Department of Cardiovascular Medicine, Mayo Clinic, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
Division of Biomedical Statistics and Informatics, Mayo Clinic, Scottsdale, AZ, USA.
Gen Thorac Cardiovasc Surg. 2020 Dec;68(12):1369-1376. doi: 10.1007/s11748-020-01375-6. Epub 2020 May 7.
We aimed to develop a risk prediction model using a machine learning to predict survival and graft failure (GF) 5 years after orthotopic heart transplant (OHT).
Using the International Society of Heart and Lung Transplant (ISHLT) registry data, we analyzed 15,236 patients who underwent OHT from January 2005 to December 2009. 342 variables were extracted and used to develop a risk prediction model utilizing a gradient-boosted machine (GBM) model to predict the risk of GF and mortality 5 years after hospital discharge. After excluding variables missing at least 50% of the observations and variables with near zero variance, 87 variables were included in the GBM model. Ten fold cross-validation repeated 5 times was used to estimate the model's external performance and optimize the hyperparameters simultaneously. Area under the receiver operator characteristic curve (AUC) for the GBM model was calculated for survival and GF 5 years post-OHT.
The median duration of follow-up was 5 years. The mortality and GF 5 years post-OHT were 27.3% (n = 4161) and 28.1% (n = 4276), respectively. The AUC to predict 5-year mortality and GF is 0.717 (95% CI 0.696-0.737) and 0.716 (95% CI 0.696-0.736), respectively. Length of stay, recipient and donor age, recipient and donor body mass index, and ischemic time had the highest relative influence in predicting 5-year mortality and graft failure.
The GBM model has a good accuracy to predict 5-year mortality and graft failure post-OHT.
我们旨在开发一种使用机器学习预测原位心脏移植（OHT）后 5 年生存率和移植物衰竭（GF）的风险预测模型。
利用国际心肺移植协会（ISHLT）登记数据，我们分析了 2005 年 1 月至 2009 年 12 月期间接受 OHT 的 15236 例患者。提取了 342 个变量，并利用梯度提升机（GBM）模型开发了一个风险预测模型，以预测出院后 5 年 GF 和死亡率的风险。排除至少 50%的观察值缺失和方差接近零的变量后，87 个变量被纳入 GBM 模型。重复 5 次的 10 倍交叉验证用于估计模型的外部性能并同时优化超参数。计算了 GBM 模型的 5 年 OHT 后生存率和 GF 的接收者操作特征曲线下面积（AUC）。
中位随访时间为 5 年。OHT 后 5 年的死亡率和 GF 分别为 27.3%（n=4161）和 28.1%（n=4276）。预测 5 年死亡率和 GF 的 AUC 分别为 0.717（95%CI 0.696-0.737）和 0.716（95%CI 0.696-0.736）。住院时间、受体和供体年龄、受体和供体体重指数以及缺血时间对预测 5 年死亡率和移植物衰竭具有最高的相对影响。
GBM 模型对 OHT 后 5 年死亡率和移植物衰竭具有良好的预测准确性。
