title: Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis.
abstract: 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.
authors.affiliation: Department of Anesthesiology and Critical Care, Taipei Veteran General Hospital, Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. ycsidney@gmail.com
authors.forename: Ying Che
authors.initials: YC
authors.lastname: Huang
authors.forename: Kuang Yi
authors.initials: KY
authors.lastname: Chang
authors.forename: Shih Pin
authors.initials: SP
authors.lastname: Lin
authors.forename: Kung
authors.initials: K
authors.lastname: Chen
authors.forename: Kwok Hon
authors.initials: KH
authors.lastname: Chan
authors.forename: Polun
authors.initials: P
authors.lastname: Chang
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003422:Critical Care / Q000706:statistics & numerical data*; D016638:Critical Illness; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D010343:Patient Admission; D011336:Probability; D011379:Prognosis; D012372:ROC Curve; D012044:Regression Analysis; D012189:Retrospective Studies; D013624:Taiwan; D013997:Time Factors
keywords: Advisory system; Blood cell count; Discrete time event history analysis; Intensive Care Unit; Mortality probability; Prediction model