Journal of Clinical Medicine Article Outcomes of Frail Patients While Waiting for Kidney Transplantation: Differences between Physical Frailty Phenotype and FRAIL Scale María José Pérez-Sáez 1, *,† , Dolores Redondo-Pachón 1,† , Carlos E. Arias-Cabrales 1 , Anna Faura 1 , Anna Bach 1 , Anna Buxeda 1 , Carla Burballa 1 , Ernestina Junyent 1 , Marta Crespo 1 , Ester Marco 2,3 , Leocadio Rodríguez-Mañas 4 and Julio Pascual 1,‡ on behalf of the FRAIL-MAR Study Group 1 Nephrology Department, Hospital del Mar, 08003 Barcelona, Spain; doloredondop@gmail.com (D.R.-P.); cariascabrales@psmar.cat (C.E.A.-C.); afaura@psmar.cat (A.F.); abach@psmar.cat (A.B.); abuxeda@psmar.cat (A.B.); cburballa@psmar.cat (C.B.); ejunyent@psmar.cat (E.J.); mcrespo@psmar.cat (M.C.); julpascual@gmail.com (J.P.) 2 Physical Medicine & Rehabilitation Department, Parc de Salut Mar (Hospital del Mar-Hospital de L’ESPERANÇA), 08003 Barcelona, Spain; emarco@psmar.cat 3 Rehabilitation Research Group, Hospital del Mar Research Institute, Universitat Autònoma de Barcelona, 08003 Barcelona, Spain 4 Geriatrics Department, Hospital Universitario de Getafe, 28905 Madrid, Spain; leocadio.rodriguezs@salud.madrid.org * Correspondence: mperezsaez@psmar.cat  † These authors contributed equally to this work.  ‡ Membership of FRAIL-MAR Study Group is provided in the Acknowledgments. Citation: Pérez-Sáez, M.J.; Redondo-Pachón, D.; Arias-Cabrales, Abstract: Frailty is associated with poorer outcomes among patients waiting for kidney transplanta- C.E.; Faura, A.; Bach, A.; Buxeda, A.; tion (KT). Several different tools to measure frailty have been used; however, their predictive value is Burballa, C.; Junyent, E.; Crespo, M.; unknown. This is a prospective longitudinal study of 449 KT candidates evaluated for frailty by the Marco, E.; et al. Outcomes of Frail Physical Frailty Phenotype (PFP) and the FRAIL scale. During the study period, 296 patients received Patients While Waiting for Kidney a KT, while 153 remained listed. Patients who did not get receive a transplant were more frequently Transplantation: Differences between frail according to PFP (16.3 vs. 7.4%, p = 0.013). Robust patients had fewer hospital admissions during Physical Frailty Phenotype and the 1st year after listing (20.8% if PFP = 0 vs. 43.4% if ≥1, and 27.1% if FRAIL = 0 vs. 48.9% if ≥1) and FRAIL Scale. J. Clin. Med. 2022, 11, 672. https://doi.org/10.3390/ fewer cardiovascular events (than FRAIL ≥ 1) or major infectious events (than PFP ≥ 1). According to jcm11030672 PFP, scoring 1 point had an impact on patient survival and chance of transplantation in the univariate analysis. The multivariable analysis corroborated the result, as candidates with PFP ≥ 3 had less Academic Editors: Mariusz Kusztal likelihood of transplantation (HR 0.45 [0.26–0.77]). The FRAIL scale did not associate with any of and Kultigin Turkmen these outcomes. In KT candidates, pre-frailty and frailty according to both the PFP and the FRAIL Received: 13 December 2021 scale were associated with poorer results while listed. The PFP detected that frail patients were less Accepted: 26 January 2022 likely to receive a KT, while the FRAIL scale did not. Published: 28 January 2022 Publisher’s Note: MDPI stays neutral Keywords: frailty; FRAIL; kidney transplant waiting list with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction The prevalence of frailty among advanced chronic kidney disease (CKD) patients has been well established as high, and it is significantly higher in those who require Copyright: © 2022 by the authors. hemodialysis (HD) as renal replacement therapy [1]. A wide variety of frailty scales have Licensee MDPI, Basel, Switzerland. been used in population-based studies, and similar heterogeneity has been found in studies This article is an open access article with renal patients [2]. The Physical Frailty Phenotype (PFP) defined by Fried et al. [3] distributed under the terms and is the most frequently applied among both dialysis and kidney transplant (KT) patients, conditions of the Creative Commons Attribution (CC BY) license (https:// although many others have also been utilized [2]. creativecommons.org/licenses/by/ Differences among frailty scales are mainly due to the different aspects or domains 4.0/). that they take into consideration when evaluating a patient for frailty [2,4]. Hence, there J. Clin. Med. 2022, 11, 672. https://doi.org/10.3390/jcm11030672 https://www.mdpi.com/journal/jcm J. Clin. Med. 2022, 11, 672 2 of 15 are scales focused on patient comorbidities, cognition, social aspects, and objective mea- surements of individual physical reserve. This can result in different frailty phenotypes. Regardless of which domains the scales evaluate, i.e., which frailty phenotype are defining, health outcomes have been shown to be poorer in frail patients [2]. On the other hand, the time and resources consumed by each frailty tool are different. While some of them can be performed in less than one minute, others may take longer and require the use of devices and/or the involvement of health-care professionals for assessment [5]. The agreement between scales has been proven to be only fair in the best-case sce- nario [6,7], and population-based studies have targeted their efforts toward finding the best frailty metric adapted to each specific setting [4,5]. In pursuit of a disease-specific frailty metric, a few studies have evaluated which might be the best-known frailty tool to apply in the CKD population, with poor correlation with outcomes when comparing different frailty scales being reported thus far [8,9]. Potentially, depending on which frailty pheno- type better fits with renal patients, one scale could have better sensitivity than another in predicting poor outcomes. On the other hand, depending on the individual clinical practice of each center, one scale might be more affordable to perform than others. KT candidates present with worse health outcomes while listed [10–12] and have lower access to transplantation [13,14] depending on their frailty status. In addition, results after transplantation are influenced by patients’ baseline frailty status [15–17]. However, it is unknown which scale has the best correlation with these outcomes and, therefore, which would be the most suitable to apply in this specific population. Our aim in this study was to analyze different clinical outcomes among KT candidates while waiting for transplantation according to two different frailty scales: the PFP, which is the scale most used in the CKD population, and the FRAIL scale, which is less time consuming and more affordable to perform in clinical practice. 