Methods:
Data from the National Inpatient Sample (NIS) were used for this study. NIS database has been made possible through sponsorship of federal agency for Healthcare Research and Quality (AHRQ). The main purpose of AHRQ is to enhance the quality, appropriateness, and effectiveness of health care services (9). NIS is publicly available all-payer administrative claims-based database. National estimates of the entire US hospitalized population were calculated using the Agency for Healthcare Research and Quality sampling and weighting method. Institutional review board approval was not required for this study given the de-identified nature of the NIS and its public availability.
We analyzed the NIS data from January 2007 to December 2017 using the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) and International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes. Patients who sustained in-hospital SCA were identified by applying ICD-9-CM and ICD-10-CM codes of 99.60, 99.63 and 5A12012, respectively to any procedure field. These codes indicate utilization of cardiopulmonary resuscitation (CPR) and well representative of in-hospital SCA from administrative datasets as shown by the earlier studies (10,11). ESRD patients were then subsequently identified using ICD-9-CM and ICD-10-CM codes of 585.6 and N18.6, respectively. Patients were excluded if they were less than 18 years of age or had acute kidney injury (AKI) and prior history of renal transplantation. Baseline characteristics and outcomes were compared in ESRD patients who sustained in-hospital SCA to non-ESRD patients with in-hospital SCA. Propensity score matching was also done to balance confounding variables and outcomes were again assessed in both groups. Trends in in-patient mortality and length of stay (LOS) were also assessed. Predictors of in-patient mortality in ESRD patients after a SCA event were also analyzed.
Age, race, median income, urban/rural hospital, US region and Elixhauser comorbidities were selected for analysis. Descriptive statistics were presented as frequencies with percentages for categorical variables and as means with standard deviations or median with interquartile range as appropriate for continuous variables. Baseline characteristics were compared using a Pearson𝜒2 test for categorical variables and independent samples t-test or non-parametric tests for continuous variables as appropriate. Median LOS, median cost of stay and mortality were calculated. The median cost of stay was adjusted for inflation (in comparison to December 2017). Simple linear regression or Chi-Square test was used for trend analysis over the study years as appropriate. To mitigate the risk of confounding and selection bias, a nearest neighbor 1:1 propensity score (PS) matching was done using a caliper width of 0.2. In this way, ESRD and non-ESRD patients were well matched with respect to demographic variables as shown in table 1. Predictors of mortality in ESRD patients who sustained in-hospital SCA were analyzed using a logistic regression model. A forward stepwise entry model was used for this purpose. Initially, all variables, which were significantly associated with mortality with a p value of less than 0.05 in univariate analysis, were entered in the model from the baseline table. Subsequently only those variables are retained in the model which were associated with mortality with a p value of less than 0.10 during forward entry. A type I error of less than 0.05 was considered statistically significant. All statistical analyses were performed using statistical package for social science (SPSS) version 26 (IBM Corp) and R version 3.5.