dysfunction-2 (PELOD-2) score, cardiac troponin levels at 48 hours were taken from medical records.RESULTS: There were 54 samples analyzed. VIS scores were positively correlated (p<0.001) to troponin T and troponin I levels at 48 hours (r=0.670 and r=0.606, respectively). VIS at 48 hours and maximum VIS were related to mortality (p=0.001 and p<0.001, respectively). VIS score at 48 hours could be used as a predictive factor for mortality (area under the curve (AUC): 79.7%, p<0.001) with a cut-off point at 11 (74.4% sensitivity and 80% specificity). High VIS at 48 hours indicated poor outcomes of sepsis in children with odd ratio (OR) value: 1.99 (95% confidence interval (CI): 1.25-3.19).CONCLUSION: Vasoactive-inotropic score was suitable as an alternative to cardiac troponin T and troponin I levels at 48 hours to early detect myocardial dysfunctions and mortality in children.KEYWORDS: troponin, vasoactive-inotropic score, sepsis, children, mortality
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Herrán-Monge, Rubén, Arturo Muriel-Bombín, Marta M. García-García, Pedro A. Merino-García, Miguel Martínez-Barrios, David Andaluz, Juan Carlos Ballesteros, et al. "Epidemiology and Changes in Mortality of Sepsis After the Implementation of Surviving Sepsis Campaign Guidelines." Journal of Intensive Care Medicine 34, no. 9 (June 26, 2017): 740–50. http://dx.doi.org/10.1177/0885066617711882.
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Purpose: To determine the epidemiology and outcome of severe sepsis and septic shock after 9 years of the implementation of the Surviving Sepsis Campaign (SSC) and to build a mortality prediction model. Methods: This is a prospective, multicenter, observational study performed during a 5-month period in 2011 in a network of 11 intensive care units (ICUs). We compared our findings with those obtained in the same ICUs in a study conducted in 2002. Results: The current cohort included 262 episodes of severe sepsis and/or septic shock, and the 2002 cohort included 324. The prevalence was 14% (95% confidence interval: 12.5-15.7) with no differences to 2002. The population-based incidence was 31 cases/100 000 inhabitants/year. Patients in 2011 had a significantly lower Acute Physiology and Chronic Health Evaluation II (APACHE II; 21.9 ± 6.6 vs 25.5 ± 7.07), Logistic Organ Dysfunction Score (5.6 ± 3.2 vs 6.3 ± 3.6), and Sequential Organ Failure Assessment (SOFA) scores on day 1 (8 ± 3.5 vs 9.6 ± 3.7; P < .01). The main source of infection was intraabdominal (32.5%) although microbiologic isolation was possible in 56.7% of cases. The 2011 cohort had a marked reduction in 48-hour (7% vs 14.8%), ICU (27.2% vs 48.2%), and in-hospital (36.7% vs 54.3%) mortalities. Most relevant factors associated with death were APACHE II score, age, previous immunosuppression and liver insufficiency, alcoholism, nosocomial infection, and Delta SOFA score. Conclusion: Although the incidence of sepsis/septic shock remained unchanged during a 10-year period, the implementation of the SSC guidelines resulted in a marked decrease in the overall mortality. The lower severity of patients on ICU admission and the reduced early mortality suggest an improvement in early diagnosis, better initial management, and earlier antibiotic treatment.
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Gao, Ruitao, Huachao Yan, and Zhou Yang. "Evaluation of tractor driving vibration fatigue based on multiple physiological parameters." PLOS ONE 16, no. 7 (July 14, 2021): e0254636. http://dx.doi.org/10.1371/journal.pone.0254636.
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The vibration generated by tractor field operations will seriously affect the comfort and health of the driver. The low frequency vibration generated by the engine and ground excitation is similar to the natural frequency of human organs. Long term operation in this environment will resonate with the organs and affect drivers’ health. To investigate this possibility, in this paper we carried out a collection experiment of human physiological indicators relevant to vibration fatigue. Four physiological signals of surface electromyography, skin electricity, skin temperature, and photoplethysmography signal were collected while the subjects experienced vibration. Several features of physiological signals as well as the law of signal features changing with fatigue are studied. The test results show that with the increase of human fatigue, the overall physiological parameters show the following trends: The median frequency of the human body surface electromyography and the slope of skin surface temperature decreases, the value of skin conductivity and the mean value of the photoplethysmography signal increases. Furthermore, this paper proposes a vibration comfort evaluation method based on multiple physiological parameters of the human body. An artificial neural network model is trained with test samples, and the prediction accuracy rate reaches 88.9%. Finally, the vibration conditions are changed by the shock-absorbing suspension of a tractor, verifying the effectiveness of the physiological signal changing with the vibration of the human body. The established prediction model can also be used to objectively reflect the discomfort of the human body under different working conditions and provide a basis for structural design optimization.
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Kuzmichev, B. Yu, O. S. Polunina, L. P. Voronina, T. V. Prokofieva, and E. A. Polunina. "Prognosis of development of complications of cardiogenic shock and pulmonary edema in patients with myocardial infarction on background of obstructive pulmonary disease." Medical alphabet, no. 36 (January 13, 2021): 34–37. http://dx.doi.org/10.33667/2078-5631-2020-36-34-37.
