Xia Wei, Liu Weici, He Zhao, Song Chenghu, Liu Jiwei, Chen Ruo, Chen Jingyu, Wang Xiaokun, Xu Hongyang, Mao Wenjun
Department of Intensive Care Unit, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China.
Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China.
Transplantation. 2025 Aug 1;109(8):1458-1470. doi: 10.1097/TP.0000000000005326. Epub 2025 Jan 10.
Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.
This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio. Independent risk factors for PGD3 were determined by integrating the univariate logistic regression and least absolute shrinkage and selection operator regression analyses. Subsequently, 9 ML models were used to construct prediction models for PGD3 based on selected variables. Their prediction performances were further evaluated. Besides, model stratification performance was assessed with 3 posttransplant metrics. Finally, the SHapley Additive exPlanations algorithm was used to understand the predictive importance of selected variables.
We identified 9 independent clinical risk factors as selected variables. Among 9 ML models, the random forest (RF) model displayed optimal performance (area under the curve [AUC] = 0.9415, sensitivity [Se] = 0.8972, specificity [Sp] = 0.8795 in the training cohort; AUC = 0.7975, Se = 0.7520, Sp = 0.7313 in the internal validation cohort; and AUC = 0.8214, Se = 0.8235, Sp = 0.6667 in the external validation cohort). Further assessments on calibration and clinical usefulness indicated the promising applicability of the RF model in PGD3 prediction. Meanwhile, the RF model also performed best in terms of risk stratification for postoperative support (extracorporeal membrane oxygenation time: P < 0.001, mechanical ventilation time: P = 0.006, intensive care unit time: P < 0.001).
The RF model had the optimal performance in PGD3 prediction and postoperative risk stratification for patients after Lung Tx.
原发性移植肺功能障碍（PGD）在肺移植（Lung Tx）后72小时内发生，极大地影响患者的预后。本研究旨在建立一个准确的机器学习（ML）模型，用于预测肺移植后3级PGD（PGD3）。
这项回顾性研究纳入了2018年7月至2023年10月期间接受肺移植的802例患者（推导队列640例，外部验证队列162例），640例患者按7:3比例随机分配到训练队列和内部验证队列。通过整合单因素逻辑回归和最小绝对收缩和选择算子回归分析，确定PGD3的独立危险因素。随后，使用9种ML模型基于选定变量构建PGD3预测模型。进一步评估它们的预测性能。此外，用3个移植后指标评估模型分层性能。最后，使用SHapley加性解释算法来了解选定变量的预测重要性。
我们确定了9个独立的临床危险因素作为选定变量。在9种ML模型中，随机森林（RF）模型表现出最佳性能（训练队列中曲线下面积[AUC]=0.9415；敏感度[Se]=0.8972；特异度[Sp]=0.8795；内部验证队列中AUC=0.7975；Se=0.7520；Sp=0.7313；外部验证队列中AUC=0.8214；Se=0.8235；Sp=0.6667）。在校准和临床实用性方面的进一步评估表明，RF模型在PGD3预测中具有良好适用性。同时，RF模型在术后支持的风险分层方面也表现最佳（体外膜肺氧合时间：P<0.001；机械通气时间：P=0.006；重症监护病房时间：P<0.001）。
RF模型在肺移植术后患者的PGD3预测和术后风险分层方面具有最佳性能。
