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Background To reduce readmissions for heart failure (HF) among HF patients, most at-risk individuals could be targeted to benefit from adapted interventions. A better understanding of HF readmission predictors could help clinicians and regulators identify patients most at-risk. We focused, in particular, on distinguishing HF severity from overall health-state severity. Methods We studied predictors of HF readmission available in administrative data in a nationwide cohort of patients aged 65 years or older surviving an index hospitalization for HF in 2015 (N = 70 657). To take into account the competing mortality risk, we estimated subdistribution hazard ratios (sdHRs) of HF readmission and cause-specific hazard ratios (csHRs) for HF readmission and for death without HF readmission, over a 1-year follow-up period. We then computed cumulative incidences and daily rates of HF readmission for specific risk-groups. Results 31.8% of patients were readmitted at least once for HF, among which 27.2% (8.6% of study cohort) were readmitted 30 days after discharge. 17.6% of patients died without any HF readmission. HF severity and overall health-state severity were the strongest HF readmission predictors (sdHRs 2.66 [95% CI: 2.52-2.81] and 1.37 [1.30-1.45] respectively, when comparing extreme categories). HF severity and length of index stay were more strongly associated with the rate (csHRs) of HF readmissions, whereas overall health-state severity and age were more strongly associated with the competing rate of death without HF readmission. Risk-groups defined upon HF severity and overall health-state severity had approximately 40% of separation in HF readmission proportion (21.9% versus 60.4%). Conclusions Our results stress the importance of considering both HF severity and overall morbidity and of accounting for the competing mortality risk to identify patients at-risk of HF readmission. Such patients could benefit from targeted transitional or post-discharge HF care. Key messages Heart failure patients can be stratified into risk-groups of readmission using administrative data. Identifying at-risk patients could help clinicians and regulators to target interventions.

Identifying patients at risk of readmission for heart failure in the French national claim database