Expected and observed in-hospital mortality in heart failure patients before and during the COVID-19 pandemic: Introduction of the machine learning-based standardized mortality ratio at Helios hospitals.

ABSTRACT:

Background

Reduced hospital admission rates for heart failure (HF) and evidence of increased in-hospital mortality were reported during the COVID-19 pandemic. The aim of this study was to apply a machine learning (ML)-based mortality prediction model to examine whether the latter is attributable to differing case mixes and exceeds expected mortality rates.

Methods and results

Inpatient cases with a primary discharge diagnosis of HF non-electively admitted to 86 German Helios hospitals between 01/01/2016 and 08/31/2020 were identified. Patients with proven or suspected SARS-CoV-2 infection were excluded. ML-based models were developed, tuned, and tested using cases of 2016-2018 (n = 64,440; randomly split 75%/25%). Extreme gradient boosting showed the best model performance i

SUBMITTER: Konig S 

PROVIDER: S-EPMC8799043 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature