人工智能增强心电图在高急症环境中非侵入性检测高钾血症的验证。

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings.

机构信息

Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota.

Department of Cardiovascular Disease, Mayo Clinic, Rochester, Minnesota.

出版信息

Clin J Am Soc Nephrol. 2024 Aug 1;19(8):952-958. doi: 10.2215/CJN.0000000000000483. Epub 2024 Jun 21.

DOI:10.2215/CJN.0000000000000483

PMID:39116276

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11321728/

Abstract

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.

摘要

背景

人工智能（AI）心电图（ECG）分析可用于检测高钾血症。在本次验证中，我们分别在两个高急症环境中评估了该算法的性能。

方法

分别对 2021 年 2 月至 8 月的急诊部（ED）队列和 2017 年 8 月至 2018 年 2 月的混合重症监护病房（ICU）队列进行了识别和分析。对于每组，均确定了在彼此 4 小时内收集的实验室采集的钾和 12 导联心电图之间的配对。随后，将先前开发的 AI ECG 算法应用于 12 导联 ECG 的导联 1 和 2，以筛查高钾血症（钾>6.0mEq/L）。

结果

ED 队列（N=40128）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，在 ED 队列中，AI-ECG 的灵敏度、特异性、阳性预测值（PPV）、阴性预测值（NPV）和阳性似然比（LR+）分别为 80%、80%、3%、99.8%和 4.0。低 eGFR（<30ml/min）亚组分析得出的 AUC、灵敏度、特异性、PPV、NPV 和 LR+分别为 ED 队列中的 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，AI-ECG 的灵敏度、特异性、PPV、NPV 和 LR+分别为 82%、82%、14%、99%和 4.6。低 eGFR 亚组分析得出的 AUC、灵敏度、特异性、PPV、NPV 和 LR+分别为 ICU 队列中的 0.85、88%、67%、29%、97%和 2.7。