Construction and validation of a predictive model for preoperative frailty risk in liver transplant recipients

Construction and validation of a predictive model for preoperative frailty risk in liver transplant recipients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction and validation of a predictive model for preoperative frailty risk in liver transplant recipients Bingliang Zhang, Huihui Sun, Lianyu lou, Jinshan Zhuang, Guofang Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890299/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Early frailty detection and tailored interventions to address frailty may increase the survival rate of liver transplant patients during the waiting period and lower infection and death rates following transplantation, with the aim of developing and testing a model to predict the likelihood that a liver transplant recipient would be too weak to undergo surgery. Methods: Facilitation of the procedure for patients on the liver transplant waiting list at a tertiary hospital in Qingdao from January 2021 to December 2022 was chosen for the study. The modeling group participants were recruited from January 2021 to June 2022 (258 cases), while the validation group participants were selected from October 2022 to December 2022 (65 instances). A nomogram was created to display the results of the risk prediction model's single-factor and logistic regression studies. The adequacy of the model was evaluated using the Hosmer-Lemeshow test, and its ability to make accurate predictions was evaluated using receiver operating characteristic (ROC) curves. Results: The incidence of preoperative frailty in liver transplant recipients was 39.32%. Age ( OR = 0.121), serum ALB concentration ( OR =-0.586), physical activity ( OR =-0.002), depression ( OR = 1.055), anxiety ( OR = 0.557), and hepatic encephalopathy ( OR = 1.731) were risk factors ( P 0.05) indicated that the model fit well. The AUC was 0.997, with a 95% CI ranging from 0.992 to 0.998 and P < 0.001. The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963. The external validation results showed a sensitivity of 0.889, specificity of 0.950, and accuracy of 84.7%. Conclusion: This risk prediction model has a high rate of accuracy, making it a useful benchmark for assessing patients' risk of developing postoperative frailty after receiving a liver transplant. liver transplantation frailty risk prediction model surgery Figures Figure 1 Figure 2 Figure 3 Introduction Liver transplantation is the most successful treatment for end-stage liver disease. End-stage liver disease, such as fatigue, starvation, muscle loss, frailty, or deterioration of multiple system functions, can cause difficulties during the waiting period for a liver transplant and may be caused by long-term muscle loss and physical function degeneration[ 1 ]. A patient's reserve of stress and resistance diminishes as a result of the cumulative deterioration of many physiological systems, which may have unfavorable effects[ 2 ]. This biological illness is known as frailty, fatigue, mental issues, or osteoporosis, and a lack of physical endurance is the major way that weakness manifests. Research has shown that frailty is positively correlated with the mortality rate of liver transplant recipients during the waiting period, incidence of hepatic encephalopathy, and mortality rate after transplantation[ 3 ]. According to statistics, the incidence rate of weakness during the waiting period for liver transplant recipients is approximately 18%-43%. Every increase in Fried's weakness score increases the risk of death before transplantation by 45%[ 4 ]. Therefore, early frailty detection and tailored interventions to address frailty may increase the survival rate of liver transplant patients during the waiting period and lower the infection and death rates following transplantation. However, current clinical practice does not assess preoperative fragility in liver transplant candidates. This study aimed to investigate the preoperative frailty factors that affect liver transplant recipients, develop a risk prediction model for preoperative frailty in liver transplant recipients, and provide theoretical underpinnings for preoperative frailty in liver transplant recipient prevention and early intervention. Methods Source of Data The survey approach entails data gathering by two qualified researchers who obtain general patient data from the HIS system. Face-to-face distribution and collection of survey data are needed. Consistent guideline language was used to explain the survey's goal, importance, content, and safety measures to patients and their families before the survey. Our research was performed in accordance with the Declaration of Helsinki, the confidentiality of patient information was verified, and the patients signed an informed consent form. The project was approved by the Ethical Review Committee of the Affiliated Hospital of Qingdao University (QYFY W2LL 28049) to confirm the correctness and thoroughness of the questionnaire after it was completed and checked for faults. Informed consent was obtained from the INR. Patients The survey participants used a convenient sampling method. From January 2021 to December 2022, patients were selected from a tertiary hospital organ transplantation center in Qingdao to receive a preoperative liver transplantation waitlist. Participants enrolled in the study between January 2021 and June 2022 composed the modeling group, while those enrolled between October 2022 and December 2022 composed the validation group. The inclusion criteria were as follows: (1) ≥ 18 years of age; (2) inclusion in the waiting list for liver transplantation; (3) clear awareness, no cognitive barriers, and no barriers to language communication; and (4) provided informed consent. The exclusion criteria were as follows: (1) second liver transplant recipient; (2) severe heart, lung, or kidney dysfunction; and (3) other organ transplant recipient. According to the literature review, there are approximately 10 predictive variables. The incidence of preoperative frailty in liver transplant recipients ranges from 18–43%[ 5 ]. According to the sample size requirements of the prediction model, at least 5–10 samples are required for each risk factor[ 6 ]. The sample size of this study was N = 10 × 10 ÷ 0.43 = 232. Considering a 10% loss to follow-up rate, this study included 258 study subjects in the modeling group. There were 65 patients in the validation cohort whose sample size in the modeling and validation phases met the 4:1 ratio. Data collection Frailty Fried’s frailty phenotype can be used to assess the preoperative frailty of liver transplant patients[ 7 ] and consists of a total of five items: unidentified weight loss, decreased physical activity, fatigue, slower walking speed, and decreased grip strength. These items were scored 1 point for each item with abnormal evaluation, 0 points for normal evaluation, 0 points for total item score as nonfrailty, 1–2 points as prefrailty, and 3–5 points as frailty. General information The researchers constructed a general information questionnaire based on a literature review of the potential causes of preoperative frailty in liver transplant patients. The patients were reviewed by liver transplantation specialists. Age, sex, education, method of covering medical expenses, per capita monthly household income, marital status, primary diseases (including posthepatic cirrhosis, alcoholic cirrhosis, autoimmune cirrhosis, liver cancer, and polycystic liver), and complications (including hepatic encephalopathy, gastrointestinal hemorrhage, cardiovascular and cerebrovascular diseases, diabetes, and hypertension) were also considered. Hemoglobin, BMI, Child-Pugh grade, MELD score, liver function test results, and albumin test results were also included. Mental health factors The mental health factors included anxiety and depression and were assessed using the Hospital Anxiety and Depression Scale[8]. Two subscales and 14 items were used. Both sadness and anxiety were assessed using 7 items each. Each subscale received a total of 21 points based on a 4-point rating (0–3 points). No anxiety or depression was indicated by a score of 0–7, mild anxiety or depression by a score of 8–10, moderate anxiety or depression by a score of 11–14, and severe anxiety or depression by a score of 15–21. Sleep conditions Sleep condition was assessed via the Pittsburgh Sleep Quality Index (PSQI) [ 9 ], which comprises 23 components, including subjective sleep quality, time to fall asleep, amount of time spent sleeping, effectiveness of sleep, sleep disorders, usage of sleep medications, and effect on daily function. The total score ranged from 0–21 points, with a total score of ≥ 7 indicating sleep disorders and < 7 indicating good sleep quality. A single score greater than one in each dimension indicates that there are obstacles to this item. Nutritional status Nutritional status was assessed using the Short-form Mini-Nutritional Assessment [ 10 ], with a total score of 0–14, with ≥ 12 indicating normal nutrition, 8–11 indicating nutritional risk, and 0–7 indicating malnutrition. Activity conditions Activity conditions were assessed using the International Physical