2. Materials and Methods 2.1. Study Design This was a prospective cohort study of patients with advanced CKD at Hospital del Mar, Barcelona, Spain. Between June 2016 and June 2020, 449 patients were evaluated for frailty at the time they were referred for inclusion on the KT waiting list. During the study period, 153 patients remained on the KT waiting list and 296 received a KT. We grouped ‘waitlisted’ patients and included all KT candidates, regardless of their final status at the end of follow-up. Median time to follow-up was 26 months in those who did not get transplanted [interquartile range (IQR) 16–39] and 15 months (4–27) in those who received a transplant. Clinical and epidemiological variables were collected from our local database. 2.2. Ethics The Institutional Review Board of Hospital del Mar approved the study, and all enrolled participants provided written informed consent. The study followed the principles of the declaration of Helsinki, only relying on the official center database. 2.3. Frailty Assessment Two different frailty assessment tools were used: PFP3 and the FRAIL scale [18]. PFP comprises five components: shrinking (self-report of unintentional weight loss of 4.5 Kg during the past year), weakness (grip strength below an established cut-off based on sex and body mass index (BMI)), exhaustion (self-report), low activity (kilocalories per week below an established cut-off) and slowed walking speed (walking time of 4.5 m below an established cut-off by sex and height). The FRAIL scale includes five questions (all of them self-reported) assessing fatigue, resistance, ambulation, illness, and loss of weight. In both scales, each component or question scores 0/1 depending on its presence or absence. Robust patients were defined as a score 0, pre-frail as those who ranked 1–2, and frail patients were defined by a score ≥ 3. In order to increase the power of the study, pre-frail J. Clin. Med. 2022, 11, 672 3 of 15 and frail categories were joined for the analysis, as has previously been done in other cohorts [19,20]. 2.4. Study Variables For baseline characteristics, we included demographics (age, sex, ethnicity and BMI); social data (education as defined by four categories: none, primary education, secondary education, and tertiary education, family or social support as defined by its presence or absence, economic incomes as defined by three categories: non-regular incomes, retired with pension, and active worker with salary); and clinical data (comorbidities such as hypertension, diabetes mellitus, chronic cardiac and pulmonary diseases, type of renal replacement therapy, etc.). In addition, we assessed basic activities of daily living using the Barthel scale (disability if score ≤ 90) [21,22] and instrumental activities of daily living using the Lawton–Brody scale [23] (disability if <8 in women and <5 in men). Regarding outcomes, we analyzed hospital admissions during the first year after listing (considered as >24 h admitted to a hospital and assessed in 277 patients who spent ≥ 12 months on the waiting list); major events while listed were as follows: cardio- vascular events (stroke, heart failure episode, coronary ischemic event, peripheral vascular disease revascularization and/or amputation), major infectious episodes (including only those that required intravenous treatment with antibiotics), cancer onset, and dialysis access problems (including those related to the peritoneal catheter and those related to the vascular access for hemodialysis), and were analyzed only in those who remained on the KT waiting list (n = 153) along with chance of exclusion from the KT waiting list (only in those who remained on the KT waiting list (n = 153)), chance of transplantation (considering the whole cohort, n = 449), and patient mortality while listed (only in those who remained listed (n = 153)). 2.5. Statistics Continuous variables were expressed as mean ± standard deviation (SD) or as median and IQR, according to normal distribution. Categorical data were expressed as absolute numbers and percentages. Comparisons of baseline characteristics between two groups were made using the Chi-square or Fisher’s exact test to analyze categorical variables, Student’s t-test for continuous variables with normal distribution, and Mann–Whitney test for non-parametric variables. ANOVA test was used to compare quantitative variables with normal distribution when three categories were present. Patient survival and chance of transplantation were estimated using Kaplan–Meier curves, applying the log-rank test. Univariate and multivariable Cox regressions were performed to estimate the hazard ratio (HR) and confidence intervals (CI) for patient survival or transplantation probability. The following variables were included in the univariate analysis: patient age, sex and comorbidity, dialysis vintage and frailty status according both PFP and FRAIL scale. These variables were chosen based on their clinical relevance. In the multivariable analysis, only those variables with a p-value < 0.1 were considered. Statistical analysis was performed using SPSS version 27 software (IBM, Armonk, NY, USA); p-values < 0.05 were considered statistically significant. 