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Composite Outcomes of Mortality and Readmission in Patients with Heart Failure: Retrospective Review of Administrative Datasets

Authors Roshanghalb A, Mazzali C, Lettieri E

Received 24 March 2020

Accepted for publication 22 May 2020

Published 24 June 2020 Volume 2020:13 Pages 539—547


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

Afsaneh Roshanghalb,1 Cristina Mazzali,2 Emanuele Lettieri1

1Department of Management, Economics & Industrial Engineering, Politecnico di Milano, Milan, Italy; 2Quality and Clinical Risk Unit, Niguarda Hospital, Milan, Italy

Correspondence: Afsaneh Roshanghalb Email

Background: Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative.
Methods: Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset.
Results: Our principal findings demonstrate that the model’s discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729– 0.748)) and readmissions (AUC=0.578, CI (0.562– 0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666– 0.684)).
Conclusion: Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.

Keywords: administrative health data, heart failure, mortality, patient readmission

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