Development and Validation of a Web-Based Severe COVID-19 Risk Prediction Model.

BACKGROUND: Coronavirus disease 2019 (COVID-19) carries high morbidity and mortality globally. Identification of patients at risk for clinical deterioration upon presentation would aid in triaging, prognostication, and allocation of resources and experimental treatments. RESEARCH QUESTION: Can we develop and validate a web-based risk prediction model for identification of patients who may develop severe COVID-19, defined as intensive care unit (ICU) admission, mechanical ventilation, and/or death? METHODS: This retrospective cohort study reviewed 415 patients admitted to a large urban academic medical center and community hospitals. Covariates included demographic, clinical, and laboratory data. The independent association of predictors with severe COVID-19 was determined using multivariable logistic regression. A derivation cohort (n=311, 75%) was used to develop the prediction models. The models were tested by a validation cohort (n=104, 25%). RESULTS: The median age was 66 years (Interquartile range [IQR] 54-77) and the majority were male (55%) and non-White (65.8%). The 14-day severe COVID-19 rate was 39.3%; 31.7% required ICU, 24.6% mechanical ventilation, and 21.2% died. Machine learning algorithms and clinical judgment were used to improve model performance and clinical utility, resulting in the selection of eight predictors: age, sex, dyspnea, diabetes mellitus, troponin, C-reactive protein, D-dimer, and aspartate aminotransferase. The discriminative ability was excellent for both the severe COVID-19 (training area under the curve [AUC]=0.82, validation AUC=0.82) and mortality (training AUC= 0.85, validation AUC=0.81) models. These models were incorporated into a mobile-friendly website. CONCLUSIONS: This web-based risk prediction model can be used at the bedside for prediction of severe COVID-19 using data mostly available at the time of presentation.

Initial Experience in Monitoring QT Intervals Using a Six-lead Contactless Mobile Electrocardiogram in an Inpatient Setting.

Mobile electrocardiograms (ECGs) (mECGs) using smartphone applications are an emerging technology. In the coronavirus disease 2019 (COVID-19) era, minimizing patient contact has gained increasing importance. Additionally, increased QT/corrected QT (QTc) monitoring has concurrently been required. The KardiaMobile 6L ECG device, cleared by the United States Food and Drug Administration (FDA) for recording ECGs, along with the KardiaStation tablet application is a platform (AliveCor, Mountain View, CA, USA) that addresses these two issues. A team of residents, fellows, hospitalists, and cardiologists identified inpatients in need of QT/QTc interval monitoring to pilot the adoption of a system composed of a KardiaMobile 6L ECG device with the accompanying KardiaStation tablet application. Concurrent standard ECGs provided validation. Adoption and performance issues were recorded. Four patients agreed to participate in QT/QTc interval monitoring, three of whom were positive for severe acute respiratory syndrome coronavirus 2 viral infection. After basic instructions were given to the patients and their clinical nurses, all patients recorded mECGs successfully. Patients were able to record their own mECG tracings at least once without any assistance. The 12-lead ECGs and mECGs each showed the correct rhythm, and the measured QTc intervals on each modality were consistently acceptable (< 500 ms). Contactless ECGs were successfully uploaded to KardiaStation for QT/QTc interval measurement and archiving. In this study, we showed that an FDA-cleared product, KardiaMobile 6L, has the ability to provide high-quality contactless ECGs for reliable QT/QTc interval measurements. Hospitalized patients were able to perform recordings when requested after receiving simple instructions at the time of first use. This technology has applications during the COVID-19 pandemic and beyond.