3. Results KT candidates presenting with at least one PFP criterion (pre-frail or frail patients) were 71.3%, while this percentage was lower when using FRAIL scale (45%). The number of patients presenting ≥ 3 criteria (frail patients) was 10.5% according to PFP and 3.6% according to FRAIL. Table 1 displays the whole cohort characterization and frailty criteria distribution according to PFP and the FRAIL scale. J. Clin. Med. 2022, 11, 672 4 of 15 Table 1. Baseline characteristics and frailty characterization of the total cohort at the time of evaluation for the KT WL (n = 449). Comparisons were made between patients who remained as KT candidates (n = 153), and those who received a KT during the study period (n = 296). All WL 1 KT p-Value a (n = 449) (n = 153) (n = 296) Sociodemographics Age (years, mean ± sd) 60.4 ± 14.1 61.5 ± 13.5 59.8 ± 14.3 0.20 Sex (female, n (%)) 141 (31.4) 50 (23.7) 91 (30.7) 0.67 Ethnicity (Caucasian, n (%)) 420 (93.5) 141 (92.2) 279 (94.3) 0.21 BMI (Kg/m2 , mean ± sd) 27.9 ± 5.2 28.5 ± 5.2 27.7 ± 5.1 0.17 Education (no/primary, n (%)) 282 (62.8) 99 (64.7) 183 (61.8) 0.54 Deficient family support, n (%) 64 (14.2) 19 (12.4) 45 (15.3) 0.41 Socioeconomic status (non-regular incomes, n (%)) 41 (9.1) 23 (15) 18 (6.1) 0.002 Comorbidities Hypertension, n (%) 432 (96.2) 148 (96.7) 284 (96.3) 0.80 Diabetes mellitus, n (%) 164 (36.5) 63 (41.2) 101 (34.2) 0.15 Heart failure, n (%) 26 (5.8) 16 (10.5) 10 (3.4) 0.002 Ischemic coronary disease, n (%) 73 (16.3) 32 (20.9) 41 (13.9) 0.055 Peripheral vasculopathy, n (%) 41 (9.1) 16 (10.5) 25 (8.4) 0.48 Cerebral vasculopathy, n (%) 35 (7.8) 13 (8.5) 22 (7.4) 0.69 Chronic obstructive pulmonary disease, n (%) 34 (7.6) 15 (9.8) 19 (6.4) 0.19 RRT modality, n (%) Hemodialysis 295 (65.7) 107 (69.9) 188 (63.5) Peritoneal dialysis 93 (20.7) 22 (14.4) 71 (24) 0.06 Low clearance 61 (13.6) 24 (17.5) 37 (12.5) Dialysis vintage (months, median [IQR]) 7 (0–14) 6 (−2–13.5) 7 (1–14) 0.19 Disabilities Disability for activities of daily living *, n (%) 35 (8.7) 16 (12.3) 19 (7) 0.077 Disability for instrumental activities of daily living #, n (%) 88 (22) 33 (26) 55 (20.1) 0.19 Physical Frailty Phenotype Shrinking, n (%) 77 (17.1) 27 (17.6) 50 (16.9) 0.84 Exhaustion, n (%) 108 (24.1) 43 (28.1) 65 (22) 0.14 Low physical activity, n (%) 25 (5.6) 12 (7.8) 13 (4.4) 0.13 Slowness, n (%) 61 (13.6) 29 (19) 32 (10.8) 0.02 Weakness, n (%) 243 (54.1) 90 (58.8) 153 (51.7) 0.15 Hand grip (Kg, mean ± sd) 26.8 (9.4) 26.3 (9.8) 27.1 (9.2) 0.30 PFP 3 categories (0, 1–2, and ≥3) 0 129 (28.7) 39 (25.5) 90 (30.4) 1–2 273 (60.8) 89 (58.2) 184 (62.2) 0.013 ≥3 47 (10.5) 25 (16.3) 22 (7.4) PFP 2 categories (0 and ≥1) 0 129 (28.7) 39 (25.5) 90 (30.4) 0.27 ≥1 320 (71.3) 114 (74.5) 206 (69.6) J. Clin. Med. 2022, 11, 672 5 of 15 Table 1. Cont. All WL 1 KT p-Value a (n = 449) (n = 153) (n = 296) FRAIL scale Fatigue, n (%) 108 (24.1) 43 (28.1) 65 (22) 0.14 Resistance, n (%) 42 (9.4) 24 (15.7) 18 (6.1) <0.001 Ambulation, n (%) 20 (4.5) 10 (6.5) 10 (3.4) 0.12 Illness, n (%) 55 (12.2) 18 (11.8) 37 (12.5) 0.82 Loss of weight, n (%) 86 (19.2) 31 (20.3) 55 (18.6) 0.66 FRAIL 3 categories (0, 1–2, and ≥3) 0 247 (55) 82 (53.6) 165 (55.7) 1–2 186 (41.4) 64 (41.8) 122 (41.2) 0.68 ≥3 16 (3.6) 7 (4.6) 9 (3) FRAIL 2 categories (0 and ≥1) 0 247 (55) 82 (53.6) 165 (55.7) 0.66 ≥1 202 (45) 71 (46.4) 131 (44.3) KT, kidney transplant; WL, waiting list; BMI, body mass index; RRT, renal replacement therapy; IQR, interquartile range; PFP, Physical Frailty Phenotype. 1 WL patients included 66 patients who were included in the KT WL at the end of follow-up, 23 patients who were excluded during the follow-up because of clinical issues (n = 18) or because they wanted to be (n = 5), 23 patients who were never waitlisted because of clinical problems detected during the transplant work-up, and 8 who were never waitlisted because they did not reach an eGFR below 15 mL/min. * Barthel ≤ 90; # Lawton-Brody < 8 if women and <5 if men; a Comparisons were made between WL and KT categories. 3.1. Comparisons among Candidates Who Received a KT during the Study Period and Those Who Remained Non-Transplanted Out of a total of 449 KT candidates, 296 received a KT during the study period while 153 remained listed (Table 1). The WL patients included 66 who were included in the KT WL at the end of follow-up, 23 patients who were excluded during the follow-up because of clinical issues (n = 18) or because they wanted to be (n = 5), 23 patients who were never waitlisted because of clinical problems detected during the transplant work-up, and 8 who were never waitlisted because they did not reach an estimated glomerular filtration rate (eGFR) below 15 mL/min. Patients who eventually received a transplant had better socioeconomic status than those who remained waitlisted (6.1 vs. 15% with non-regular incomes, respectively) and less frequently presented with heart failure (3.4 vs. 10.5%). Regarding PFP criteria distribution and total score, they presented with less slowness (10.8 vs. 19% of waitlisted patients), and fewer were frail (7.4 vs. 16.3%). On the contrary, although they scored better for resistance according to the FRAIL scale (6.1 vs. 15.7%), the FRAIL criteria distribution at the time of evaluation was similar between those who later received a KT and those who remained on the waiting list. 3.2. Clinical Outcomes of Frail Patients While Waiting for Transplantation Two-hundred and seventy-seven candidates spent ≥12 months on the KT waiting list. Among robust patients, 20.8 and 27.1% required at least one hospital admission during their first year after evaluation according to the PFP and FRAIL scale, respectively (Table 2). Considering pre-frail and frail patients together, this percentage increased to 43.4% (PFP) and 48.9% (FRAIL). J. Clin. Med. 2022, 11, 672 6 of 15 Table 2. Hospitalizations while first year on the KT WL according to PFP and FRAIL scale (n = 277). PFP 0 ≥1 p-Value (n = 72) (n = 205) Hospitalization requirement during 1st year on the WL, n (%) 15 (20.8) 89 (43.4) <0.001 