Activity Questionnaire [ 11 ]. The physical activity level was calculated as weekly physical activity level (MET min/w) = related metabolic equivalent task (METS) of physical activity × weekly frequency (d/w) × daily time (min/d). The METs for walking, moderate-intensity activities, and high-intensity activities were 3.3, 4, and 8, respectively. Weekly total energy consumption was ≥ 3000 MET/min for high-intensity physical activity, ≥ 600 MET/min for moderate physical activity, and < 600 MET/min for low-intensity physical activity. Social support Social support was assessed using the Perceived Social Support Scale[ 12 ], which includes the three dimensions of family support, friend support, and other support, with 12 items. Using the Likert 7-point scoring method, the total score range was 12–84 points. The higher the total score is, the greater the perceived level of social support. Statistical analysis To ensure the correctness of the data, the program verified the original data entered by the two humans. The broad data were normally distributed. While the general data were not normally distributed and are expressed as medians and interquartile ranges, they are expressed as the means and standard deviations. In the single-factor analysis, one-way analysis of variance (ANOVA) was used to analyze the quantitative data that were normally distributed, the rank-sum test was used to test the nonnormal distribution, and the chi-square test was used to test the grade data. Factors with P < 0.05 in the univariate analysis were included in the logistic regression analysis. To evaluate the risk characteristics, a nomogram was created using R (4.2.3) and the RMS software suite. The Hosmer-Lemeshow test and area under the curve (AUC) of the receiver operating characteristic (ROC) curve were used. were used to assess the degree of fit and predictive power of the model. Sensitivity, specificity, and accuracy were used to assess the model's predictive ability, with a P value of < 0.05. considered to be statistically significant. Results Participant characteristics The general data modeling group of the survey subjects included 258 patients aged 26 to 70 (50.82 ± 10.79) years, including 198 males (76.74%) and 60 females (23.26%). The primary disease information included 26 patients (10.08%) with alcoholic cirrhosis, 112 patients (43.41%) with hepatitis cirrhosis, 19 patients (7.36%) with autoimmune cirrhosis, 96 patients (37.21%) with liver cancer, and 5 patients (1.94%) with polycystic liver disease. The grade was Grade A in 117 patients (45.35%), Grade B in 75 patients (29.07%), and Grade C in 66 patients (25.58%). The BMI was 20.96 ± 1.64 kg/m2, and the mean MELD score was 13.70 ± 5.71 points. Education level was as follows: one at or below primary school (0.39%), 36 at junior high school (13.95%), 119 at high school or technical secondary school (46.12%), and 102 at or above university (39.53%). There were 10 unmarried (3.88%) and 248 married (96.12%) patients. There were 78 patients with gastrointestinal hemorrhage (30.23%), 97 with hepatic encephalopathy (37.50%), 34 with diabetes (13.18%), 38 with hypertension (14.73%), 77 with bone disease (29.84%), 46 with respiratory diseases (17.83%), and 36 with cardiovascular and cerebrovascular diseases (13.95%). Please refer to Table 1 for general information. A total of 65 patients aged 29–66 years (48.21 ± 9.57) were included in the validation cohort, which included 52 males (80.00%) and 13 females (20.00%). The primary diseases included seven cases of alcoholic cirrhosis (10.77%), 28 cases of hepatitis cirrhosis (43.08%), four cases of autoimmune cirrhosis (6.15%), 25 cases of liver cancer (38.46%), and one case of polycystic liver disease (1.54%). The grade was Grade A for 31 patients (47.67%), Grade B for 20 patients (30.77%), and Grade C for 14 patients (21.54%). Prevalence of frailty and related variables Among the 258 liver transplant recipients in the preoperative frailty modeling group, 110 had no frailty, 41 had pre-frailty, and 107 had frailty, for a frailty incidence rate of 41.47%. Among the 65 patients in the validation cohort, 28 were not frail, 17 were pre-frail, and 20 were frail. The incidence rate of frailty was 30.76%. Overall, 138, 58, and 127 patients were not frail or pre-frailty, respectively, and 127 patients were frail. The incidence rate of frailty was 39.32%. Univariate analysis of preoperative frailty in liver transplant recipients revealed negative outcomes on average and pre-frailty, with frailty occurring before liver transplantation as the dependent variable. Age, main disease, grade, MELD score, body mass index, albumin level, anxiety score, depression score, sleep status, perceived social support score, nutritional score, physical activity score, combined gastrointestinal bleeding, combined hepatic encephalopathy score, and other findings are shown. Liver transplant patients were more likely to have preoperative frailty if they had diabetes, bone disease, or respiratory illnesses ( P < 0.05). Further details are provided in Table 1 . Logistic regression of patients with liver transplants Multivariate analysis of preoperative frailty in liver transplant recipients revealed factors ( P < 0.05) in the univariate analysis as independent variables, with normal and pre-frail status as adverse outcomes and pre-transplant frailty as the dependent variable for logistic regression analysis. The specific assignment methods are shown in Table 2 (normal or pre-frailty = 0, frailty = 1). This study showed that age, the serum ALB concentration, anxiety, depression, concomitant hepatic encephalopathy, and physical activity were found to be independent influencing factors ( P < 0.05) for preoperative frailty in liver transplant recipients, as shown in Table 3 . Predictive model development The predictive model for preoperative frailty risk in liver transplant recipients was as follows: P = 1/{1 + exp [-9.927 + 0.121] × age − 0.586 × albumin − 0.002 × physical activity + 1.055 × depression + 0.557 × anxiety + 1.731 × concomitant hepatic encephalopathy. Therefore, a nomogram for predicting preoperative frailty risk in liver transplant recipients was constructed based on these variables (Fig. 1 ). Fitting and prediction effect analysis of the preoperative frailty risk prediction model for liver transplant recipients; Hosmer–Lemeshow test χ2 = 3.139, P = 0.925 (> 0.05), indicating that the model has a good fit. The area under the ROC curve (AUC) was 0.997 (95% confidence interval [CI], 0.992–0.998; P < 0.001). The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963. The ROC curve is shown in Fig. 2 . Predictive model validation In the validation cohort of liver transplant recipients, the Hosmer-Lemeshow test was used to predict preoperative frailty risk (χ2 = 2.720, P = 0.910 (> 0.05)). The area under the ROC curve (AUC) was 0.971, with 95% CI ranging from 0.938 to 0.988 ( P < 0.001). The optimal critical value and sensitivity were 0.889, and the specificities were 0.304, 0.889, and 0.950. The ROC curve is shown in Fig. 3 . This model predicts that 25 of 65 patients will experience weakness, while 40 will not. The results showed that 20 patients experienced weakness, whereas 45 did not. The accuracy rate was 84.7%. Discussion The incidence of preoperative weakness in liver transplant recipients was relatively high. In this study, 323 patients were included in the modeling and validation groups, 127 of whom were in the weakened state. The incidence of weakness was 39.23%, which was higher than the results reported by Sinclair[13)]and Lai [ 1 ] (31.6%, 17%). This may be due to differences in regional culture, medical status, and other factors, which may have led to inconsistent results. On the other hand, there were more complications in the liver transplant recipients included in this study. Research shows that the combination of hepatic encephalopathy and diabetes increases the incidence of frailty, so the incidence of frailty in this study was high. Research has shown that preoperative weakness in liver transplant recipients can significantly increase hospital stay and waiting period mortality[ 14 ]. Liver transplant recipients experience rapid disease progression during the waiting period, often accompanied by gastrointestinal bleeding, hepatic encephalopathy, renal insufficiency, and infection. Weakness can reduce the reserve capacity of liver transplant recipients to resist these complications during the waiting period, thereby increasing the incidence of these complications, prolonging hospital stay, and increasing mortality during the waiting period. Foreign studies have shown that assessing and intervening in frailty in liver transplant recipients can significantly reduce readmission and overall mortality within 30 days[ 15 ]. Therefore, medical staff must dynamically monitor frailty in liver transplant recipients before surgery. In addition to paying attention to the rapid progression of liver transplant recipients, dynamic monitoring and identification of risk factors for preoperative frailty in liver transplant recipients should also be performed. Older liver transplant recipients are prone to developing preoperative weakness. The results of this study showed that age