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.

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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.

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Background Quality and patient safety are the driving forces for resilient healthcare organizations. However, the healthcare leadership is central to the role of establishing the values of quality and patient safety in the organization. This task becomes extremely challenging when the safety culture has to be built across a large hospital network. Methods A comprehensive patient safety program, the Apollo Quality Program(AQP), structured in the form of a patient-safety dashboard was used as a tool to establish and strengthen the fabric of quality and safety across a large hospital network in India. The dashboard consisted of essential patient safety parameters that were measurable and objective. This dashboard was implemented across 41-hospitals of the network and improvement data monitored. These 41-hospitals varied in size and on basis of their bed strength they were categorized into 3 groups(A,B and C). For this study, the results have been presented from 2011 to 2021. Results The overall AQP scores improved indicating holistic enhancement of patient safety across Apollo Hospitals. Sustained progress, through the last nine years, was observed for various patient safety parameters in the AQP dashboard, across 41-hospitals of the network. Conclusion AQP is an innovative methodology that incorporates all the essential tenets of patient-safety. The programme led to a progressive improvement in patient-safety over the nine-years of its implementation. The enhancement was visible through compliance to the various parameters of AQP. The AQP empowered the leadership to retrospect and analyse each of their units’ performance for patient-safety and quality in systematic manner.