Number of hospitalizations, n (%) 0 56 (77.8) 115 (56.1) 1 9 (12.5) 56 (27.3) 0.09 2 2 (2.8) 22 (10.7) 3 3 (4.2) 9 (4.4) >3 2 (2.8) 3 (3) FRAIL Scale 0 ≥1 p-Value (n = 144) (n = 133) Hospitalization requirement during 1st year on the WL, n (%) 39 (27.1) 65 (48.9) <0.001 Number of hospitalizations, n (%) 0 104 (72.2) 67 (50.4) 1 29 (20.1) 36 (27.1) <0.001 2 4 (2.8) 20 (15) 3 4 (2.8) 8 (6) >3 3 (2.1) 2 (1.5) KT, kidney transplant; WL, waiting list; PFP, Physical Frailty Phenotype. We analyzed clinical outcomes while listed in those who remained waitlisted (n = 153). Patients scoring ≥ 1 for PFP had more frequent major infectious events than robust ones (35.1% vs. 17.9%, respectively). Contrarily, FRAIL scale better discriminated between those patients who had a cardiovascular event while listed (49.3% if they scored ≥ 1 vs. 28% if they scored 0) and those who did not (Table 3). As a result of these events, the rate of waiting list exclusion was higher among those who were pre-frail or frail according to PFP (57 vs. 23.1% of robust patients) and according to the FRAIL scale (63.4 vs. 35.4%) (Table 3). Table 3. Clinical outcomes while listed according to PFP and FRAIL scale (n = 153). PFP 0 ≥1 p-Value (n = 39) (n = 114) Cardiovascular event, n (%) 10 (25.6) 48 (42.1) 0.07 Major infection event, n (%) 7 (17.9) 40 (35.1) 0.04 Neoplasia, n (%) 2 (5.1) 13 (11.4) 0.25 Dialysis access problem, n (%) 4 (10.3) 22 (19.3) 0.19 Any event, n (%) 10 (25.6) 59 (51.9) 0.005 Number of events > 1, n (%) 5 (12.8) 29 (25.4) 0.10 WL exclusion during the follow-up, n (%) 9 (23.1) 65 (57) <0.001 FRAIL scale 0 ≥1 p-Value (n = 82) (n = 71) Cardiovascular event, n (%) 23 (28) 35 (49.3) 0.007 Major infection event, n (%) 22 (26.8) 25 (35.2) 0.26 Neoplasia, n (%) 8 (9.8) 7 (9.9) 0.98 Dialysis access problem, n (%) 9 (11) 17 (23.9) 0.033 Any event, n (%) 31 (37.8) 38 (53.1) 0.05 Number of events > 1, n (%) 13 (15.9) 21 (29.6) 0.03 WL exclusion during the follow-up, n (%) 29 (35.4) 45 (63.4) <0.001 PFP, Physical Frailty Phenotype; WL, waiting list. Any event includes: cardiovascular event, major infection event, neoplasia and dialysis access problem. J. Clin. Med. 2022, 11, 672 7 of 15 J. Clin. Med. 2022, 11, x FOR PEER REVIEW 7 of 15 3.3. Mortality of Frail Patients While Waiting for Transplantation Thirty-four patients had died at the end of follow-up. Of those, one patient was actively The included univariatein analysis the KT waiting list, mortality of patient two were while not included listed (nbecause = 153)their eGFR was according not to PFP below 15 mL/min, five had never been included because of clinical issues, score at the time of inclusion showed that robust patients had lower mortality (7.7 vs. and 26 had been previously 27.2% if PFPexcluded due toFigure ≥ 1, p = 0.039, clinical1A), problems. although this difference lost its statistical signifi- The univariate analysis of patient cance when the three categories were considered mortality(Figure while listed 1B). In(ncontrast, = 153) according the FRAILtoscale PFP score at the time of inclusion showed that robust patients had lower mortality (7.7 vs. 27.2% failed to detect this difference with both two and three categories (Figure 1C,D). When an if PFP ≥ 1, p = 0.039, Figure 1A), although this difference lost its statistical significance adjusted multivariable Cox analysis was performed, only age (HR 1.098 per year) and when the three categories were considered (Figure 1B). In contrast, the FRAIL scale failed to cardiovascular disease (HR 3.43 if any cardiovascular disease was present) were associ- detect this difference with both two and three categories (Figure 1C,D). When an adjusted ated with patient mortality. PFP ≥ 1 almost reached statistical significance for higher risk multivariable Cox analysis was performed, only age (HR 1.098 per year) and cardiovascular of mortality (HR 4.07, p = 0.09) (Table 4). disease (HR 3.43 if any cardiovascular disease was present) were associated with patient Twelve patients died of infection (five of them of COVID-19), ten patients of cardio- mortality. PFP ≥ 1 almost reached statistical significance for higher risk of mortality vascular causes, eight of unknown causes (probably sudden death), and four of cancer. (HR 4.07, p = 0.09) (Table 4). (A) (B) Figure 1. Cont. J. Clin. Med. 2022, 11, x FOR PEER REVIEW 8o J. Clin. Med. 2022, 11, 672 8 of 15 (C) (D) Figure Figure1.1. Kaplan–Meier Kaplan–Meier curves curves of patient of patient survivalsurvival while while listed listedtoaccording according their PFP andto FRAIL their PFPscaleand FRA scale scorescore (n = Time (n = 153). 153).toTime to follow-up follow-up (median (median IQR) IQR) 26 (16–39) 26 (16–39) months. months. (A) Patient (A) Patient survival survival amo among robust robust (PFP =(PFP 0) and= both 0) and bothand pre-frail pre-frail and frail frail patients patients together (PFPtogether (PFP(B) ≥ 1); p = 0.039; ≥ 1); p = 0.039; Patient (B)among survival Patient survi among robust robust (PFP = 0),(PFP = 0),(PFP pre-frail pre-frail = 1–2),(PFP = 1–2), and frail and(PFP patients frail≥patients (PFP 3); p = 0.11; (C)≥Patient 3); p = survival 0.11; (C)among Patient survi among robust robust (FRAIL (FRAIL = 0) pre-frail = 0) and both and both andpre-frail and together frail patients frail patients ≥ 1); p = (FRAIL (FRAILtogether 0.086; (D)≥Patient 1); p = 0.086; Patient survivalsurvival among among robust robust (FRAIL (FRAIL = 0), = (FRAIL pre-frail 0), pre-frail = 1–2),(FRAIL and frail=patients 1–2), and frail≥patients (FRAIL (FRAIL ≥ 3 3); p = 0.22. = 0.22. Twelve patients died of infection (five of them of COVID-19), ten patients of cardio- vascular causes, eight of unknown causes (probably sudden death), and four of cancer. Table 4. Univariate and multivariable analysis of patient death while listed (n = 153). Time to follo up 26 (16–39) months. Univariate Multivariable HR CI 95% p-Value HR CI 95% p-Valu Age (per year) 1.008 1.003–1.013 0.03 1.098 1.003–1.015 