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Objective. To create a personalized mathematical model of the development of complications – cardiogenic shock and pulmonary edema in patients with myocardial infarction (MI) with chronic obstructive pulmonary disease (COPD) depending of the homocysteine (HCY) level and the COPD phenotype.Materials and methods. The study included 88 patients with MI and COPD with various phenotypes: 25 patients with emphysematous phenotype, 22 patients with a mixed phenotype, 20 patients with chronic bronchitis phenotype, 21 patients with eosinophilia and bronchial asthma (BA). As a control group, 50 somatically healthy individuals were examined. Gender anamnestic, clinical, and laboratory – instrumental indicators were studied and analyzed to develop a predictive mathematical model. The level of HCY was determined by enzyme-linked immunosorbent assay in all patients.Results. It was found that in patients with MI and COPD with different COPD phenotypes, the level of HCY was statistically significantly higher than in the control group. The highest level of HCY was in patients with the chronic bronchitis phenotype and was 45 [14.1; 51.9] mmol/l, which was statistically significantly higher than in patients with the phenotype with eosinophilia and BA, with emphysematous and mixed phenotypes. Predictor factors were selected using the logit regression method from gender-anamnestic, clinical, and laboratory – instrumental indicators to create a mathematical model with the highest prediction accuracy. HCY level and COPD phenotype were predictors of the mathematical model for predicting the development of complications – cardiogenic shock and pulmonary edema in patients with MI and COPD. It was also found that the threshold value of HCY for predicting the development of cardiogenic shock and pulmonary edema in patients with MI and COPD was 0.82 ± 0.51 confidence interval [0.72–0.91] mmol/l (p < 0.001).Conclusion. The personalized mathematical model initiated for predicting the development of complications-cardiogenic shock and pulmonary edema in patients with MI and COPD, depending of the HCY level and the COPD phenotype, has a high sensitivity (85%) and prognostic significance (92%), which allows us to recommend it for use in clinical practice.
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Gad, Ghada I., Dina M. Shinkar, Manal M. Kamel El-Din, and Hoda M. Nagi. "The Utility of Soluble CD14 Subtype in Early Diagnosis of Culture-Proven Early-Onset Neonatal Sepsis and Prediction of Outcome." American Journal of Perinatology 37, no. 05 (March 21, 2019): 497–502. http://dx.doi.org/10.1055/s-0039-1683863.
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Abstract Objective This study aimed to evaluate soluble cluster of differentiation 14 subtype (sCD14-ST), also named presepsin, as an early marker for the diagnosis of culture-proven early-onset sepsis (EOS) in neonates and to assess its relation to disease severity and mortality. Study Design Out of 60 neonates with risk factors of EOS, 31 neonates were diagnosed as having culture-proven EOS. They were compared with 20 nonseptic controls. We obtained blood samples on day 1 of life for sCD14-ST measurement and sepsis screening. Blood samples were repeated on day 3 in EOS neonates. Results sCD14-ST was significantly higher in EOS neonates than controls (p < 0.001). Neonates who later developed septic shock had significantly higher day 1 sCD14-ST level than those who did not (p < 0.001). Furthermore, neonates who died had significantly higher day 1 sCD14-ST than survivors (p < 0.001). On day 3, there was a significant decline in sCD14-ST levels than initial levels among survivors. There was a significant positive correlation between day 1 sCD14-ST level and Tollner's sepsis severity score. Conclusion sCD14-ST could be used as a powerful diagnostic and prognostic marker of EOS. Its quantitative measurement at birth could be a good predictor of sepsis severity and mortality.
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Wang, Jun, Dongmei Wang, Mei Qiu, Jing Lou, Rong Fang, Qian Wang, Rong Shi, et al. "Adoption of Computerized Tomography Quantitative Imaging Data in the Evaluation of Severe Acute Pancreatitis by Pleural Effusion." Journal of Medical Imaging and Health Informatics 10, no. 9 (August 1, 2020): 2096–100. http://dx.doi.org/10.1166/jmihi.2020.3163.
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In order to study the pleural effusion (PE) in Severe acute pancreatitis (SAP) in patients with the clinical value of diagnosis, in this study, 78 patients with SAP who were included in Shandong Jinan Municipal Hospital of Traditional Chinese Medicine from January 31, 2017 to December 30, 2019 were selected as the experimental group (EG) and 78 patients with mild acute pancreatitis (MAP) as the control group (CG). The PE was diagnosed by computerized tomography (CT) imaging technology, and the patients with PE in the two groups were divided into small group, medium group and large group according to the volume of PE. The concurrent symptoms of SAP were recorded, and the relation between the occurrence of PE and the complications of SAP was analyzed by a multivariate Logistic regression model (LRM). It was concluded that the concurrent rate of PE in the EG was greatly higher than that in the CG, and the number of cases in the medium and multiple groups was also greatly higher than that in the CG (P < 0.05). There were statistically significant differences (SSD) in acute renal failure, hypovolemic shock and pancreatic pseudocyst in the PE small amount group, medium amount group and large amount group (P < 0.05). The complications of SAP in patients with acute renal failure, hypovolemic shock and pancreatic pseudocyst were positively correlated with age and PE (P < 0.05), which showed that CT could present the PE more directly, which was beneficial to the diagnosis of SAP. In addition, the increased amount of PE indicated the aggravation of SAP and the occurrence of concurrent symptoms, which is helpful for early assessment of patients’ condition and prediction of poor prognosis as an independent indicator to evaluate the early stage of SAP.
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Schrutka, Lore, Felix Rohmann, Christina Binder, Thomas Haberl, Ben Dreyfuss, Gottfried Heinz, Irene M. Lang, et al. "Discriminatory power of scoring systems for outcome prediction in patients with extracorporeal membrane oxygenation following cardiovascular surgery†." European Journal of Cardio-Thoracic Surgery 56, no. 3 (February 20, 2019): 534–40. http://dx.doi.org/10.1093/ejcts/ezz040.
