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Artículos de revistas sobre el tema "Complication prediction"

Autor: Grafiati

Publicado: 7 de junio de 2025

Última modificación: 2 de agosto de 2025

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1

Woodfield, John C., Peter M. Sagar, Dinesh K. Thekkinkattil, Praveen Gogu, Lindsay D. Plank, and Dermot Burke. "Accuracy of the Surgeons’ Clinical Prediction of Postoperative Major Complications Using a Visual Analog Scale." Medical Decision Making 37, no. 1 (2016): 101–12. http://dx.doi.org/10.1177/0272989x16651875.

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Background. Although the risk factors that contribute to postoperative complications are well recognized, prediction in the context of a particular patient is more difficult. We were interested in using a visual analog scale (VAS) to capture surgeons’ prediction of the risk of a major complication and to examine whether this could be improved. Methods. The study was performed in 3 stages. In phase I, the surgeon assessed the risk of a major complication on a 100-mm VAS immediately before and after surgery. A quality control questionnaire was designed to check if the VAS was being scored as a l

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Peixoto, Hugo, Lara Silva, Soraia Pereira, Tiago Jesus, Vitor Neves Lopes, and António Carlos Abelha. "Death and Morbidity Prediction Using Data Mining in Perforated Peptic Ulcers." International Journal of Reliable and Quality E-Healthcare 9, no. 1 (2020): 37–49. http://dx.doi.org/10.4018/ijrqeh.2020010104.

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Peptic ulcers are not the most common complication in gastrointestinal mucosa, but these defects stand out as being the complication with the highest mortality rate. Several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed to predict the probability of mortality. In this study, a data mining process is performed in the medical data available, in order to evaluate how the scoring systems perform when trying to predict mortality and patients' state complication. Furthermore, the presented paper studies the two scoring syst

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Zuo, Ming, Wei Zhang, Qi Xu, and Dehua Chen. "Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning." Journal of Healthcare Engineering 2022 (April 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/5129125.

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Objective. Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods. This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep pe

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Devana, Sai K., Akash A. Shah, Changhee Lee, et al. "Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Primary Anatomic Total Shoulder Replacements." Journal of Shoulder and Elbow Arthroplasty 6 (January 2022): 247154922210754. http://dx.doi.org/10.1177/24715492221075444.

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Background The demand and incidence of anatomic total shoulder arthroplasty (aTSA) procedures is projected to increase substantially over the next decade. There is a paucity of accurate risk prediction models which would be of great utility in minimizing morbidity and costs associated with major post-operative complications. Machine learning is a powerful predictive modeling tool and has become increasingly popular, especially in orthopedics. We aimed to build a ML model for prediction of major complications and readmission following primary aTSA. Methods A large California administrative data

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Schallmoser, Simon, Thomas Zueger, Mathias Kraus, Maytal Saar-Tsechansky, Christoph Stettler, and Stefan Feuerriegel. "Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study." Journal of Medical Internet Research 25 (February 27, 2023): e42181. http://dx.doi.org/10.2196/42181.

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Background Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. Objective This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. Methods In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and dis

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Hong, Qing-Qi, Su Yan, Yong-Liang Zhao, et al. "Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer." World Journal of Gastroenterology 30, no. 1 (2024): 79–90. http://dx.doi.org/10.3748/wjg.v30.i1.79.

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BACKGROUND Laparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue. AIM To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications. METHODS In total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the tra

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7

Kim, Jae Weon, Dong-Hoon Suh, and Jae Hoon Kim. "Prediction of major surgical complications by comprehensive geriatric assessment in elderly patients with gynecologic cancers: A prospective cohort study." Journal of Clinical Oncology 30, no. 15_suppl (2012): e15503-e15503. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e15503.

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e15503 Background: To evaluate the ability of a preoperative comprehensive geriatric assessment (CGA) to predict the risk of postoperative complications in elderly patients who underwent primary surgery for gynecologic cancers. Methods: Fifty-six consecutive patients (24 ovarian, 15 cervical, 6 endometrial, 5 uterine sarcoma, 4 vulvar, and 2 metastatic cancers) older than 70 years scheduled to take surgery electively for gynecologic cancer were preoperatively assessed by CGA. Every category of CGA was evaluated for in-hospital postoperative complications and mortality within 30 days of surgery

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Healthcare Engineering, Journal of. "Retracted: Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning." Journal of Healthcare Engineering 2023 (September 20, 2023): 1. http://dx.doi.org/10.1155/2023/9891682.

