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Development of a Predictive Model for Hyperglycemia in Nondiabetic Recipients After Liver Transplantation.

ABSTRACT: Background:Posttransplant hyperglycemia has been associated with increased risks of transplant rejection, infections, length of stay, and mortality. Methods:To establish a predictive model to identify nondiabetic recipients at risk for developing postliver transplant (LT) hyperglycemia, we performed this secondary, retrospective data analysis of a single-center, prospective, randomized, controlled trial of glycemic control among 107 adult LT recipients in the inpatient period. Hyperglycemia was defined as a posttransplant glucose level greater than 200 mg/dL after initial discharge up to 1 month following surgery. Candidate variables with P less than 0.10 in univariate analyses were used to build a multivariable logistic regression model using forward stepwise selection. The final model chosen was based on statistical significance and additive contribution to the model based on the Bayesian Information Criteria. Results:Forty-three (40.2%) patients had at least 1 episode of hyperglycemia after transplant after the resolution of the initial postoperative hyperglycemia. Variables selected for inclusion in the model (using model optimization strategies) included length of hospital stay (odds ratio [OR], 0.83; P < 0.001), use of glucose-lowering medications at discharge (OR, 3.76; P = 0.03), donor female sex (OR, 3.18; P = 0.02) and donor white race (OR, 3.62; P = 0.01). The model had good calibration (Hosmer-Lemeshow goodness-of-fit test statistic = 9.74, P = 0.28) and discrimination (C-statistic = 0.78; 95% confidence interval, 0.65-0.81, bias-corrected C-statistic = 0.78). Conclusions:Shorter hospital stay, use of glucose-lowering medications at discharge, donor female sex and donor white race are important determinants in predicting hyperglycemia in nondiabetic recipients after hospital discharge up to 1 month after liver transplantation.

SUBMITTER: Zelada H

PROVIDER: S-EPMC6233666 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Development of a Predictive Model for Hyperglycemia in Nondiabetic Recipients After Liver Transplantation.

Zelada Henry H VanWagner Lisa B LB Pollack Teresa T Higginbotham Devan D Zhao Lihui L Yang Amy A Molitch Mark E ME Wallia Amisha A

Transplantation direct 20180920 10

<h4>Background</h4>Posttransplant hyperglycemia has been associated with increased risks of transplant rejection, infections, length of stay, and mortality.<h4>Methods</h4>To establish a predictive model to identify nondiabetic recipients at risk for developing postliver transplant (LT) hyperglycemia, we performed this secondary, retrospective data analysis of a single-center, prospective, randomized, controlled trial of glycemic control among 107 adult LT recipients in the inpatient period. Hyp ...[more]

PMID: 30498770

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Project description:BackgroundAn individual prognostic model that includes inflammation caused by the delayed recovery of liver function after surgery for the early recurrence of hepatocellular carcinoma (HCC) following liver transplantation (LT) has not been well determined. Our aim was to develop a nomogram model for predicting individual survival and early recurrence following LT for patients.MethodsRetrospective data, including clinical pathology and follow-up data, on HCC patients were collected between October 2016 and October 2019 at Huashan Hospital Affiliated to Fudan University. A nomogram estimating recurrence post-transplantation was constructed using multivariate Cox regression analysis.ResultsA total of 210 patients were included in the present study. The multivariate estimators of recurrence consisted of age, maximum tumor diameter, tumor thrombus, microvascular invasion (MVI), alanine aminotransferase and alpha-fetoprotein on postoperative day 7. Nomogram of recurrence-free survival was developed. The calibration and discrimination of the novel model were assessed with the calibration curves and concordance index (C-index). Its reliability and advantages were evaluated by comparing it with the conventional American Joint Committee on Cancer (AJCC) 8th edition staging system using integrated discrimination improvement (IDI) and net reclassification improvement (NRI). In comparison to the AJCC 8th edition staging system, the C-index (development set: 0.796 vs. 0.643, validation set: 0.741 vs. 0.563), the area under the receiver operating characteristic curve (AUC) of the validation set (1-year AUC: 0.732 vs. 0.586, 2-year AUC: 0.705 vs. 0.504), the development set (1-year AUC: 0.799 vs. 0.551, 2-year AUC: 0.801 vs. 0.512), and this model's calibration plots all showed improved performance. In addition, NRI and IDI verified that the nomogram is an accurate prognostic tool. Subsequently, a web calculator was generated to assess the risk of tumor recurrence post-LT.ConclusionsThe nomogram, based on clinical and pathological factors, showed good accuracy in estimating prognostic recurrence and can be used to guide individual patient follow-up and treatment.

Dataset Information

Development of a Predictive Model for Hyperglycemia in Nondiabetic Recipients After Liver Transplantation.

Publications

Development of a Predictive Model for Hyperglycemia in Nondiabetic Recipients After Liver Transplantation.

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