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Abstract OBJECTIVES Although extracorporeal membrane oxygenation (ECMO) represents a rapidly evolving treatment option in patients with refractory heart or lung failure, survival remains poor and appropriate risk stratification challenging because established risk prediction models have not been validated for this specific population. METHODS This observational single-centre registry included a total of 240 patients treated with venoarterial ECMO therapy following cardiovascular surgery and analysed the discriminatory power of the European System of Cardiac Operative Risk Evaluation (EuroSCORE) additive, the EuroSCORE II, the Sequential Organ Failure Assessment (SOFA) score, the Simplified Acute Physiology Score (SAPS) II, the SAPS III, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Risk of renal failure, Injury to the kidney, Failure of kidney function, Loss of kidney function and End-stage renal failure (RIFLE) classification, the survival after venoarterial ECMO (SAVE) score, the prEdictioN of Cardiogenic shock OUtcome foR AMI patients salvaGed by VA-ECMO (ENCOURAGE) score and the Society of Thoracic Surgeons (STS) risk model for outcome prediction. RESULTS During a median follow-up time of 37 months (interquartile range 19–67), 65% of the patients died. Only the SAVE score and the SAPS II were significantly associated with the 30-day mortality rate with a hazard ratio (HR) of 1.06 [95% confidence interval (CI) 1.02–1.11; P = 0.002] for the SAVE score and an HR of 1.02 (95% CI 1.01–1.03; P = 0.004) for the SAPS II with a modest discriminatory power displayed by a C-index of 0.61 and 0.57, respectively. Seven out of 10 scoring systems revealed significant association with long-term mortality, with the SAVE score and the SAPS II remaining the strongest predictors of long-term mortality with an HR of 1.06 (95% CI 1.03–1.09; P < 0.001, C-index 0.61) for the SAVE score and an HR of 1.02 (95% CI 1.01–1.03; P < 0.001, C-index 0.58) for the SAPS II. CONCLUSIONS Risk assessment based on established risk models in patients with ECMO remains difficult. Only the SAPS II and the SAVE score were exclusively found to be suitable for short- and long-term outcome prediction in this specific vulnerable patient population.
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Squizzato, Alessandro. "New Prospective for the Management of Low-Risk Pulmonary Embolism: Prognostic Assessment, Early Discharge, and Single-Drug Therapy with New Oral Anticoagulants." Scientifica 2012 (2012): 1–12. http://dx.doi.org/10.6064/2012/502378.
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Patients with pulmonary embolism (PE) can be stratified into two different prognostic categories, based on the presence or absence of shock or sustained arterial hypotension. Some patients with normotensive PE have a low risk of early mortality, defined as <1% at 30 days or during hospital stay. In this paper, we will discuss the new prospective for the optimal management of low-risk PE: prognostic assessment, early discharge, and single-drug therapy with new oral anticoagulants. Several parameters have been proposed and investigated to identify low-risk PE: clinical prediction rules, imaging tests, and laboratory markers of right ventricular dysfunction or injury. Moreover, outpatient management has been suggested for low-risk PE: it may lead to a decrease in unnecessary hospitalizations, acquired infections, death, and costs and to an improvement in health-related quality of life. Finally, the main characteristics of new oral anticoagulant drugs and the most recent published data on phase III trials on PE suggest that the single-drug therapy is a possible suitable option. Oral administration, predictable anticoagulant responses, and few drug-drug interactions of direct thrombin and factor Xa inhibitors may further simplify PE home therapy avoiding administration of low-molecular-weight heparin.
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Jesser, Kelsey J., and Rachel T. Noble. "VibrioEcology in the Neuse River Estuary, North Carolina, Characterized by Next-Generation Amplicon Sequencing of the Gene Encoding Heat Shock Protein 60 (hsp60)." Applied and Environmental Microbiology 84, no. 13 (April 20, 2018): e00333-18. http://dx.doi.org/10.1128/aem.00333-18.
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ABSTRACTOf marine eubacteria, the genusVibriois intriguing because member species are relevant to both marine ecology and human health. Many studies have touted the relationships ofVibrioto environmental factors, especially temperature and salinity, to predict totalVibrioabundance but lacked the taxonomic resolution to identify the relationships among species and the key drivers ofVibriodynamics. To improve next-generation sequencing (NGS) surveys ofVibrio, we have conducted both 16S small subunit rRNA and heat shock protein 60 (hsp60) amplicon sequencing of water samples collected at two well-studied locations in the Neuse River Estuary, NC. Samples were collected between May and December 2016 with enhanced sampling efforts in response to two named storms. Usinghsp60sequences, 21Vibriospecies were identified, including the potential human pathogensV. cholerae,V. parahaemolyticus, andV. vulnificus. Changes in theVibriocommunity mirrored seasonal and storm-related changes in the water column, especially in response to an influx of nutrient-rich freshwater to the estuary after Hurricane Matthew, which initiated dramatic changes in the overallVibriocommunity. Individual species dynamics were wide ranging, indicating that individualVibriotaxa have unique ecologies and that totalVibrioabundance predictors are insufficient for risk assessments of potentially pathogenic species. Positive relationships betweenVibrio, dinoflagellates, andCyanobacteriawere identified, as were intraspecies associations, which further illuminated the interactions of cooccurringVibriotaxa along environmental gradients.IMPORTANCEThe objectives of this research were to utilize a novel approach to improve sequence-based surveys ofVibriocommunities and to demonstrate the usefulness of this approach by presenting an analysis ofVibriodynamics in the context of environmental conditions, with a particular focus on species that cause disease in humans and on storm effects. The methods presented here enabled the analysis ofVibriodynamics with excellent taxonomic resolution and could be incorporated into future ecological studies and risk prediction strategies for potentially pathogenic species. Next-generation sequencing ofhsp60and other innovative sequence-based approaches are valuable tools and show great promise for studyingVibrioecology and associated public health risks.
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Afify, Mohammed Farouk M., Sheren Esam Maher, Nora Mohamed Ibrahim, and Waleed Mahamoud Abd El-Hamied. "Serum Neutrophil Gelatinase-Associated Lipocalin in Infants and Children with Sepsis-Related Conditions with or without Acute Renal Dysfunction." Clinical Medicine Insights: Pediatrics 10 (January 2016): CMPed.S39452. http://dx.doi.org/10.4137/cmped.s39452.
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Purpose To validate serum neutrophil gelatinase-associated lipocalin (NGAL) as an early biomarker for acute kidney injury (AKI) in sepsis-related conditions and its predictive and prognostic values. Patients and Methods This study included 65 patients, who were clinically evaluated for sepsis, severe sepsis, or septic shock, and 20 apparently healthy served as controls. Patients were divided into two groups: Group I (AKI-sepsis): 65 newly admitted patients diagnosed as sepsis, who were further divided into three subgroups according to the severity: systemic inflammatory response syndrome, severe sepsis, and septic shock, and Group II (control group): 20 apparently healthy subjects matched for age and sex, serum creatinine and serum NGAL concentrations were estimated initially within 24 hours of admission and after 72 hours of admission in all patients and control groups. Results Serum NGAL increased significantly with increasing severity of renal impairment. Receiver-operating characteristic analysis suggested that serum NGAL cutoff value of 40 ng/mL within the first 24 hours of admission is highly specific and sensitive for predicting AKI, with sensitivity of 90.9% and specificity of 75.8%. Conclusion We concluded that early measurement of serum NGAL level in sepsis can serve as a clinically useful marker for early prediction of AKI and for grading of its severity.
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Goswami, Nandu, Andrew Philip Blaber, Helmut Hinghofer-Szalkay, and Victor A. Convertino. "Lower Body Negative Pressure: Physiological Effects, Applications, and Implementation." Physiological Reviews 99, no. 1 (January 1, 2019): 807–51. http://dx.doi.org/10.1152/physrev.00006.2018.
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This review presents lower body negative pressure (LBNP) as a unique tool to investigate the physiology of integrated systemic compensatory responses to altered hemodynamic patterns during conditions of central hypovolemia in humans. An early review published in Physiological Reviews over 40 yr ago (Wolthuis et al. Physiol Rev 54: 566–595, 1974) focused on the use of LBNP as a tool to study effects of central hypovolemia, while more than a decade ago a review appeared that focused on LBNP as a model of hemorrhagic shock (Cooke et al. J Appl Physiol (1985) 96: 1249–1261, 2004). Since then there has been a great deal of new research that has applied LBNP to investigate complex physiological responses to a variety of challenges including orthostasis, hemorrhage, and other important stressors seen in humans such as microgravity encountered during spaceflight. The LBNP stimulus has provided novel insights into the physiology underlying areas such as intolerance to reduced central blood volume, sex differences concerning blood pressure regulation, autonomic dysfunctions, adaptations to exercise training, and effects of space flight. Furthermore, approaching cardiovascular assessment using prediction models for orthostatic capacity in healthy populations, derived from LBNP tolerance protocols, has provided important insights into the mechanisms of orthostatic hypotension and central hypovolemia, especially in some patient populations as well as in healthy subjects. This review also presents a concise discussion of mathematical modeling regarding compensatory responses induced by LBNP. Given the diverse applications of LBNP, it is to be expected that new and innovative applications of LBNP will be developed to explore the complex physiological mechanisms that underline health and disease.
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Huang, Sheng-Wen, Huey-Pin Tsai, Su-Jhen Hung, Wen-Chien Ko, and Jen-Ren Wang. "Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning." PLOS Neglected Tropical Diseases 14, no. 12 (December 23, 2020): e0008960. http://dx.doi.org/10.1371/journal.pntd.0008960.
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Background Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. Methodology/Principal findings Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. Conclusions/Significance We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.
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Yamashita, Shimpei, Yasuo Kohjimoto, Yuya Iwahashi, Takashi Iguchi, Satoshi Nishizawa, Kazuro Kikkawa, and Isao Hara. "Noncontrast Computed Tomography Parameters for Predicting Shock Wave Lithotripsy Outcome in Upper Urinary Tract Stone Cases." BioMed Research International 2018 (December 2, 2018): 1–6. http://dx.doi.org/10.1155/2018/9253952.
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Kidney stones are a major public health concern with continuously increasing worldwide prevalence. Shock wave lithotripsy (SWL) is the first line treatment choice for upper urinary tract calculi with ureteroscopy and has advantages of safety and noninvasiveness, but the treatment success rate of SWL is lower than that of other therapies. It is therefore important to identify predictive factors for SWL outcome and select a suitable treatment choice for patients with upper urinary tract calculi. In recent years, computed tomography (CT) has become the gold standard for diagnosis of upper urinary tract calculi. Several factors based on CT images, including skin-to-stone distance, mean stone density, stone heterogeneity index, and variation coefficient of stone density, have been reported to be useful for predicting SWL outcome. In addition, a new method of analysis, CT texture analysis, is reportedly useful for predicting SWL outcomes. This review aims to summarize CT parameters for predicting the outcome of shock wave lithotripsy in stone cases in the upper urinary tract.
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IVANCEVIC, VLADIMIR G. "NEW MECHANICS OF GENERIC MUSCULO-SKELETAL INJURY." Biophysical Reviews and Letters 04, no. 03 (July 2009): 273–87. http://dx.doi.org/10.1142/s1793048009001022.
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Prediction and prevention of musculo-skeletal injuries is an important aspect of preventive health science. Using as an example a human knee joint, this paper proposes a new coupled-loading-rate hypothesis, which states that a generic cause of any musculo-skeletal injury is a Euclidean jolt, or SE(3)-jolt, an impulsive loading that hits a joint in several coupled degrees-of-freedom simultaneously. Informally, it is a rate-of-change of joint acceleration in all six-degrees-of-freedom simultaneously, times the corresponding portion of the body mass. In the case of a human knee, this happens when most of the body mass is on one leg with a semi-flexed knee — and then, caused by some external shock, the knee suddenly "jerks"; this can happen in running, skiing, sports games (e.g., soccer, rugby) and various crashes/impacts. To show this formally, based on the previously defined covariant force law and its application to traumatic brain injury (Ref. 52), we formulate the coupled Newton–Euler dynamics of human joint motions and derive from it the corresponding coupled SE(3)-jolt dynamics of the joint in case. The SE(3)-jolt is the main cause of two forms of discontinuous joint injury: (i) mild rotational disclinations and (ii) severe translational dislocations. Both the joint disclinations and dislocations, as caused by the SE(3)-jolt, are described using the Cosserat multipolar viscoelastic continuum joint model.
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Tang, Shengpu, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, and Jenna Wiens. "Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data." Journal of the American Medical Informatics Association 27, no. 12 (October 11, 2020): 1921–34. http://dx.doi.org/10.1093/jamia/ocaa139.
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Abstract Objective In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR. Materials and Methods Largely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines. Results Across tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757–0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments. Conclusions FIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.