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Kim, Jae Weon, Dong-Hoon Suh, Mi-Kyung Kim, et al. "Prediction of major surgical complications by comprehensive geriatric assessment in elderly patients with gynecologic cancers: A prospective cohort study." Journal of Clinical Oncology 31, no. 15_suppl (2013): e20530-e20530. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e20530.

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e20530 Background: To evaluate the ability of a preoperative comprehensive geriatric assessment (CGA) to predict the risk of postoperative complications in elderly patients who underwent primary surgery for gynecologic cancers. Methods: Sixty consecutive patients (26 ovarian, 16 cervical, 7 endometrial, 5 uterine sarcoma, 4 vulvar, and 2 metastatic cancers) older than 70 years scheduled to take surgery electively for gynecologic cancer were preoperatively assessed by CGA. Every category of CGA was evaluated for in-hospital postoperative complications and mortality within 30 days of surgery. Ma

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Coia Jadresic, M., and J. Baker. "DEVELOPMENT OF A PREDICTION MODEL (NZSPINE) FOR SIGNIFICANT ADVERSE OUTCOME AFTER SPINE SURGERY." Orthopaedic Proceedings 105-B, SUPP_3 (2023): 5. http://dx.doi.org/10.1302/1358-992x.2023.3.005.

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Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation studies have shown mixed results. We present the development, validation, and comparative evaluation of novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery.Data was gathered retrospectively from medical records of patients who underwent spine surgery at Waikato Hospital between January 2019 and December 2020 (n = 488). Variables were selected a priori based on previous evidence and clinical judgement. Postoperative adverse events were class

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Kutovyi, O. B., and K. O. Denysova. "PROSPECTS OF EARLY COMPLICATION AFTER PANCREATICODUODENECTOMY PREDICTION." Bulletin of Problems Biology and Medicine 1, no. 1 (2022): 136. http://dx.doi.org/10.29254/2077-4214-2022-1-163-136-140.

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van de Beld, Jorn-Jan, David Crull, Julia Mikhal, et al. "Complication Prediction after Esophagectomy with Machine Learning." Diagnostics 14, no. 4 (2024): 439. http://dx.doi.org/10.3390/diagnostics14040439.

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Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respect

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Kim, Kwang Hyeon, Suk Lee, Jang Bo Shim, et al. "Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study." Journal of Radiotherapy in Practice 16, no. 2 (2017): 161–70. http://dx.doi.org/10.1017/s1460396916000583.

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AbstractPurposeThe aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system.Materials and methodsWe analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapyTM treatment plans for prostate cancer were established, and a hundred modelled

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Veeravagu, Anand, Amy Li, Christian Swinney, et al. "Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool." Journal of Neurosurgery: Spine 27, no. 1 (2017): 81–91. http://dx.doi.org/10.3171/2016.12.spine16969.

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OBJECTIVEThe ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort.METHODSThe spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 pati

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Dzakiyullah, Nur Rachman, Mohd Aboobaider Burhanuddin, Raja Rina Raja Ikram, Novanto Yudistira, Muhammad Rifqi Fauzi, and Dwijoko Purbohadi. "Multi-Label Risk Prediction Diabetes Complication Using Machine Learning Models." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 16 (2024): 66–88. https://doi.org/10.3991/ijoe.v20i16.51643.

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Early diagnosis of diabetic complications based on risk factors is essential but remains understudied, particularly in the context of multi-label classification (MLC). This study leverages data from the behavioral risk factor surveillance system (BRFSS) from 2016 to 2021 to classify seven diabetes complications using MLC techniques combined with multiple machine learning (ML) models. We analyzed 33 variables per dataset year after thorough statistical analysis and preprocessing. Seven ML models were employed: Artificial neural network (ANN), random forest (RF), decision tree (DT), K-nearest ne

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Pratama, Bagus, Alvira Balqis Soraya, Desta Eko Indrawan, et al. "77. Correlation of Adherence to Antihypertensive Medications and 10-year Risk Prediction of a Fatal or Non-fatal Major Cardiovascular Event in Hypertensive Patient." Journal of Hypertension 42, Suppl 2 (2024): e20. http://dx.doi.org/10.1097/01.hjh.0001027088.70686.6c.

