Comput Methods Programs Biomed - Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis.

Tópicos

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Resumo

As studies have pointed out, severity scores are imperfect at predicting individual clinical chance of survival. The clinical condition and pathophysiological status of these patients in the Intensive Care Unit might differ from or be more complicated than most predictive models account for. In addition, as the pathophysiological status changes over time, the likelihood of survival day by day will vary. Actually, it would decrease over time and a single prediction value cannot address this truth. Clearly, alternative models and refinements are warranted. In this study, we used discrete-time-event models with the changes of clinical variables, including blood cell counts, to predict daily probability of mortality in individual patients from day 3 to day 28 post Intensive Care Unit admission. Both models we built exhibited good discrimination in the training (overall area under ROC curve: 0.80 and 0.79, respectively) and validation cohorts (overall area under ROC curve: 0.78 and 0.76, respectively) to predict daily ICU mortality. The paper describes the methodology, the development process and the content of the models, and discusses the possibility of them to serve as the foundation of a new bedside advisory or alarm system.

Resumo Limpo

studi point sever score imperfect predict individu clinic chanc surviv clinic condit pathophysiolog status patient intens care unit might differ complic predict model account addit pathophysiolog status chang time likelihood surviv day day will vari actual decreas time singl predict valu address truth clear altern model refin warrant studi use discretetimeev model chang clinic variabl includ blood cell count predict daili probabl mortal individu patient day day post intens care unit admiss model built exhibit good discrimin train overal area roc curv respect valid cohort overal area roc curv respect predict daili icu mortal paper describ methodolog develop process content model discuss possibl serv foundat new bedsid advisori alarm system

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