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: 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.

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.

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Background: Acute liver failure (ALF) is characterized by severe and sudden liver cell dysfunction. Baseline demographic, clinical, and biochemical factors associated with the survival of ALF patients were identified in a few selected Western studies, but very few studies have been done in India. The aim of the current study is to provide an overview of the factors associated with the survival of ALF patients and to suggest an optimum cutoff value for clinically significant parameters. Materials and Methods: The patients suffering from ALF were reviewed in this study. The factors studied were age, sex, total serum bilirubin, serum creatinine, serum albumin, urea, aspartate aminotransferase, alanine aminotransferase (ALT), and recent hepatitis E virus infection. Results: Total n = 41; Male 73%; median age 43 years. The median survival time of patients in the age group of 18–40 years was 238 days. The median survival time of patients >40 years of age was 129.10 days. Elevated serum urea and serum ALT levels at the time of admission were found to be significant predictors of mortality in patients suffering from ALF in our study. In Receiver Operator Characteristic (ROC) curve analysis, the optimum cutoff value of urea was found to be 42 mg/dL, and ALT was found to be 400 IU/L. Conclusions: Elevated serum urea and serum ALT levels at the time of admission were found to be significant predictors of mortality in patients suffering from ALF in our study. The use of these two parameters, along with King's criteria for the prognosis of ALF, can be more useful in the management of such patients in India.

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Background and purpose: Prechtl's general movement assessment is a tool to identify infants at risk of abnormal neurodevelopmental outcomes especially cerebral palsy. There is a need for further studies to establish its effectiveness in clinical practice. The main objective of this study was to find the diagnostic accuracy of prechtl's general movement assessment to predict neuromotor outcomes of preterm babies at one year corrected age when done in a standard clinical practice setting. The secondary objective was to find the inter-rater reliability of general movement assessment between two raters in a clinical setting. Methods: 116 preterm infants (55 females and 61 males) born below gestational age 35 weeks participated in this study. Prechtl's general movement assessment was done at two points of time -once between 33 to 40 weeks post menstrual age and later between 3to4 months corrected age. Babies were reassessed at 12 months (±1week) corrected age using the Infant Neurological International Battery and Alberta Infant Motor Scale to identify neuromotor dysfunction. To find the inter-rater reliability, 75 video recordings at preterm/term age and 73 recordings at fidgety age were viewed and rated independently by two raters. Results: Statistical analysis using the Fishers' exact test and Pearson's chi-square test showed significant association (p<.001) between Prechtl's General movement assessment and neuromotor outcomes at one year corrected age. General movement assessment at preterm age and fidgety age showed sensitivity of 85% (each) ,specificity of 85% & 99%, positive predictive value of 27% & 85 %, and negative predictive value of 98% & 99% respectively in predicting neuromotor outcomes. Substantial agreement was found between two trained raters. Kappa values were 0.78 and 0.72 for assessments done at preterm/ term age and three months corrected age respectively. Conclusion:The results suggest that Prechtl's general movement assessment done in routine clinical settings can reliably predict neuromotor outcomes of premature babies at one year corrected age. Thus, it has practical applications to identify premature babies at high risk of abnormal neurodevelopment in infancy.