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Background: Hypertension is the one of high mortality uncommunicable disease (35%) due to cardiovascular complication events. That complications are caused by low of standard hypertension treatment including lack of adherence to antihypertensive medication. The risk prediction of cardiovascular complication event could be simply done with WHO/ISH risk prediction chart. Patient adherence to antihypertensive medication is the main factor determining the success of therapy to preventing of complication. Objective: To find out the correlation of adherence to antihypertensive medications and 10-yea

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Johnson, Cassandra, Insiyah Campwala, and Subhas Gupta. "Examining the validity of the ACS-NSQIP Risk Calculator in plastic surgery: lack of input specificity, outcome variability and imprecise risk calculations." Journal of Investigative Medicine 65, no. 3 (2016): 722–25. http://dx.doi.org/10.1136/jim-2016-000224.

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American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) created the Surgical Risk Calculator, to allow physicians to offer patients a risk-adjusted 30-day surgical outcome prediction. This tool has not yet been validated in plastic surgery. A retrospective analysis of all plastic surgery-specific complications from a quality assurance database from September 2013 through July 2015 was performed. Patient preoperative risk factors were entered into the ACS Surgical Risk Calculator, and predicted outcomes were compared with actual morbidities. The difference in aver

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Rocans, Rihards P., Janis Zarins, Evita Bine, et al. "The Controlling Nutritional Status (CONUT) Score for Prediction of Microvascular Flap Complications in Reconstructive Surgery." Journal of Clinical Medicine 12, no. 14 (2023): 4794. http://dx.doi.org/10.3390/jcm12144794.

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Microvascular flap surgery is a widely acknowledged procedure for significant defect reconstruction. Multiple flap complication risk factors have been identified, yet there are limited data on laboratory biomarkers for the prediction of flap loss. The controlling nutritional status (CONUT) score has demonstrated good postoperative outcome assessment ability in diverse surgical populations. We aim to assess the predictive value of the CONUT score for complications in microvascular flap surgery. This prospective cohort study includes 72 adult patients undergoing elective microvascular flap surge

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Wilson, Jefferson R., Paul M. Arnold, Anoushka Singh, Sukhvinder Kalsi-Ryan, and Michael G. Fehlings. "Clinical prediction model for acute inpatient complications after traumatic cervical spinal cord injury: a subanalysis from the Surgical Timing in Acute Spinal Cord Injury Study." Journal of Neurosurgery: Spine 17, Suppl1 (2012): 46–51. http://dx.doi.org/10.3171/2012.4.aospine1246.

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Object While the majority of existing reports focus on complications sustained during the chronic stages after traumatic spinal cord injury (SCI), the objective in the current study was to characterize and quantify acute inpatient complications. In addition, the authors sought to create a prediction model using clinical variables documented at hospital admission to predict acute complication development. Methods Analyses were based on data from the Surgical Timing in Acute Spinal Cord Injury Study (STASCIS) data registry, which contains prospective information on adult patients with cervical S

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Bonde, Mikkel, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, and Martin Sillesen. "Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients." PLOS ONE 19, no. 12 (2024): e0316402. https://doi.org/10.1371/journal.pone.0316402.

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Introduction Pancreaticoduodenectomy (PD) for patients with pancreatic ductal adenocarcinoma (PDAC) is associated with a high risk of postoperative complications (PoCs) and risk prediction of these is therefore critical for optimal treatment planning. We hypothesize that novel deep learning network approaches through transfer learning may be superior to legacy approaches for PoC risk prediction in the PDAC surgical setting. Methods Data from the US National Surgical Quality Improvement Program (NSQIP) 2002–2018 were used, with a total of 5,881,881 million patients, including 31,728 PD patients

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Volchak, Aleksandr A., and Ivan Kirvel. "Lake water level variations in Belarus." Limnological Review 13, no. 2 (2013): 115–26. http://dx.doi.org/10.2478/limre-2013-0013.

