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Introduction: COVID-19 patients with cancer had poorer outcomes due to immunosuppression during cancer care, poor general condition, and other comorbidities. The study was conducted to present the real-world analysis of the effect of treatment interruptions on the outcomes of patients treated with radiation therapy during the first wave of the COVID-19 pandemic in a tertiary care institute in India., Materials and Methods: The study is a retrospective observational cohort study on cancer patients undergoing radiation therapy from March 2020 to January 2021. The study's primary outcome was to analyze the effect of treatment interruptions on the outcomes of patients treated with radiation therapy during the first wave of COVID-19 pandemic., Results: Between March 2020 to January 2021, 218 eligible patients undergoing radiation therapy were found for the study. Among the 218 patients, 25 patients (11.47%) were found positive for COVID-19, while 193 patients (88.53%) were negative for COVID-19. Among COVID-19-positive patients, ten patients had < 3 weeks of treatment interruption, while 15 patients had > 3 weeks of treatment interruptions. After recovering from COVID-19, treatment was resumed and completed for 15 (60.00%) of the COVID-19-positive patients. In comparison, 13 patients (52.00%) were lost to follow-up. Three of the COVID-19-positive patients died. The disease was clinically controlled in 12 (48.00%) of the COVID-19-positive patients, and the patients reported locoregional disease progression in 10 (40.00%). Among the 193 COVID-19-negative patients, 32 patients (16.58%) had treatment interruption. Twelve patients (37.50%) had treatment interruptions for less than 1 week. There was a significant difference in the delay of radiation treatment delivery by 2 weeks (11 fractions) in COVID-19-positive patients compared to only two fractions delay in COVID-19-negative patients., Conclusion: COVID-19 impacted the treatment outcomes in both COVID-19-positive and COVID-19-negative cohorts of patients. There was a longer duration of treatment interruptions in the COVID-19-positive patients, leading to fewer patients completing the radiation treatment and thereby increased locoregional disease progression. There was a significant difference in the delay in treatment between the two groups., (© 2022. The Author(s).)

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Managing of radiotherapy department in many cancer centers in India has become very challenging during the COVID-19 pandemic. A radiotherapy center has to deal with multiple problems such as long treatment duration of each patient, high caseload on each radiotherapy machine, a limited number of qualified technical staff available, and equipment maintenance. For the department's smooth running, both the patient and healthcare worker must be safe from contacting COVID-19. A robust and planned strategy is required for prevention, screening, and awareness among all. To access our preparedness and evolve by gaining from other radiotherapy centers, a study was conducted using questionnaires and responses collected from different cancer centers in India., Competing Interests: None