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Background <p>Early hospital readmissions (EHR) within 30 days after kidney transplantation are common and add clinical and financial burden. Early identification of high-risk recipients can guide post-discharge care. We aimed to develop a machine learning model to predict 30-day hospital readmission in adult kidney transplant recipients.</p> Methods <p>We conducted a retrospective cohort study of 2,110 adult kidney transplant recipients treated at Colombiana de Trasplantes between July 2008 and December 2023. Patients with allograft thrombosis at the time of transplantation were excluded. Clinical and demographic data were extracted from electronic records. Missing values were imputed using multivariate imputation by chained equations with classification and regression trees (MICE-CART). We then implemented a 10-fold cross-validation framework, restricting oversampling to the training folds to prevent data leakage. Two machine learning models (Random Forest and XGBoost) were trained and compared against a baseline logistic regression model on both discrimination and calibration metrics.</p> Results <p>Overall, 14.5% of patients were readmitted within 30 days. Readmitted recipients more often had diabetes mellitus, longer pre-transplant dialysis, delayed graft function, and surgical complications. Evaluated via 10-fold cross-validation, XGBoost yielded the highest discrimination (AUC 0.750; 95% CI 0.722–0.778). Random Forest (AUC 0.744) and baseline logistic regression (AUC 0.747) performed comparably. Both algorithms identified initial hospitalization length, dialysis duration, GFR at day 7, recipient age, and surgical reintervention as the most important predictors.</p> Conclusions <p>A machine learning model that combines pre- and early post-transplant variables can identify kidney transplant recipients at higher risk of 30-day readmission. Based on this model, we developed a web-based risk calculator that returns individualized risk estimates to support discharge planning and follow-up. External validation in independent cohorts is needed before clinical adoption.</p>

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Anticipating the unexpected: a predictive model for early hospital readmissions in kidney transplant recipients using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Jessica Pinto-Ramírez,
  • Andrea Garcia-Lopez,
  • Santiago Cabas,
  • Yenny Báez-Suarez,
  • Néstor Pedraza,
  • Andrea Gómez-Montero,
  • Juan García,
  • Fernando Girón-Luque

摘要

Background

Early hospital readmissions (EHR) within 30 days after kidney transplantation are common and add clinical and financial burden. Early identification of high-risk recipients can guide post-discharge care. We aimed to develop a machine learning model to predict 30-day hospital readmission in adult kidney transplant recipients.

Methods

We conducted a retrospective cohort study of 2,110 adult kidney transplant recipients treated at Colombiana de Trasplantes between July 2008 and December 2023. Patients with allograft thrombosis at the time of transplantation were excluded. Clinical and demographic data were extracted from electronic records. Missing values were imputed using multivariate imputation by chained equations with classification and regression trees (MICE-CART). We then implemented a 10-fold cross-validation framework, restricting oversampling to the training folds to prevent data leakage. Two machine learning models (Random Forest and XGBoost) were trained and compared against a baseline logistic regression model on both discrimination and calibration metrics.

Results

Overall, 14.5% of patients were readmitted within 30 days. Readmitted recipients more often had diabetes mellitus, longer pre-transplant dialysis, delayed graft function, and surgical complications. Evaluated via 10-fold cross-validation, XGBoost yielded the highest discrimination (AUC 0.750; 95% CI 0.722–0.778). Random Forest (AUC 0.744) and baseline logistic regression (AUC 0.747) performed comparably. Both algorithms identified initial hospitalization length, dialysis duration, GFR at day 7, recipient age, and surgical reintervention as the most important predictors.

Conclusions

A machine learning model that combines pre- and early post-transplant variables can identify kidney transplant recipients at higher risk of 30-day readmission. Based on this model, we developed a web-based risk calculator that returns individualized risk estimates to support discharge planning and follow-up. External validation in independent cohorts is needed before clinical adoption.