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Abstract Lake level is one of the most important lake characteristics which allows the results of different effects to be identified and detected. In this work time series of the water levels of Belorussian lakes were analysed in order to detect pattern variations, to evaluate quantitatively the transformation of the hydrological regime of lake ecosystems and to develop prediction models. The possibility of plotting predicting models of lake water levels one year in advance was shown. The complication in plotting predicting models is in its individuality, the huge volume of initial data and th

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Soruba Rani, G., K. Padma, and Nancy F. "Uterine Artery Doppler At 11-14 weeks in Prediction of Preeclampsia." Indian Journal of Obstetrics and Gynecology 10, no. 2 (2022): 85–90. http://dx.doi.org/10.21088/ijog.2321.1636.10222.9.

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Abstract Identification of women at high risk for preeclampsia improves pregnancy outcome, early prediction of preeclampsia prevents dreadful maternal complication thereby reduces maternal mortality & morbidity and fetal complication. A prospective study conducted in Govt. Rajaji Hospital Madurai from January 2021 – September 2021. 300 antenatal women were interrogated between 11-14 weeks of GA with uterine artery Doppler. Out of which 55.3% of High risk women were predicted as preeclampsia & 27% of low risk women were predicted as preeclampsia. These patients were treated with T. Aspi

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Yousefi, Leila, and Allan Tucker. "Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction." Intelligent Data Analysis 26, no. 2 (2022): 501–24. http://dx.doi.org/10.3233/ida-205570.

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Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type

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Schonfeld, Ethan, Aaradhya Pant, Aaryan Shah, et al. "Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery." Journal of Clinical Medicine 13, no. 3 (2024): 656. http://dx.doi.org/10.3390/jcm13030656.

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Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using

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S. Fuentes, Sergio M., Luis A. F. Chávez, Eduardo M. M. López, Christian D. C. Cardona, and Laís L. M. Goti. "The impact of artificial intelligence in general surgery: enhancing precision, efficiency, and outcomes." International Journal of Research in Medical Sciences 13, no. 1 (2024): 293–97. https://doi.org/10.18203/2320-6012.ijrms20244129.

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The integration of artificial intelligence (AI) into general surgery has brought significant advancements in surgical precision, postoperative complication prediction, and intraoperative assistance. Despite its potential, AI faces challenges regarding its broad implementation in clinical practice. This systematic review aims to assess the impact of AI on clinical outcomes in general surgery, including diagnostic accuracy, complication prediction, and surgical error reduction. A systematic review was conducted using PubMed, Scopus, and Web of Science databases, focusing on studies published bet

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Ke, Janny X. C., Tim T. H. Jen, Sihaoyu Gao, et al. "Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy." PLOS ONE 19, no. 12 (2024): e0314526. https://doi.org/10.1371/journal.pone.0314526.

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Background Patients undergoing colectomy are at risk of numerous major complications. However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring. We sought to develop and internally validate Cox proportional hazards models to predict time-to-complication of major complications within 30 days after elective colectomy. Methods We studied a retrospective cohort from the multicentered American College of Surgeons National Surgic

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Jensen, Derek, Stefan Graw, Sida Niu, Vassili Glazyrine, Devin Koestler, and Eugene K. Lee. "Preoperative risk factors predicting postoperative complications in radical cystectomy for bladder cancer." Journal of Clinical Oncology 35, no. 6_suppl (2017): 395. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.395.

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395 Background: Radical cystectomy is an extensive operation with complications reported in up to 30.5% of patients. High complication rates contribute to increased costs, patient morbidity and mortality. Accurate prospective predictions of patients’ risk for post−surgical complications have the potential to identify at risk patients. Risk estimators have been developed but often involve an extensive number of factors or produce expansive results that are not clinically useful. Methods: 330 patients who underwent radical cystectomy for bladder cancer from January 2008 to July 2014 were include

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Weller, Grant B., Jenna Lovely, David W. Larson, Berton A. Earnshaw, and Marianne Huebner. "Leveraging electronic health records for predictive modeling of post-surgical complications." Statistical Methods in Medical Research 27, no. 11 (2017): 3271–85. http://dx.doi.org/10.1177/0962280217696115.

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Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health record data. However, these data provide opportunities to use probabilistic methods for early prediction of adverse health events, which may be more informative than deterministic algorithms. Electronic health record data from a set of 9598 colorectal surgery cases from 2010 to 2014 were used to predict

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Barker, Fred G., William E. Butler, Sue Lyons, et al. "Dose—volume prediction of radiation-related complications after proton beam radiosurgery for cerebral arteriovenous malformations." Journal of Neurosurgery 99, no. 2 (2003): 254–63. http://dx.doi.org/10.3171/jns.2003.99.2.0254.

