Relevant bibliographies by topics / Delirium prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Delirium prediction'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Delirium prediction"

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Linkaitė, Gabrielė, Mantas Riauka, Ignė Bunevičiūtė, and Saulius Vosylius. "Evaluation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for the patients in the intensive care unit." Acta medica Lituanica 25, no. 1 (2018): 14–22. http://dx.doi.org/10.6001/actamedica.v25i1.3699.

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Introduction. Delirium not only compromises patient care, but is also associated with poorer outcomes: increased duration of mechanical ventilation, higher mortality, and greater long-term cognitive dysfunction. The PRE-DELIRIC model is a tool used to calculate the risk of the development of delirium. The classification of the patients into groups by risk allows efficient initiation of preventive measures. The goal of this study was to validate the PRE-DELIRIC model using the CAM-ICU (The Confusion Assessment Method for the Intensive Care Unit) method for the diagnosis of delirium. Materials a
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Amerongen, Hilde van Nieuw, Sandra Stapel, Jan Jaap Spijkstra, Dagmar Ouweneel, and Jimmy Schenk. "Comparison of Prognostic Accuracy of 3 Delirium Prediction Models." American Journal of Critical Care 32, no. 1 (2023): 43–50. http://dx.doi.org/10.4037/ajcc2023213.

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Background Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed. Objectives To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation. Methods This retrospective study involved adult patients admitted to the intensive care unit during a 2-year
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Pagali, Sandeep R., Donna Miller, Karen Fischer, et al. "Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool." Mayo Clinic Proceedings 96, no. 5 (2021): 1229–35. http://dx.doi.org/10.1016/j.mayocp.2020.08.049.

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Jauk, Stefanie, Diether Kramer, Birgit Großauer, et al. "Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study." Journal of the American Medical Informatics Association 27, no. 9 (2020): 1383–92. http://dx.doi.org/10.1093/jamia/ocaa113.

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Abstract Objective Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. Materials and Methods Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 p
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Van, den Boogaard M., L. Schoonhoven, E. Maseda, et al. "Recalibration of the delirium prediction model for ICU patients (PRE-DELIRIC): a multinational observational study." Intensive Care med 40, no. 3 (2014): 361–9. https://doi.org/10.1007/s00134-013-3202-7.

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<strong>Purpose:&nbsp;</strong>Recalibration and determining discriminative power, internationally, of the existing delirium prediction model (PRE-DELIRIC) for intensive care patients. <strong>Methods:&nbsp;</strong>A prospective multicenter cohort study was performed in eight intensive care units (ICUs) in six countries. The ten predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 h after ICU admission. The confusion assessment method for the intensive care unit (CAM-ICU) was used to identi
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Boettger, Soenke, Rafael Meyer, André Richter, et al. "Screening for delirium with the Intensive Care Delirium Screening Checklist (ICDSC): Symptom profile and utility of individual items in the identification of delirium dependent on the level of sedation." Palliative and Supportive Care 17, no. 1 (2018): 74–81. http://dx.doi.org/10.1017/s1478951518000202.

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AbstractObjectiveThe importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.MethodIn this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, a
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Arai, *Naohiro, Yuki Sugiura, Shinichiro Nakajima, et al. "PREDICTION OF POSTOPERATIVE DELIRIUM BY BLOOD METABOLOME." International Journal of Neuropsychopharmacology 28, Supplement_1 (2025): i264. https://doi.org/10.1093/ijnp/pyae059.466.

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Abstract Background Delirium is a notable risk factor for cognitive dysfunction and poor prognosis. Despite its importance, there is currently no established blood marker that can predict postoperative delirium in the preoperative period. Aims &amp; Objectives We aimed to examine that water-soluble metabolites, lipids, and cytokines in peripheral blood could uniquely classify postoperative delirium. In addition, we investigated whether changes in neuroinflammation-related and water-soluble metabolites in the indoleamine 2,3-dioxygenase (IDO) pathway could predict postoperative delirium. Method
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Yan, Pengbo. "Research Progress in the Construction of Delirium Risk Warning Model for ICU Patients Based on Decision Tree: A Review of the Literature." Journal of Modern Nursing Practice and Research 4, no. 4 (2024): 20. http://dx.doi.org/10.53964/jmnpr.2024020.

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Intensive Care Unit (ICU) delirium is a cerebral syndrome characterized by acute disturbance of consciousness, with an incidence of 38%-87%. The occurrence of delirium can lead to prolonged hospital stay, accidental extubation rate, mortality and other adverse consequences. Therefore, early identification of delirium and active correction of reversible causes appear to be particularly important. At present, the risk prediction models for delirium in ICU constructed at home and abroad mainly use logistic regression to build delirium risk prediction models for patients admitted to ICU≥24h. Howev
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Green, Cameron, William Bonavia, Candice Toh, and Ravindranath Tiruvoipati. "Prediction of ICU Delirium." Critical Care Medicine 47, no. 3 (2019): 428–35. http://dx.doi.org/10.1097/ccm.0000000000003577.

