External validation of the COVID-19 4C mortality score in an urban United States cohort.

BACKGROUND: Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population. METHODS: This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge. RESULTS: This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89). CONCLUSIONS: Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.

Association of race/ethnicity and socioeconomic status with COVID-19 30-day mortality at a Philadelphia medical center using a retrospective cohort study.

COVID-19 has disproportionately affected low-income communities and people of color. Previous studies demonstrated that race/ethnicity and socioeconomic status (SES) are not independently correlated with COVID-19 mortality. The purpose of our study is to determine the effect of race/ethnicity and SES on COVID-19 30-day mortality in a diverse, Philadelphian population. This is a retrospective cohort study in a single-center tertiary care hospital in Philadelphia, PA. The study includes adult patients hospitalized with polymerase-chain-reaction-confirmed COVID-19 between March 1, 2020 and June 6, 2020. The primary outcome was a composite of COVID-19 death or hospice discharge within 30 days of discharge. The secondary outcome was intensive care unit (ICU) admission. The study included 426 patients: 16.7% died, 3.3% were discharged to hospice, and 20.0% were admitted to the ICU. Using multivariable analysis, race/ethnicity was not associated with the primary nor secondary outcome. In Model 4, age greater than 75 (odds ratio [OR]: 11.01; 95% confidence interval [CI]: 1.96-61.97) and renal disease (OR: 2.78; 95% CI: 1.31-5.90) were associated with higher odds of the composite primary outcome. Living in a "very-low-income area" (OR: 0.29; 95% CI: 0.12-0.71) and body mass index (BMI) 30-35 (OR: 0.24; 95% CI: 0.08-0.69) were associated with lower odds of the primary outcome. When controlling for demographics, SES, and comorbidities, race/ethnicity was not independently associated with the composite primary outcome. Very-low SES, as extrapolated from census-tract-level income data, was associated with lower odds of the composite primary outcome.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

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.

PANCYTOPENIA AND LYMPHOID ORGAN HYPERPLASIA IN A PATIENT WITH GRAVES DISEASE: RESPONSE TO ANTITHYROID DRUG THERAPY.

OBJECTIVE: In rare instances, cytopenias manifest as a complication of thyrotoxicosis. Here, we report a case of Graves disease (GD) thyrotoxicosis presenting as pancytopenia that resolved with antithyroid therapy. METHODS: A 35-year-old male presented with fever and chills following an outpatient colonoscopy. Initial blood work revealed pancytopenia. Workup included viral antigen titers, blood cultures, rheumatologic antibodies, inflammatory markers, immunocompetency, nutrient deficiency, metal toxicity, and malignancy. Bone marrow aspirate was analyzed by microscope, flow cytometry, fluorescence in situ hybridization, and genetic analysis. Computed tomography scan of the chest, abdomen, and pelvis was obtained. Thyroid labs included thyroid-stimulating hormone, total triiodothyronine, free thyroxine, thyroid-stimulating immunoglobulin, anti-thyroid peroxidase antibody, and radioiodine uptake scan. RESULTS: All workup above was non-revelatory except as follows. Imaging revealed thymic hyperplasia and splenomegaly. Thyroid labs revealed thyroid-stimulating hormone <0.02 µIU/mL (reference range is 0.30 to 5.00 µIU/mL), free thyroxine of 4.7 ng/dL (reference range is 0.7 to 1.7 ng/dL), total triiodothyronine of 191 pg/mL (reference range is 90 to 180 pg/mL), thyroid-stimulating immunoglobulin of 522% (reference range is <140%). Bone marrow biopsy was consistent with a reactive process suggesting an infectious or autoimmune process. Radioiodine uptake scan confirmed GD. He was discharged on antithyroid medication. Two-month follow-up labs revealed improved cell counts; his absolute neutrophil count was 1.94 × 109 cells/L (reference range is 1.50 to 8.00 × 109 cells/L), hemoglobin was 12.9 g/dL (reference range is 14.0 to 17.0 g/dL), and platelets were 153 × 109 cells/L (reference range is 140 to 400 × 109 cells/L). Definitive treatment was obtained with 12 mCi of 131-iodine. CONCLUSION: Pancytopenia and lymphoid organ hyperplasia (splenomegaly, thymic hyperplasia, and lymphadenopathy) have been previously reported to be associated with thyrotoxicosis secondary to GD, rarely simultaneously, and manifest from both thyrotoxic and immunologic mechanisms. After excluding alternative life-threatening pathologies, in such presentations, GD should be considered and treated if confirmed.

Circadian misalignment affects sleep and medication use before and during spaceflight.

Sleep deficiency and the use of sleep-promoting medication are prevalent during spaceflight. Operations frequently dictate work during the biological night and sleep during the biological day, which contribute to circadian misalignment. We investigated whether circadian misalignment was associated with adverse sleep outcomes before (preflight) and during spaceflight missions aboard the International Space Station (ISS). Actigraphy and photometry data for 21 astronauts were collected over 3,248 days of long-duration spaceflight on the ISS and 11 days prior to launch (n=231 days). Sleep logs, collected one out of every 3 weeks in flight and daily on Earth, were used to determine medication use and subjective ratings of sleep quality. Actigraphy and photometry data were processed using Circadian Performance Simulation Software to calculate the estimated endogenous circadian temperature minimum. Sleep episodes were classified as aligned or misaligned relative to the estimated endogenous circadian temperature minimum. Mixed-effects regression models accounting for repeated measures were computed by data collection interval (preflight, flight) and circadian alignment status. The estimated endogenous circadian temperature minimum occurred outside sleep episodes on 13% of sleep episodes during preflight and on 19% of sleep episodes during spaceflight. The mean sleep duration in low-Earth orbit on the ISS was 6.4±1.2 h during aligned and 5.4±1.4 h (P<0.01) during misaligned sleep episodes. During aligned sleep episodes, astronauts rated their sleep quality as significantly better than during misaligned sleep episodes (66.8±17.7 vs. 60.2±21.0, P<0.01). Sleep-promoting medication use was significantly higher during misaligned (24%) compared with aligned (11%) sleep episodes (P<0.01). Use of any medication was significantly higher on days when sleep episodes were misaligned (63%) compared with when sleep episodes were aligned (49%; P<0.01). Circadian misalignment is associated with sleep deficiency and increased medication use during spaceflight. These findings suggest that there is an immediate need to deploy and assess effective countermeasures to minimize circadian misalignment and consequent adverse sleep outcomes both before and during spaceflight.