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Object. The use of radiosurgery for the treatment of cerebral arteriovenous malformations (AVMs) and other lesions demands an accurate understanding of the risk of radiation-related complications. Some commonly used formulas for predicting risk are based on extrapolation from small numbers of animal experiments, pilot human treatment series, and theoretical radiobiological considerations. The authors studied the incidence of complications after AVM radiosurgery in relation to dose, volume, and other factors in a large patient series. Methods. A retrospective review was conducted in 1329 patien

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Tariq, R., S. Malik, and S. Khanna. "A180 SYSTEMATIC REVIEW OF MACHINE LEARNING-BASED PREDICTIVE MODELS FOR CLOSTRIDIOIDES DIFFICILE INFECTION." Journal of the Canadian Association of Gastroenterology 7, Supplement_1 (2024): 141–42. http://dx.doi.org/10.1093/jcag/gwad061.180.

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Abstract Background Clostridioides difficile infection (CDI) is a significant healthcare-associated infection that poses a substantial burden on patients and healthcare systems. Despite extensive research, accurately predicting CDI incidence and its associated complications remains a challenge. Electronic health records (EHRs) contain a wealth of clinical data that could potentially aid in predicting CDI and its outcomes. Machine-learning (ML) models have emerged as promising tools in healthcare, offering the potential to harness this data and enhance our ability to predict CDI incidence and c

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Rachata, Napa, Punnarumol Temdee, Worasak Rueangsirarak, and Chayapol Kamyod. "Fuzzy based Risk Predictive Model for Cardiovascular Complication of Patient with Type 2 Diabetes Mellitus and Hypertension." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 1 (2019): 49–58. http://dx.doi.org/10.37936/ecti-cit.2019131.132114.

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Cardiovascular diseases are chronic diseases that cause serious morbidity and mortality worldwide. Unfortunately, the patients with type 2 diabetes mellitus and hypertension have a high risk of having a cardiovascular complication. For these reasons, patients with type 2 diabetes mellitus and hypertension should be aware of cardiovascular complication along their healthcare journey. To prevent cardiovascular complication from diabetes and hypertension, accurate risk prediction is required for a long term self-management process. Consequently, this paper proposes a fuzzy logic based method for

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Parekh, U., and H. Sarkar. "Machine learning tools for complication prediction in spine surgery." Brain and Spine 1 (2021): 100807. http://dx.doi.org/10.1016/j.bas.2021.100807.

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Psutka, Sarah P., Roman Gulati, Michael A. S. Jewett, et al. "A novel clinical decision aid to support personalized treatment selection for patients with CT1 renal cortical masses: Results from a multi-institutional competing risks analysis including performance status and comorbidity." Journal of Clinical Oncology 38, no. 6_suppl (2020): 610. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.610.

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610 Background: Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. Using a contemporary multi-institutional cohort, we developed treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). Methods: Preoperative clinical and radiological features were collected for eligible patients aged 18-91 years treated at four academic centers from 200

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Brink, Huguette S., Aart Jan van der Lely, and Joke van der Linden. "The potential role of biomarkers in predicting gestational diabetes." Endocrine Connections 5, no. 5 (2016): R26—R34. http://dx.doi.org/10.1530/ec-16-0033.

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Gestational diabetes (GD) is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardio

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Tongaria, Khushboo, Ashok Kumar, and Simar Kaur. "Prediction of adverse effects of preeclampsia." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 9, no. 11 (2020): 4420. http://dx.doi.org/10.18203/2320-1770.ijrcog20204786.

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Background: To predict the adverse maternal, perinatal and combined (both maternal and perinatal) outcome in preeclampsia by using various clinical and laboratory variables. Methods: Five hundred fifty women diagnosed with preeclampsia were included and twenty-four women were excluded from the study due to exclusion criteria, six women decline to participate, twenty women were lost to follow up, three women withdrew consent, so a total of 497 women were followed up in the study.Results: Mean age of study population was 26.82±4.48 years. Majority of women with preeclampsia delivered vaginally.

