Mortality prediction models for community-dwelling older adults: A systematic review

Introduction: As complexity and comorbidities increase with age, the increasing number of community-dwelling older adults poses a challenge to healthcare professionals in making trade-offs between beneficial and harmful treatments, monitoring deteriorating patients and resource allocation. Mortality predictions may help inform these decisions. So far, a systematic overview on the characteristics of currently existing mortality prediction models, is lacking. Objective: To provide a systematic overview and assessment of mortality prediction models for the community-dwelling older population. Methods: A systematic search of terms related to predictive modelling and older adults was performed until March 1st, 2024, in four databases. We included studies developing multivariable all-cause mortality prediction models for community-dwelling older adults (aged ≥65 years). Data extraction followed the CHARMS Checklist and Quality assessment was performed with the PROBAST tool. Results: A total of 22 studies involving 38 unique mortality prediction models were included, of which 14 models were based on a cumulative deficit-based frailty index and 9 on machine learning. C-statistics of the models ranged from 0.60 to 0.93 for all studies versus 0.61–0.78 when a frailty index was used. Eight models reached c-statistics higher than 0.8 and reported calibration. The most used variables in all models were demographics, symptoms, diagnoses and physical functioning. Five studies accounting for eleven models had a high risk of bias. Conclusion: Some mortality prediction models showed promising results for use in practice and most studies were of sufficient quality. However, more uniform methodology and validation studies are needed for clinical implementation.