Selected article for: "admission ratio and logistic regression"

Author: Kansara, Nikunj; Nandapurkar, Ashok B; Maniyar, Rahul; Yadav, Arun Kumar
Title: Prediction of mortality by age and multi-morbidities among confirmed COVID-19 patients: Secondary analysis of surveillance data in Pune, Maharashtra, India.
  • Cord-id: zsj63sdf
  • Document date: 2021_1_1
  • ID: zsj63sdf
    Snippet: Maharashtra has reported the maximum number of COVID-19 cases in India. This study was conducted to describe the predictors of death among the confirmed cases of COVID-19 by carrying out a secondary analysis of surveillance data of 11,278 lab-confirmed COVID-19 cases and admitted in dedicated COVID hospitals and dedicated COVID health-care centers between April 4, 2020, and July 17, 2020, in Pune district of Maharashtra. A total of 1270 (11.2%, 95% confidence interval [CI]: 10.7-11.9) deaths out
    Document: Maharashtra has reported the maximum number of COVID-19 cases in India. This study was conducted to describe the predictors of death among the confirmed cases of COVID-19 by carrying out a secondary analysis of surveillance data of 11,278 lab-confirmed COVID-19 cases and admitted in dedicated COVID hospitals and dedicated COVID health-care centers between April 4, 2020, and July 17, 2020, in Pune district of Maharashtra. A total of 1270 (11.2%, 95% confidence interval [CI]: 10.7-11.9) deaths out of 11,278 patients were reported. Out of the 1270 deaths, 825 (65%) were male and 788 (62%) had one or more comorbidities. Logistic regression was done for predictors of death, and males (adjusted odds ratio: 1.6, 95% CI: 1.4-1.8), those with symptoms at the time of admission (adjusted odds ratio: 2.9, 95% CI: 2.5-3.4), and those with the presence of two or more comorbidities (adjusted odds ratio: 2.7, 95% CI: 2.2-3.4) were having a higher risk of death.

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