Preliminary Development of the Lung Transplant Frailty Index

• Published in: The Journal of heart and lung transplantation Vol. 41; no. 4; pp. S48 - S49
• Main Authors: Singer, J.P., Diamond, J.M., Anderson, M.R., Benvenuto, L.J., Arcasoy, S.M., Lederer, D.J., Gao, Y., Calabrese, D.R., Hays, S.R., Kukreja, J., Venado, A., Kolaitis, N.A., Leard, L.E., Shah, R.J., Trinh, B.N., Perez, A., Golden, J.A., Kleinhenz, M., Betancourt, L., Oyster, M., Brown, M.C., Kalman, L., Zeleski, D., Adler, J., Medikonda, N., Tevald, M.A., Balar, P., Patel, S., Wang, P., Greenland, J.R., Christie, J.D., Katz, P.P.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2022
• ISSN: 1053-2498; 1557-3117
• DOI: 10.1016/j.healun.2022.01.110

Existing measures of frailty developed in community dwelling older adults may misclassify frailty in lung transplant candidates. We aimed to develop a novel frailty index for lung transplantation with improved performance characteristics compared to existing measures. In a three-center cohort study of lung transplant candidates, we measured the Short Physical Performance Battery (SPPB) and Fried Frailty Phenotype (FFP) measures; body composition by bioelectrical impedance; and serum biomarkers associated with frailty (hemoglobin, albumin, and C-reactive protein). We evaluated individual variable domains to identify those associated with the criterion of candidate delisting for becoming too ill or death. Next, we built composite multidimensional models by testing the addition of domain variables in clusters: (1) performance measures (gait speed, balance, grip strength, low physical activity level); (2) body composition (low skeletal muscle, high % body fat), and (3) biomarkers. Domain cut-points were determined using receiver operating curve analyses. Three threshold selection methods for variable retention were evaluated; where methods differed, we prioritized specificity for delisting/death. Logistic regression analyses then estimated the odds of death/delisting across candidate Lung Transplant Frailty Index models. In 533 participants, we identified two candidate Lung Transplant Frailty Index (LT-FI) measures that maximized specificity, area under the curve, and had stronger associations with delisting and death compared to either the SPPB or FFP (Table). Candidate LT-FI measures that include measures of physical performance, body composition, and serum biomarkers have improved discrimination for candidate delisting or death compared to the SPPB and FFP. Validating these candidate LT-FI measures’ capacity to identify people at risk for peri- and post-operative complications and at other centers will help to define the optimal measure of frailty in lung transplantation.