Dataset Information

Predicting Kidney Transplant Evaluation Non-attendance.

ABSTRACT: Non-attendance to kidney transplant evaluation (KTE) appointments is a barrier to optimal care for those with kidney failure. We examined the medical and socio-cultural factors that predict KTE non-attendance to identify opportunities for integrated medical teams to intervene. Patients scheduled for KTE between May, 2015 and June, 2018 completed an interview before their initial KTE appointment. The interview assessed various social determinants of health, including demographic (e.g., income), medical (e.g. co-morbidities), transplant knowledge, cultural (e.g., medical mistrust), and psychosocial (e.g., social support) factors. We used multiple logistic regression analysis to determine the strongest predictor of KTE non-attendance. Our sample (N = 1119) was 37% female, 76% non-Hispanic White, median age 59.4 years (IQR 49.2-67.5). Of note, 142 (13%) never attended an initial KTE clinic appointment. Being on dialysis predicted higher odds of KTE non-attendance (OR 1.76; p = .02; 64% of KTE attendees on dialysis vs. 77% of non-attendees on dialysis). Transplant and nephrology teams should consider working collaboratively with dialysis units to better coordinate care, (e.g., resources to attend appointment or outreach to emphasize the importance of transplant) adjusting the KTE referral and evaluation process to address access issues (e.g., using tele-health) and encouraging partnership with clinical psychologists to promote quality of life for those on dialysis.

SUBMITTER: Ford CG

PROVIDER: S-EPMC10035980 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting Kidney Transplant Evaluation Non-attendance.

Ford C Graham CG Leyva Yuridia Y Kruger Eric S ES Zhu Yiliang Y Croswell Emilee E Kendall Kellee K Puttarajapa Chethan C Dew Mary Amanda MA Ng Yue Harn YH Unruh Mark L ML Myaskovsky Larissa L

Journal of clinical psychology in medical settings 20230323 1

Non-attendance to kidney transplant evaluation (KTE) appointments is a barrier to optimal care for those with kidney failure. We examined the medical and socio-cultural factors that predict KTE non-attendance to identify opportunities for integrated medical teams to intervene. Patients scheduled for KTE between May, 2015 and June, 2018 completed an interview before their initial KTE appointment. The interview assessed various social determinants of health, including demographic (e.g., income), m ...[more]

PMID: 36959431

Similar Datasets

Project description:BackgroundGraft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.MethodsWe built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.ResultsFor prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.ConclusionThe model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.

Project description:IntroductionDepressive symptoms, even without a clinical diagnosis of depression, are common in kidney failure patients and may be a barrier to completing the complex process of kidney transplant (KT) evaluation. We assessed depressive symptom burden and association between depressive symptoms and access to KT waitlist by age.MethodsIn a prospective cohort of 3728 KT patients (aged 18-88 years), the Center for Epidemiologic Studies-Depression (CES-D) scale was used to measure depressive symptoms at evaluation. Depressive symptom severity was defined as follows: none: 0; minimal: 1 to 15; mild: 16 to 20; moderate: 21 to 25; severe: 26 to 60. Hazard ratios (HRs) of active listing within 1 year after evaluation were estimated using Cox proportional hazards models, adjusted for clinical and social factors.ResultsAt evaluation, 85.8% of the patients reported at least minimal depressive symptoms; the proportion was lower among older patients: 18 to 29 years = 92.0%; 30 to 39 years = 88.3%; 40 to 49 years = 87.2%; 50 to 59 years = 87.0%; 60 to 69 years = 83.4%; and ≥70 years = 82.0%. Chance of active listing decreased with more severe depressive symptoms (log-rank, P < 0.001). After adjustment, every 5-point higher CES-D score (more depressive symptoms) was associated with a 13% lower chance of listing (HR = 0.87, 95% CI: 0.85-0.90); the strongest association was found among patients aged ≥70 years (adjusted HR [aHR] = 0.73, 95% CI: 0.62-0.86). Furthermore, minimal (HR = 0.69, 95% CI: 0.60-0.79), mild (HR = 0.57, 95% CI: 0.44-0.72), moderate (HR = 0.53, 95% CI: 0.39-0.71), and severe (HR = 0.44, 95% CI: 0.34-0.57) depressive symptoms were all associated with a lower chance of listing.ConclusionOlder candidates were less likely to report depressive symptoms at KT evaluation. Regardless of age, candidates who did report depressive symptoms, and even minimal symptoms, had a lower chance of listing. Transplant centers should routinely screen patients for depressive symptoms and refer the affected patients to mental health services to improve access to KT.

Project description:BackgroundOver one thousand pediatric kidney transplant candidates are added to the waitlist annually, yet the prospective time spent waiting is unknown for many. Our study fills this gap by identifying variables that impact waitlist time and by creating an index to predict the likelihood of a pediatric candidate receiving a transplant within 1 year of listing. This index could be used to guide patient management by giving clinicians a potential timeline for each candidate's listing based on a unique combination of risk factors.MethodsA retrospective analysis of 3757 pediatric kidney transplant candidates from the 2014 to 2020 OPTN/UNOS database was performed. The data was randomly divided into a training set, comprising two-thirds of the data, and a testing set, comprising one-third of the data. From the training set, univariable and multivariable logistic regressions were used to identify significant predictive factors affecting wait times. A predictive index was created using variables significant in the multivariable analysis. The index's ability to predict likelihood of transplantation within 1 year of listing was validated using ROC analysis on the training set. Validation of the index using ROC analysis was repeated on the testing set.ResultsA total of 10 variables were found to be significant. The five most significant variables include the following: blood group, B (OR 0.65); dialysis status (OR 3.67); kidney disease etiology, SLE (OR 0.38); and OPTN region, 5 (OR 0.54) and 6 (OR 0.46). ROC analysis of the index on the training set yielded a c-statistic of 0.71. ROC analysis of the index on the testing set yielded a c-statistic of 0.68.ConclusionsThis index is a modest prognostic model to assess time to pediatric kidney transplantation. It is intended as a supplementary tool to guide patient management by providing clinicians with an individualized prospective timeline for each candidate. Early identification of candidates with potential for prolonged waiting times may help encourage more living donation including paired donation chains.

Dataset Information

Predicting Kidney Transplant Evaluation Non-attendance.

Publications

Predicting Kidney Transplant Evaluation Non-attendance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets