Development and validation of predictive models for mortality and length of hospitalisation in adults with community-onset bacteraemia under the COVID-19 pandemic: Application of early data dynamics

Abstract

Abstract Background Bacteraemia is associated with increased morbidity and mortality and contributes substantially to healthcare costs. The development of a scoring system to predict the short-term mortality and the length of hospitalisation (LOS) in patients with bacteraemia is essential to improve quality of care and reduce variance in hospital bed occupancy. Methods This multicentre study of patients hospitalised with community-onset bacteraemia retrospectively enrolled derivation and validation cohorts in the pre-COVID-19 and COVID-19 eras. This study developed five models to compare the performances of various scoring algorithms. Model I incorporated all variables available on day 0, Model II incorporated all variables available on day 3, and Models III, IV, and V incorporated the variables that changed from day 0 to day 3. This study adopted the statistical and machine learning (ML) methods to determine the crucial determinants of 30-day mortality and LOS in patients with community-onset bacteraemia, respectively. Results A total of 3,639 (81.4%) and 834 (18.6%) patients were included in the derivation and validation cohorts, respectively. Model IV best predicted 30-day mortality in both cohorts; it achieved the best performance (i.e., the largest area under the receiver operating characteristic [ROC] curve) according to the results of the logistic regression and most ML methods. The most frequently identified variables incorporated into Model IV were deteriorated consciousness from day 0 to day 3 and deteriorated respiration from day 0 to day 3. The generalised linear models and the majorities of ML methods also identified Model V as having the best performance (i.e., the lowest mean square error) in predicting LOS. The most frequently identified variables incorporated into Model V were deteriorated consciousness from day 0 to day 3, a body temperature ≤ 36.0°C or ≥ 39.0°C on day 3, and a diagnosis of complicated bacteraemia. Conclusions For hospitalised adults with community-onset bacteraemia, clinical variables that dynamically changed from day 0 to day 3 were crucial in predicting both the short-term mortality and their LOS.