Characterizing the volume of surgery and post-operative complications during the COVID-19 pandemic.

PURPOSE: The COVID-19 pandemic led to unprecedented changes in volume and quality of surgery. Utilizing the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database, the current study assesses the impact of COVID-19 on surgical volume during each quarter of 2020 in comparison to 2019. Quality of surgical care during 2020 was also investigated by assessing postoperative complications, readmissions, and reoperations during 2020 in comparison to the previous 5 years. MATERIALS AND METHODS: The NSQIP database was queried from 2015 to 2020. Descriptive statistics and a chi-squared test were utilized to compare demographic variables. A seasonal autoregressive integrated moving average time-series model was fit to assess the trend and seasonality of complications from 2015 to 2019 and was used to forecast the proportion of complications in the year 2020 and compared the forecast with the actual proportions graphically. RESULTS: There were fewer patients operated on in 2020 compared to 2019, with the most dramatic drop in Q2 with a nearly 27% decrease. Patients with ASA class 3 or greater were operated on at a greater proportion in every quarter of 2020. Q2 of 2020 represented the highest proportion of any operative complications since 2015 at ~13%. Q4 of 2020 demonstrated a return to 2020 Q1 complication proportions. CONCLUSION: Surgical volume was heavily affected in 2020, particularly in Q2. Patients during Q2 of 2020 were generally of a higher ASA class and had increased operative complications. Operative volume and overall surgical complication rate normalized over the next two quarters.

Combining antibody markers for serosurveillance of SARS-CoV-2 to estimate seroprevalence and time-since-infection.

Serosurveillance is an important epidemiologic tool for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), used to estimate infection rates and the degree of population immunity. There is no general agreement on which antibody biomarker(s) should be used, especially with the rollout of vaccines globally. Here, we used random forest models to demonstrate that a single spike or receptor-binding domain (RBD) antibody was adequate for classifying prior infection, while a combination of two antibody biomarkers performed better than any single marker for estimating time-since-infection. Nucleocapsid antibodies performed worse than spike or RBD antibodies for classification, but can be useful for estimating time-since-infection, and in distinguishing infection-induced from vaccine-induced responses. Our analysis has the potential to inform the design of serosurveys for SARS-CoV-2, including decisions regarding a number of antibody biomarkers measured.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.