ArticleCritical care (London, England)2007
Predicting mortality in intensive care unit survivors using a subjective scoring system.
Article in Critical care (London, England), 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Analyzing Information Disparities across Modalities in Mortality Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center.Journal of the Chinese Medical Association : JCMA · 2024Article
- An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study.Frontiers in pediatrics · 2022Article
- Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.Scientific reports · 2021Article
- Factors associated with physicians' predictions of six-month mortality in critically ill patients.Journal of the Intensive Care Society · 2020Article
- Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.International journal of medical informatics · 2020Article
- A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients.BMC medical informatics and decision making · 2014Article
- A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system.BMC medical informatics and decision making · 2010Article
- Ward mortality after ICU discharge: a multicenter validation of the Sabadell score.Intensive care medicine · 2010Article
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2 authors.
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Abstract
Most prognostic models rely on variables recorded within 24 hours of admission to predict the mortality rate of patients in the intensive care unit (ICU). Although a significant number of patients die after discharge from the ICU, there is a paucity of data related to predicting hospital mortality based on information obtained at ICU discharge. It is likely that experienced intensivists may be able to predict the likelihood of hospital death at ICU discharge accurately if they incorporate patients' age, preferences regarding life support, comorbidities, prehospital quality of life, and clinical course in the ICU into their prediction. However, if it is to be generalizable and reproducible and to perform well without bias, then a good prediction model should be based on objectively defined variables.
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