Background:Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms.Methods:We constructed 3 ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUROCs) for the ML models against the AUROCs generated by the Toronto HCM Genotype Score (the Toronto score) and Mayo HCM Genotype Predictor (the Mayo score) using the Delong test and net reclassification improvement.Results:Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUROC of 0.92 (95% CI, 0.85–0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUROC, 0.77 [95% CI, 0.65–0.90], P=0.004, net reclassification improvement: P<0.001) and the Mayo score (AUROC, 0.79 [95% CI, 0.67–0.92], P=0.01, net reclassification improvement: P=0.001). The gradient boosted decision tree ML model also achieved significant net reclassification improvement over the Toronto score (P<0.001) and the Mayo score (P=0.03), with an AUROC of 0.87 (95% CI, 0.75–0.99). Compared with the Toronto and Mayo scores, all 3 ML models had higher sensitivity, positive predictive value, and negative predictive value.Conclusions:Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared with conventional scoring systems in an external validation test set.

中文翻译：

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使用机器学习预测肥厚型心肌病患者的基因型阳性

背景：基因检测可以确定家族筛查策略，对肥厚型心肌病（HCM）具有预后和诊断价值。然而，它也可能造成重大的社会心理负担。传统的评分系统提供了预测基因型阳性的适度能力。我们研究的目的是通过应用机器学习 (ML) 算法开发 HCM 患者基因型阳性的新预测模型。方法：我们使用来自哥伦比亚大学的 102 名患者的现成临床和心脏成像数据构建了 3 个 ML 模型接受过基因检测的 HCM（训练集）。我们验证了来自麻省总医院（测试集）的 76 名 HCM 患者的模型性能。在测试集中，我们使用 Delong 检验和净重分类改进将 ML 模型的接收者操作特征曲线 (AUROC) 与 Toronto HCM 基因型评分（多伦多评分）和 Mayo HCM 基因型预测器（Mayo 评分）生成的 AUROC 下面积进行了比较. 结果：总体而言，178 例患者中有 63 例（35%）为基因型阳性。在训练集中开发的随机森林 ML 模型在预测测试集中的基因型阳性方面表现出 0.92（95% CI，0.85-0.99）的 AUROC，显着优于多伦多得分（AUROC，0.77 [95% CI，0.65-0.90） ], 178 名患者中有 63 名（35%）为基因型阳性。在训练集中开发的随机森林 ML 模型在预测测试集中的基因型阳性方面表现出 0.92（95% CI，0.85-0.99）的 AUROC，显着优于多伦多得分（AUROC，0.77 [95% CI，0.65-0.90） ], 178 名患者中有 63 名（35%）为基因型阳性。在训练集中开发的随机森林 ML 模型在预测测试集中的基因型阳性方面表现出 0.92（95% CI，0.85-0.99）的 AUROC，显着优于多伦多得分（AUROC，0.77 [95% CI，0.65-0.90） ],P = 0.004，净重分类改善：P <0.001）和 Mayo 评分（AUROC，0.79 [95% CI，0.67-0.92]，P = 0.01，净重分类改善：P = 0.001）。梯度提升的决策树 ML 模型也比多伦多得分（P <0.001）和 Mayo 得分（P = 0.03）实现了显着的净重分类改进，AUROC 为 0.87（95% CI，0.75-0.99）。与 Toronto 和 Mayo 评分相比，3 种 ML 模型均具有更高的敏感性、阳性预测值和阴性预测值。结论：与传统的外部评分系统相比，我们的 ML 模型在预测 HCM 患者基因型阳性方面的能力更强。验证测试集。