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 indicated by a receiver operating characteristic area under the curve of 0.882 (95% confidence interval [CI]: 0.872–0.893). The model was applied on data sets of 2019 and 2020 ( n = 28,556 cases) and the hospital standardized mortality ratio (HSMR) was computed as the observed to expected death ratio. Observed mortality rates were 5.84% (2019) and 6.21% (2020), HSMRs based on an individual case‐based mortality probability were 100.0 (95% CI: 93.3–107.2; p = 1.000) for 2019 and 99.3 (95% CI: 92.5–106.4; p = .850) for 2020. Within subgroups of age or hospital volume, there were no significant differences between observed and expected deaths. When stratified for pandemic phases, no excess death during the COVID‐19 pandemic was observed. Conclusion Applying an ML algorithm to calculate expected inpatient mortality based on administrative data, there was no excess death above expected event rates in HF patients during the COVID‐19 pandemic.
【저자키워드】 machine learning, heart failure, COVID‐19 pandemic, mortality prediction, administrative data, 【초록키워드】 pandemic, Mortality, hospital, Diagnosis, discharge, COVID‐19, Probability, Algorithm, Patient, death, age, mortality rate, characteristic, Evidence, deaths, data set, expected mortality, subgroup, Inpatient, Volume, no significant difference, 95% confidence interval, SARS‐CoV‐2 infection, event rate, Randomly, hospital admission rate, Result, tested, reported, indicated, applied, expected, stratified, Applying, calculate, Helio, Observed, were excluded, 【제목키워드】 Mortality, hospital, COVID‐19, Patient, Helio,