Hodge, Dave

Abstract Background Diagnosing myocardial infarction (MI) in patients with chronic kidney disease (CKD) is difficult as these patients often have elevated baseline high-sensitivity cardiac troponin T (hs-cTnT) concentrations. Methods Observational U.S. cohort study of emergency department (ED) patients undergoing hs-cTnT measurement. Cases with >1 hs-cTnT increase >99th percentile were adjudicated following the Fourth Universal Definition of MI. Diagnostic performance of baseline and serial 2- and 6-hour hs-cTnT thresholds for ruling-in acute MI was compared between those without and with CKD (eGFR <60 ml/min/1.73m2). Results The study cohort included 1992 patients, amongst whom 501 (25%) had CKD. There were 701 (82%) patients with myocardial injury, 64 (7.5%) with type 1, and 91 (11%) with type 2 MI. In CKD patients with baseline hs-cTnT thresholds of >52, >100, >200 or >300 ng/L, PPVs for acute MI were 37% [95% CI 28-46], 53% (95% CI 39-67), 73% (95% CI 50-89) and 80% (95% CI 44-98), and in those without CKD, 64% (95% CI 50-77), 69% (95% CI 49-85), 59% (95% CI 33-82) and 54% (95% CI 25-81). In CKD patients with a 2-hour hs-cTnT delta of >10, >20 or >30 ng/L, the corresponding PPVs were 65% (95% CI 50-78), 86% (95% CI 68-96) and 88% (95% CI 68-97), and in those without CKD, 65% (95% CI 51-78), 73% (95% CI 57-86) and 75% (95% CI 58-88) (Figure). Conclusion Diagnostic performance of standard baseline and serial 2-hour hs-cTnT thresholds to rule-in MI is suboptimal in patients with CKD. It significantly improves when using higher baseline thresholds and delta values.

Abstract Background Artificial intelligence-augmented electrocardiogram (AI-ECG) algorithms have been developed from the standard 12-lead ECG and validated for the recognition of left ventricular systolic dysfunction (LVSD), defined as LV ejection fraction (LVEF)≤35%. Whether AI-ECG facilitates identification of LVSD and is associated with adverse outcomes in emergency department (ED) patients undergoing high-sensitivity cardiac troponin (hs-cTnT) testing is uncertain. Purpose To investigate the diagnostic and prognostic performance of AI-ECG in ED patients undergoing hs-cTnT measurement. Methods Observational US cohort study of ED patients undergoing hs-cTnT measurement. Cases with hs-cTnT increases >sex-specific 99th percentiles were adjudicated following the Fourth Universal Definition of Myocardial Infarction (MI). Post-discharge major adverse cardiac events (MACE) included death, MI, heart failure (HF) hospitalization, stroke or transient ischemic attack, and new onset atrial fibrillation/flutter during 2-years follow-up. The AI-ECG network output, which is a continuous number between 0–1, that provides a probability of LVSD, was obtained for each patient from the first ECG during the index presentation. An AI-ECG threshold of ≥0.256 indicates a positive screen that correlates with a high probability of LVSD. Results Among 1977 patients, 1729 (87%) had a negative AI-ECG screen, while 248 (13%) had a positive AI-ECG screen. Patients with a positive AI-ECG screen were older and had more comorbidities. As compared to patients with hs-cTnT≤99th percentile in whom AI-ECG was positive in 5.8%, those with hs-cTnT>99th percentile had a positive AI-ECG in 22% of cases (p<0.0001). Based on adjudicated diagnoses, the frequency of a positive AI-ECG was 20% in myocardial injury, 38% in type 1 MI, and 20% in type 2 MI. At 2-years follow-up, as compared to patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE (48% vs. 21%, p<0.0001, adjusted HR 1.39, 95% CI 1.11–1.75) (Figure 1), mainly because of more deaths (43% vs. 30%, p=0.004) and HF hospitalizations (36% vs. 13%, p<0.0001). A positive AI-ECG was associated with a higher risk for MACE (60% vs. 41%, p<0.0001, adjusted HR 1.30, 95% CI 1.02–1.64) in those with hs-cTnT increases >99th percentile, but not in those without hs-cTnT increases. Among patients with an echocardiogram during index presentation or within 30-days (n=452), the diagnostic accuracy of AI-ECG for LVEF ≤35% was 81.4% (95% CI 77.5, 84.9) with a negative predictive value of 96.5% (95% CI 94.0, 98.2). A normal LVEF (>50%) was observed in 87% of those with a negative AI ECG, whereas in those with a positive AI-ECG LVEF was reduced (<50%) in 60%. Conclusions Among ED patients evaluated with hs-cTnT, a positive AI-ECG screen for LVSD identifies patients at high risk of MACE. These findings are largely because of more deaths and HF hospitalizations in those with hs-cTnT increases >sex-specific 99th percentiles. Funding Acknowledgement Type of funding sources: None.