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Wang, Min, Tianyu Liu, Zheng Niu, Jingzhi Zuo, and Dunyi Qi. "Utility of venous-to-arterial carbon dioxide changes to arteriovenous oxygen content ratios in the prognosis of severe sepsis and septic shock: A systematic review and meta-analysis." Hong Kong Journal of Emergency Medicine 28, no. 4 (March 1, 2021): 241–53. http://dx.doi.org/10.1177/1024907921994970.
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Background: Sepsis patients with insufficient tissue perfusion and hypoxia should be identified and resuscitated immediately. Recently, venous-to-arterial carbon dioxide pressure changes and the arteriovenous oxygen content difference ratio (Pcv-aCO2/Ca-vO2) as a predictor of tissue perfusion recovery and poor prognosis. Objectives: Pcv-aCO2/Ca-vO2 is a substitute for respiratory entropy, the elevation of which indicates a lack of tissue perfusion. Pcv-aCO2/Ca-vO2 can be used as an indicator to predict the prognosis of patients with sepsis or septic shock, but its prognostic value has not been fully evaluated. Here, we have performed a meta-analysis to assess its predictive value for mortality. Methods: Meta-analysis of Observational Studies in Epidemiology group guidelines were followed for this meta-analysis. We searched the comprehensive electronic databases of PubMed, EMBASE, Web of Science, and Cochrane libraries from inception to March 2019, using the terms including “venous-arterial,” “carbon dioxide,” “Shock, Septic,” and related keywords. The Newcastle-Ottawa scale was used for quality evaluation of the literature. A meta-analysis was performed using RevMan 5.3 and Stata 14.0 software to evaluate the effects of Pcv-aCO2/Ca-vO2 on short-term mortality, sequential organ failure assessment, and acute physiology and chronic health evaluation scores in patients with sepsis or septic shock. Results: The final analysis included 13 clinical studies involving a total of 940 subjects. The results of the meta-analysis showed that non-surviving patients had higher Pcv-aCO2/Ca-vO2 than survivors after fluid resuscitation (standardized mean difference = 0.68, 95% confidence interval = 0.24–1.12) and blood samples taken 6 h after resuscitation showed a greater risk of mortality (risk ratio = 1.89, 95% confidence interval = 1.48–2.41) and sequential organ failure assessment scores (mean difference = 1.58, 95% confidence interval = 0.88–2.28, P < 0.01) in patients with high Pcv-aCO2/Ca-vO2. These differences were statistically significant. Conclusion: This meta-analysis indicates that Pcv-aCO2/Ca-vO2 has predictive value for mortality in patients with sepsis or septic shock. Further studies are now required to determine the optimal threshold for predicting sepsis mortality. Prospero Registration: The protocol for this systematic review was registered on PROSPERO (CRD 42019128134).
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Martínez-Paz, Pedro, Marta Aragón-Camino, Esther Gómez-Sánchez, Mario Lorenzo-López, Estefanía Gómez-Pesquera, Rocío López-Herrero, Belén Sánchez-Quirós, et al. "Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock." Journal of Clinical Medicine 9, no. 5 (April 28, 2020): 1276. http://dx.doi.org/10.3390/jcm9051276.
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Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.
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Mahmud, Shahid, Rahat Iqbal, and Faiyaz Doctor. "Cloud enabled data analytics and visualization framework for health-shocks prediction." Future Generation Computer Systems 65 (December 2016): 169–81. http://dx.doi.org/10.1016/j.future.2015.10.014.
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Song, Jia, Yun Cui, Chunxia Wang, Jiaying Dou, Huijie Miao, Xi Xiong, and Yucai Zhang. "Predictive value of thyroxine for prognosis in pediatric septic shock: a prospective observational study." Journal of Pediatric Endocrinology and Metabolism 33, no. 5 (May 26, 2020): 653–59. http://dx.doi.org/10.1515/jpem-2019-0502.
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AbstractBackgroundThyroid hormone plays an important role in the adaptation of metabolic function to critically ill. The relationship between thyroid hormone levels and the outcomes of septic shock is still unclear. The aim of this study was to assess the predictive value of thyroid hormone for prognosis in pediatric septic shock.MethodsWe performed a prospective observational study in a pediatric intensive care unit (PICU). Patients with septic shock were enrolled from August 2017 to July 2019. Clinical and laboratory indexes were collected, and thyroid hormone levels were measured on PICU admission.ResultsNinety-three patients who fulfilled the inclusion criteria were enrolled in this study. The incidence of nonthyroidal illness syndrome (NTIS) was 87.09% (81/93) in patients with septic shock. Multivariate logistic regression analysis showed that T4 level was independently associated with in-hospital mortality in patients with septic shock (OR: 0.965, 95% CI: 0.937–0.993, p = 0.017). The area under receiver operating characteristic (ROC) curve (AUC) for T4 was 0.762 (95% CI: 0.655–0.869). The cutoff threshold value of 58.71 nmol/L for T4 offered a sensitivity of 61.54% and a specificity of 85.07%, and patients with T4 < 58.71 nmol/L showed high mortality (60.0%). Moreover, T4 levels were negatively associated with the pediatric risk of mortality III scores (PRISM III), lactate (Lac) level in septic shock children.ConclusionsNonthyroidal illness syndrome is common in pediatric septic shock. T4 is an independent predictor for in-hospital mortality, and patients with T4 < 58.71 nmol/L on PICU admission could be with a risk of hospital mortality.
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Lin, Sunny Jui-Shan, Yung-Yen Cheng, Chih-Hung Chang, Cheng-Hung Lee, Yi-Chia Huang, and Yi-Chang Su. "Traditional Chinese Medicine Diagnosis “Yang-Xu Zheng”: Significant Prognostic Predictor for Patients with Severe Sepsis and Septic Shock." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/759748.
