Prolonged QT predicts prognosis in COVID-19.

BACKGROUND: Coronavirus disease-2019 (COVID-19) causes severe illness and multi-organ dysfunction. An abnormal electrocardiogram is associated with poor outcome, and QT prolongation during the illness has been linked to pharmacological effects. This study sought to investigate the effects of the COVID-19 illness on the corrected QT interval (QTc). METHOD: For 293 consecutive patients admitted to our hospital via the emergency department for COVID-19 between 01/03/20 -18/05/20, demographic data, laboratory findings, admission electrocardiograph and clinical observations were compared in those who survived and those who died within 6 weeks. Hospital records were reviewed for prior electrocardiograms for comparison with those recorded on presentation with COVID-19. RESULTS: Patients who died were older than survivors (82 vs 69.8 years, p < 0.001), more likely to have cancer (22.3% vs 13.1%, p = 0.034), dementia (25.6% vs 10.7%, p = 0.034) and ischemic heart disease (27.8% vs 10.7%, p < 0.001). Deceased patients exhibited higher levels of C-reactive protein (244.6 mg/L vs 146.5 mg/L, p < 0.01), troponin (1982.4 ng/L vs 413.4 ng/L, p = 0.017), with a significantly longer QTc interval (461.1 ms vs 449.3 ms, p = 0.007). Pre-COVID electrocardiograms were located for 172 patients; the QTc recorded on presentation with COVID-19 was longer than the prior measurement in both groups, but was more prolonged in the deceased group (448.4 ms vs 472.9 ms, pre-COVID vs COVID, p < 0.01). Multivariate Cox-regression analysis revealed age, C-reactive protein and prolonged QTc of >455 ms (males) and >465 ms (females) (p = 0.028, HR 1.49 [1.04-2.13]), as predictors of mortality. QTc prolongation beyond these dichotomy limits was associated with increased mortality risk (p = 0.0027, HR 1.78 [1.2-2.6]). CONCLUSION: QTc prolongation occurs in COVID-19 illness and is associated with poor outcome.

Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study.

Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.