t substantially lower positive predictive value. Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia....

中文翻译：

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在高敏锐度环境下验证人工智能增强心电图无创检测高钾血症


t 显着降低阳性预测值。背景 人工智能 (AI) 心电图 (ECG) 分析可以检测高钾血症。在此验证中，我们评估了算法在两种高敏锐度设置下的性能。方法分别确定并分析急诊科（ED）队列（2021年2月至8月）和混合重症监护病房（ICU）队列（2017年8月至2018年2月）。对于每组，鉴定了 4 小时内获得的实验室收集的钾和 12 导联心电图对。随后将之前开发的 AI 心电图算法应用于 12 导联心电图中的 1 导联和 2 导联，以筛查高钾血症（钾>6.0 mEq/L）。结果 ED 队列 (N=40,128) 的平均年龄为 60 岁，48% 为男性，1% (N=351) 患有高钾血症。 AI增强心电图（AI-ECG）检测高钾血症的曲线下面积（AUC）为0.88，敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和阳性似然比（LR+） ）在 ED 队列中分别为 80%、80%、3%、99.8% 和 4.0。低 eGFR (<30 ml/min) 亚分析得出 ED 队列中的 AUC、敏感性、特异性、PPV、NPV 和 LR+ 分别为 0.83、86%、60%、15%、98% 和 2.2。 ICU 队列 (N=2636) 的平均年龄为 65 岁，60% 为男性，3% (N=87) 患有高钾血症。 AI-ECG 的 AUC 为 0.88，在 ICU 队列中的敏感性、特异性、PPV、NPV 和 LR+ 分别为 82%、82%、14%、99% 和 4.6。低 eGFR 亚分析得出 ICU 队列中的 AUC、敏感性、特异性、PPV、NPV 和 LR+ 分别为 0.85、88%、67%、29%、97% 和 2.7。 结论 AI-ECG 算法表现出较高的 NPV，表明它有助于排除高钾血症，但 PPV 较低，表明它不足以治疗高钾血症。