journal of biomedical informatics
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In-hospital Death Prediction by Multilevel Logistic Regression in Patients with Acute Coronary Syndromes

Author(s): Jindra Reissigová, Zdenek Monhart, Jana Zvárová, Petr Hanzlícek, Hana Grünfeldová, Petr Janský, Jan Vojácek, Petr Widimský

Background: The odds of death of patients with acute coronary syndromes (ACS) in non-PCI (percutaneous coronary intervention) hospitals in the Czech Republic change depending on a number of factors (age, heart rate, systolic blood pressure, creatinine, Killip class, the diagnosis, and the number of recommended medications and treatment of ACE-inhibitor or sartan).

Objectives: We present a detailed description of multilevel logistic regression applied in the derivation of the conclusion described in the Background, namely we compare multilevel logistic regression with logistic regression.

Methods: The above mentioned clinical findings have been derived on the basis of data from the three-year (7/2008-6/2011) registry of acute coronary syndromes ALERT-CZ (Acute coronary syndromes – Longitudinal Evaluation of Real-life Treatment in non-PCI hospitals in the Czech Republic). A total of 32 hospitals contributed into the registry. The number of patients with ACS (n=6013) in the hospitals varied from 15 to 827.

Results: The likelihood ratio test showed that the independence of medical outcomes across hospitals cannot be assumed (p<0.001, the variance partition coefficient VPC=8.9%). For this reason, we chose multilevel logistic regression to analyse data, specifically logistic mixed regression (the hospital identity was a random effect). The calibration properties of this model were very good (Hosmer-Lemeshow test, p=0.989). The total discriminant ability of the model was 91.8%.

Conclusions: Considering some differences among hospitals, it was appropriate to take into account patient affiliation to various hospitals and to use multilevel logistic regression instead of logistic regression.