Background Many studies have been carried out in modelling COVID-19 pandemic. However, region-wise average duration of recovery from COVID-19 has not been attempted; hence, an effort has been made to estimate state-wise recovery duration of India's COVID-19 patients. Determining the recovery time in each region is intended to assist healthcare professionals in providing better care and planning of logistics. Methods This study used database provided by Kaggle, which takes data from the Ministry of Health & Family Welfare. The simple Linear Regression model between incidence, prevalence, and duration was used to assess the duration of COVID-19 disease in various Indian states. Results The fitted model suits ideal for most of the states, except for some union territories and northeastern states. The average time to recover from disease was ranging from 5 to 36 days in Indian states/union territories except for Madhya Pradesh. Tamil Nadu has an average recovery time of 7 days with an value of 0.96, followed by Odisha, Karnataka, West Bengal, Kerala and Chhattisgarh and the average recovery duration was estimated as 7, 13, 17, 11, 14 and 12 days respectively. Conclusion The average recovery from COVID-19 was ten or less days in twenty percentage of states, whereas in forty-four percentage of states/union territories had an average recovery duration between ten to twenty days. However, around twentyfour percentage of states/union territory recovered between twenty to thirty days. In the rest of Indian states/union territories, the average duration of recovery was more than thirty days.
Background: Non-traditional image markers can improve the traditional cardiovascular risk estimation, is untested in Kerala based participants. Objective: To identify the relationship between the ‘Modified CV risk’ categories with traditional and non-traditional image-based risk markers. The correlation and improvement in reclassification, achieved by pooling atherosclerotic non-traditional markers with Intermediate (≥7.5% and <20%) and High (≥20%) 10-year participants is evaluated. Method: The cross-sectional study with 594 participants has the ultrasound measurements recorded from the medical archives of clinical locations at Ernakulum district, Kerala. With carotid Intima-Media Thickness (cIMT) measurement, the Plaque (cP) complexity was computed using selected plaque characteristics to compute the carotid Total Plaque Risk Score (cTPRS) for superior risk tagging. Statistical analysis was done using RStudio, the classification accuracy was verified using the decision tree algorithm. Result: The mean age of the participants was (58.14±10.05) years. The mean cIMT was (0.956±0.302) mm, with 65.6% plaque incidence. With 94.90% variability around its mean, the Multinomial Logistic Regression model identifies cIMT and cTPRS, age, diabetics, Familial Hypercholesterolemia(FH), Hypertension treatment, presence of Rheumatoid Arthritis (RA), Chronic Kidney Disease (CKD) as significant (p#60;0.05). cIMT and cP were found significant for ‘Intermediate High’, ‘High’ and ‘Very High’ ‘Modified CV risk’ categories. However, age, diabetes, gender and use of hypertension treatment are significant for the ‘Intermediate’ ‘Modified CV risk’ category. The overall performance of the MLR model was 80.5%. The classification accuracy verified using the decision tree algorithm has 78.7% accuracy. Conclusion: The use of atherosclerotic markers shows a significant correlation suitable for a next-level reclassification of the traditional CV risk.
Background: The accuracy of Joint British Society calculator3 (JBS3) cardiovascular risk prediction may vary within Indian population, and is not yet studied using south Indian Kerala based population data. Objectives: To evaluate the cardiovascular disease (CV) risk estimation using the traditional CVD risk factors (TRF) in Kerala based population. Methods: This cross sectional study has 977 subjects aged between 30 and 80 years. The traditional CVD risk markers are recorded from the medical archives of clinical locations at Ernakulum district, in Kerala The 10 year risk categories used are low (<7.5%), intermediate (≥7.5% and <20%), and high (≥20%). The lifetime classifications low lifetime (≤39%) and high lifetime (≥40%) are used. The study was evaluated using statistical analysis. Chi-square test was done for dependent and categorical CVD risk variable comparison. Multivariate ordinal logistic regression for 10-year risk model and odds logistic regression analysis for lifetime model was used to identify significant risk variables. Results: The mean age of the study population is 52.56±11.43 years. The risk predictions has 39.1% in low, 25.0% in intermediate, and 35.9% had high 10-year risk. The low lifetime risk had 41.1% and 58.9% is high lifetime risk. Reclassifications to high lifetime are higher from intermediate 10-year risk category. The Hosmer-Lemeshow goodness-of-fit statistics indicates a good model fit. Conclusion: The risk prediction and timely intervention with appropriate therapeutic and lifestyle modification is useful in primary prevention. Avoiding short-term incidences and reclassifications to high lifetime can reduce the CVD mortality rates.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.