Key result
AI-ECG alert intervention cuts 90-day all-cause mortality ~17% vs standard care in hospitalized patients.
Why the study?
Early identification of vulnerable hospitalized patients can improve outcomes but remains a substantial challenge in clinical practice, motivating evaluation of an AI-enabled ECG alert to flag patients at high risk of mortality.
Does an AI-enabled ECG alert intervention reduce 90-day all-cause mortality in hospitalized emergency and inpatient department patients compared to standard care?
RCT (n=15,965)
Single-blind
Patient-level 1:1 randomization based on MD5 hash transform of medical record number
Yes
Does an AI-enabled ECG alert intervention reduce 90-day all-cause mortality in hospitalized emergency and inpatient department patients compared to standard care?
This pragmatic RCT demonstrates that real-time AI-enabled ECG mortality risk alerts to physicians significantly reduce 90-day all-cause and cardiac mortality in hospitalized patients, driven by increased intensive care delivery to high-risk individuals.
Answers are grounded in this paper and cite their sources.
AI-ECG alerts reduced mortality in hospitalized patients; provides first RCT evidence for real-time AI risk stratification in acute care.
The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .
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Lin et al. (2024) conducted an RCT in Hospitalized patients (emergency department or inpatient department) undergoing routine ECG (n=15,965). AI-enabled electrocardiogram (AI-ECG) alert intervention vs. Standard care with concealed AI-ECG reports and no warning alerts was evaluated on All-cause mortality within 90 days (HR 0.83, 95% CI 0.70-0.99). Implementation of an AI-enabled ECG alert intervention reduced 90-day all-cause mortality compared to standard care (3.6% vs 4.3%; HR 0.83, 95% CI 0.70-0.99) in hospitalized patients.
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