ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) pandemic has challenged researchers performing clinical trials to develop innovative approaches to mitigate infectious risk while maintaining rigorous safety monitoring. Methods: In this report we describe the implementation of a novel exclusively remote randomized clinical trial (ClinicalTrials.gov NCT04354428) of hydroxychloroquine and azithromycin for the treatment of the SARS-CoV-2-mediated COVID-19 disease which included cardiovascular safety monitoring. All study activities were conducted remotely. Self-collected vital signs (temperature, respiratory rate, heart rate, and oxygen saturation) and electrocardiographic (ECG) measurements were transmitted digitally to investigators while mid-nasal swabs for SARS-CoV-2 testing were shipped. ECG collection relied on a consumer device (KardiaMobile 6L, AliveCor Inc.) that recorded and transmitted six-lead ECGs via participants' internet-enabled devices to a central core laboratory, which measured and reported QTc intervals that were then used to monitor safety. Results: Two hundred and thirty-one participants uploaded 3245 ECGs. Mean daily adherence to the ECG protocol was 85.2% and was similar to the survey and mid-nasal swab elements of the study. Adherence rates did not differ by age or sex assigned at birth and were high across all reported race and ethnicities. QTc prolongation meeting criteria for an adverse event occurred in 28 (12.1%) participants, with 2 occurring in the placebo group, 19 in the hydroxychloroquine group, and 7 in the hydroxychloroquine + azithromycin group. Conclusions: Our report demonstrates that digital health technologies can be leveraged to conduct rigorous, safe, and entirely remote clinical trials.
ABSTRACT
BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.
ABSTRACT
BACKGROUND: Observational studies have suggested increased arrhythmic and cardiovascular risk with the combination use of hydroxychloroquine (HCQ) and azithromycin in patients with coronavirus disease 2019 (COVID-19). OBJECTIVE: The arrhythmic safety profile of HCQ monotherapy, which remains under investigation as a therapeutic and prophylactic agent in COVID-19, is less established and we sought to evaluate this. METHODS: In 245 consecutive patients with COVID-19 admitted to the University of Washington hospital system between March 9, 2020, and May 10, 2020, we identified 111 treated with HCQ monotherapy. Patients treated with HCQ underwent a systematic arrhythmia and QT interval surveillance protocol including serial electrocardiograms (ECG) (baseline, following second HCQ dose). The primary endpoint was in-hospital sustained ventricular arrhythmia or arrhythmic cardiac arrest. Secondary endpoints included clinically significant QTc prolongation. RESULTS: A total of 111 patients with COVID-19 underwent treatment with HCQ monotherapy (mean age 62 ± 16 years, 44 women [39%], serum creatinine 0.9 [interquartile range 0.4] mg/dL). There were no instances of sustained ventricular arrythmia or arrhythmic cardiac arrest. In 75 patients with serial ECGs, clinically significant corrected QT (QTc) prolongation was observed in a minority (n = 5 [7%]). In patients with serial ECGs, there was no significant change in the QTc interval in prespecified subgroups of interest, including those with prevalent cardiovascular disease or baseline use of renin-angiotensin-aldosterone axis inhibitors. CONCLUSIONS: In the context of a systematic monitoring protocol, HCQ monotherapy in hospitalized COVID-19 patients was not associated with malignant ventricular arrhythmia. A minority of patients demonstrated clinically significant QTc prolongation during HCQ therapy.