a novel coronavirus disease (COVID-19) emerged in Wuhan, China, and spread globally, resulting in the first World Health Organization (WHO)-classified pandemic in over a decade. 1 As of April 2020, the United States has the most confirmed COVID-19 cases worldwide, but public health interventions and testing availability have varied across the country. 2Santa Clara County, California, has a population density of approximately 1,400 people per square mile and a high median household income ($116,178/year) and is part of the San Francisco Bay Area. 3 It was one of the first counties where COVID-19 was detected in the United States, with its first case (on January 31, 2020) being the seventh case nationwide. 4 The San Francisco Bay Area was also the first region in the United States to implement "shelter in place" orders on March 16, 2020, which consisted of widespread school and business closures and social distancing measures including prohibition of all nonessential travel and gatherings. 4 The objective of this study was to describe the demographics, clinical characteristics, and outcomes of emergency department (ED) patients who tested positive for COVID-19 at a medical center in Santa Clara County with the aim of identifying clinical patterns and assessing possible effects of local public health measures.This was an observational, cross-sectional study of ED patients with a laboratory-confirmed diagnosis of COVID-19 at a single academic hospital (Stanford Health Care). Our ED is a tertiary care, Level I trauma center that treated approximately 56,000 adults and 23,000 children in 2019. The hospital has 86 intensive care unit (ICU) beds.A novel polymerase chain reaction (PCR) laboratory test to diagnose COVID-19 was developed at the Stanford Clinical Virology Laboratory and approved for clinical use by the Food and Drug Administration (FDA). It utilizes a nasopharyngeal swab specimen that is collected by a health care provider, preserved in viral transport medium, and tested via reverse-transcriptase-PCR (RT-PCR). The test screens for the presence of RNA encoding an envelope protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus of COVID-19, and is followed by a confirmatory test for the SARS-CoV-2 RNA polymerase. 5
Background In India, acute respiratory illnesses, including pneumonia, are the leading cause of early childhood death. Emergency medical services are a critical component of India's public health infrastructure; however, literature on the prehospital care of pediatric patients in lowand middle-income countries is minimal. The aim of this study is to describe the demographic and clinical characteristics associated with 30-day mortality among a cohort of pediatric patients transported via ambulance in India with an acute respiratory complaint. Methods Pediatric patients less than 18 years of age using ambulance services in one of seven states in India, with a chief complaint of "shortness of breath", or a "fever" with associated "difficulty breathing" or "cough", were enrolled prospectively. Patients were excluded if evidence of choking, trauma or fire-related injury, patient was absent on ambulance arrival, or refused transport. Primary exposures included demographic, environmental, and clinical indicators, including hypoxemia and respiratory distress. The primary outcome was 7 and 30-day mortality. Multivariable logistic regression, stratified by transport type, was constructed to estimate associations between demographic and clinical predictors of mortality. Results A total of 1443 patients were enrolled during the study period: 981 (68.5%) were transported from the field, and 452 (31.5%) were interfacility transports. Thirty-day response was 83.4% (N = 1222). The median age of all patients was 2 years (IQR: 0.17-10); 93.9% (N = 1347) of patients lived on family incomes below the poverty level; and 54.1% (N = 706) were male. Cumulative mortality at 2, 7, and 30-days was 5.2%, 7.1%, and 7.7%, respectively; with 94 deaths by 30 days. Thirty-day mortality was greatest among those 0-28 days (N = 38,17%);
ObjectivesEstimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network.Setting99 emergency departments (EDs) across the USA.ParticipantsPatients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised.Primary outcomeDeath within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component.ResultsOf 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%).ConclusionsWe independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.
Background: Traumatic injury continues to be a leading cause of mortality and morbidity in low-income and middle-income countries (LMIC). The World Health Organization has called for a strengthening of prehospital care in order to improve outcomes from trauma. In this study we sought to profile traumatic injury seen in the prehospital setting in India and identify predictors of mortality in this patient population. Methods: We conducted a prospective observational study of a convenience sample of patients using a single emergency medical services (EMS) system for traumatic injuries across seven states in India from November 2015 through January 2016. Any patient with a chief complaints indicative of a traumatic injury was eligible for enrollment. Our primary outcome was 30-day mortality. Results: We enrolled 2905 patients. Follow-up rates were 76% at 2 days, 70% at 7 days, and 70% at 30 days. The median age was 36 years (IQR: 25-50) and were predominately male (72%, N = 2088), of lower economic status (97%, N = 2805 used a government issued ration card) and were from rural or tribal areas (74%, N = 2162). Cumulative mortality at 2, 7, and 30 days, was 3%, 4%, and 4% respectively. Predictors of 30-day mortality were prehospital abnormal mental status (OR 7.5 (95% CI: 4-14)), presence of hypoxia or hypotension (OR 4.0 (95% CI: 2.2-7)), on-scene mobility (OR 2.8 (95% CI: 1.3-6)), and multisystem injury inclusive of head injury (OR 2.3 (95% CI: 1.1-5)). Conclusions: EMS in an LMIC can transport trauma patients from poor and rural areas that traditionally struggle to access timely trauma care to facilities in a timeframe consistent with current international recommendations. Information readily obtained by EMTs predicts 30-day mortality within this population and could be utilized for triaging patients with the potential to reduce morbidity and mortality.
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