Statistical analysis:
Categorical outcomes were analyzed by chi-square test with continuity correction or Fisher’s exact test, wherever one or more expected cell size was less than 5. Numerical variables were tested for normality using the test for normality (Kolmogorov–Smirnov). Normally distributed variables were compared by unpaired t-test after evaluating equality of variance by Levene’s test (F test), whereas skewed variables were analyzed with the Mann–Whitney U-test. Univariate analysis was done to identify predictors of long stay. Logistic regression with backward stepwise elimination was done to identify independent predictors of long stay and a p-value of less than .05 was taken as significant. A weighted prediction score was devised using beta coefficients of independent variables derived from the logistic model (multiplied by 10 and rounded off to nearest integer)16. The discriminatory power and calibration of the prediction score was evaluated using area under Receiver Operating Characteristic (ROC) curve. The optimal cut-off for the weighted score was calculated using the ROC curve and estimated sensitivity, specificity and positive predictive value of the cut off17. The analysis was done using statistical software packages IBM-SPSS v.25 (SPSS Inc, NY, USA).