Study Data:

In this retrospective analysis, we obtained data from the 2016 and 2017 National Inpatient Sample (NIS), which is sponsored by the Agency for Healthcare Research and Quality as a part of the Healthcare Cost and Utilization Project (HCUP). NIS is the largest publicly available all-payer administrative database, containing data on more than 7 million hospitalizations (unweighted); when weighted, it represents about 35 million hospitalizations nationally. It provides information on clinical and resource utilization with safeguards to protect data for individual patients, physicians, and hospitals. Beginning in October 2015, the NIS started using the International Classification of Diseases, Tenth Edition, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) to reflect the implementation of ICD-10-CM/PCS by hospital systems. Using the Agency for Healthcare Research and Quality sampling and weighting method, national estimates of the entire U.S. hospitalized population were calculated. [16]

Study design:

Given the de-identified nature of the NIS data, our study was exempt from approval from the Institutional Review Board. We identified all patients (≥18 years of age) who had a discharge diagnosis of ESRD (n =2187954), using their respective ICD-10-CM/PCS codes. We divided the total sample into two groups: ESRD with LGIB (n=242075) and ESRD only (n=1945879). We identified patients with LGIB using appropriate diagnosis codes. The ICD-10-CM/PCS codes used in this study are displayed in supplementary tables 1 and 2.
For baseline characteristics, we used patient demographics (age, race, and sex), the Charlson Comorbidity Index, insurance status, hospital characteristics, and relevant comorbidities coronary artery disease (CAD) or CAD equivalent, hypertension (HTN), obesity, dyslipidemia, diabetes mellitus (D.M.), chronic lung disease, tobacco smoking, alcohol use, peripheral vascular disease , blood thinner use (anticoagulants/antithrombotics/antiplatelets) and congestive heart failure (CHF) (Table 1). Comorbidities were identified using their respective ICD-10-CM/PCS codes (supplementary table 1)).

Outcomes:

The primary outcome of interest was all-cause in-hospital mortality and predictors of mortality. The secondary outcomes included the incidence of sepsis, acute coronary syndrome, shock requiring vasopressors, acute respiratory failure, disseminated intravascular coagulation, and mechanical ventilation. Complications were identified using their respective ICD-10-CM/PCS (supplementary tables 1 and 2). We also studied the length of hospital stay (LOS) and average hospital costs.

Statistical Analysis:

We conducted all statistical analyses as per the recommended methods accounting for the intricate survey design of the NIS database. [17] Categorical data are reported as frequency and percentage, and continuous data as mean with standard deviation and standard error. Categorical variables were analyzed using Pearson’s Chi-square test, and continuous variables were analyzed using the Student’s t-test. Unadjusted odds ratios for the primary and secondary outcomes were calculated using univariate logistic regression. Multivariable logistic regression was used to adjust for potential confounders in the final model. Statistical significance was set at a two-sided p-value of <0.05. STATA/ MP 15.10 (Stata Corp LLC) was used for statistical analysis. All analyses in our study were weighted using provided discharge weights to produce national estimates. Hospital costs were inflation-adjusted for 2018 using the Consumer Price Index (provided by the U.S. Department of Labor).
To account for the differences in baseline characteristics, we used propensity score matching. [18 19] To establish a propensity-matched cohort, we used the treatment outcomes as the outcome variable and potential confounders as covariates. A 1:1 propensity score-match was performed using a caliper width of 0.1 using the psmatch2 command. Appropriate Caliper was calculated by multiplying 0.2 with a standard deviation of the logit of the propensity score. Pstest was used to generate the unmatched and matched variable. The standardized difference of <10% checked with pbalchk command suggested adequacy of the match between two groups among the measured covariates. Regression analysis was done using a generalized linear model using all covariates in succession in the final model, including patient-level discharge weights. We added covariates one by one in the model, and if the coefficient changed by more than 20%, we included that covariate in the final model. A full list of covariates used in the regression analysis and confounders in the multivariable regression model is shown in the supplemental table 2.