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Matsumoto, Koutarou, Yasunobu Nohara, Mikako Sakaguchi, et al. "Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow." Applied Sciences 13, no. 3 (2023): 1564. http://dx.doi.org/10.3390/app13031564.

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Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients wer
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Dissertations / Theses on the topic "Delirium prediction"

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Sheikhalishahi, Seyedmostafa. "Machine learning applications in Intensive Care Unit." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339274.

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The rapid digitalization of the healthcare domain in recent years highlighted the need for advanced predictive methods particularly based upon deep learning methods. Deep learning methods which are capable of dealing with time- series data have recently emerged in various fields such as natural language processing, machine translation, and the Intensive Care Unit (ICU). The recent applications of deep learning in ICU have increasingly received attention, and it has shown promising results for different clinical tasks; however, there is still a need for the benchmark models as far as a handful
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Ha, Albert Sangji. "A Contemporary, Population-Based Analysis of the Incidence, Cost, Outcomes, and Preoperative Risk Prediction of Postoperative Delirium Following Major Urologic Cancer Surgeries." Thesis, Harvard University, 2017. http://nrs.harvard.edu/urn-3:HUL.InstRepos:32676128.

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Introduction Postoperative delirium is associated with poor outcomes and increased healthcare costs across numerous surgical and medical disciplines. Although characterized in other surgical fields, the population-based incidence, outcomes, and cost of delirium have not been assessed in major urologic cancer surgeries. We sought to evaluate the incidence, outcomes, and cost of postoperative delirium after major urologic cancer surgeries, specifically after radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC) in the USA. We have also devel
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Cunningham, Emma Louise. "Predicting the risk of post-operative delirium : use of neuropsychology, serum and CSF biomarkers and genetics to predict risk of post-operative delirium." Thesis, Queen's University Belfast, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695315.

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Delirium following surgery is common and is associated with negative outcomes. Across surgical populations pre-operative cognitive impairment is a consistent risk factor for post-operative delirium. This study tests the hypothesis that the quantification of brain vulnerability, using neuropsychological tests, plasma and cerebrospinal fluid (CSF) biomarkers, and Apolipoprotein E status, can quantify the risk of post-operative delirium following elective primary arthroplasty surgery. An observational cohort study of patients over 65 years of age, admitted for elective primary hip or knee arthrop
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Book chapters on the topic "Delirium prediction"

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van den Boogaard, Mark, and John W. Devlin. "Prediction Models for Delirium in Critically Ill Adults." In Delirium. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25751-4_5.

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Coelho, Alexandra, Ana Cristina Braga, and José Mariz. "GLM’s in Data Science as a Tool in the Prediction of Delirium." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53025-8_40.

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Amadin, Frank Iwebuke, and Moses Eromosele Bello. "A Neuro Fuzzy Approach for Predicting Delirium." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01054-6_50.

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Coelho, Alexandra, and Ana Cristina Braga. "Predictive Application for Early Delirium Detection Subtypes Using GLM’s." In Computational Science and Its Applications – ICCSA 2024 Workshops. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65154-0_23.

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Mehrotra, Anchit, and Natasha Keric. "Delirium as a Predictor of Mortality in Mechanically Ventilated Patients in the Intensive Care Unit." In 50 Landmark Papers. CRC Press, 2019. http://dx.doi.org/10.1201/9780429316944-88.

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Jauk Stefanie, Kramer Diether, Schulz Stefan, and Leodolter Werner. "Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models." In Studies in Health Technology and Informatics. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-880-8-249.

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The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of a risk prediction model for delirium, as diabetes is known to be one of the most relevant variables for delirium prediction. We used four data sets varying in their correctness and completeness of diabetes coding as input for different machine learning algorithms. Although there was a higher prevalence of diabetes in delirium patients, the model p
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Matsumoto, Koutarou, Yasunobu Nohara, Mikako Sakaguchi, et al. "Development of Machine Learning Prediction Models for Self-Extubation After Delirium Using Emergency Department Data." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti231115.

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Delirium is common in the emergency department, and once it develops, there is a risk of self-extubation of drains and tubes, so it is critical to predict delirium before it occurs. Machine learning was used to create two prediction models in this study: one for predicting the occurrence of delirium and one for predicting self-extubation after delirium. Each model showed high discriminative performance, indicating the possibility of selecting high-risk cases. Visualization of predictors using Shapley additive explanation (SHAP), a machine learning interpretability method, showed that the predi
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Veeranki Sai P.K., Hayn Dieter, Kramer Diether, Jauk Stefanie, and Schreier Günter. "Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients." In Studies in Health Technology and Informatics. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-858-7-124.

