Incorporating initial treatments improves performance of a mortality prediction model for patients with sepsis

Pharmacoepidemiology and Drug Safety
Tara LaguPeter K Lindenauer

Abstract

Mortality prediction models can be used to adjust for presenting severity of illness in observational studies of treatment effectiveness. We aimed to determine the incremental benefit of adding information about critical care services to a sepsis mortality prediction model. In a retrospective cohort of 166 931 eligible sepsis patients at 309 hospitals, we developed nested logistic regression models to predict mortality at the patient level. Our initial model included only demographic information. We then added progressively more detailed information such as comorbidities and initial treatments. We calculated each model's area under the receiver operating characteristic curve (AUROC) and also used a sheaf coefficient analysis to determine the relative effect of each additional group of variables. Model discrimination increased as more detailed patient information was added. With demographics alone, the AUROC was 0.59; adding comorbidities increased the AUROC to 0.67. The final model, which took into account mixed (hierarchical) effects at the hospital level as well as initial treatments administered within the first two hospital days, resulted in an AUROC of 0.78. The standardized sheaf coefficient for the initial treatments was...Continue Reading

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Citations

Feb 6, 2014·PloS One·Michael B RothbergPeter K Lindenauer
Jun 1, 2013·Healthcare : the Journal of Delivery Science and Innovation·Tara LaguPeter K Lindenauer
May 11, 2012·Pharmacoepidemiology and Drug Safety·Sebastian SchneeweissScott R Smith
Apr 10, 2014·Journal of Hospital Medicine : an Official Publication of the Society of Hospital Medicine·Tara LaguPeter K Lindenauer
Feb 18, 2016·Journal of Intensive Care Medicine·Christine A Motzkus, Roger Luckmann
Jan 26, 2021·International Journal of Clinical Practice·Concepción Beneyto-RipollMaría de Los Ángeles Carbonell-Torregrosa

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