Development and external validation of a prognostic model for time to readmission or death in multimorbid patients

To develop and externally validate a prognostic model built on important factors predisposing multimorbid patients to all-cause readmission and/or death. In addition to identify patients who may benefit most from a comprehensive clinical pharmacist intervention.

A multivariable prognostic model was developed based on data from a randomised controlled trial investigating the effect of pharmacist-led medicines management on readmission rate in multimorbid, hospitalised patients. The derivation set comprised 386 patients randomised in a 1:1 manner to the intervention group, i.e. with a pharmacist included in their multidisciplinary treatment team, or the control group receiving standard care at the ward. External validation of the model was performed using data from an independent cohort, in which 100 patients were randomised to the same intervention, or standard care. The setting was an internal medicines ward at a university hospital in Norway.

The number of patients who were readmitted or had died within 18 months after discharge was 297 (76.9 %) in the derivation set, i.e. the randomized controlled trial, and 69 (71.1 %) in the validation set, i.e. the independent cohort. Charlson comorbidity index (CCI; low, moderate or high), previous hospital admissions within the previous six months and heart failure were the strongest prognostic factors and were included in the final model. The efficacy of the pharmaceutical intervention did not prove significant in the model. A prognostic index (PI) was constructed to estimate the hazard of readmission or death (low, intermediate or high-risk groups). Overall, the external validation replicated the result. We were unable to identify a subgroup of the multimorbid patients with better efficacy of the intervention.

A prognostic model including CCI, previous admissions and heart failure can be used to obtain valid estimates of risk of readmission and death in patients with multimorbidity.