0.003 Sex (ref: male) 0.637 0.309–1.316 0.22 J. Clin. Med. 2022, 11, 672 9 of 15 Table 4. Univariate and multivariable analysis of patient death while listed (n = 153). Time to follow-up 26 (16–39) months. Univariate Multivariable HR CI 95% p-Value HR CI 95% p-Value Age (per year) 1.008 1.003–1.013 0.03 1.098 1.003–1.015 0.003 Sex (ref: male) 0.637 0.309–1.316 0.22 Race (ref: Caucasian) 3.30 1.13–9.61 0.03 1.35 0.19 Hypertension 20.99 0.02–228.17 0.51 Diabetes mellitus 1.32 0.67–2.60 0.43 Any cardiovascular disease 2.37 1.20–4.68 0.01 3.43 1.35–8.66 0.009 Chronic obstructive pulmonary disease 1.57 0.61–4.06 0.35 Hemodialysis as RRT modality (ref: HD) 0.72 0.27–1.87 0.50 Dialysis vintage (per month) 1.004 0.99–1.01 0.43 PFP (ref: 0) ≥1 3.24 0.99–10.61 0.05 4.07 0.78–21.15 0.09 PFP (ref: 0) 1–2 2.87 0.71–11.4 0.13 4.05 0.77–21.19 0.09 ≥3 3.35 1.01–11.10 0.04 4.37 0.64–29.14 0.13 FRAIL scale (ref: 0) ≥1 1.83 0.90–3.70 0.09 1.51 0.62–3.70 0.35 FRAIL scale (ref: 0) 1–2 1.81 0.88–3.70 0.15 1.51 0.66–3.47 0.32 ≥3 2.06 0.46–9.27 0.43 1.95 0.38–9.98 0.41 HR, hazard ratio; CI, confidence interval; RRT, renal replacement therapy; HD, hemodialysis; PFP, Physical Frailty Phenotype. Four multivariable analyses were performed, including (1) PFP ≥ 1; (2) PFP 1–2 and ≥ 3; (3) FRAIL scale ≥ 1; and (4) FRAIL scale 1–2 and ≥3. Age and any cardiovascular disease remained significant in all four models. 3.4. Chance of Transplantation among KT Candidates and Its Relationship with Frailty Status Considering the whole cohort (n = 449), patients scoring ≥1 for PFP had a lower chance of obtaining a KT than those who were robust (Figure 2A), and this chance was gradually lower if more criteria were present (Figure 2B). However, the FRAIL scale was not associated with transplant probability (Figure 2C,D). In the multivariable analysis, cardiovascular disease was negatively associated with the chance of transplantation during follow-up (HR 0.72), while dialysis vintage was positively associated (HR 1.005 per month). PFP ≥ 3 candidates had a lower chance of receiving a transplant as well (HR 0.45) (Table 5). Table 5. Univariate and multivariable analysis of chance of the transplantation while listed (n = 449). Time to follow-up 15 (4–27) months. Univariate Multivariable HR CI 95% p-Value HR CI 95% p-Value Age (per year) 0.99 0.98–1.01 0.64 Sex (ref: male) 0.80 0.62–1.02 0.08 0.81 0.62–1.06 0.14 Hypertension 1.01 0.55–1.84 0.97 Diabetes mellitus 0.83 0.65–1.06 0.14 Any cardiovascular disease 0.72 0.55–0.94 0.01 0.72 0.54–0.95 0.02 Chronic obstructive pulmonary disease 0.82 0.51–1.30 0.40 PFP (ref: 0) ≥1 3.24 0.99–10.61 0.05 4.07 0.78–21.15 0.09 PFP (ref: 0) 1–2 2.87 0.71–11.4 0.13 4.05 0.77–21.19 0.09 J. Clin. Med. 2022, 11, 672 10 of 15 ≥3 3.35 1.01–11.10 0.04 4.37 0.64–29.14 0.13 FRAIL scale (ref: 0) ≥1 1.83 0.90–3.70 0.09 1.51 0.62–3.70 0.35 Table 5. Cont. FRAIL scale (ref: 0) 1–2 1.81 0.88–3.70 Univariate 0.15 1.51 0.66–3.47 Multivariable 0.32 ≥3 2.06 HR 0.46–9.27 CI 95% 0.43 p-Value 1.95 HR 0.38–9.98 CI 95% 0.41 p-Value HR, hazard Hemodialysis as RRT modality ratio; CI, confidence (ref: HD) 0.94 interval; 0.63–1.41RRT, renal 0.78replacement therapy; HD, hemodialysis; PFP, Physical Dialysis vintage (per Frailty Phenotype. month) 1.005 Four 1.001–1.008 multivariable analyses 0.01 were 1.005 performed, including (1) PFP 1.001–1.009 0.007≥ 1; (2) PFP 1–2 and ≥ 3; (3) FRAIL scale ≥ 1; and (4) FRAIL scale 1–2 and ≥ 3. Age and any cardiovascular PFP (ref: 0) disease remained significant in all four models. ≥1 0.76 0.60–0.98 0.03 0.78 0.59–1.03 0.08 PFP (ref: 0) 1–2 3.4. Chance of Transplantation 0.82 among KT Candidates 0.64–1.06 0.13 and Its0.83 Relationship with Frailty0.19 0.63–1.09 Status ≥3 Considering the0.49 whole cohort (n = 449), 0.30–0.78 patients 0.45 0.003 scoring ≥1 for PFP had 0.26–0.77 a lower 0.004 FRAIL scale chance (ref: 0) of obtaining a KT than those who were robust (Figure 2A), and this chance was ≥1 gradually lower if more 0.88 criteria 0.70–1.11 were present0.29 (Figure 2B). 0.84 However, 0.65–1.08 the FRAIL 0.19 scale was FRAIL scale not associated with transplant probability (Figure 2C,D). In the multivariable analysis, (ref: 0) 1–2 cardiovascular disease 0.88was negatively 0.70–1.11 associated0.30 with the 0.85chance0.66–1.10 0.23 dur- of transplantation ≥3 0.87 0.44–0.71 0.70 0.70 0.32–1.51 0.37 ing follow-up (HR 0.72), while dialysis vintage was positively associated (HR 1.005 per HR, hazard ratio; CI, confidence interval; RRT, renal replacement therapy; HD, hemodialysis; PFP, Physical month). PFPPhenotype. Frailty ≥ 3 candidates had a analyses 4 multivariable lower chance of receiving were performed, a transplant including: asPFP (1) PFP ≥ 1; (2) well 1–2(HR and 0.45) ≥ 3; 5).FRAIL scale ≥ 1; and (4) FRAIL scale 1–2 and ≥3. Any cardiovascular disease and dialysis vintage both (Table (3) remained significant in all four models. (A) Figure 2. Cont. J. Clin. Med. J. Clin. 2022, Med. 11, 11, 2022, x FOR 672 PEER REVIEW 10 11of of15 15 (B) (C) Figure 2. Cont. J. Clin. Med. J. Clin. 2022, Med. 11, 11, 2022, x FOR 672 PEER REVIEW 11 12of of15 15 (D) Figure Figure2. 2. Kaplan–Meier Kaplan–Meiercurves curvesofofpatient chance of patient chance oftransplantation transplantationwhile while listed listed according according to their to their PFP PFPandand FRAIL FRAIL scale scale score score (n =(n449). = 449). Time Time to follow-up to follow-up 15 (4–27) 15 (4–27) months. months. (A) Chance (A) Chance of transplan- of transplantation tation amongamong robust robust (PFP (PFP = 0)both = 0) and and pre-frail both pre-frail andpatients and frail frail patients together together (PFP ≥ (PFP 1); p =≥ 0.034; 1); p =(B) 0.034; (B) Chance Chance of transplantation among robust (PFP = 0), pre-frail (PFP = 1–2), and frail patients (PFP ≥ 3); of transplantation among robust (PFP = 0), pre-frail (PFP = 1–2), and frail patients (PFP ≥ 3); p = 0.008; (C) Chance of transplantation among robust (FRAIL = 0) and both pre-frail and frail pa- p = 0.008; (C) Chance of transplantation among robust (FRAIL = 0) and both pre-frail and frail tients together (FRAIL ≥ 1); p= 0.28; (D) Chance of transplantation among robust (FRAIL = 0), pre- patients together (FRAIL ≥ 1); p= 0.28; (D) Chance of transplantation among robust (FRAIL = 0), frail (FRAIL = 1–2), and frail patients (FRAIL ≥ 3); p = 0.56. pre-frail (FRAIL = 1–2), and frail patients (FRAIL ≥ 3); p = 0.56. Table 5. Univariate and multivariable analysis of chance of the transplantation while listed (n = 449). 