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Pathogenesis of sepsis includes complex interaction between pathogen activities and host response, manifesting highly variable signs and symptoms, possibly delaying diagnosis and timely life-saving interventions. This study applies traditional Chinese medicine (TCM)Zhengdiagnosis in patients with severe sepsis and septic shock to evaluate its adaptability and use as an early predictor of sepsis mortality. Three-year prospective observational study enrolled 126 septic patients. TCMZhengdiagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and blood samples for host response cytokines measurement (tumor necrosis factor-α, Interleukin-6, Interleukin-8, Interleukin-10, Interleukin-18) were collected within 24 hours after admission to Intensive Care Unit. Main outcome was 28-day mortality; multivariate logistic regression analysis served to determine predictive variables of the sepsis mortality. APACHE II score, frequency ofNutrient-phase heat, andQi-XuandYang-Xu Zhengswere significantly higher in nonsurvivors. The multivariate logistic regression analysis identifiedYang-Xu Zhengas the outcome predictor. APACHE II score and levels of five host response cytokines between patients with and withoutYang-Xu Zhengrevealed significant differences. Furthermore, cool extremities and weak pulse, both diagnostic signs ofYang-Xu Zheng, were also proven independent predictors of sepsis mortality. TCM diagnosis “Yang-Xu Zheng” may provide a new mortality predictor for septic patients.
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Yang, Guo Liang, Ren Shu Yang, Chuan Huo, and Yu Long Che. "Simulation Research of Blasting Vibration Prediction with Cylindrical Dynamite." Applied Mechanics and Materials 249-250 (December 2012): 1008–11. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1008.
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Blasting vibration not only damages architectural structures but also harms people heath. Numerical simulation provides an effective method to study explosion and shock problems. Herein, the paper performed numerical simulation to predict blasting vibration. At first, analyzed the propagation velocity of stress wave, which validated the feasibility of the model. The simulation and test agreed well both at vibration waveform and at the order of magnitude of both nodes 1 and 2. The simulation could provide reasonable accuracy. The achievement provided reference for other researches.
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Seif, Alix E., Susan R. Rheingold, Brian T. Fisher, Yuan-Shung V. Huang, Yimei Li, Leslie S. Kersun, L. Charles Bailey, Anne F. Reilly, and Richard Aplenc. "Induction Mortality In Pediatric Acute Lymphoblastic Leukemia (ALL): a Retrospective Cohort Analysis From the Pediatric Health Systems Information (PHIS) Database, 1999–2009." Blood 116, no. 21 (November 19, 2010): 3239. http://dx.doi.org/10.1182/blood.v116.21.3239.3239.
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Abstract Abstract 3239 We sought to define factors identifying children at increased risk for mortality during the induction phase of chemotherapy for ALL. We identified a cohort of 11,145 pediatric patients with newly diagnosed ALL using PHIS data from 1999–2009. The PHIS database contains de-identified records of all admissions from 42 children's hospitals across the United States, representing 85% of pediatric admissions to freestanding children's hospitals. Data are internally validated by PHIS, and participating hospitals must have an error rate of <2%. Patients were further validated by manual comparison of induction chemotherapy to published ALL induction standards. Information included in the database are patient demographics, ICD-9 codes, and resource utilization codes for pharmacy, radiology and other procedures. Our strategy to identify newly diagnosed ALL patients included all patients from birth to age 18.99 years with an ICD-9 code for ALL (204.xx) who received one of a series of 3- or 4-drug induction chemotherapy regimens based on standard practices for the era studied. Data were analyzed from the first 30 days after initial admission for ALL. Table 1 shows demographic characteristics, as well as clinical characteristics hypothesized to predict increased mortality. There were 172 patients who died during the study period (1.54%; Table 2). Age <1 year was associated with markedly increased mortality compared to ages 1–9.99 years; patients ages 10–18.99 also had increased mortality. Sex, race and Hispanic ethnicity were not predictive of mortality. We hypothesized that Down syndrome, anthracycline exposure and dexamethasone exposure would be risk factors; however, none of these exposures significantly predicted increased mortality compared to patients without these exposures. Certain interventions were strong predictors of mortality, such as intubation, use of pressors, intubation with concomitant pressors, extracorporeal membrane oxygenation (ECMO), and hemodialysis (Table 2). We tabulated the frequency of ICD-9 codes during the admission in which mortality was observed. The most commonly associated diagnoses were: respiratory failure, acidosis, aplastic anemia, pleural effusion, hemorrhagic stroke, renal failure, sepsis, coagulopathy, complications of a vascular device, hyponatremia, hypotension/shock, pneumonia, and seizure. We are in the process of further analyzing these diagnostic codes for prediction of mortality risk. In summary, we present the largest published dataset of induction mortality in pediatric ALL. Additionally, this is the first use of a nationally representative pediatric ALL dataset that includes patients irrespective of clinical trial enrollment. We are currently performing multivariate analyses using ICD-9 discharge, procedure and utilization codes to define risk factors for induction mortality. Using these analyses, we will develop a model that may allow practitioners to intervene against poor outcomes in high-risk patients. Finally, these data can provide national benchmarks for ALL induction mortality and complication rates that will be critically important in this era of increasing emphasis on patient safety. Table 1: Demographic characteristics of PHIS ALL cohort, 1999–2009 Patient characteristic N (%) Age at diagnosis, median 5.7 years <1 year 410 (3.68) 1-9.99 years 7543 (67.68) 10-18.99 years 3192 (28.64) Female 4835 (43.38) Race Caucasian 8430 (75.66) African American 938 (8.42) Asian/Pacific Islander 327 (2.93) Native American 78 (0.70) Other/unknown 808 (7.25) Missing 562 (5.04) Hispanic ethnicity 2376 (21.32) Down syndrome 304 (2.73) Anthracycline exposure 4645 (41.68) Dexamethasone exposure 5393 (48.39) Hospitalization data Mean ± SD Median (range) Duration of 1st admission 13.31 ± 14.82 9 (1-283) Number of hospitalizations in 1st 30 days 1.35 ± 0.59 1 (1-5) Table 2: Risk of mortality by demographics and need for intervention Risk factor Mortality rate, N (%) RR (95% CI) Overall 172 (1.54) – Age at diagnosis <1 year 23 (5.61) 7.17 (4.48, 11.49) 1-9.99 years 59 (0.78) 1.0 10-18.99 years 90 (2.82) 3.61 (2.60, 4.99) Any Intubation Yes 99 (26.68) 39.38 (29.64, 52.32) No 73 (0.68) 1.0 Pressors Yes 74 (16.34) 17.82 (13.38, 23.74) No 98 (0.92) 1.0 Any Intubation + pressors Yes 60 (45.11) 44.36 (34.10, 57.69) No 112 (1.02) 1.0 ECMO Yes 6 (75.00) 50.32 (32.81, 77.17) No 166 (1.49) 1.0 Hemodialysis Yes 29 (15.59) 11.95 (8.24, 17.33) No 143 (1.30) 1.0 Disclosures: No relevant conflicts of interest to declare.
