Key result
An AI-enabled ECG method accurately detected severe dyscalcemia, and ECG-hypercalcemia in patients with initially normal calcium predicted higher all-cause mortality (HR 2.05; 95% CI 1.55-2.70).
Why the study?
Detecting hypocalcemia and hypercalcemia relies on blood tests with turnaround time, prompting development of an AI-enabled ECG method for rapid dyscalcemia detection and outcome prediction.
Does an AI-enabled ECG method accurately detect severe dyscalcemias and predict clinical outcomes?
Cohort (n=121,848)
Yes
Does an AI-enabled ECG method accurately detect severe dyscalcemias and predict clinical outcomes?
An AI-enabled ECG model can accurately detect severe dyscalcemias and identify patients at higher risk for mortality, AMI, and heart failure even when initial blood calcium levels are normal.
May support AI-ECG for dyscalcemia detection and risk stratification; leaves open prospective validation before practice change.
CONTEXT: Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time. OBJECTIVE: The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction. METHODS: This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification. RESULTS: ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although < 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation. CONCLUSION: The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).
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Lin et al. (2022) conducted a cohort in Dyscalcemia (n=121,848). Artificial intelligence-enabled electrocardiography (AI-ECG) was evaluated on Accuracy of AI-ECG to predict aCa and follow up for all-cause mortality, new-onset AMI, and new-onset HF (HR 2.05, 95% CI 1.55-2.70). An AI-enabled ECG method accurately detected severe dyscalcemia, and ECG-hypercalcemia in patients with initially normal calcium predicted higher all-cause mortality (HR 2.05; 95% CI 1.55-2.70).
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