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Van der Cruyssen, Fréderic, Pieter-Jan Verhelst, and Reinhilde Jacobs. "The Use of Artificial Intelligence in Third Molar Surgery Risk Assessment." Dental Update 51, no. 1 (2024): 28–33. http://dx.doi.org/10.12968/denu.2024.51.1.28.

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Third molar removal complication rates can be as high as 30%. Risk assessment tools may lower these rates. Artificial intelligence (AI) driven prediction models are a promising approach to predict possible unfavourable outcomes and cone beam computed tomography imaging may play an important role. AI prediction models are showing excellent results in research settings. To be implemented in clinical practice they will need to overcome some robustness, security, liability, and practical issues. If they do, AI prediction models can be integrated in electronic patient record systems by alerting cli

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Luo, Xin, Jijia Sun, Hong Pan, et al. "Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining." PLOS ONE 18, no. 8 (2023): e0289749. http://dx.doi.org/10.1371/journal.pone.0289749.

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In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk compl

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Siddaiah-Subramanya, Manjunath, Yashashwi Sinha, Sivesh K. Kamarajah, Abdulrahman Ghoneim, James Halle-Smith, and Benjamin HL Tan. "Incremental Shuttle Walk Test and Body Composition Measures: Useful Predictive Factors For Complications After Oesophago-Gastric Cancer Surgery?" Foregut: The Journal of the American Foregut Society 1, no. 4 (2021): 314–20. http://dx.doi.org/10.1177/26345161211063448.

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Background: Oesophagogastric cancer resection carries a morbidity, as high as 60%. Better patient selection, not only with regards to clinical stage but also fitness, reduces morbidity, and improves outcome. Assessment of body composition measures in particular sarcopenia and the incremental shuttle walk test (ISWT) are 2 such tools to evaluate patients’ fitness. We investigate the usefulness of these 2 tools in predicting post-operative outcomes following oesophagogastric resection. Methods: All patients who underwent oesophagogastric cancer resection between 2017 and 2019 and consented to pa

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Savu, Elena, Liviu Vasile, Mircea-Sebastian Serbanescu, et al. "Clinicopathological Analysis of Complicated Colorectal Cancer: A Five-Year Retrospective Study from a Single Surgery Unit." Diagnostics 13, no. 12 (2023): 2016. http://dx.doi.org/10.3390/diagnostics13122016.

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Patients with primary colorectal cancer can present with obstructions, tumor bleeding, or perforations, which represent acute complications. This paper aimed to analyze and compare the clinical and pathological profiles of two patient groups: one with colorectal cancer and a related complication and another without any specific complication. We performed a five-year retrospective study on colorectal cancer patients admitted to a surgery unit and comparatively explored the main clinical and pathological features of the tumors belonging to the two groups. A total of 250 patients with colorectal

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Noble, Peter A., Blake D. Hamilton, and Glenn Gerber. "Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients." PLOS ONE 19, no. 5 (2024): e0301812. http://dx.doi.org/10.1371/journal.pone.0301812.

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Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outc

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Sri, Kusumadewi, Rosita Linda, and Gustri Wahyuni Elyza. "Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 532–42. https://doi.org/10.11591/ijai.v12.i2.pp532-542.

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It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cho

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Ben Abdelkrim, Mehdi, Mohamed Amine Elghali, Amany Moussa, and Ahmed Ben Abdelaziz. "Contextual Validation of the Prediction of Postoperative Complications of Colorectal Surgery by the “ACS NSQIP^®Risk Calculator” in a Tunisian Center." Cancer Informatics 21 (January 2022): 117693512211351. http://dx.doi.org/10.1177/11769351221135153.

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Context: Models for predicting individual risks of surgical complications are advantageous for operative decision making and the nature of postoperative management procedures. Objective: Validate the “ACS NSQIP® Risk Calculator” in the prediction of postoperative complications during colorectal cancer surgery, operated during the years 2015 to 2019. Methods: this is a prognostic validation study of the “ACS NSQIP®” applied retrospectively to patients operated on for colorectal cancer in the surgical department of Farhat Hached hospital, during the 2015 and 2019 5-year term. Three levels of adj

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S N, Shivappriya, Sneha Nagarajan, Srima E S, and Sriram K. "Prediction of Pregnancy Complication and Child Mortality Using Regression Analysis." IFAC-PapersOnLine 58, no. 3 (2024): 32–37. http://dx.doi.org/10.1016/j.ifacol.2024.07.120.

