ABSTRACT
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.)
ABSTRACT
Purpose To estimate the risk of hospital-acquired COVID-19 transmission in a population of orthopaedic trauma patients during the first wave of the pandemic. Patients and Methods This is a retrospective cohort study of 109 patients who underwent an emergent orthopedic procedure by a single orthopedic traumatologist between March 1, 2020 and May 15, 2020 during the first peak of the pandemic. After applying inclusion and exclusion criteria, a total of 82 patients (67 inpatients and 15 ambulatory) were identified for final analysis. The primary outcome measured was postoperative Coronavirus (COVID-19) status. Secondary outcome measures included length of stay and discharge disposition. Results The mean age and length of stay in the hospital group was 59.5 years (± 21.7) and 4.3 days (± 4.6), respectively, versus 47.9 years (± 9.8) in the ambulatory group. 7.3% (6/82) of the inpatients subsequently tested or screened positive for COVID-19 at 2 weeks post-operatively, compared to 0/15 ambulatory patients (P=0.58). Of the 6 inpatients who tested positive, 4 (66.7%) were discharged to a rehabilitation center. Diabetes (P=0.05), hypertension (P=0.02), and congestive heart failure (P=0.005) were associated with transmission. Conclusion In this analysis, there was a nosocomial transmission rate of 7% compared to zero in the ambulatory surgery center, however this was not found to be statistically significant. This data supports the use of precautions such as frequent screening, hand washing, and masks to reduce transmission when COVID-19 rates are high. There is a lower risk of nosocomial COVID-19 transmission for patients treated as an outpatient and elective surgical procedures may be safer in this setting.