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Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our latest studies, we showed that delirium prediction using machine learning algorithms is possible based on the patients' health history. The aim of this study is to compare the influence of nursing assessment data on prediction models with clinical and
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Lindenmeyer, Adrian, Sai Veeranki, Stefan Franke, Thomas Neumuth, Diether Kramer, and Daniel Schneider. "Knowledge Uncertainty Estimation for Reliable Clinical Decision Support: A Delirium Risk Prognosis Case Study." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250192.

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Introduction: Predictive models hold significant potential in healthcare, but their adoption in clinical settings is hampered by limited trust due to their inability to recognize when presented with unfamiliar data. Estimating knowledge uncertainty (KU) can mitigate this issue. This study aims to assess the capabilities of two targeted approaches, specifically Ensemble Neural Networks (ENN) and Spectral Normalized Neural Gaussian Processes (SNGP), in quantifying KU and detecting out-of-distribution (OoD) data within the context of delirium risk prediction. Methodology: Using a cohort of hospit
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Dostovic, Zikrija, Dzevdet Smajlovic, Ernestina Dostovic, and Omer C. "Risk Factors for Delirium in the Acute Stroke." In Mental Illnesses - Understanding, Prediction and Control. InTech, 2012. http://dx.doi.org/10.5772/29885.

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Conference papers on the topic "Delirium prediction"

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Mito, Shogo, Miho Miyajima, Hirofumi Tomioka, et al. "Postoperative Delirium Prediction Based on Preoperative Electrocardiogram and Electroencephalogram." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848992.

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Azevedo, Wylson, Eduardo Augusto Schutz, Mayara Menezes Attuy, Thamara Graziela Flores, and Melissa Agostini Lampert. "Prediction model to delirium in hospitalized elderly people." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.478.

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Introduction: Delirium has a high prevalence in hospitalized elderly patients. This is due to low hospital detection and the absence of a screening instrument. Objective: evaluate predictive variables in the development of delirium in na in-hospital environment. Methods: Cross-sectional study. Data collection was carried out between 2015-2016, with a sample of 493 elderly people. The variables used were age, sex, the reason for hospitalization, Identification of Elderly at Risk (ISAR), delirium during hospitalization using the Confusion Assessment Method, frailty using the Edmonton Scale, the
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Davoudi, Anis, Tezcan Ozrazgat-Baslanti, Ashkan Ebadi, Alberto C. Bursian, Azra Bihorac, and Parisa Rashidi. "Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data." In 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2017. http://dx.doi.org/10.1109/bibe.2017.00014.

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Lucini, Filipe R., Kirsten M. Fiest, Henry T. Stelfox, and Joon Lee. "Delirium prediction in the intensive care unit: a temporal approach." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176042.

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Kohistani, Z., S. Repschläger, W. Kai, et al. "Postoperative Delirium Prediction through Machine Learning in Patients Undergoing Aortocoronary Bypass Surgery." In 50th Annual Meeting of the German Society for Thoracic and Cardiovascular Surgery (DGTHG). Georg Thieme Verlag KG, 2021. http://dx.doi.org/10.1055/s-0041-1725694.

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Prendergast, N., P. Tiberio, K. Rengel, et al. "Derivation of a Clinical Prediction Rule for Sedative-Associated Delirium During Acute Respiratory Failure." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a2862.

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Kohistani, Z., S. Kebir, S. Repschläger, M. Hamiko, and F. Bakhtiary. "Comparison of Machine Learning Models for Delirium Prediction in Patients Undergoing Aortocoronary Bypass Surgery." In 51st Annual Meeting of the German Society for Thoracic and Cardiovascular Surgery (DGTHG). Georg Thieme Verlag KG, 2022. http://dx.doi.org/10.1055/s-0042-1742792.

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Contreras, Miguel, Brandon Silva, Benjamin Shickel, et al. "Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records." In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2023. http://dx.doi.org/10.1109/bhi58575.2023.10313445.

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Prendergast, N., P. J. Tiberio, C. A. Onyemekwu, et al. "Multiple Statistical Approaches to a Clinical Prediction Rule for Sedative-Associated Delirium During Acute Respiratory Failure." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a2284.

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Gergen, D. J., and E. L. Burnham. "Evaluating the Alcohol Use Disorders Identification Test (AUDIT-C) as a Delirium Prediction Tool in the Critically Ill." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a3574.

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