Prognostic Interplay of Functional Status and Multimorbidity Among Older Patients Discharged From Hospital

Objectives: The purpose of this study was to investigate the prognostic weight of multimorbidity and functional impairment over long-term mortality among older patients discharged from acute care hospitals. Design: A prospective multicenter observational study. Setting and Participants: Our series consisted of 1967 adults aged ≥65 years consecutively admitted to acute care wards in Italy, in the context of the Report-AGE project. Methods: After signing a written informed consent, all patients underwent comprehensive geriatric assessment by Inter-RAI Minimum Data Set acute care. The primary endpoint of the present study was long-term mortality. Patients were grouped into 3 functional clusters and 3 disease clusters using the K-medians cluster analysis. The association of functional clusters, disease clusters, and Charlson score categories with long-term mortality was investigated through Cox regression analysis and the intercluster classification agreement was further estimated. Finally, the additive effect of either disease clusters or Charlson score on predictive ability of functional clusters was assessed by using changes in Harrell's C-index and categorical Net Reclassification Index (NRI). Results: Functional clusters, disease clusters, and Charlson score were significant predictors of long-term mortality, but the interclassification agreement was poor. Functional clusters predicted mortality with greater accuracy [C-index 0.66, 95% confidence interval (CI) 0.65–0.68] compared with disease clusters (C-index 0.54, 95% CI 0.53–0.56), and Charlson score (C-index 0.58, 95% CI 0.56–0.59). Adding multimorbidity (NRI 0.23, 95% CI 0.14–0.31) or Charlson score (NRI 0.13, 95% CI 0.03–0.20) to functional cluster model slightly improved the accuracy of prediction. Conclusions and Implications: Functional impairment may better predict prognosis compared with multimorbidity, which may be relevant to optimally address individuals’ needs and to design tailored preventive interventions.