Symptomatic post-vaccination SARS-CoV-2 infections in healthcare workers- A multicenter cohort study.

BACKGROUND AND AIMS: During the COVID-19 vaccination program in India, the healthcare workers were given the first priority. There are concerns regarding the occurrence of breakthrough infections after vaccination. We aimed to investigate the effictiveness of COVID-19 vaccines in preventing and reducing the severity of post-vaccination infections. METHODS: This retrospective test-negative case-control study examined 28342 vaccinated healthcare workers for symptomatic SARS-CoV-2 infections between January 16 to June 15, 2021. They worked at 43 Apollo Group hospitals in 24 Indian cities. These cohorts received either ChAdOx nCOV-19 (Recombinant) or the whole virion inactivated Vero cell vaccines. Various demographic, vaccination related and clinical parameters were evaluated. RESULTS: Symptomatic symptomatic post-vaccination infections occurred in a small number of vaccinated cohorts (5.07%, p < 0.001), and these were predominantly mild and did not result in hospitalization (p < 0.0001), or death. Both vaccines provided similar protection, with symptomatic infections in 5.11% and 4.58%, following ChAdOx nCOV-19 (Recombinant) and the whole virion inactivated Vero cell vaccines, respectively (p < 0.001). Nursing and Clinical staff and cohorts >50 years contracted more infections (p < 0.001). Two-dose vaccination has significantly lower odds of developing symptomatic infection (0.83, 95%CI - 0.72 to 0.97). Maximum infections occurred during the peak of the second COVID-19 wave from mid-April to May 2021 (p < 0.001). No significant difference existed in the infection between sex, vaccine type, and the number of vaccine doses received (p ≥ 0.05). CONCLUSION: Symptomatic infections occurred in a small percentage of healthcare workers after COVID vaccination. Vaccination protected them from not only infection but also severe disease.

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired-8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient's initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. 'Time to event' using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired-8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is  http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52-3.53], Male Gender (HR 1.72, 95% CI 1.06-2.85), Respiratory Distress (HR 1.79, 95% CI 1.32-2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83-1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72-5.38), Coronary Artery Disease (HR 1.56, 95% CI - 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03-2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87-4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23-2.32), INR (HR 1.71, 95% CI 1.31-2.13), LDH (HR 4.02, 95% CI 2.66-6.07) and Ferritin (HR 2.48, 95% CI 1.32-4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on 'time to event' at the admission, accurately predicting patient outcomes.

A Retrospective Observational Study to Determine the Early Predictors of In-hospital Mortality at Admission with COVID-19.

INTRODUCTION: Coronavirus disease-2019 (COVID-19) systemic illness caused by a novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has been spreading across the world. The objective of this study is to identify the clinical and laboratory variables as predictors of in-hospital death at the time of admission in a tertiary care hospital in India. MATERIALS AND METHODS: Demographic profile, clinical, and laboratory variables of 425 patients admitted from April to June 2020 with symptoms and laboratory-confirmed diagnosis through real-time polymerase chain reaction (RT-PCR) were studied. Descriptive statistics, an association of these variables, logistic regression, and CART models were developed to identify early predictors of in-hospital death. RESULTS: Twenty-two patients (5.17%) had expired in course of their hospital stay. The median age [interquartile range (IQR)] of the patients admitted was 49 years (21-77 years). Gender distribution was male - 73.38% (mortality rate 5.83%) and female-26.62% (mortality rate 3.34%). The study shows higher association for age (>47 years) [odds ratio (OR) 4.52], male gender (OR 1.78), shortness of breath (OR 2.02), oxygen saturation <93% (OR 9.32), respiratory rate >24 (OR 5.31), comorbidities like diabetes (OR 2.70), hypertension (OR 2.12), and coronary artery disease (OR 3.18) toward overall mortality. The significant associations in laboratory variables include lymphopenia (<12%) (OR 8.74), C-reactive protein (CRP) (OR 1.99), ferritin (OR 3.18), and lactate dehydrogenase (LDH) (OR 3.37). Using this statistically significant 16 clinical and laboratory variables, the logistic regression model had an area under receiver operating characteristic (ROC) curve of 0.86 (train) and 0.75 (test). CONCLUSION: Age above 47 years, associated with comorbidities like hypertension and diabetes, with oxygen saturation below 93%, tachycardia, and deranged laboratory variables like lymphopenia and raised CRP, LDH, and ferritin are important predictors of in-hospital mortality. HOW TO CITE THIS ARTICLE: Jain AC, Kansal S, Sardana R, Bali RK, Kar S, Chawla R. A Retrospective Observational Study to Determine the Early Predictors of In-hospital Mortality at Admission with COVID-19. Indian J Crit Care Med 2020;24(12):1174-1179.