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Azfar, M. Feroz, M. Faisal Khan, S. Shahid Habib, Z. Al Aseri, A. Mohammad Zubaidi, D. Ocampo Aguila, M. Owais Suriya, and Hameed Ullah. "Prognostic value of ADAMTS13 in patients with severe sepsis and septic shock." Clinical & Investigative Medicine 40, no. 2 (April 26, 2017): 49. http://dx.doi.org/10.25011/cim.v40i2.28195.
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Purpose: ADAMTS13 level was evaluated as a predictor of mortality in patients with severe sepsis and septic shock, and compared with Acute Physiology and Chronic Health Evaluation II (APACHE II) scores. Methods: This prospective observational study was conducted in the Medical and Surgical Intensive Care Units of King Khalid University Hospital. Detailed clinical evaluations were performed on 84 patients (56.08±18.18 years of age) with severe sepsis and septic shock. ADAMTS13 levels were determined (three blood samples at 24 hours intervals) and APACHE II scores, hematological profiles, indices of organ hypo-perfusion, renal functions and coagulation profiles were recorded. Primary outcome was 30 days ICU mortality and secondary outcomes were its comparison with APACHE II score, length of ICU stay and use of vasopressor agents. Results: Hypertension (53.6%) and diabetic mellitus (45.2%) were the commonest comorbidities. The median ADAMTS13 levels were 336.65, 339.35 and 313.9, respectively. ROC analysis showed maximum area under the curve for second ADAMTS13 (AUC=0.760) compared with first (AUC=0.660) and third samples (AUC=0.707) and APACHE II scores (AUC=0.662). Patients were divided into low and high ADAMTS13 groups according to the best cut-off point. Mortality was high in the low ADAMTS13 level group [OR=4.5]and was significantly associated with age, DBP, ADAMTS13, APACHE II score, DIC score and platelet count. ADAMTS13 (OR=5.3), APACHE II (OR=4.13) and DIC scores (OR=7.32) were significant risk factors for mortality. Conclusions: Low ADAMTS13 was associated with increased mortality in patients with severe sepsis and septic shock and was comparable to APACHE II scores for predicting mortality.
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Faustino, E. Vincent S. "Predicting septic shock upon arrival to the emergency department." Journal of Pediatrics 217 (February 2020): 1–3. http://dx.doi.org/10.1016/j.jpeds.2019.11.043.
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Harikrishnan, M. P., C. R. Anil Kumar, M. K. Anand, and J. Earali. "EFFECTS OF HEMOTOXIC SNAKE BITE ENVENOMATION ON HAEMATOLOGICAL PARAMETERS VARIABILITY IN PREDICTING COMPLICATIONS." International Journal of Medicine and Medical Research 6, no. 2 (May 18, 2021): 22–29. http://dx.doi.org/10.11603/ijmmr.2413-6077.2020.2.11509.
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Background. Snake bite envenomation is a major public health problem in India with a high mortality rate. The major complications following a hemotoxic snake bite are disseminated intravascular coagulation (DIC), shock, acute kidney injury (AKI), acute respiratory distress syndrome (ARDS) and coagulopathy. The study explores a possible correlation of the haematological parameters studied to complications like DIC, AKI, acute renal failure (ARF), ARDS, shock and gastrointestinal (GI) bleed. Objective. The aim of the study was to find out the effect of snakebite envenomation on the major haematological parameters of the body in relation to complications. Methods. This cross-sectional study was conducted during a period of 18 months. 150 patients were included in the study after obtaining their informed consents. Data collection was done using a proforma. The study also compared clotting time (CT) by capillary tube method and whole blood clotting time at 20 minutes (WBCT20). SPSS software was used for statistical analysis. Results. Among the people with complications, the majority (52%) of participants had AKI, 26% of them requiring dialysis, 16.7% participants had GI bleed, 11.3% participants had shock and 10% participants had DIC. Conclusions. A prolonged bleeding time was found to be one of the most helpful haematological parameters in predicting shock and AKI. Clotting time by both capillary tube and WBCT20 methods were comparable in predicting complications. Objective: The objective of this study was to find the effect of snakebite envenomation on the major haematological parameters of the body. Material and method: This cross-sectional study was conducted over a period of 18months. A total of 150 patients were included in the study after obtaining informed consent. Data collection was done using a proforma. SPSS software was used for statistical analysis. Results: Among the people with complications, the majority (52%) of participants had AKI, followed by 26% participants who required Dialysis, 16.7% participants had GI bleed, 11.3% participants had the shock and 10% participants had DIC. Conclusion: A prolonged bleeding time was found to be one of the most helpful haematological parameters in predicting the shock and AKI.
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Hlapčić, Iva, Andrea Hulina-Tomašković, Marija Grdić Rajković, Sanja Popović-Grle, Andrea Vukić Dugac, and Lada Rumora. "Association of Plasma Heat Shock Protein 70 with Disease Severity, Smoking and Lung Function of Patients with Chronic Obstructive Pulmonary Disease." Journal of Clinical Medicine 9, no. 10 (September 25, 2020): 3097. http://dx.doi.org/10.3390/jcm9103097.