4. Discussion Time to follow-up 15 (4–27) months. In this prospective study carried out in a cohort of 449 KT candidates, we analyzed Univariate Multivariable frailty impact in terms of both clinical outcomes and patient survival while listed according to two differentHR CI 95% metrics (PFP and FRAIL p-Value HR at the scale) performed CI 95% p-Valuelist time of waiting Age (per year) evaluation. We found0.99 higher 0.98–1.01 0.64 rates of hospital admissions and major clinical events among Sex (ref: male) those candidates 0.80 who were0.62–1.02 0.08 according pre-frail and frail 0.81to both 0.62–1.06 0.14 was scales. Mortality Hypertension four-fold higher if 1.01patients scored 0.55–1.84 ≥ 1 for PFP 0.97 (p = 0.09), and if they scored ≥ 3 for PFP Diabetes mellitus they had 55% less 0.83probability of 0.65–1.06 transplantation 0.14 while listed. The FRAIL scale was not associated with 0.72 Any cardiovascular disease these outcomes. 0.55–0.94 0.01 0.72 0.54–0.95 0.02 Studies regarding KT candidates have established frailty as a major condition that Chronic obstructive pulmonary disease 0.82 0.51–1.30 0.40 impacts patient survival while listed [10–13]. Although many scales have been used both Hemodialysis as RRT modality in(ref: HD) KT candidates 0.94 0.63–1.41 and recipients 0.78the monopoly, probably because most of [2], PFP has Dialysis vintage (per month) 1.005 1.001–1.008 0.01 the research comes from the same group [10,11,13]. However, 1.005 no 1.001–1.009 0.007 studies have compared PFP (ref: 0) different scales and their potential association with outcomes. ≥1 0.76 prevalence In US cohorts, 0.60–0.98of frail KT 0.03 candidates0.78according 0.59–1.03 0.08been to PFP scale has PFP (ref: 0) established between 13.3 and 21% [10,11,13,17,24]. In our study, frail patients represented 1–2 10.5% (PFP) and0.82 3.6% (FRAIL) of the whole 0.13 0.64–1.06 cohort. In addition, 0.83 differences 0.63–1.09 were found 0.19 in ≥3 frailty prevalence among 0.49 KT active 0.30–0.78 candidates. 0.003 Patients who 0.45 finally receive 0.26–0.77 a transplant 0.004 presented with ≥3 Fried criteria in 7.4% of cases vs. 16.3% of those who remained listed. FRAIL scale (ref: 0) In contrast, the FRAIL scale revealed a lower percentage of frail patients, and without ≥1 0.88those 0.70–1.11 differences between who were transplanted0.29 and those0.84 who were 0.65–1.08 0.19must not. Clinicians FRAIL scale (ref: 0)be aware of this difference in frailty prevalence between PFP and the FRAIL scale, which 1–2 reaches 71.3% vs. 0.88 0.70–1.11 45% when 0.30 are considered pre-frail patients 0.85 0.66–1.10 because (probably 0.23 PFP is ≥3 0.87 FRAIL, more sensitive than 0.44–0.71 0.70 measurements). as it involves objective 0.70 0.32–1.51 0.37 HR, hazard ratio; CI, confidence interval; RRT, renal replacement therapy; HD, hemodialysis; PFP, Physical Frailty Phenotype. 4 multivariable analyses were performed, including: (1) PFP ≥ 1; (2) PFP J. Clin. Med. 2022, 11, 672 13 of 15 Hospital admission rate prior to KT is a strong determinant of waitlist mortality and transplant probability [25] and of poorer patient and graft survival after transplanta- tion [25,26]. Frailty has been associated with hospitalizations in hemodialysis patients [27]. We described a higher rate of admissions during the first year after evaluating for trans- plantation in those KT candidates who were pre-frail and frail by both PFP and the FRAIL scale. Furthermore, PFP pre-frail and frail patients suffered more infectious events, while FRAIL pre-frail and frail patients suffered more cardiovascular events. Both scales were able to discriminate those candidates who were removed from the waiting list, even when only one point of score was present. Thus far, it seems that both PFP and FRAIL scale would have the same predictive value for bad outcomes during the time the patient is waiting for KT. In larger US cohorts, frailty as determined by the PFP has shown an impact on patient mortality after listing, with an increased risk between 70 and 119% among those KT candidates who were frail [11,13]. In addition, a high comorbidity burden has been described as impacting patient survival only in robust candidates, as when frailty is present this comorbidity loses its significance [10]. We found that only frailty according to the PFP was associated with patient mortality in the univariate analysis (7.7% in robust patients and 27.2% in pre-frail or frail ones), although this significance was lost in the multivariable analysis (probably due to the small number of patients). Therefore, this difference might be due to other confounders such us comorbidity or disability, both of which are more frequent within frail patents. On the contrary, FRAIL failed to discriminate survival probability while listed. This could be related to a smaller number of patients being frail according to the FRAIL scale, or to the different frailty phenotype both scales are catching, as the FRAIL scale accounts for comorbidity and ignores physical reserve. Likelihood of transplantation is lower when frailty is present. Haugen et al. described results regarding frailty according to the PFP and access to transplantation in KT active candidates. From a total of 4558 patients, 21% turned out to be frail, and they presented with 32% less probability to undergo a KT [13]. Chance of transplantation in our cohort was determined by PFP and FRAIL scale status. We