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Stenberg, Erik, Yang Cao, Eva Szabo, Erik Näslund, Ingmar Näslund, and Johan Ottosson. "Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery." Obesity Surgery 28, no. 7 (2018): 1869–75. http://dx.doi.org/10.1007/s11695-017-3099-2.

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LEE, Huisong, Oh Chul KWON, In Woong HAN, and Jin Seok HEO. "Development of complication prediction platform after pancreatoduodenectomy using artificial intelligence." Annals of Hepato-Biliary-Pancreatic Surgery 27, no. 1 (2023): S152. http://dx.doi.org/10.14701/ahbps.2023s1.kahbps-2.

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Tang, Baoyu, Yuyu Yuan, Jincui Yang, Lirong Qiu, Shasha Zhang, and Jinsheng Shi. "Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records." Sensors 22, no. 21 (2022): 8454. http://dx.doi.org/10.3390/s22218454.

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Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in the

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Bugarin, Amador, Akash A. Shah, Sai Devana, Changhee Lee, and Nelson F. SooHoo. "Development of a Machine Learning Algorithm for Prediction of Complications after Ankle Arthrodesis." Foot & Ankle Orthopaedics 7, no. 1 (2022): 2473011421S0012. http://dx.doi.org/10.1177/2473011421s00122.

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Category: Ankle Arthritis; Ankle Introduction/Purpose: Ankle arthrodesis and total ankle replacement are the most commonly performed procedures for surgical management of ankle arthritis. Arthrodesis provides effective pain relief but the rate of complications after arthrodesis is higher as it is more commonly performed in patients with comorbidities that preclude ankle replacement. Accurately risk- stratifying patients who undergo ankle arthrodesis would be of great utility, given the significant cost and morbidity associated with developing major perioperative complications. There is a pauci

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Saqeb, Khan Md Nazmus. "Serum Procalcitonin in the Prediction of Severity and Outcome of Acute Pancreatitis." Bangladesh Critical Care Journal 9, no. 1 (2021): 16–21. http://dx.doi.org/10.3329/bccj.v9i1.53051.

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Background: Different modalities are available for predicting severity and outcome of acute pancreatitis. A single marker with high sensitivity and specificity is yet to be identified. Aim: This study intends to find out the utility of serum procalcitonin in predicting the severity and outcome of acute pancreatitis. Methods: 117 patients admitted with acute pancreatitis were included.Clinical parameters and biochemical tests were recorded on admission, on day-3 & day-5 of admission. CT scan was performed in all patients. Serum procalcitonin was done on admission. Multifactorial

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Zehnder, Pascal, Ulrike Held, Tim Pigott, et al. "Development of a model to predict the probability of incurring a complication during spine surgery." European Spine Journal 30, no. 5 (2021): 1337–54. http://dx.doi.org/10.1007/s00586-021-06777-5.

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Abstract Purpose Predictive models in spine surgery are of use in shared decision-making. This study sought to develop multivariable models to predict the probability of general and surgical perioperative complications of spinal surgery for lumbar degenerative diseases. Methods Data came from EUROSPINE's Spine Tango Registry (1.2012–12.2017). Separate prediction models were built for surgical and general complications. Potential predictors included age, gender, previous spine surgery, additional pathology, BMI, smoking status, morbidity, prophylaxis, technology used, and the modified Mirza inv

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Scheurer, Fabrice, Sascha Halvachizadeh, Till Berk, Hans-Christoph Pape, and Roman Pfeifer. "Chest CT Findings and SARS-CoV-2 Infection in Trauma Patients—Is There a Prediction towards Higher Complication Rates?" Journal of Clinical Medicine 11, no. 21 (2022): 6401. http://dx.doi.org/10.3390/jcm11216401.

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Background: Polytrauma patients with SARS-CoV-2 infections may be associated with an increased complication rate. The main goal of this study was to analyze the clinical course of trauma patients with COVID infection and a positive CT finding. Methods: This was a retrospective in-hospital study. Polytrauma patients diagnosed with SARS-CoV-2 infections were included in our analysis. The outcome parameters were pulmonary complication during admission, pulmonary embolism, pleural effusion, pneumonia, mortality, length of stay and readmission < 30 days. Results: 48 patients were included in the

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