is an independent risk factor for preoperative weakness in liver transplant recipients, and the older the patient is, the greater the risk of weakness ( OR = 1.128, P = 0.025). A study by Lai [ 1 ] showed that the frailty of patients with liver cirrhosis is positively correlated with age. The older the patient is, the greater the risk of frailty, which is consistent with the results of this study. The reason may be that as age increases, the body's resistance and organ function gradually decrease, increasing the risk of weakness. In addition, aging has led to more diseases. Old age often leads to hypertension, cardiovascular and cerebrovascular diseases, diabetes, and other diseases, which increase the risk of weakness. As age increases, the quality and function of skeletal muscles in the body gradually decrease, increasing susceptibility to sarcopenia. Long-term sarcopenia ultimately leads to weakness in patients. Therefore, medical staff should focus on the elderly population, strengthen early frailty assessments of elderly patients waiting for liver transplant recipients, and develop targeted intervention measures for the elderly population. Low-albumin liver transplant recipients are prone to developing preoperative weakness. The results of this study showed that the serum ALB concentration is a protective factor against preoperative weakness in liver transplant recipients and that the lower the serum ALB concentration is, the greater the risk of weakness ( OR = 0.557, P = 0.002). These results are consistent with those reported by Sinclair[ 13 ]. Human serum albumin (HSA) is the most abundant protein in human plasma. It maintains plasma osmotic pressure and is the primary regulator of immune and vascular barriers. Albumin is generated in the liver, and the function of patients with end-stage liver disease is severely impaired. When albumin is insufficiently generated, its normal structure and function are also lost, which leads to a reduction in plasma colloid osmotic pressure, damage to the vascular barrier, leakage of a large amount of body fluid into the abdominal cavity leading to ascites, and a large number of ascites, leading to patient unwillingness to leave bed and exercise, ultimately leading to a decline in physical activity and the risk of frailty[ 16 ]. Research has shown that the serum ALB concentration is an important prognostic factor for liver transplant recipients and is closely related to liver transplant recipient mortality, hospital stay, and infection incidence[ 17 ]. Research has shown that low ALB levels, which are included in the frailty assessment, are associated with decreased walking speed and grip strength[ 18 ]. Therefore, medical staff should monitor albumin levels in liver transplant recipients and provide timely and reasonable supplementation. Liver transplant recipients with lower physical activity levels are more likely to experience preoperative frailty. The results of this study showed that physical activity is a protective factor against preoperative frailty in liver transplant recipients and that the greater the intensity of physical activity is, the lower the risk of frailty ( OR = 0.998, P = 0.021). Research has shown that persistent physical activity can increase peak oxygen uptake, improve cardiopulmonary endurance, enhance muscle strength, and reduce the occurrence of weakness in liver transplant recipients[ 19 ]. Preoperative exercise in liver transplant recipients can increase protein synthesis, significantly prevent further loss of muscle mass, and maintain physical exercise function and cardiovascular health. However, liver transplant recipients lack awareness of exercise before surgery. Exercise may lead to complications, such as gastrointestinal bleeding, resulting in lower-intensity physical activity. Therefore, medical staff should strengthen education on preoperative exercise for liver transplant recipients and work with rehabilitation therapists to develop targeted exercise plans to ensure exercise safety. Liver transplant recipients with concomitant hepatic encephalopathy are prone to preoperative frailty. The results of this study showed that concomitant hepatic encephalopathy is a risk factor for preoperative frailty in liver transplant recipients, and the risk of frailty in patients with combined hepatic encephalopathy is 5.648 times greater than that in patients without combined hepatic encephalopathy ( OR = 5.648, P = 0.027), consistent with the results of Xu[ 20 ]. Research has shown that blood ammonia can interfere with the synthesis of muscle proteins by increasing the activity of muscle growth inhibitors[ 21 ]. Therefore, patients with high blood ammonia levels experience faster muscle loss, which leads to weakness. Therefore, medical staff should pay attention to monitoring blood ammonia concentrations, identifying the occurrence of HE early, and preventing and intervening. Anxiety and depression in liver transplant recipients are associated with preoperative frailty. The results of this study showed that anxiety is a risk factor for preoperative frailty in liver transplant recipients. The more severe the anxiety level was, the greater the risk of frailty ( OR = 1.746, P = 0.029). Depression is also a risk factor for preoperative frailty in liver transplant recipients. The greater the level of depression was, the greater the risk of frailty ( OR = 2.871, P = 0.007). Cron showed a significant correlation between the incidence of frailty and depression, which is consistent with the results of this study[ 22 ]. Due to the specific complications of end-stage liver disease, such as ascites, gastrointestinal bleeding, hepatic encephalopathy, and uncertainty surrounding surgery, the incidence of negative emotions, such as anxiety and depression, is greater in patients with end-stage liver disease. Research has shown that depression increases inflammatory mediators, such as C-reactive protein and interleukin-6 cytokines, which are related to sarcopenia and frailty. Inflammatory mediators inhibit the synthesis and metabolic processes involved in muscle tissue formation, leading to decreased muscle mass and impaired function[ 5 ]. Therefore, medical staff should pay more attention to the negative emotions of liver transplant recipients before surgery, conduct dynamic psychological assessments, and collaborate with psychological counselors to provide targeted psychological interventions. The ability of the preoperative risk prediction model to predict mortality in liver transplant recipients was better. This study used the Hosmer–Lemeshow test to judge the model's goodness of fit. The Hosmer-Lemeshow test results (χ2 = 3.139, P = 0.925, P > 0.005) indicated that the predictive model for preoperative frailty risk in liver transplant recipients constructed in this study was a good fit. This study validated the discriminant validity of the model by using an ROC curve. The area under the ROC curve was 0.997, with a 95% CI of 0.992–0.998 and P < 0.001. The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963, indicating that the model has high predictive ability. This study validated this model in 65 patients. The results showed a sensitivity of 0.889, specificity of 0.950, and accuracy of 84.7%, indicating that the model can predict the risk of preoperative weakness in liver transplant recipients in clinical practice. This model can provide a scientific and effective tool for quickly identifying the occurrence of preoperative weakness in liver transplant recipients in clinical practice. Conclusion Age, albumin level, concurrent hepatic encephalopathy, physical activity, anxiety, and depression were risk factors for preoperative frailty. Based on logistic regression and column charts, this study developed a risk prediction model for preoperative frailty in liver transplant recipients. The model is concise, simple to use, highly sensitive, specific, well fit, accurate, and highly accurate. This study could provide a scientific basis for quickly identifying the occurrence of preoperative frailty in liver transplant recipients in clinical practice and developing targeted intervention measures. Abbreviations ROC ： receiver operating characteristic AUC ： area under the curve MELD ： model for end-Stage liver Disease OR ： odd ratio ALB ： albumin HIS ： hospital information system CI: confidence interval BMI ： body mass index Declarations Ethical Approval and consent to participate This project was approved by the Ethical Review Committee of the Affiliated Hospital of Qingdao University (QYFY W2LL 28049).This was a retrospective study based on the HIS. Our research was performed in accordance with the Declaration of Helsinki. The confidentiality of the patient information was verified, and the patients signed an informed consent form. Availability of data and materials The data support the findings of this study and are available from the corresponding author upon reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Sources of funding This work was supported by the National Natural Science Foundation of China (Grant No. 81900575). Acknowledgments We thank the Affiliated Hospital of Qingdao University for providing open data and all the investigators who participated in the study. References Lai JC, Sonnenday CJ, Tapper EB, Duarte-Rojo A, Dunn MA, Bernal W, et al. Frailty in Liver Transplantation: an Expert Opinion Statement from the American Society of Transplantation Liver and Intestinal Community of Practice. Am J Transplant. 