Trial reportNature communications2026
AI-enabled electrocardiogram alert for potassium imbalance treatment: a pragmatic randomized controlled trial.
Trial report in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05118022 (Artificial Intelligence Identified Dyskalemia Using Electrocardiogram), which is not on this map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence Identified Dyskalemia Using Electrocardiogram (AIDE) Prompts Immediate Treatment
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Life-threatening dyskalemia, defined as an abnormal serum potassium concentration, is common in emergency settings that requires timely recognition and treatment and can be detected via AI-enabled electrocardiography. We conducted a pragmatic, open-label, randomized controlled trial with physician-level randomization to evaluate whether a real-time AI-enabled electrocardiography alert could improve physicians' management of dyskalemia. Over a six-month period in 2022, 70 emergency physicians were randomized (35 intervention, 35 control) and provided care to 14,989 patients (7506 in the intervention group and 7483 in the control group) at an academic medical center and a community hospital (ClinicalTrials.gov NCT05118022). The trial had two primary outcomes: the rate of hyperkalemia-related treatment and hypokalemia-related treatment within three hours. The intervention consisted of a real-time pop-up alert in the electronic health records that categorized patients at risk of moderate-to-severe hyperkalemia (≥6.0 mmol/L) or hypokalemia (≤3.0 mmol/L). Physicians in the control group did not receive alerts. Overall, the rate of hyperkalemia-related treatment was not significantly greater in the intervention group (8.0%) than in the control group (7.7%) (hazard ratio 1.05; 95% CI 0.94-1.17; p = 0.420). Similarly, the rate of hypokalemia-related treatment did not differ significantly (2.1% vs. 2.4%; hazard ratio 0.91; 95% CI 0.74-1.13; p = 0.392). Among patients identified by AI-enabled electrocardiography as having hyperkalemia, however, hyperkalemia-related treatment occurred more frequently in the intervention group (69.1% vs. 41.6%; hazard ratio 2.23; 95% CI 1.44-3.46; p < 0.001). This trial demonstrates that a real-time AI-enabled electrocardiography alert facilitated earlier treatment among patients identified as high risk for hyperkalemia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.