Development and Validation of a Model to Predict Malignancy Within the First Year After Adult Heart Transplantation. (March 2023)

• Record Type: Journal Article
• Authors: Baker, William L.; Moore, Timothy E.; Baron, Eric; Jennings, Douglas L.; Jaiswal, Abhishek
• Is Part Of: Progress in transplantation. Volume 33:Number 1(2023)
• Page Start: 69
• Page End: 77
• Publication Date: 2023-03
• Subjects: clinical outcomes -- cancer -- statistics -- regression -- risk -- research -- quantitative methods -- regression -- statistics -- inferential -- parametric and nonparametric -- clinical outcomes -- cardiovascular disease; Transplantation of organs, tissues, etc -- Periodicals; Transplantation; Organ Procurement; Tissue Donors; Transplantation of organs, tissues, etc; Electronic journals; Periodicals; Periodicals; 362.1783
• DOI: 10.1177/15269248221145042
• Languages: English
• ISSNs: 1526-9248

Abstract

Purpose: Malignancy after heart transplantation is associated with poor outcomes. At present, no prediction model exists for any malignancy within the first year after transplant.Methods: We studied adults who underwent heart transplantation included in the multicenter, national Scientific Registry of Transplant Recipients from January 2000 through April 2021. Possible predictors of malignancy were identified based on their known association with malignancy. Multiple imputations were conducted for missing values using predictive mean matching. A multivariable logistic regression model for predicting malignancy development within the first year after transplant was developed and internally validated via 500 bootstrapped samples to estimate the optimism-corrected measures of model accuracy and performance.Results: Among the 47 212 recipients comprising 16% females, 76% whites, 7% with prior malignancy, and a median age of 56 years; 865 (2.3% of those with non-missing data) developed malignancy within the first year after transplant. Prior malignancy, older age at heart transplantation, white race, and nonischemic heart failure etiology were the strongest predictors of new malignancy. The optimism-corrected model had modest discrimination (C-statistic: 0.70, 95% CI: 0.69-0.72) and good calibration and performance (calibration slope: 0.96; Cox-Snell R 2 : 0.063), particularly at lower predicted risk. A nomogram for the practicing clinician was developed.Conclusions: Using selection variables previously linked to cutaneous malignancy, our model was modestly predictive of the development of any malignancy in the first year after heart transplantation. Future research could identify factors that may improve malignancy prediction, including incorporation of time-to-event data.