Dataset Information

Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

ABSTRACT:

Background and objectives

Tertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms.

Design, setting, participants, & measurements

We used two separate cohorts: a derivation cohort with the data of kidney allograft recipients (n=669) who underwent kidney transplantation at Severance Hospital, Seoul, Korea between January 2009 and December 2015 and a multicenter registry dataset (the Korean Cohort Study for Outcome in Patients with Kidney Transplantation) as an external validation cohort (n=542). Tertiary hyperparathyroidism was defined as post-transplant parathyroidectomy. The derivation cohort was split into 75% training set (n=501) and 25% holdout test set (n=168) to develop prediction models and integer-based score.

Results

Tertiary hyperparathyroidism requiring parathyroidectomy occurred in 5% and 2% of the derivation and validation cohorts, respectively. Three top predictors (dialysis duration, pretransplant intact parathyroid hormone, and serum calcium level measured at the time of admission for kidney transplantation) were identified to create an integer score system (dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level [DPC] score; 0-15 points) to predict tertiary hyperparathyroidism. The median DPC score was higher in participants with post-transplant parathyroidectomy than in those without (13 versus three in derivation; 13 versus four in external validation; P<0.001 for all). Pretransplant dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level score predicted post-transplant parathyroidectomy with comparable performance with the best-performing machine learning model in the test set (area under the receiver operating characteristic curve: 0.94 versus 0.92; area under the precision-recall curve: 0.52 versus 0.47). Serial measurement of DPC scores (≥13 at least two or more times, 3-month interval) during 12 months prior to kidney transplantation improved risk classification for post-transplant parathyroidectomy compared with single-time measurement (net reclassification improvement, 0.28; 95% confidence interval, 0.02 to 0.54; P=0.03).

Conclusions

A simple integer-based score predicted the risk of tertiary hyperparathyroidism in kidney allograft recipients, with improved classification by serial measurement compared with single-time measurement.

Clinical trial registry name and registration number

Korean Cohort Study for Outcome in Patients with Kidney Transplantation (KNOW-KT), NCT02042963 PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_10_CJN15921221.mp3.

SUBMITTER: Hong N

PROVIDER: S-EPMC9269627 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

Hong Namki N Lee Juhan J Kim Hyung Woo HW Jeong Jong Ju JJ Huh Kyu Ha KH Rhee Yumie Y

Clinical journal of the American Society of Nephrology : CJASN 20220610 7

<h4>Background and objectives</h4>Tertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms.<h4>Design, setting, participants, & measurements</h4>We used two separ ...[more]

PMID: 35688469

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Project description:BackgroundTertiary hyperparathyroidism after kidney transplantation has been associated with graft dysfunction, cardiovascular morbidity, and osteopenia; however, its true prevalence is unclear. The objective of our study was to evaluate the prevalence of and risk factors for tertiary hyperparathyroidism.MethodsA prospective cohort of 849 adult kidney transplantation recipients (December 2008-February 2020) was used to estimate the prevalence of hyperparathyroidism 1-year post-kidney transplant. Tertiary hyperparathyroidism was defined as hypercalcemia (≥10mg/dL) and hyperparathyroidism (parathyroid hormone≥70pg/mL) 1-year post-kidney transplantation. Modified Poisson regression models were used to evaluate risk factors associated with the development of both persistent hyperparathyroidism and tertiary hyperparathyroidism.ResultsAmong kidney transplantation recipients, 524 (61.7%) had persistent hyperparathyroidism and 182 (21.5%) had tertiary hyperparathyroidism at 1-year post-kidney transplantation. Calcimimetic use before kidney transplantation was associated with 1.30-fold higher risk of persistent hyperparathyroidism (adjusted prevalence ratio = 1.30, 95% CI: 1.12-1.51) and 1.84-fold higher risk of tertiary hyperparathyroidism (adjusted prevalence ratio = 1.84, 95% CI: 1.25-2.72). Pre-kidney transplantation parathyroid hormone ≥300 pg/mL was associated with 1.49-fold higher risk of persistent hyperparathyroidism (adjusted prevalence ratio = 1.49, 95% CI = 1.19-1.85) and 2.21-fold higher risk of tertiary hyperparathyroidism (adjusted prevalence ratio = 2.21, 95% CI = 1.25-3.90). Pre-kidney transplantation tertiary hyperparathyroidism was associated with an increased risk of post-kidney transplantation tertiary hyperparathyroidism (adjusted prevalence ratio = 1.71, 95% CI = 1.29-2.27), but not persistent hyperparathyroidism. Furthermore, 73.0% of patients with persistent hyperparathyroidism and 61.5% with tertiary hyperparathyroidism did not receive any treatment at 1-year post-kidney transplantation.ConclusionPersistent hyperparathyroidism affected 61.7% and tertiary hyperparathyroidism affected 21.5% of kidney transplantation recipients; however, the majority of patients were not treated. Pre-kidney transplantation parathyroid hormone levels ≥300pg/mL and the use of calcimimetics are associated with the development of tertiary hyperparathyroidism. These findings encourage the re-evaluation of recommended pre-kidney transplantation parathyroid hormone thresholds and reconsideration of pre-kidney transplantation secondary hyperparathyroidism treatments to avoid the adverse sequelae of tertiary hyperparathyroidism in kidney transplantation recipients.

Project description:BackgroundRenal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients' longevity and quality of life.ObjectivesThe primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT.MethodUnicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death.Results18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences (P > 0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher (P = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m2) versus elderlies (53.3 ± 21.1 mL/min/1.73 m2). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m2 presented a statistical significance for age ≥60 years (P = 0.01) and reduction in serum haemoglobin (P = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F 1 score: 0.79).ConclusionRenal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

Project description:BackgroundThis study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach.MethodsConsensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters.ResultsConsensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival.ConclusionsWith the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.

Dataset Information

Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

Background and objectives

Design, setting, participants, & measurements

Results

Conclusions

Clinical trial registry name and registration number

Publications

Machine Learning-Derived Integer-Based Score and Prediction of Tertiary Hyperparathyroidism among Kidney Transplant Recipients: An Integer-Based Score to Predict Tertiary Hyperparathyroidism.

Similar Datasets

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