2019;19:1896–906. Fried LP, Tangen CM,Walston J. Frailty in older adults:evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56. Orman ES, Ghabril M, Chalasani N. Poor performance status is associated with increased mortality in patients with cirrhosis. Clin Gastroenterol Hepatol. 2016;14:1189–95. Lai JC, Feng S, Terrault NA, Lizaola B, Hayssen H, Covinsky K. Frailty Predicts Waitlist Mortality in Liver Transplant Candidates. Am J Transplant. 2014;14:1870–9. Duarte-Roj A, Ruiz-Margáin A, Montano-Loza AJ, Ricardo U, Kim WR. Exercise and physical activity for patie ts with end- stage liver disease: improving functional st at us and sarcopenia while on the transplant waiting list. Liver Transpl. 2018;24:122–39. Jiang WH, Jin CD,Li SN, Yang SF,Yan CC,Zhu JH. Construction and validation of a risk prediction model for central venous catheter-associated deep venous thromboses in children with congenital heart disease after surgery. Chin J Nurs. 2022;57:2217–24. Fried LP, Tangen CM, Walston J. Frailty in older adults: evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001;56:M146–57. Buysse DJ. Reynolds CF,Monk TH,Berman SR,Kupfer DJ.The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193. Kaiser MJ, Bauer JM, Ramsch C. Bauer JM,Sieber CC.Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009;13:782–8. IPAQ group.Intemationalphysicalactivity questionnaire [EB/. OL]2002.http: //www.ipaq.ki.se༏downloads.htm1 . Blumenthal JA, Burg MM, Barefoot J, Williams RB, Haney T, Zimet G. Social support, type A behavior, and coronary artery disease. Psychosom Med. 1987;49:331–40. Sinclair M, Poltavskiy E, Dodge JL,Lai JC. Frailty is independently associated with increased hospitalization days in patients on liver transplant waitlists. World J Gastroenterol. 2017;23:89. Ponziani. Francesca, Romana, Gasbarrini, Antonio. Sarcopenia in Patients with Advanced Liver Disease. The Curr Protein Pept Sc. 2018;19:681–91. Montano-Loza AJ, Meza-Junco J, Baracos VE, Prado CMM, Ma M, Meeberg G. Severe muscle depletion predicts postoperative length of stay but is not associated with survival after liver transplantation. Liver Transpl. 2014;20:640–8. Fernández J, Clària J, Amorós A, Arroyo V. Effects of albumin treatment on systemic and portal hemodynamics and systemic inflammation in patients with decompensated cirrhosis. Gastroenterology. 2019;157:149–62. Abbas M, Pires D, Peters A, Morel C, Hurst S, Holmes A. Conflicts of interest in infection prevention and control research: no smoke without fire. A narrative review. Intensive Care Med. 2018;44:1679–90. McAdams-DeMarco MA, King EA, Luo X, Haugen C, Segev DL. Frailty, length of stay, and mortality in kidney transplant recipients: a national registry and prospective cohort study. Ann Surg. 2017;266:1084–90. Román E, Torrades MT, Nadal MJ, Cárdenas G,Cárdenas, Vidal G. Randomized Pilot Study: Effects of an Exercise Programme and Leucine Supplementation in Patients with Cirrhosis. Dig Dis Sci. 2014;59:1966–75. Yao XUCQ, Mohand F, Wong Y, Kent R, Seetharaman D. Evaluating the Associations Between the Liver Frailty Index and Karnofsky Performance Status With Waitlist Mortality. Transpl Direct. 2021;7:e651. Avinash NE, Silva et al. Ammonia lowering reverses sarcopenia of cirrhosis by restoring skeletal muscle proteostasis. Hepatology , 2017;65:2045–2058. Cron DC, Friedman JF, Winder GS, Thelen AE, Derck JE, Fakhoury JW, et al. Depression and Frailty in Patients With End-Stage Liver Disease Referred for Transplant Evaluation. Am J Transplant. 2016;16:1805–11. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3890299","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268790651,"identity":"c95c7261-4011-46b7-9ac9-5ec30892da53","order_by":0,"name":"Bingliang Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Bingliang","middleName":"","lastName":"Zhang","suffix":""},{"id":268790652,"identity":"78e1c445-b5eb-457b-96b7-d605f32c995e","order_by":1,"name":"Huihui Sun","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Sun","suffix":""},{"id":268790653,"identity":"53c0d5fd-9173-4901-b395-a645ea6a8f0b","order_by":2,"name":"Lianyu lou","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Lianyu","middleName":"","lastName":"lou","suffix":""},{"id":268790654,"identity":"a617445b-53c1-4bf2-a128-589f4a5d31d4","order_by":3,"name":"Jinshan Zhuang","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Jinshan","middleName":"","lastName":"Zhuang","suffix":""},{"id":268790655,"identity":"878e7aab-4f48-422b-93aa-864059cdc78f","order_by":4,"name":"Guofang Liu","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Guofang","middleName":"","lastName":"Liu","suffix":""},{"id":268790656,"identity":"73a8a9e9-0598-4dd5-b27a-79757fd8b7ed","order_by":5,"name":"Wenjuan Sun","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Wenjuan","middleName":"","lastName":"Sun","suffix":""},{"id":268790657,"identity":"045c7394-847e-44fc-823e-76f4892f4ea1","order_by":6,"name":"Hui Lin","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University Organ Transplant Center","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Lin","suffix":""},{"id":268790658,"identity":"7012b37c-247d-4bb2-961a-89d9336e032b","order_by":7,"name":"Lili Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFACxsYDCQUScgwMCWwgLg8DA3MDIS0NBxIMJIzhWniAIgTtOcBgwJDYANXCQFCLwfHmhgMPDCzS+47nmD348OeOjD37wQaGnzvwaDlzEOyw3Jln3pgbzmx7xsPDk9jA2HsGtxazG4kQLRtu5JhJ8zYcBvolsYGZsQ2PlvsPwVrSDUBaeP4AtfA/JKDlBiTEEiBa2IBaJAjYYn8G4jDDmWeelUmC/XLjYcPBXjxaJNuPP3z4o6JOnu948jYJYIjZs/cnH3zwE48WBDiAQZKkZRSMglEwCkYBMgAA/wpanJaPUEgAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Lili","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-01-23 07:37:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890299/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50174326,"identity":"6910f93e-64d5-4c6b-b1c9-af3a1aac790d","added_by":"auto","created_at":"2024-01-25 16:08:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61303,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3890299/v1/9b94043468ae171fcdfb65e6.jpg"},{"id":50173333,"identity":"2afa8af4-2ecf-436f-8d81-cda484b3201d","added_by":"auto","created_at":"2024-01-25 16:00:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25293,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the modeling group\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3890299/v1/5731451bdd030028db8dbe30.jpg"},{"id":50173334,"identity":"a06914f2-44ad-4a2c-9960-fe3c4ce1e357","added_by":"auto","created_at":"2024-01-25 16:00:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20428,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the validation group\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3890299/v1/54bc670a1b71d693303dab6e.jpg"},{"id":50645271,"identity":"2639a394-ebfc-4d35-9f8e-d8e77fe27176","added_by":"auto","created_at":"2024-02-05 07:10:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":422369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890299/v1/2edeaf06-df57-4c0e-86f6-536489eaed70.pdf"},{"id":50173335,"identity":"c9cffb2f-8da3-4cf8-8f10-f6612fda0874","added_by":"auto","created_at":"2024-01-25 16:00:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22603,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3890299/v1/1ded6fc9bb9da8d3f61938ee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and validation of a predictive model for preoperative frailty risk in liver transplant recipients","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLiver transplantation is the most successful treatment for end-stage liver disease. End-stage liver disease, such as fatigue, starvation, muscle loss, frailty, or deterioration of multiple system functions, can cause difficulties during the waiting period for a liver transplant and may be caused by long-term muscle loss and physical function degeneration[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A patient's reserve of stress and resistance diminishes as a result of the cumulative deterioration of many physiological systems, which may have unfavorable effects[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This biological illness is known as frailty, fatigue, mental issues, or osteoporosis, and a lack of physical endurance is the major way that weakness manifests. Research has shown that frailty is positively correlated with the mortality rate of liver transplant recipients during the waiting period, incidence of hepatic encephalopathy, and mortality rate after transplantation[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to statistics, the incidence rate of weakness during the waiting period for liver transplant recipients is approximately 18%-43%. Every increase in Fried's weakness score increases the risk of death before transplantation by 45%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, early frailty detection and tailored interventions to address frailty may increase the survival rate of liver transplant patients during the waiting period and lower the infection and death rates following transplantation. However, current clinical practice does not assess preoperative fragility in liver transplant candidates. This study aimed to investigate the preoperative frailty factors that affect liver transplant recipients, develop a risk prediction model for preoperative frailty in liver transplant recipients, and provide theoretical underpinnings for preoperative frailty in liver transplant recipient prevention and early intervention.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource of Data\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe survey approach entails data gathering by two qualified researchers who obtain general patient data from the HIS system. Face-to-face distribution and collection of survey data are needed. Consistent guideline language was used to explain the survey's goal, importance, content, and safety measures to patients and their families before the survey. Our research was performed in accordance with the Declaration of Helsinki, the confidentiality of patient information was verified, and the patients signed an informed consent form. The project was approved by the Ethical Review Committee of the Affiliated Hospital of Qingdao University (QYFY W2LL 28049) to confirm the correctness and thoroughness of the questionnaire after it was completed and checked for faults. Informed consent was obtained from the INR.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe survey participants used a convenient sampling method. From January 2021 to December 2022, patients were selected from a tertiary hospital organ transplantation center in Qingdao to receive a preoperative liver transplantation waitlist. Participants enrolled in the study between January 2021 and June 2022 composed the modeling group, while those enrolled between October 2022 and December 2022 composed the validation group. The inclusion criteria were as follows: (1)\u0026thinsp;\u0026ge;\u0026thinsp;18 years of age; (2) inclusion in the waiting list for liver transplantation; (3) clear awareness, no cognitive barriers, and no barriers to language communication; and (4) provided informed consent. The exclusion criteria were as follows: (1) second liver transplant recipient; (2) severe heart, lung, or kidney dysfunction; and (3) other organ transplant recipient. According to the literature review, there are approximately 10 predictive variables. The incidence of preoperative frailty in liver transplant recipients ranges from 18\u0026ndash;43%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to the sample size requirements of the prediction model, at least 5\u0026ndash;10 samples are required for each risk factor[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The sample size of this study was N\u0026thinsp;=\u0026thinsp;10 \u0026times; 10\u0026thinsp;\u0026divide;\u0026thinsp;0.43\u0026thinsp;=\u0026thinsp;232. Considering a 10% loss to follow-up rate, this study included 258 study subjects in the modeling group. There were 65 patients in the validation cohort whose sample size in the modeling and validation phases met the 4:1 ratio.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eFrailty\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFried\u0026rsquo;s frailty phenotype can be used to assess the preoperative frailty of liver transplant patients[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and consists of a total of five items: unidentified weight loss, decreased physical activity, fatigue, slower walking speed, and decreased grip strength. These items were scored 1 point for each item with abnormal evaluation, 0 points for normal evaluation, 0 points for total item score as nonfrailty, 1\u0026ndash;2 points as prefrailty, and 3\u0026ndash;5 points as frailty.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe researchers constructed a general information questionnaire based on a literature review of the potential causes of preoperative frailty in liver transplant patients. The patients were reviewed by liver transplantation specialists. Age, sex, education, method of covering medical expenses, per capita monthly household income, marital status, primary diseases (including posthepatic cirrhosis, alcoholic cirrhosis, autoimmune cirrhosis, liver cancer, and polycystic liver), and complications (including hepatic encephalopathy, gastrointestinal hemorrhage, cardiovascular and cerebrovascular diseases, diabetes, and hypertension) were also considered. Hemoglobin, BMI, Child-Pugh grade, MELD score, liver function test results, and albumin test results were also included.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMental health factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe mental health factors included anxiety and depression and were assessed using the Hospital Anxiety and Depression Scale[8]. Two subscales and 14 items were used. Both sadness and anxiety were assessed using 7 items each. Each subscale received a total of 21 points based on a 4-point rating (0\u0026ndash;3 points). No anxiety or depression was indicated by a score of 0\u0026ndash;7, mild anxiety or depression by a score of 8\u0026ndash;10, moderate anxiety or depression by a score of 11\u0026ndash;14, and severe anxiety or depression by a score of 15\u0026ndash;21.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSleep conditions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSleep condition was assessed via the Pittsburgh Sleep Quality Index (PSQI) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which comprises 23 components, including subjective sleep quality, time to fall asleep, amount of time spent sleeping, effectiveness of sleep, sleep disorders, usage of sleep medications, and effect on daily function. The total score ranged from 0\u0026ndash;21 points, with a total score of \u0026ge;\u0026thinsp;7 indicating sleep disorders and \u0026lt;\u0026thinsp;7 indicating good sleep quality. A single score greater than one in each dimension indicates that there are obstacles to this item.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNutritional status\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNutritional status was assessed using the Short-form Mini-Nutritional Assessment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with a total score of 0\u0026ndash;14, with \u0026ge;\u0026thinsp;12 indicating normal nutrition, 8\u0026ndash;11 indicating nutritional risk, and 0\u0026ndash;7 indicating malnutrition.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eActivity conditions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eActivity conditions were assessed using the International Physical Activity Questionnaire [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The physical activity level was calculated as weekly physical activity level (MET min/w)\u0026thinsp;=\u0026thinsp;related metabolic equivalent task (METS) of physical activity \u0026times; weekly frequency (d/w) \u0026times; daily time (min/d). The METs for walking, moderate-intensity activities, and high-intensity activities were 3.3, 4, and 8, respectively. Weekly total energy consumption was \u0026ge;\u0026thinsp;3000 MET/min for high-intensity physical activity, \u0026ge; 600 MET/min for moderate physical activity, and \u0026lt;\u0026thinsp;600 MET/min for low-intensity physical activity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSocial support\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSocial support was assessed using the Perceived Social Support Scale[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which includes the three dimensions of family support, friend support, and other support, with 12 items. Using the Likert 7-point scoring method, the total score range was 12\u0026ndash;84 points. The higher the total score is, the greater the perceived level of social support.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ensure the correctness of the data, the program verified the original data entered by the two humans. The broad data were normally distributed. While the general data were not normally distributed and are expressed as medians and interquartile ranges, they are expressed as the means and standard deviations. In the single-factor analysis, one-way analysis of variance (ANOVA) was used to analyze the quantitative data that were normally distributed, the rank-sum test was used to test the nonnormal distribution, and the chi-square test was used to test the grade data. Factors with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the logistic regression analysis. To evaluate the risk characteristics, a nomogram was created using R (4.2.3) and the RMS software suite. The Hosmer-Lemeshow test and area under the curve (AUC) of the receiver operating characteristic (ROC) curve were used. were used to assess the degree of fit and predictive power of the model. Sensitivity, specificity, and accuracy were used to assess the model's predictive ability, with a P value of \u0026lt;\u0026thinsp;0.05. considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe general data modeling group of the survey subjects included 258 patients aged 26 to 70 (50.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.79) years, including 198 males (76.74%) and 60 females (23.26%). The primary disease information included 26 patients (10.08%) with alcoholic cirrhosis, 112 patients (43.41%) with hepatitis cirrhosis, 19 patients (7.36%) with autoimmune cirrhosis, 96 patients (37.21%) with liver cancer, and 5 patients (1.94%) with polycystic liver disease. The grade was Grade A in 117 patients (45.35%), Grade B in 75 patients (29.07%), and Grade C in 66 patients (25.58%). The BMI was 20.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 kg/m2, and the mean MELD score was 13.70\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71 points. Education level was as follows: one at or below primary school (0.39%), 36 at junior high school (13.95%), 119 at high school or technical secondary school (46.12%), and 102 at or above university (39.53%). There were 10 unmarried (3.88%) and 248 married (96.12%) patients. There were 78 patients with gastrointestinal hemorrhage (30.23%), 97 with hepatic encephalopathy (37.50%), 34 with diabetes (13.18%), 38 with hypertension (14.73%), 77 with bone disease (29.84%), 46 with respiratory diseases (17.83%), and 36 with cardiovascular and cerebrovascular diseases (13.95%). Please refer to Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for general information.\u003c/p\u003e\n \u003cp\u003eA total of 65 patients aged 29\u0026ndash;66 years (48.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.57) were included in the validation cohort, which included 52 males (80.00%) and 13 females (20.00%). The primary diseases included seven cases of alcoholic cirrhosis (10.77%), 28 cases of hepatitis cirrhosis (43.08%), four cases of autoimmune cirrhosis (6.15%), 25 cases of liver cancer (38.46%), and one case of polycystic liver disease (1.54%). The grade was Grade A for 31 patients (47.67%), Grade B for 20 patients (30.77%), and Grade C for 14 patients (21.54%).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePrevalence of frailty and related variables\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAmong the 258 liver transplant recipients in the preoperative frailty modeling group, 110 had no frailty, 41 had pre-frailty, and 107 had frailty, for a frailty incidence rate of 41.47%. Among the 65 patients in the validation cohort, 28 were not frail, 17 were pre-frail, and 20 were frail. The incidence rate of frailty was 30.76%. Overall, 138, 58, and 127 patients were not frail or pre-frailty, respectively, and 127 patients were frail. The incidence rate of frailty was 39.32%.\u003c/p\u003e\n \u003cp\u003eUnivariate analysis of preoperative frailty in liver transplant recipients revealed negative outcomes on average and pre-frailty, with frailty occurring before liver transplantation as the dependent variable. Age, main disease, grade, MELD score, body mass index, albumin level, anxiety score, depression score, sleep status, perceived social support score, nutritional score, physical activity score, combined gastrointestinal bleeding, combined hepatic encephalopathy score, and other findings are shown. Liver transplant patients were more likely to have preoperative frailty if they had diabetes, bone disease, or respiratory illnesses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further details are provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eLogistic regression of patients with liver transplants\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eMultivariate analysis of preoperative frailty in liver transplant recipients revealed factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the univariate analysis as independent variables, with normal and pre-frail status as adverse outcomes and pre-transplant frailty as the dependent variable for logistic regression analysis. The specific assignment methods are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (normal or pre-frailty\u0026thinsp;=\u0026thinsp;0, frailty\u0026thinsp;=\u0026thinsp;1). This study showed that age, the serum ALB concentration, anxiety, depression, concomitant hepatic encephalopathy, and physical activity were found to be independent influencing factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for preoperative frailty in liver transplant recipients, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive model development\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe predictive model for preoperative frailty risk in liver transplant recipients was as follows: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1/{1\u0026thinsp;+\u0026thinsp;exp [-9.927\u0026thinsp;+\u0026thinsp;0.121] \u0026times; age \u0026minus;\u0026thinsp;0.586 \u0026times; albumin \u0026minus;\u0026thinsp;0.002 \u0026times; physical activity\u0026thinsp;+\u0026thinsp;1.055 \u0026times; depression\u0026thinsp;+\u0026thinsp;0.557 \u0026times; anxiety\u0026thinsp;+\u0026thinsp;1.731 \u0026times; concomitant hepatic encephalopathy. Therefore, a nomogram for predicting preoperative frailty risk in liver transplant recipients was constructed based on these variables (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFitting and prediction effect analysis of the preoperative frailty risk prediction model for liver transplant recipients; Hosmer\u0026ndash;Lemeshow test \u0026chi;2\u0026thinsp;=\u0026thinsp;3.139, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.925 (\u0026gt;\u0026thinsp;0.05), indicating that the model has a good fit. The area under the ROC curve (AUC) was 0.997 (95% confidence interval [CI], 0.992\u0026ndash;0.998; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963. The ROC curve is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictive model validation\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn the validation cohort of liver transplant recipients, the Hosmer-Lemeshow test was used to predict preoperative frailty risk (\u0026chi;2\u0026thinsp;=\u0026thinsp;2.720, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.910 (\u0026gt;\u0026thinsp;0.05)). The area under the ROC curve (AUC) was 0.971, with 95% CI ranging from 0.938 to 0.988 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal critical value and sensitivity were 0.889, and the specificities were 0.304, 0.889, and 0.950. The ROC curve is shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. This model predicts that 25 of 65 patients will experience weakness, while 40 will not. The results showed that 20 patients experienced weakness, whereas 45 did not. The accuracy rate was 84.7%.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe incidence of preoperative weakness in liver transplant recipients was relatively high. In this study, 323 patients were included in the modeling and validation groups, 127 of whom were in the weakened state. The incidence of weakness was 39.23%, which was higher than the results reported by Sinclair[13)]and Lai [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (31.6%, 17%). This may be due to differences in regional culture, medical status, and other factors, which may have led to inconsistent results. On the other hand, there were more complications in the liver transplant recipients included in this study. Research shows that the combination of hepatic encephalopathy and diabetes increases the incidence of frailty, so the incidence of frailty in this study was high. Research has shown that preoperative weakness in liver transplant recipients can significantly increase hospital stay and waiting period mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Liver transplant recipients experience rapid disease progression during the waiting period, often accompanied by gastrointestinal bleeding, hepatic encephalopathy, renal insufficiency, and infection. Weakness can reduce the reserve capacity of liver transplant recipients to resist these complications during the waiting period, thereby increasing the incidence of these complications, prolonging hospital stay, and increasing mortality during the waiting period. Foreign studies have shown that assessing and intervening in frailty in liver transplant recipients can significantly reduce readmission and overall mortality within 30 days[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, medical staff must dynamically monitor frailty in liver transplant recipients before surgery. In addition to paying attention to the rapid progression of liver transplant recipients, dynamic monitoring and identification of risk factors for preoperative frailty in liver transplant recipients should also be performed.\u003c/p\u003e \u003cp\u003eOlder liver transplant recipients are prone to developing preoperative weakness. The results of this study showed that age is an independent risk factor for preoperative weakness in liver transplant recipients, and the older the patient is, the greater the risk of weakness (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.128, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). A study by Lai [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] showed that the frailty of patients with liver cirrhosis is positively correlated with age. The older the patient is, the greater the risk of frailty, which is consistent with the results of this study. The reason may be that as age increases, the body's resistance and organ function gradually decrease, increasing the risk of weakness. In addition, aging has led to more diseases. Old age often leads to hypertension, cardiovascular and cerebrovascular diseases, diabetes, and other diseases, which increase the risk of weakness. As age increases, the quality and function of skeletal muscles in the body gradually decrease, increasing susceptibility to sarcopenia. Long-term sarcopenia ultimately leads to weakness in patients. Therefore, medical staff should focus on the elderly population, strengthen early frailty assessments of elderly patients waiting for liver transplant recipients, and develop targeted intervention measures for the elderly population.