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Extracellular heat shock protein 70 (eHsp70) might modulate immune responses in chronic obstructive pulmonary disease (COPD). The aim of the study was to explore eHsp70 concentration in stable COPD, its association with disease severity and smoking status as well as its diagnostic performance in COPD assessment. Plasma samples were collected from 137 COPD patients and 95 healthy individuals, and concentration of eHsp70 was assessed by commercially available enzyme-linked immunosorbent assay (ELISA) kit (Enzo Life Science, Farmingdale, NY, USA). COPD patients were subdivided regarding airflow obstruction severity and symptoms severity according to the Global Initiative for COPD (GOLD) guidelines. eHsp70 concentration increased in COPD patients when compared to controls and increased with the severity of airflow limitation as well as symptoms burden and exacerbation history. eHsp70 concentration did not differ among COPD patients based on smoking status, yet it increased in healthy smokers compared to healthy nonsmokers. In addition, eHsp70 negatively correlated with lung function parameters forced expiratory volume in one second (FEV1) and FEV1/ forced vital capacity (FVC), and positively with COPD multicomponent indices BODCAT (BMI, airflow obstruction, dyspnea, CAT score), BODEx (BMI, airflow obstruction, dyspnea, previous exacerbations), CODEx (Charlson’s comorbidity index, airflow obstruction, dyspnea, previous exacerbations) and DOSE (dyspnea, airflow obstruction, smoking status, previous exacerbations) With great predictive value (OR = 7.63) obtained from univariate logistic regression, eHsp70 correctly classified 76% of cases. eHsp70 is associated with COPD prediction and disease severity and might have the potential for becoming an additional biomarker in COPD assessment.
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Gómez, Pedro A., Javier de-la-Cruz, David Lora, Luis Jiménez-Roldán, Gregorio Rodríguez-Boto, Rosario Sarabia, Juan Sahuquillo, et al. "Validation of a prognostic score for early mortality in severe head injury cases." Journal of Neurosurgery 121, no. 6 (December 2014): 1314–22. http://dx.doi.org/10.3171/2014.7.jns131874.
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Object Traumatic brain injury (TBI) represents a large health and economic burden. Because of the inability of previous randomized controlled trials (RCTs) on TBI to demonstrate the expected benefit of reducing unfavorable outcomes, the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in TBI) and CRASH (Corticosteroid Randomisation After Significant Head Injury) studies provided new methods for performing prognostic studies of TBI. This study aimed to develop and externally validate a prognostic model for early death (within 48 hours). The secondary aim was to identify patients who were more likely to succumb to an early death to limit their inclusion in RCTs and to improve the efficiency of RCTs. Methods The derivation cohort was recruited at 1 center, Hospital 12 de Octubre, Madrid (1990–2003, 925 patients). The validation cohort was recruited in 2004–2006 from 7 study centers (374 patients). The eligible patients had suffered closed severe TBIs. The study outcome was early death (within 48 hours post-TBI). The predictors were selected using logistic regression modeling with bootstrapping techniques, and a penalized reduction was used. A risk score was developed based on the regression coefficients of the variables included in the final model. Results In the validation set, the final model showed a predictive ability of 50% (Nagelkerke R2), with an area under the receiver operating characteristic curve of 89% and an acceptable calibration (goodness-of-fit test, p = 0.32). The final model included 7 variables, and it was used to develop a risk score with a range from 0 to 20 points. Age provided 0, 1, 2, or 3 points depending on the age group; motor score provided 0 points, 2 (untestable), or 3 (no response); pupillary reactivity, 0, 2 (1 pupil reacted), or 6 (no pupil reacted); shock, 0 (no) or 2 (yes); subarachnoid hemorrhage, 0 or 1 (severe deposit); cisternal status, 0 or 3 (compressed/absent); and epidural hematoma, 0 (yes) or 2 (no). Based on the risk of early death estimated with the model, 4 risk of early death groups were established: low risk, sum score 0–3 (< 1% predicted mortality); moderate risk, sum score 4–8 (predicted mortality between 1% and 10%); high risk, sum score 9–12 (probability of early death between 10% and 50%); and very high risk, sum score 13–20 (early mortality probability > 50%). This score could be used for selecting patients for clinical studies. For example, if patients with very high risk scores were excluded from our study sample, the patients included (eligibility score < 13) would represent 80% of the original sample and only 23% of the patients who died early. Conclusions The combination of Glasgow Coma Scale score, CT scanning results, and secondary insult data into a prognostic score improved the prediction of early death and the classification of TBI patients.
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Hagedoorn, Nienke N., Joany M. Zachariasse, and Henriette A. Moll. "Association between hypotension and serious illness in the emergency department: an observational study." Archives of Disease in Childhood 105, no. 6 (April 4, 2019): 545–51. http://dx.doi.org/10.1136/archdischild-2018-316231.
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BackgroundThe value of routine blood pressure measurement in the emergency department (ED) is unclear.ObjectiveTo determine the association between hypotension in addition to tachycardia and the Shock Index for serious illness.DesignObservational study.SettingUniversity ED (2009–2016).Participants, methods and main outcomesRoutine data collected from consecutive children <16 years. Using logistic regression, we assessed the association between hypotension (adjusted for tachycardia) and Shock Index (ratio heart rate/blood pressure [BP]) for serious illness. The predictive accuracy (sensitivity, specificity) for hypotension and Shock Index was determined for serious illness, defined as intensive care unit (ICU) and hospital admissions.ResultsWe included 10 698 children with measured BP. According to three age-adjusted clinical cut-offs (Advanced Paediatric Life Support, Paediatric Advanced Life Support and Paediatric Early Warning Score), hypotension was significantly associated with ICU admission when adjusted for tachycardia (range OR 2.6–5.3). Hypotension showed low sensitivity (range 0.05–0.12) and high specificity (range 0.95–0.99) for ICU admission. Combining hypotension and tachycardia did not change the predictive value for ICU admission. Similar results were found for hospitalisation. Shock index was associated with serious illness. However, no specific cut-off value was identified in different age groups.ConclusionsHypotension, adjusted for tachycardia, is associated with serious illness, although its sensitivity is limited. Shock index showed an association with serious illness, but no acceptable cut-off value could be identified. Routine BP measurement in all children to detect hypotension has limited value in the ED. Future studies need to confirm which patients could benefit from BP measurement.
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Wu, Yong, Qigai Yin, Xiaobao Zhang, Pin Zhu, Hengfei Luan, and Ying Chen. "Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock." BioMed Research International 2020 (December 1, 2020): 1–9. http://dx.doi.org/10.1155/2020/7170464.
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Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.