Background We aimed to describe the characteristics and in-hospital outcomes of ST-segment elevation myocardial infarction (STEMI) patients during the Covid-19 era. Methods We conducted a prospective, multicenter study involving 13 intensive cardiac care units, to evaluate consecutive STEMI patients admitted throughout an 8-week period during the Covid-19 outbreak. These patients were compared with consecutive STEMI patients admitted during the corresponding period in 2018 who had been prospectively documented in the Israeli bi-annual National Acute Coronary Syndrome Survey. The primary end-point was defined as a composite of malignant arrhythmia, congestive heart failure, and/or in-hospital mortality. Secondary outcomes included individual components of primary outcome, cardiogenic shock, mechanical complications, electrical complications, re-infarction, stroke, and pericarditis. Results The study cohort comprised 1466 consecutive acute MI patients, of whom 774 (53%) were hospitalized during the Covid-19 outbreak. Overall, 841 patients were diagnosed with STEMI: 424 (50.4%) during the Covid-19 era and 417 (49.6%) during the parallel period in 2018. Although STEMI patients admitted during the Covid-19 period had fewer co-morbidities, they presented with a higher Killip class (p value = .03). The median time from symptom onset to reperfusion was extended from 180 minutes (IQR 122–292) in 2018 to 290 minutes (IQR 161–1080, p < .001) in 2020. Hospitalization during the Covid-19 era was independently associated with an increased risk of the combined endpoint in the multivariable regression model (OR 1.65, 95% CI 1.03–2.68, p value = .04). Furthermore, the rate of mechanical complications was four times higher during the Covid-19 era (95% CI 1.42–14.8, p-value = .02). However, in-hospital mortality remained unchanged (OR 1.73, 95% CI 0.81–3.78, p-value = .16). Conclusions STEMI patients admitted during the first wave of Covid-19 outbreak, experienced longer total ischemic time, which was translated into a more severe disease status upon hospital admission, and a higher rate of in-hospital adverse events, compared with parallel period.

Introduction Early reports described decreased admissions for acute cardiovascular events during the SarsCoV-2 pandemic. We aimed to explore whether the lockdown enforced during the SARSCoV-2 pandemic in Israel impacted the characteristics of presentation, reperfusion times, and early outcomes of ST-elevation myocardial infarction (STEMI) patients. Methods A multicenter prospective cohort comprising all STEMI patients treated by primary percutaneous coronary intervention admitted to four high-volume cardiac centers in Israel during lockdown (20/3/2020–30/4/2020). STEMI patients treated during the same period in 2019 served as controls. Results The study comprised 243 patients, 107 during the lockdown period of 2020 and 136 during the same period in 2019, with no difference in demographics and clinical characteristics. Patients admitted in 2020 had higher admission and peak troponin levels, had a 2.4 fold greater likelihood of Door-to-balloon times> 90 min (95%CI: 1.2–4.9, p = 0.01) and 3.3 fold greater likelihood of pain-to-balloon times> 12 hours (OR 3.3, 95%CI: 1.3–8.1, p<0.01). They experienced higher rates hemodynamic instability (25.2% vs 14.7%, p = 0.04), longer hospital stay (median, IQR [4, 3–6 Vs 5, 4–6, p = 0.03]), and fewer early (<72 hours) discharge (12.4% Vs 32.4%, p<0.001). Conclusions The lockdown imposed during the SARSCoV-2 pandemic was associated with a significant lag in the time to reperfusion of STEMI patients. Measures to improves this metric should be implemented during future lockdowns.

Objective: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing. Approach: The approach leverages digital biomarkers and recent advances in machine learning (ML) for mass AF diagnosis from overnight-hours of single-channel electrocardiogram (ECG) recording. Four databases, totaling n = 3088 patients and p = 26 913 h of continuous single-channel electrocardiogram raw data were used. Three of the databases (n = 125, p = 2513) were used for training a ML model in recognizing AF events from beat-to-beat time series. Visit 1 of the sleep heart health study database (SHHS1, n = 2963, p = 24 400) was used as the test set to evaluate the feasibility of identifying prominent AF from polysomnographic recordings. By combining AF diagnosis history and a cardiologist’s visual inspection of individuals suspected of having AF (n = 118), a total of 70 patients were diagnosed with prominent AF in SHHS1. Main results: Model prediction on SHHS1 showed an overall S e = 0.97 , S p = 0.99 , NPV = 0.99 and PPV = 0.67 in classifying individuals with or without prominent AF. PPV was non-inferior (p = 0.03) for individuals with an apnea-hypopnea index (AHI) ≥15 versus AHI < 15 . Over 22% of correctly identified prominent AF rhythm cases were not previously documented as AF in SHHS1. Significance: Individuals with prominent AF can be automatically diagnosed from an overnight single-channel ECG recording, with an accuracy unaffected by the presence of moderate-to-severe obstructive sleep apnea. This approach enables identifying a large proportion of AF individuals that were otherwise missed by regular care.

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