Cumulative Deficits Frailty Index Predicts Outcomes for Solid Organ Transplant Candidates

Background. Despite comprehensive multidisciplinary candidacy assessments to determine appropriateness for solid organ transplantation, limitations persist in identifying candidates at risk of adverse outcomes. Frailty measures may help inform candidacy evaluation. Our main objective was to create a solid organ transplant frailty index (FI), using the cumulative deficits model, from data routinely collected during candidacy assessments. Secondary objectives included creating a social vulnerability index (SVI) from assessment data and evaluating associations between the FI and assessment, waitlist, and posttransplant outcomes. Methods. In this retrospective cohort study of solid organ transplant candidates from Toronto General Hospital, cumulative deficits FI and SVI were created from data collected during candidacy evaluations for consecutive kidney, heart, liver, and lung transplant candidates. Regression modeling measured associations between the FI and transplant listing, death or removal from the transplant waitlist, and survival after waitlist placement. Results. For 794 patients, 40 variable FI and 10 variable SVI were created (258 lung, 222 kidney, 201 liver, and 113 heart transplant candidates). The FI correlated with assessment outcomes; patients with medical contraindications (mean FI 0.35 +/- 0.10) had higher FI scores than those listed (0.29 +/- 0.09), P < 0.001. For listed patients, adjusted for age, sex, transplant type, and SVI, higher FI was associated with an increased risk of death (pretransplant or posttransplant) or delisting (hazard ratio 1.03 per 0.01 FI score, 95% confidence interval, 1.01-1.05, P = 0.01). Conclusions. A cumulative deficits FI can be derived from routine organ transplant candidacy evaluations and may identify candidates at higher risk of adverse outcomes.

Automated summary

This study found that a cumulative deficits frailty index can be derived from routine solid organ transplant candidacy evaluations, which can help identify candidates at higher risk of adverse outcomes.