Stuckey Thomas D, Meine Frederick J, McMinn Thomas R, Depta Jeremiah P, Bennett Brett A, McGarry Thomas F, Carroll William S, Suh David D, Steuter John A, Roberts Michael C, Gillins Horace R, Fathieh Farhad, Burton Timothy, Nemati Navid, Shadforth Ian P, Ramchandani Shyam, Bridges Charles R, Rabbat Mark G
Cone Health Heart and Vascular Center, Greensboro, NC 27401, USA.
Novant Health New Hanover Regional Medical Center, Wilmington, NC 28401, USA.
Diagnostics (Basel). 2024 May 8;14(10):987. doi: 10.3390/diagnostics14100987.
Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals ( = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.
许多临床研究表明，用于识别冠状动脉疾病（CAD）的检测方法在性能上存在很大差异。冠状动脉计算机断层扫描血管造影（CCTA）已被确定为一种有效的排除检测方法，但在美国，尤其是农村地区，其应用并不广泛。与城市地区相比，农村地区的患者在医疗保健系统中未得到充分服务，这使得农村地区的患者成为优先需要获得易于获取的诊断方法的人群。我们之前开发了一种机器学习算法，用于在不使用辐射或负荷的情况下，识别有症状患者是否存在CAD（根据功能意义定义）。该算法需要215秒在静息状态下采集的时间同步光电容积脉搏波描记图和正交电压梯度信号。本研究的目的是在来自IDENTIFY试验的大型、盲态数据集中，验证该算法在冻结状态（即不重新训练）下的性能。IDENTIFY是一项多中心、选择性盲法、非随机、前瞻性、存储库研究，旨在从左心导管检查或CCTA前7天内有CAD症状的受试者中获取带有配对元数据的信号。使用一组未见过的患者信号（n = 1816）验证了该算法的敏感性和特异性。选择预先设定的终点以证明排除性能与CCTA相当。验证集中的ROC-AUC为0.80（95%CI：0.78 - 0.82）。在男性和女性亚组中均保持了这一性能。在预先设定的切点处，敏感性为0.85（95%CI：0.82 - 0.88），特异性为0.58（95%CI：0.54 - 0.62），达到了预先设定的终点。假设疾病患病率为4%，阴性预测值为0.99。算法性能与使用CCTA的三级中心检测相当。选择合适的切点可使女性与男性具有相同的敏感性和特异性表现。因此，嵌入该算法的医疗设备可能满足对CAD进行无创、一线即时检测（无任何辐射或负荷）的未满足需求，从而为患者、医生和医疗保健系统带来显著益处。
