Symptoms Predicting SARS-CoV-2 Test Results in Resident Physicians and Fellows in New York City

Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in physician trainees from a large healthcare system in New York. We used survey data on symptoms history and SARS-CoV-2 testing results collected retrospectively from 328 physician trainees in the Mount Sinai Health System, over the period 1 February 2020 to 31 July 2020. Prospective data on symptoms reported prior to SARS-CoV-2 test results were available from the employee health service COVID-19 registry for 186 trainees and analyzed to confirm absence of recall bias. We estimated the associations between symptoms and IgG antibody and/or reverse transcriptase polymerase chain reaction test results using Bayesian generalized linear mixed effect regression models adjusted for confounders. We identified symptoms predicting a positive SARS-CoV-2 test result using extreme gradient boosting (XGBoost). Cough, chills, fever, fatigue, myalgia, headache, shortness of breath, diarrhea, nausea/vomiting, loss of smell, loss of taste, malaise and runny nose were associated with a positive SARS-CoV-2 test result. Loss of taste, myalgia, loss of smell, cough and fever were identified as key predictors for a positive SARS-CoV-2 test result in the XGBoost model. Inclusion of sociodemographic and occupational risk factors in the model improved prediction only slightly (from AUC = 0.822 to AUC = 0.838). Loss of taste, myalgia, loss of smell, cough and fever are key predictors for symptom-based screening of SARS-CoV-2 infection in healthcare settings with remote screening and/or limited testing capacity. [ FROM AUTHOR] Copyright of COVID is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Were surgical outcomes for acute appendicitis impacted by the COVID-19 pandemic?

BACKGROUND: The COVID-19 pandemic disrupted healthcare systems throughout the world. We examine whether appendectomy outcomes in 2020 and 2021 were affected by the pandemic. METHODS: We conducted a retrospective cohort study of 30-day appendectomy outcomes using the ACS-NSQIP database from 2019 through 2021. Logistic regression and linear regression analyses were performed to create models of post-operative outcomes. RESULTS: There were no associations between the time period of surgery and death, readmission, reoperation, deep incisional SSI, organ space SSI, sepsis, septic shock, rate of complicated appendicitis, failure to wean from the ventilator, or days from admission to operation. During the first 21 months of the pandemic (April 2020 through December 2021), there was a decreased length of hospital stay (p = 0.016), increased operative time (p < 0.001), and increased likelihood of laparoscopic versus open surgery (p < 0.001) in compared to 2019. CONCLUSIONS: There were minimal differences in emergent appendectomy outcomes during the first 21 months of the pandemic when compared to 2019. Surgical systems in the US successfully adapted to the challenges presented by the COVID-19 pandemic.

Factors Associated with SARS-CoV-2 Infection in Physician Trainees in New York City during the First COVID-19 Wave.

Occupational and non-occupational risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been reported in healthcare workers (HCWs), but studies evaluating risk factors for infection among physician trainees are lacking. We aimed to identify sociodemographic, occupational, and community risk factors among physician trainees during the first wave of coronavirus disease 2019 (COVID-19) in New York City. In this retrospective study of 328 trainees at the Mount Sinai Health System in New York City, we administered a survey to assess risk factors for SARS-CoV-2 infection between 1 February and 30 June 2020. SARS-CoV-2 infection was determined by self-reported and laboratory-confirmed IgG antibody and reverse transcriptase-polymerase chain reaction test results. We used Bayesian generalized linear mixed effect regression to examine associations between hypothesized risk factors and infection odds. The cumulative incidence of infection was 20.1%. Assignment to medical-surgical units (OR, 2.51; 95% CI, 1.18-5.34), and training in emergency medicine, critical care, and anesthesiology (OR, 2.93; 95% CI, 1.24-6.92) were independently associated with infection. Caring for unfamiliar patient populations was protective (OR, 0.16; 95% CI, 0.03-0.73). Community factors were not statistically significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for physician trainees responding to the COVID-19 pandemic.