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.

SARS-CoV-2 infection after COVID-19 immunization in healthcare workers: A retrospective, pilot study.

BACKGROUND & OBJECTIVES: COVID-19 pandemic has taken a significant toll on the health of the people across the globe, including India, and is still continuing with its rapidly evolving second wave. Although the COVID-19 vaccines effectively prevent infection, yet some cases of infections have been reported post-vaccination, raising concerns about their efficacy and safety. This study was aimed to investigate the occurrence of SARS-CoV-2 infection among the symptomatic-vaccinated healthcare workers (HCWs) and to analyze the severity of their disease. METHODS: This retrospective study was done at a single multispecialty hospital, on the HCWs who have had COVID-19 vaccination, during the initial period of the vaccination drive (January 16 to April 24, 2021). The symptomatic post-vaccination infections in these HCWs were evaluated. RESULTS: Eighty five of 3235 (2.63%) vaccinated HCWs acquired the SARS-CoV-2 infection after vaccination, during the study period. Of these, 65 (76.5%) were fully vaccinated (FV), and 20 (23.5%) were partially vaccinated (PV) and the protection rate of vaccination was 97.4 per cent [95 % confidence interval (CI)=96.8-97.9]. The odds ratio of acquiring infection among females was higher at 1.84 (95% CI=1.17-2.88; P=0.008) mainly because of their greater involvement in the patient care as nursing personnel. The chances of infections were the highest in the medical and nursing personnel, as compared to paramedical, administrative and supporting staff (P<0.001). Among the HCWs studied, only two required hospitalization (0.06%), none needed an intensive care unit (ICU) admission and there were no deaths. INTERPRETATION & CONCLUSIONS: The COVID-19 infection after vaccination occurred in a smaller subset (2.63%) of HCWs, in both PV and the FV groups. These infections were primarily minor and did not lead to severe disease. Overall, the vaccination with ChAdOx1 nCoV-19 vaccine (recombinant) prevented SARS-CoV-2 severe infection in the HCWs, leading to ICU admission and deaths.

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.