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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. [ABSTRACT FROM AUTHOR]

The potential for heterogenous pathobiology underlying physical frailty might explain the inconsistent associations observed between frailty and poor outcomes in lung transplantation. We hypothesize that molecular phenotyping could refine frailty measurement and ultimately tailor interventions. In a three-center cohort of lung transplant candidates, we measured frailty by the Short Physical Performance Battery (SPPB); demographic and clinical variables; body composition by bioelectrical impedance, and 17 serum biomarkers reflecting commonly cited causes of frailty including inflammation, sarcopenia, adiposity, mitochondrial and neurohormonal dysfunction. Among those pre-frail or frail (SPPB <12), we first applied latent class modelling to the baseline data and then followed participants forward in time. The best-fitting model was determined by the Bayesian Information Criteria and the Vuong-Lo-Mendell-Rubin test. We tested predictive validity of these classes with disability by the Lung Transplant Valued Life Activities scale and by candidate delisting for becoming too ill or death. In 442 participants, a 3 latent class model best fit (p = 0.046; Figure 1A). Type A was characterized by higher serum biomarkers of inflammation and sarcopenia (lower muscle mass [ASMI] and grip strength); Type B by sarcopenia, high adiposity (VFA), and lack of inflammation, and Type C was relatively fit (higher ASMI and grip strength) and without inflammation (Figure 1B). Amongst the Type A endotype, 13% of participants died or were delisted compared to 6% in both Type B and C (p = 0.15). Participants comprising Type A reported worse disability (LT-VLA 1.7) than Types B and C (1.4 and 1.3, respectively) (p < 0.001) and had shorter time to delisting or death (1.4 vs 8.0 and 4.0 months, respectively) (p < 0.001). We identified distinct biological phenotypes of frailty. A frailty phenotype characterized by systemic inflammation and sarcopenia appears to be associated with worse clinical outcomes. [ABSTRACT FROM AUTHOR]