found that frail patients according to the PFP had 55% less chance to be transplanted than robust patients. The FRAIL scale did not differentiate this outcome at the time of waiting list evaluation. This study has the inherent limitations of a descriptive one-center study, thus, external validation may not be assumed. Frail patients accounted only for a small percentage of the sample, and the statistical power may be low. In addition, the degree of patient HLA sensitization might have played a role in the likelihood of transplantation; however, this information was not available. However, to our knowledge, this is the first study to compare the predictive value of bad outcomes of two different scales at the time of inclusion on the KT waiting list. Comparing scales may have importance, as they can be used depending on individual clinical practice and/or resources. PFP considers physical aspects that FRAIL does not. On the contrary, FRAIL considers comorbidity as part of frailty without measuring physical capacity. This might provide a lower percentage of frail patients (i.e., a less sensitive scale). However, it is unknown if these patients identified as frail by Fried and not by FRAIL have poorer outcomes. Although PFP appears to catch more CKD frail patients compared to the FRAIL scale (10.5 vs. 3.6%), being frail by both scales is related to poorer outcomes, and the FRAIL scale might be a good screening tool in cases where clinicians need to implement an easy-to-perform, faster option. 5. Conclusions Transplant units should incorporate frailty evaluation in patient assessment. This may be of crucial importance for proper patient information and clinician decisions, as well as for the detection of patients at risk and the implementation of interventional approaches to reverse frailty before transplantation. The decision about which scale to use at the time the candidate is evaluated will depend on many factors, as its feasibility is different in terms J. Clin. Med. 2022, 11, 672 14 of 15 of time and resources consumed. Frailty status according to the PFP scale provides more accurate insight than the FRAIL scale about KT waiting list patient prognosis. Author Contributions: Conceptualization, M.J.P.-S., D.R.-P. and J.P.; methodology, M.J.P.-S., D.R.-P. and C.E.A.-C.; formal analysis, D.R.-P.; investigation, M.J.P.-S., D.R.-P., A.B. (Anna Buxeda) and C.B.; resources, A.F., A.B. (Anna Bach) and E.J.; data curation, M.J.P.-S. and D.R.-P.; writing—original draft preparation, M.J.P.-S. and D.R.-P.; writing—review and editing, J.P., E.M., L.R.-M., M.C., C.E.A.-C., A.B. (Anna Buxeda) and C.B.; supervision, J.P. and M.C.; project administration, J.P. and E.M.; funding acquisition, J.P. and E.M. All authors have read and agreed to the published version of the manuscript. Funding: Frail-MAR project is currently supported by a FIS-FEDER grant PI19/00037 (ISCIII) and a ‘Proyecto Estrella de Mejora de la Calidad’, Parc de Salut Mar, Barcelona, Spain. M.J.P.-S. has been granted by the Spanish Society of Transplant. A.B. (Anna Buxeda) has support from a Rio Hortega contract CM19/00004, ISCIII. Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of PARC DE SALUT MAR (2018/7798/I; approved on 2 March 2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy reasons. Acknowledgments: The authors of this study appreciate the contribution of all the members of the FRAIL-MAR Study Group: María José Pérez-Sáez, Carlos E. Arias-Cabrales, Dolores Redondo, Francesc Barbosa, Higini Cao, Silvia Collado, Anna Buxeda, Carla Burballa, Marta Crespo, Julio Pascual, Anna Faura, María Vera, Anna Bach, Guillermo Pedreira, Ernestina Junyent, Montserrat Folgueiras, Yolanda Castillo, Aida Martínez, Marisol Fernández, Eva Barbero, Rosa Causadías (De- partment of Nephrology, Hospital del Mar), Alicia Calvo (Department of Cardiology, Hospital del Mar), Jesús Carazo (Department of Anesthesiology, Hospital del Mar), Albert Frances, Lluis Cec- chini (Department of Urology, Hospital del Mar), Ester Marco, Delky Meza de Valderrama, Andrea Morgado, Elena Muñoz (Department of Rehabilitation and Physical Medicine, Hospital del Mar), Xavier Nogués (Department of Internal Medicine, Hospital del Mar), Leocadio Rodríguez-Mañas (Department of Geriatrics, Hospital Universitario de Getafe, Madrid), Olga Vázquez (Department of Geriatrics, Hospital del Mar), María Dolores Muns (Dietary Unit, Department of Endocrinology and Nutrition, Hospital del Mar), Miguel Gárriz, María Polo Gómez (Psychology Department, Neu- ropsychiatric Institute, Hospital del Mar), Sara Hurtado, Maite López (Diagonal Hemodialysis Center, Fresenius Medical Care), Laura Ribera, Margarita Guino (Glories Hemodialysis Center, Fresenius Medical Care), Ramón Roca, Jordi Calls, Alicia Rovira (Department of Nephrology, Hospital de Mollet), Josep Mora, Omar Ibrik, Florentina Liria (Granollers Hemodialysis Center, Fresenius Medical Care), Thaïs López, Jaume Almirall, Carmen Moya (Department of Nephrology, Hospital Parc Taulí), Fátima Moreno, Manel Ramírez de Arellano, Sandra Rubio (Department of Nephrology, Consorci Sanitari de Terrassa), Ignacio Cidraque, Carlota Pájaro (Cetirsa Terrassa Hemodialysis Center, Fre- senius Medical Care), Núria Garra, Josep Galcerán, Marina Fenollar (Department of Nephrology, Hospital de Manresa), Sara Outón, Fabiola Dapena, Josep Jara (Department of Nephrology, Consorci Sanitari del Garraf), Rosa García, Mònica Manresa (Department of Nephrology, Hospital de Palamós). Conflicts of Interest: The authors declare no conflict of interest. References 1. Chowdhury, R.; Peel, N.; Krosch, M.; Hubbard, R. Frailty and chronic kidney disease: A systematic review. Arch. Gerontol. Geriatr. 2017, 68, 135–142. [CrossRef] 2. Harhay, M.N.; Rao, M.K.; Woodside, K.J.; Johansen, K.L.; Lentine, K.L.; Tullius, S.G.; Parsons, R.F.; Alhamad, T.; Berger, J.; Cheng, X.S.; et al. An overview of frailty in kidney transplantation: Measurement, management and future considerations. Nephrol. Dial. Transplant. 