The Use of Routine Laboratory Data to Predict In-Hospital Death in Medical Admissions

This source preferred by Gary Smith

Authors: Prytherch, D.R., Sirl, J.S., Schmidt, P.E., Featherstone, P.I., Weaver, P.C. and Smith, G.B.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T19-4GCXBRV-1&_user=1682380&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000011378&_version=1&_urlVersion=0&_userid=1682380&md5=355d4c4f8a494c5d53e1509e8fc6b948

Journal: Resuscitation

Volume: 66

Pages: 203-207

ISSN: 0300-9572

DOI: 10.1016/j.resuscitation.2005.02.011

The ability to predict clinical outcomes in the early phase of a patient's hospital admission could facilitate the optimal use of resources, might allow focused surveillance of high-risk patients and might permit early therapy. We investigated the hypothesis that the risk of in-hospital death of general medical patients can be modelled using a small number of commonly used laboratory and administrative items available within the first few hours of hospital admission. Matched administrative and laboratory data from 9497 adult hospital discharges, with a hospital discharge specialty of general medicine, were divided into two subsets. The dataset was split into a single development set, Q1 (n = 2257), and three validation sets, Q2, Q3 and Q4 (n1 = 2335, n2 = 2361, n3 = 2544). Hospital outcome (survival/non-survival) was obtained for all discharges. An outcome model was constructed from binary logistic regression of the development set data. The goodness-of-fit of the model for the validation sets was tested using receiver–operating characteristics curves (c-index) and Hosmer–Lemeshow statistics. Application of the model to the validation sets produced c-indices of 0.779 (Q2), 0.764 (Q3) and 0.757 (Q4), respectively, indicating good discrimination. Hosmer–Lemeshow analysis gave χ2 = 9.43 (Q2), χ2 = 7.39 (Q3) and χ2 = 8.00 (Q4) (p-values of 0.307, 0.495 and 0.433) for 8 degrees of freedom, indicating good calibration. The finding that the risk of hospital death can be predicted with routinely available data very early on after hospital admission has several potential uses. It raises the possibility that the surveillance and treatment of patients might be categorised by risk assessment means. Such a system might also be used to assess clinical performance, to evaluate the benefits of introducing acute care interventions or to investigate differences between acute care systems.

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