\u003c/p\u003e \u003cp\u003eLow-albumin liver transplant recipients are prone to developing preoperative weakness. The results of this study showed that the serum ALB concentration is a protective factor against preoperative weakness in liver transplant recipients and that the lower the serum ALB concentration is, the greater the risk of weakness (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.557, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). These results are consistent with those reported by Sinclair[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Human serum albumin (HSA) is the most abundant protein in human plasma. It maintains plasma osmotic pressure and is the primary regulator of immune and vascular barriers. Albumin is generated in the liver, and the function of patients with end-stage liver disease is severely impaired. When albumin is insufficiently generated, its normal structure and function are also lost, which leads to a reduction in plasma colloid osmotic pressure, damage to the vascular barrier, leakage of a large amount of body fluid into the abdominal cavity leading to ascites, and a large number of ascites, leading to patient unwillingness to leave bed and exercise, ultimately leading to a decline in physical activity and the risk of frailty[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Research has shown that the serum ALB concentration is an important prognostic factor for liver transplant recipients and is closely related to liver transplant recipient mortality, hospital stay, and infection incidence[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Research has shown that low ALB levels, which are included in the frailty assessment, are associated with decreased walking speed and grip strength[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, medical staff should monitor albumin levels in liver transplant recipients and provide timely and reasonable supplementation.\u003c/p\u003e \u003cp\u003eLiver transplant recipients with lower physical activity levels are more likely to experience preoperative frailty. The results of this study showed that physical activity is a protective factor against preoperative frailty in liver transplant recipients and that the greater the intensity of physical activity is, the lower the risk of frailty (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.998, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Research has shown that persistent physical activity can increase peak oxygen uptake, improve cardiopulmonary endurance, enhance muscle strength, and reduce the occurrence of weakness in liver transplant recipients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Preoperative exercise in liver transplant recipients can increase protein synthesis, significantly prevent further loss of muscle mass, and maintain physical exercise function and cardiovascular health. However, liver transplant recipients lack awareness of exercise before surgery. Exercise may lead to complications, such as gastrointestinal bleeding, resulting in lower-intensity physical activity. Therefore, medical staff should strengthen education on preoperative exercise for liver transplant recipients and work with rehabilitation therapists to develop targeted exercise plans to ensure exercise safety.\u003c/p\u003e \u003cp\u003eLiver transplant recipients with concomitant hepatic encephalopathy are prone to preoperative frailty. The results of this study showed that concomitant hepatic encephalopathy is a risk factor for preoperative frailty in liver transplant recipients, and the risk of frailty in patients with combined hepatic encephalopathy is 5.648 times greater than that in patients without combined hepatic encephalopathy (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.648, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), consistent with the results of Xu[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Research has shown that blood ammonia can interfere with the synthesis of muscle proteins by increasing the activity of muscle growth inhibitors[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, patients with high blood ammonia levels experience faster muscle loss, which leads to weakness. Therefore, medical staff should pay attention to monitoring blood ammonia concentrations, identifying the occurrence of HE early, and preventing and intervening.\u003c/p\u003e \u003cp\u003eAnxiety and depression in liver transplant recipients are associated with preoperative frailty. The results of this study showed that anxiety is a risk factor for preoperative frailty in liver transplant recipients. The more severe the anxiety level was, the greater the risk of frailty (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.746, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). Depression is also a risk factor for preoperative frailty in liver transplant recipients. The greater the level of depression was, the greater the risk of frailty (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.871, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Cron showed a significant correlation between the incidence of frailty and depression, which is consistent with the results of this study[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the specific complications of end-stage liver disease, such as ascites, gastrointestinal bleeding, hepatic encephalopathy, and uncertainty surrounding surgery, the incidence of negative emotions, such as anxiety and depression, is greater in patients with end-stage liver disease. Research has shown that depression increases inflammatory mediators, such as C-reactive protein and interleukin-6 cytokines, which are related to sarcopenia and frailty. Inflammatory mediators inhibit the synthesis and metabolic processes involved in muscle tissue formation, leading to decreased muscle mass and impaired function[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, medical staff should pay more attention to the negative emotions of liver transplant recipients before surgery, conduct dynamic psychological assessments, and collaborate with psychological counselors to provide targeted psychological interventions.\u003c/p\u003e \u003cp\u003eThe ability of the preoperative risk prediction model to predict mortality in liver transplant recipients was better. This study used the Hosmer\u0026ndash;Lemeshow test to judge the model's goodness of fit. The Hosmer-Lemeshow test results (χ2\u0026thinsp;=\u0026thinsp;3.139, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.925, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.005) indicated that the predictive model for preoperative frailty risk in liver transplant recipients constructed in this study was a good fit. This study validated the discriminant validity of the model by using an ROC curve. The area under the ROC curve was 0.997, with a 95% CI of 0.992\u0026ndash;0.998 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963, indicating that the model has high predictive ability. This study validated this model in 65 patients. The results showed a sensitivity of 0.889, specificity of 0.950, and accuracy of 84.7%, indicating that the model can predict the risk of preoperative weakness in liver transplant recipients in clinical practice. This model can provide a scientific and effective tool for quickly identifying the occurrence of preoperative weakness in liver transplant recipients in clinical practice.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAge, albumin level, concurrent hepatic encephalopathy, physical activity, anxiety, and depression were risk factors for preoperative frailty. Based on logistic regression and column charts, this study developed a risk prediction model for preoperative frailty in liver transplant recipients. The model is concise, simple to use, highly sensitive, specific, well fit, accurate, and highly accurate. This study could provide a scientific basis for quickly identifying the occurrence of preoperative frailty in liver transplant recipients in clinical practice and developing targeted intervention measures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eROC\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003ereceiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAUC\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003earea under the curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMELD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003emodel for end-Stage liver Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eodd ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eALB\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003ealbumin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHIS\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003ehospital information system\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003econfidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003ebody mass index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was approved by the Ethical Review Committee of the Affiliated Hospital of Qingdao University (QYFY W2LL 28049).This was a retrospective study based on the HIS. Our research was performed in accordance with the Declaration of Helsinki. The confidentiality of the patient information was verified, and the patients signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data support the findings of this study and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 81900575).