Division of Pulmonary and Critical Care, Department of Medicine, School of Medicine, University of California, San Francisco CA, USA.
Division of Pulmonary and Critical Care, Department of Medicine, School of Medicine, University of Pennsylvania, Philadelphia, Philadelphia, PA, USA.
J Heart Lung Transplant. 2023 Jul;42(7):892-904. doi: 10.1016/j.healun.2023.02.006. Epub 2023 Feb 20.
Existing measures of frailty developed in community dwelling older adults may misclassify frailty in lung transplant candidates. We aimed to develop a novel frailty scale for lung transplantation with improved performance characteristics.
We measured the short physical performance battery (SPPB), fried frailty phenotype (FFP), Body Composition, and serum Biomarkers representative of putative frailty mechanisms. We applied a 4-step established approach (identify frailty domain variable bivariate associations with the outcome of waitlist delisting or death; build models sequentially incorporating variables from each frailty domain cluster; retain variables that improved model performance ability by c-statistic or AIC) to develop 3 candidate "Lung Transplant Frailty Scale (LT-FS)" measures: 1 incorporating readily available clinical data; 1 adding muscle mass, and 1 adding muscle mass and research-grade Biomarkers. We compared construct and predictive validity of LT-FS models to the SPPB and FFP by ANOVA, ANCOVA, and Cox proportional-hazard modeling.
In 342 lung transplant candidates, LT-FS models exhibited superior construct and predictive validity compared to the SPPB and FFP. The addition of muscle mass and Biomarkers improved model performance. Frailty by all measures was associated with waitlist disability, poorer HRQL, and waitlist delisting/death. LT-FS models exhibited stronger associations with waitlist delisting/death than SPPB or FFP (C-statistic range: 0.73-0.78 vs. 0.57 and 0.55 for SPPB and FFP, respectively). Compared to SPPB and FFP, LT-FS models were generally more strongly associated with delisting/death and improved delisting/death net reclassification, with greater improvements with increasing LT-FS model complexity (range: 0.11-0.34). For example, LT-FS-Body Composition hazard ratio for delisting/death: 6.0 (95%CI: 2.5, 14.2), SPPB HR: 2.5 (95%CI: 1.1, 5.8), FFP HR: 4.3 (95%CI: 1.8, 10.1). Pre-transplant LT-FS frailty, but not SPPB or FFP, was associated with mortality after transplant.
The LT-FS is a disease-specific physical frailty measure with face and construct validity that has superior predictive validity over established measures.
现有的社区居住的老年人衰弱测量方法可能会错误分类肺移植候选者的衰弱情况。我们旨在开发一种新的用于肺移植的衰弱量表,以提高其性能特征。
我们测量了短体物理表现电池(SPPB)、油炸虚弱表型(FFP)、身体成分和血清生物标志物,这些标志物代表了潜在的虚弱机制。我们应用了一种经过验证的四步方法(确定与候补名单除名或死亡相关的虚弱领域变量的双变量关联;依次建立包含每个虚弱领域聚类变量的模型;保留通过 C 统计量或 AIC 提高模型性能能力的变量)来开发 3 种候选“肺移植衰弱量表(LT-FS)”测量方法:1 种方法结合了现成的临床数据;1 种方法增加了肌肉质量;1 种方法增加了肌肉质量和研究级生物标志物。我们通过 ANOVA、ANCOVA 和 Cox 比例风险模型比较了 LT-FS 模型与 SPPB 和 FFP 的结构和预测有效性。
在 342 名肺移植候选者中,LT-FS 模型的结构和预测有效性均优于 SPPB 和 FFP。增加肌肉质量和生物标志物可提高模型性能。所有测量方法的虚弱与候补名单残疾、较差的 HRQL 和候补名单除名/死亡有关。LT-FS 模型与候补名单除名/死亡的关联比 SPPB 或 FFP 更强(C 统计量范围:0.73-0.78 与 SPPB 和 FFP 相比,分别为 0.57 和 0.55)。与 SPPB 和 FFP 相比,LT-FS 模型通常与除名/死亡的关联更强,改善了除名/死亡的净重新分类,并且随着 LT-FS 模型复杂性的增加(范围:0.11-0.34),改善效果更大。例如,LT-FS-身体成分的除名/死亡风险比:6.0(95%CI:2.5,14.2),SPPB HR:2.5(95%CI:1.1,5.8),FFP HR:4.3(95%CI:1.8,10.1)。移植前的 LT-FS 虚弱,但不是 SPPB 或 FFP,与移植后的死亡率有关。
LT-FS 是一种具有特定疾病的身体虚弱测量方法,具有良好的面部和结构有效性,其预测有效性优于现有的测量方法。