Internal Medicine, Georgetown University, Washington, District of Columbia, USA.
Biostatistics, Mayo Clinic, Rochester, Minnesota, USA.
Open Heart. 2023 Nov 27;10(2):e002417. doi: 10.1136/openhrt-2023-002417.
Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR.
Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups.
Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate-severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74-0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age.
Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.
三尖瓣反流（TR）是一种常见的瓣膜疾病，与较高的发病率和死亡率相关。我们旨在应用机器学习（ML）评估≥中度 TR 患者的风险分层。
回顾性纳入 2005 年 1 月至 2016 年 12 月超声心动图上存在≥中度 TR 的患者。我们使用 70%的数据来训练基于 ML 的生存模型，包括 27 项临床和超声心动图特征，以在独立测试集（30%）上预测 3 年内的死亡率。为了考虑基线合并症的差异，我们按增加的 Charlson 合并症指数（CCI）分层进行预测。使用最佳性能模型在这些组中分别计算了置换特征重要性。
在 13312 例患者中，平均年龄为 72±13 岁，7406 例（55%）为女性，7409 例（56%）为中度，2646 例（20%）为中度-重度，3257 例（24%）为重度 TR。3 种 ML 模型对 1 年死亡率的总体性能良好，C 统计量为 0.74-0.75。有趣的是，CCI 组之间的性能存在差异（最低 CCI 组为 0.774，最高 CCI 组为 0.661）。随着 3 年随访，性能下降（平均 C 指数为 0.78）。此外，对这些预测贡献最大的前 10 个特征在不同 CCI 组之间略有不同，前 10 个特征包括心率、右心室收缩压、血压、利尿剂使用和年龄。
基于常见临床和超声心动图特征的机器学习可评估 TR 患者的死亡风险。在将模型纳入临床实践之前，需要进一步完善模型并进行前瞻性研究验证。