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Affiliated Hospital of Qingdao University for providing open data and all the investigators who participated in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLai JC, Sonnenday CJ, Tapper EB, Duarte-Rojo A, Dunn MA, Bernal W, et al. Frailty in Liver Transplantation: an Expert Opinion Statement from the American Society of Transplantation Liver and Intestinal Community of Practice. Am J Transplant. 2019;19:1896\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, Tangen CM,Walston J. Frailty in older adults:evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrman ES, Ghabril M, Chalasani N. Poor performance status is associated with increased mortality in patients with cirrhosis. Clin Gastroenterol Hepatol. 2016;14:1189\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai JC, Feng S, Terrault NA, Lizaola B, Hayssen H, Covinsky K. Frailty Predicts Waitlist Mortality in Liver Transplant Candidates. Am J Transplant. 2014;14:1870\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuarte-Roj A, Ruiz-Marg\u0026aacute;in A, Montano-Loza AJ, Ricardo U, Kim WR. Exercise and physical activity for patie ts with end- stage liver disease: improving functional st at us and sarcopenia while on the transplant waiting list. Liver Transpl. 2018;24:122\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang WH, Jin CD,Li SN, Yang SF,Yan CC,Zhu JH. Construction and validation of a risk prediction model for central venous catheter-associated deep venous thromboses in children with congenital heart disease after surgery. Chin J Nurs. 2022;57:2217\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, Tangen CM, Walston J. Frailty in older adults: evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001;56:M146\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuysse DJ. Reynolds CF,Monk TH,Berman SR,Kupfer DJ.The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser MJ, Bauer JM, Ramsch C. Bauer JM,Sieber CC.Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009;13:782\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPAQ group.Intemationalphysicalactivity questionnaire [EB/. OL]2002.http: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e//www.ipaq.ki.se༏downloads.htm1\u003c/span\u003e\u003cspan address=\"http:////www.ipaq.ki.se༏downloads.htm1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlumenthal JA, Burg MM, Barefoot J, Williams RB, Haney T, Zimet G. Social support, type A behavior, and coronary artery disease. Psychosom Med. 1987;49:331\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinclair M, Poltavskiy E, Dodge JL,Lai JC. Frailty is independently associated with increased hospitalization days in patients on liver transplant waitlists. World J Gastroenterol. 2017;23:89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonziani. Francesca, Romana, Gasbarrini, Antonio. Sarcopenia in Patients with Advanced Liver Disease. The Curr Protein Pept Sc. 2018;19:681\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontano-Loza AJ, Meza-Junco J, Baracos VE, Prado CMM, Ma M, Meeberg G. Severe muscle depletion predicts postoperative length of stay but is not associated with survival after liver transplantation. Liver Transpl. 2014;20:640\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez J, Cl\u0026agrave;ria J, Amor\u0026oacute;s A, Arroyo V. Effects of albumin treatment on systemic and portal hemodynamics and systemic inflammation in patients with decompensated cirrhosis. Gastroenterology. 2019;157:149\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbas M, Pires D, Peters A, Morel C, Hurst S, Holmes A. Conflicts of interest in infection prevention and control research: no smoke without fire. A narrative review. Intensive Care Med. 2018;44:1679\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcAdams-DeMarco MA, King EA, Luo X, Haugen C, Segev DL. Frailty, length of stay, and mortality in kidney transplant recipients: a national registry and prospective cohort study. Ann Surg. 2017;266:1084\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRom\u0026aacute;n E, Torrades MT, Nadal MJ, C\u0026aacute;rdenas G,C\u0026aacute;rdenas, Vidal G. Randomized Pilot Study: Effects of an Exercise Programme and Leucine Supplementation in Patients with Cirrhosis. Dig Dis Sci. 2014;59:1966\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao XUCQ, Mohand F, Wong Y, Kent R, Seetharaman D. Evaluating the Associations Between the Liver Frailty Index and Karnofsky Performance Status With Waitlist Mortality. Transpl Direct. 2021;7:e651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvinash NE, Silva et al. Ammonia lowering reverses sarcopenia of cirrhosis by restoring skeletal muscle proteostasis. \u003cem\u003eHepatology\u003c/em\u003e, 2017;65:2045\u0026ndash;2058.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCron DC, Friedman JF, Winder GS, Thelen AE, Derck JE, Fakhoury JW, et al. Depression and Frailty in Patients With End-Stage Liver Disease Referred for Transplant Evaluation. Am J Transplant. 2016;16:1805\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"liver transplantation, frailty, risk, prediction model, surgery","lastPublishedDoi":"10.21203/rs.3.rs-3890299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eEarly frailty detection and tailored interventions to address frailty may increase the survival rate of \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eliver transplant patients\u003c/span\u003e during the waiting period and lower infection and death rates following transplantation, with the aim of developing and testing a model to predict the likelihood that a liver transplant recipient would be too weak to undergo surgery.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eFacilitation of the procedure for patients on the liver transplant waiting list at a tertiary hospital in Qingdao from January 2021 to December 2022 was chosen for the study. The modeling group participants were recruited from January 2021 to June 2022 (258 cases), while the validation group participants were selected from October 2022 to December 2022 (65 instances). A nomogram was created to display the results of the risk prediction model's single-factor and logistic regression studies. The adequacy of the model was evaluated using the Hosmer-Lemeshow test, and its ability to make accurate predictions was evaluated using receiver operating characteristic (ROC) curves.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe incidence of preoperative frailty in liver transplant recipients was 39.32%. Age (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.121), serum ALB concentration (\u003cem\u003eOR\u003c/em\u003e=-0.586), physical activity (\u003cem\u003eOR\u003c/em\u003e=-0.002), depression (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.055), anxiety (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.557), and hepatic encephalopathy (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.731) were risk factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Hosmer-Lemeshow test χ2\u0026thinsp;=\u0026thinsp;3.139, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.925 (\u0026gt;\u0026thinsp;0.05) indicated that the model fit well. The AUC was 0.997, with a 95% CI ranging from 0.992 to 0.998 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The optimal cutoff value was 0.630, the sensitivity was 0.993, and the specificity was 0.963. The external validation results showed a sensitivity of 0.889, specificity of 0.950, and accuracy of 84.7%.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThis risk prediction model has a high rate of accuracy, making it a useful benchmark for assessing patients' risk of developing postoperative frailty after receiving a liver transplant.\u003c/p\u003e","manuscriptTitle":"Construction and validation of a predictive model for preoperative frailty risk in liver transplant recipients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 16:00:31","doi":"10.21203/rs.3.rs-3890299/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a0454e92-01f0-4a57-bf0c-cc1e48cae6f1","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-05T07:02:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 16:00:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3890299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890299","identity":"rs-3890299","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}