Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients

Abstract

AbstractIntroduction To establish a prediction model to predict immunosuppressive medication (IM) nonadherence in kidney transplant recipients (KTRs) based on a combined theory framework.Methods This polycentric, cross-sectional study included 1191 KTRs from October 2020 to February 2021 in China, with 1011 KTRs enrolled in the derivation set and 180 in the external validation set. Variables selected based on the combined theory of planned behavior (TPB)/health belief model (HBM) theory were analyzed by the least absolute shrinkage and selection operator (LASSO). Internal 10 cross-validation was conducted to determine the optimal lambda value. The receiver operating characteristic (ROC) curve, specificity, and sensitivity were used to evaluate the prediction model, and further assessment was run by external validation.Results IM nonadherence rate was 38.48% in the derivation set and 37.22% in the validation set. The LASSO model was developed with eight predictors for IM nonadherence: age, preoperative drinking history, education, marital status, perceived barriers, social support, perceived behavioral control, and perceived susceptibility. The model demonstrated acceptable discrimination with the area under the ROC curve of 0.797 (95% CI: 0.745–0.850) in the internal validation set and 0.757 (95% CI: 0.684–0.829) in the external validation set. The specificity and sensitivity in the internal validation and external validation set were 0.741, 0.748, 0.673, and 0.716, respectively.Conclusions The LASSO model was developed to guide identifying high-risk nonadherent patients and timely and effective interventions to improve their prognosis and survival.

Keywords

• Immunosuppressive medication
• kidney transplantation
• LASSO regression
• prediction model
• nonadherence