2020, 35, 1099–1112. [CrossRef] 3. Fried, L.P.; Tangen, C.M.; Walston, J.; Newman, A.B.; Hirsch, C.; Gottdiener, J.; Seeman, T.; Tracy, R.; Kop, W.J.; Burke, G.; et al. Frailty in Older adults: Evidence for a phenotype. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2001, 56, M146–M157. [CrossRef] 4. Cesari, M.; Gambassi, G.; van Kan, G.A.; Vellas, B. The frailty phenotype and the frailty index: Different instruments for different purposes. Age Ageing 2013, 43, 10–12. [CrossRef] J. Clin. Med. 2022, 11, 672 15 of 15 5. Oviedo-Briones, M.; Laso, R.; Carnicero, J.A.; Cesari, M.; Grodzicki, T.; Gryglewska, B.; Sinclair, A.; Landi, F.; Vellas, B.; Checa- López, M.; et al. A Comparison of Frailty Assessment Instruments in Different Clinical and Social Care Settings: The Frailtools Project. J. Am. Med. Dir. Assoc. 2020, 22, 607.e7–607.e12. [CrossRef] 6. Lim, Y.J.; Ng, Y.-S.; Sultana, R.; Tay, E.L.; Mah, S.M.; Chan, C.H.N.; Latib, A.B.; Abu-Bakar, H.M.; Ho, J.C.Y.; Kwek, T.H.H.; et al. Frailty Assessment in Community-Dwelling Older Adults: A Comparison of 3 Diagnostic Instruments. J. Nutr. Health Aging 2020, 24, 582–590. [CrossRef] 7. Ibrahim, K.; Howson, F.F.A.; Culliford, D.J.; Sayer, A.A.; Roberts, H.C. The feasibility of assessing frailty and sarcopenia in hospitalised older people: A comparison of commonly used tools. BMC Geriatr. 2019, 19, 42. [CrossRef] 8. Van Loon, I.N.; Goto, N.A.; Boereboom, F.T.; Bots, M.L.; Verhaar, M.C.; Hamaker, M.E. Frailty screening tools for elderly patients incident to dialysis. Clin. J. Am. Soc. Nephrol. 2017, 12, 1480–1488. [CrossRef] 9. Kittiskulnam, P.; Chertow, G.M.; Carrero, J.J.; Delgado, C.; Kaysen, G.A.; Johansen, K.L. Sarcopenia and its individual criteria are associated, in part, with mortality among patients on hemodialysis. Kidney Int. 2017, 92, 238–247. [CrossRef] 10. Fernández, M.P.; Miguel, P.M.; Ying, H.; Haugen, C.E.; Chu, N.; Puyol, D.M.R.; Rodríguez-Mañas, L.; Norman, S.P.; Walston, J.D.; Segev, D.L.; et al. Comorbidity, Frailty, and Waitlist Mortality among Kidney Transplant Candidates of All Ages. Am. J. Nephrol. 2019, 49, 103–110. [CrossRef] 11. McAdams-DeMarco, M.A.; Ying, H.; Thomas, A.; Warsame, F.; Shaffer, A.; Haugen, C.E.; Garonzik-Wang, J.M.; Desai, N.M.; Varadhan, R.; Walston, J.; et al. Frailty, Inflammatory Markers, and Waitlist Mortality Among Patients with End-stage Renal Disease in a Prospective Cohort Study. Transplantation 2018, 102, 1740–1746. [CrossRef] 12. Lorenz, E.C.; Cosio, F.G.; Bernard, S.L.; Bogard, S.D.; Bjerke, B.R.; Geissler, E.N.; Hanna, S.W.; Kremers, W.K.; Cheng, Y.; Stegall, M.D.; et al. The Relationship between Frailty and Decreased Physical Performance with Death on the Kidney Transplant Waiting List. Prog. Transplant. 2019, 29, 108–114. [CrossRef] 13. Haugen, C.E.; Chu, N.; Ying, H.; Warsame, F.; Holscher, C.M.; Desai, N.M.; Jones, M.R.; Norman, S.P.; Brennan, D.C.; Garonzik- Wang, J.; et al. Frailty and Access to Kidney Transplantation. Clin. J. Am. Soc. Nephrol. 2019, 14, 576–582. [CrossRef] 14. Harhay, M.N.; Reese, P.P. Frailty and cognitive deficits limit access to kidney transplantation unfair or unavoidable? Clin. J. Am. Soc. Nephrol. 2019, 14, 493–495. [CrossRef] 15. McAdams-DeMarco, M.A.; Chu, N.M.; Segev, D.L. Frailty and Long-Term Post-Kidney Transplant Outcomes. Curr. Transplant. Rep. 2019, 6, 45–51. [CrossRef] 16. McAdams-DeMarco, M.; Law, A.; King, E.; Orandi, B.; Salter, M.; Gupta, N.; Chow, E.; Alachkar, N.; Desai, N.; Varadhan, R.; et al. Frailty and Mortality in Kidney Transplant Recipients. Am. J. Transplant. 2014, 15, 149–154. [CrossRef] 17. Quint, E.E.; Zogaj, D.; Banning, L.B.; Benjamens, S.; Annema, C.; Bakker, S.J.; Nieuwenhuijs-Moeke, G.J.; Segev, D.L.; McAdams-DeMarco, M.A.; Pol, R.A. Frailty and Kidney Transplantation: A Systematic Review and Meta-analysis. Transplant. Direct 2021, 7, e701. [CrossRef] 18. Morley, J.E.; Malmstrom, T.K.; Miller, D.K. A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J. Nutr. Health Aging 2012, 16, 601–608. [CrossRef] 19. McAdams-DeMarco, M.; Law, A.; Tan, J.; Delp, C.; King, E.A.; Orandi, B.; Salter, M.; Alachkar, N.; Desai, N.; Grams, M.; et al. Frailty, Mycophenolate Reduction, and Graft Loss in Kidney Transplant Recipients. Transplantation 2015, 99, 805–810. [CrossRef] 20. McAdams-DeMarco, M.A.; Olorundare, I.O.; Ying, H.; Warsame, F.; Haugen, C.E.; Hall, R.; Garonzik-Wang, J.M.; Desai, N.M.; Walston, J.D.; Norman, S.P.; et al. Frailty and Postkidney Transplant Health-Related Quality of Life. Transplantation 2018, 102, 291–299. [CrossRef] 21. Mahoney, F.I.; Barthel, D.W. Functional evaluation: The Barthel Index. Md. State Med. J. 1965, 14, 61–65. 22. Shah, S.; Vanclay, F.; Cooper, B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J. Clin. Epidemiol. 1989, 42, 703–709. [CrossRef] 23. Lawton, M.P.; Brody, E.M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontology 1969, 9, 179–186. [CrossRef] 24. Haugen, C.E.; Thomas, A.G.; Chu, N.; Shaffer, A.A.; Norman, S.P.; Bingaman, A.W.; Segev, D.L.; McAdams-DeMarco, M. Prevalence of frailty among kidney transplant candidates and recipients in the United States: Estimates from a National Registry and Multicenter Cohort Study. Am. J. Transplant. 2019, 20, 1170–1180. [CrossRef] 25. Lynch, R.J.; Zhang, R.; Patzer, R.E.; Larsen, C.P.; Adams, A.B. First-Year Waitlist Hospitalization and Subsequent Waitlist and Transplant Outcome. Am. J. Transplant. 2016, 17, 1031–1041. [CrossRef] 26. Lynch, R.J.; Zhang, R.; Patzer, R.E.; Larsen, C.P.; Adams, A.B. Waitlist Hospital Admissions Predict Resource Utilization and Survival after Renal Transplantation. Ann. Surg. 2016, 264, 1168–1173. [CrossRef] 27. McAdams-DeMarco, M.A.; Law, A.; Salter, M.L.; Boyarsky, B.; Gimenez, L.; Jaar, B.G.; Walston, J.D.; Segev, D.L. Frailty as a novel predictor of mortality and hospitalization in individuals of all ages under-going hemodialysis. J. Am. Geriatr. Soc. 2013, 61, 896–901. [CrossRef]