H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Sci Rep. 2023 Apr 15;13(1):6164. doi: 10.1038/s41598-023-33117-y.
With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant functional status remains the same as the status at registration, results in average root mean squared errors of 14.50 and 14.11 respectively. The analysis is based on 118,401 transplant records from 2007 to 2019. To the best of our knowledge, there has been no previously published research on building a model to predict kidney pre-transplant functional status. We also find that functional status at registration and total serum albumin, have the most impact in predicting the pre-transplant functional status.
2019 年，有超过 10 万名患者在肾脏移植候补名单上，了解患者在候补名单上的功能状态是否以及如何发生变化非常重要。卡诺夫斯基绩效评分在登记时和移植前记录，用于衡量患者的功能状态，取值范围为 0 到 100，以 10 为增量。我们使用机器学习技术，构建了一个梯度提升回归模型，根据候补名单登记时的信息预测患者的移植前功能状态。该模型在 5 次滚动原点交叉验证中的预测结果平均均方根误差为 12.99，在单独的超时测试中为 12.94。相比之下，预测移植前的功能状态与登记时的状态保持不变，平均均方根误差分别为 14.50 和 14.11。该分析基于 2007 年至 2019 年的 118401 份移植记录。据我们所知，目前尚无关于构建模型预测肾脏移植前功能状态的先前研究。我们还发现，登记时的功能状态和总血清白蛋白对预测移植前的功能状态影响最大。
