Stata version 14.2 for windows

Continuous variables are present as mean ± SD and compared using Student t-test. Categorical variables are present as absolute values (percentage) and compared using chi-square statistics. Specific Age, gender, and race/ethnicity categories reference values were obtained for the entire study group, NRF, and RF groups. Also, multivariate linear regression models adjusted for demographic characteristics were performed to evaluate differences in LA indices by age, gender, and race/ethnicity and CV risk factors components. All analyses were conducted using STATA 14.2 version for Windows (StataCorp LP, College Station, TX). All tests were two-tailed and considered statistically significant when p-value ≤ 0.05.

Analyses were conducted using Stata version 14.2 for Windows (College Station, TX, USA). Descriptive statistics were used to describe the population of potentially eligible patients and the simulation results. Continuous variables were tested for normality. Chi-square, Fisher’s exact, and Kruskal-Wallis tests were used to test for differences across study years and models of LEMS cut-offs. Imbalance in the randomization lists was investigated and reported as the absolute difference between the proportion of participants assigned to either intervention [23 (link)]. To describe participant flow, the intervention start was defined as day 34 (middle of the baseline assessment window) and intervention end as day 91 (middle of three-month follow-up window). Multivariable logistic regression was used to identify predictors of early discharge from the hospital within the TASCI intervention time period (< 81 days after SCI diagnosis).

Descriptive statistics were utilized to summarize participant characteristics according to DASH score categories. Data are presented as mean ± SD for continuous variables and number (percentage) for categorical variables. Differences in baseline participant characteristics by quintile of DASH score were tested with Student’s t-test (continuous variables with normal distribution) or chi-square test (categorical variables). Log rank tests were used to compare the incidence of HF by DASH score quintile. Subsequently, Cox proportional hazards models were used to analyze the association of dietary pattern with incident HF. For all regression models, adjustment was made for demographic factors (age, sex, study site, educational level, and race/ethnicity), traditional CVD risk factors (systolic BP and diastolic BP, use of antihypertensive medications, lipids, diabetes mellitus, smoking, alcohol intake, and exercise), BMI, and eGFR. Four sets of models were used: Model 1: gender and age; Model 2: plus race, education, energy, cigarette use, site, exercise, energy and BMI; Model 3 adjusted for Model 2 adjusted for HTN, diabetes, high-density lipoprotein (known disease associations with HF); and Model 4 adjusted for Model 3 plus EF, LV mass (pathophysiology of HF). Analyses were performed using Stata, version 14.2 for Windows.