{'fraction_non_alphanumeric': 0.025954764553207266, 'fraction_numerical': 0.014831294030404153, 'mean_word_length': 4.995555555555556, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Moving toward clinical implementation of the physical frailty phenotype in kidney transplantation

To the Editor, We thank Drs. Gupta et al. for the valuable comments. In our recent manuscript,1 we sought to tailor the physical frailty phenotype (PFP) among patients with ESRD by addressing a major limitation, namely that the unintentional weight loss component does not have face validity in this population.2,3 Removing this component because it is the least common would create a different frailty phenotype. As has been previously discussed in the literature, a core principle of the clinical application of frailty must be rooted in validation4; therefore, we identified an objective measure of wasting that had similar predictive validity as the original unintentional component. Furthermore, we address the thoughtful comments by Dr. Gupta et al. as follows. First, the donor characteristics and cold ischemia time do not meet the definition of confounders and as such we did not include them in the original models. However, new sensitivity analyses that adjust for these factors, as expected, do not change our findings; for example, frail recipients based on the new PFP (nPFP) were at a 1.73fold (95%CI: 1.09– 2.74) increased risk of allcause mortality compared to nonfrail recipients. There was no difference (p = .989) in the strength of this association compared to our published adjusted HR of 1.68. Furthermore, adjusting for these additional factors did not change the cstat of 0.71. Additionally, the discrimination of the allcause mortality model including frailty and 5 other easy to measure clinical factors was cstat = 0.710. This is the same degree of discrimination as the SRTR risk adjustment adult deceaseddonor mortality model (0.71). We tried to improve upon this risk adjustment mortality model using a complex prediction model with gradient boosting that required measurement of 48 predictors. However, our team found that the cstat improved to only cstat = 0.712. Therefore, there is clinical and predictive value in measuring the PFPit captures underlying physiologic reserve with efficiency. Finally, frailty is being measured in transplant clinical practice and the measurement of validated measures of frailty is associated with better transplant center outcomes.3,5 Therefore, we seek to understand the best way to capture this underlying latent characteristic among KT patients. We advocate for the use of the nPFP that replaces subjective shrinking with an objective measure as one way to tailor this phenotype to KT patients. We agree with Dr. Gupta et al. that understanding how to best implement this tool into clinical practice is an important next step.