The relationship between the body mass index and in‐hospital mortality in patients admitted for sudden cardiac death in the United States

Abstract While obesity has been shown to be associated with elevated risk for Sudden Cardiac Death (SCD), studies examining its effect on outcomes in SCD victims have shown conflicting results. We aimed to describe the body mass index (BMI) distribution in a nationwide cohort of patients admitted for an out of hospital SCD (OHSCD), and the relationship between BMI and in‐hospital mortality. We drew data from the U.S. National Inpatient Sample (NIS), to identify cases of OHSCD. Patients were divided into six groups based on their BMI (underweight, normal weight, overweight, obese I, obese II, extremely obese). Socio‐demographic and clinical data were collected, mortality and length of stay were analyzed. Multivariate analysis was performed to identify predictors of mortality. Among a weighted total of 2330 hospitalizations for OHSCD in patients with documented BMI, the mean age was 62.3 ± 29 years, 52.4% were male and 62% were white. The overall rate of in‐hospital mortality was 69.3%. A U‐shaped relationship between the BMI and mortality was documented, as patients with 25 < BMI < 40 exhibited significantly lower mortality (60.7%) compared to the other BMI groups (75.2%), p < .001. BMI of 25 kg/m2 and below or 40 kg/m2 and above, were independent predictors of in‐hospital mortality in a multivariate analysis along with prior history of congestive heart failure and Deyo Comorbidity Index of ≥2. A U‐shaped relationship between the BMI and in‐hospital mortality was documented in patients hospitalized for an out of hospital sudden cardiac death in the United States in the recent years.


| INTRODUCTION
The effect of excess weight on morbidity and mortality has been acknowledged from over 2000 years ago. Hippocrates recognized that "sudden death is more common in those who are naturally fat than in the lean". 1 Over the last few decades, the prevalence of obesity in the United States has increased significantly, bearing dramatic social, clinical and economic implications. [2][3][4][5][6] Elevated body mass index (BMI) has been proven over the years as an independent risk factor for various cardio-vascular conditions such as ischemic heart disease, acute coronary syndrome, congestive heart failure, atrial and ventricular arrhythmia and sudden cardiac death. 7,8 Sudden cardiac death (SCD) is responsible for about 50% of the mortality from cardiovascular disease in the United States and other developed countries. 9,10 Different clinical parameters including age, co-morbidities, initial cardiac rhythm, and time to return of spontaneous circulation were investigated as predictors of survival in SCD. 11 While obesity has been shown to be associated with increased incidence and severity of major cardiovascular risk factors and elevated risk for SCD, 8,12,13 studies examining its effect on outcomes in SCD victims have shown conflicting results. [14][15][16][17][18][19][20] Some studies showed increased mortality in patients with BMI > 30 kg/m 2 admitted to the hospital following a sudden cardiac death. 17,18 At the same time, several other studies have implied that the "obesity paradox", described in various cardio-vascular conditions such as acute myocardial infarction (AMI) and heart failure, applies to patients admitted after a sudden cardiac death, showing lower mortality in obese patients. 14,16,18,19 We aimed at describing the BMI distribution and baseline characteristics in a nationwide cohort of patients, admitted for an out of hospital sudden cardiac death (OHSCD) in the United States, and the relationship between BMI and in-hospital mortality.

| Data source
The data were drawn from the National Inpatient Sample (NIS), the Healthcare Cost and Utilization Project (HCUP), and Agency for Healthcare Research and Quality (AHRQ) 21,22 datasets, consisting only of de-identified information; therefore, this study was deemed exempt from institutional review by the Human Research Committee.
The NIS is the largest collection of all-payer data on inpatient hospitalizations in the United States. The dataset represents an approximate 20% stratified sample of all inpatient discharges from U.S. hospitals. 23 This information includes patient-level and hospital-level factors such as patient demographic characteristics, primary and secondary diagnoses and procedures, co-morbidities, length of stay (LOS), hospital region, hospital teaching status, hospital bed size, and cost of hospitalization.
National estimates can be calculated using the patient-level and hospital-level sampling weights that are provided by the HCUP.
For the purpose of this study, we obtained data for the years 2015 (last quarter) and 2016. International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) was used from the last quarter of 2015 and thereafter for reporting diagnoses and procedures in the NIS database during the study period. For each index hospitalization, the database provides a principal discharge diagnosis and a maximum of 29 additional diagnoses, in addition to a maximum of 15 procedures. The reason we only included the data coded with ICD-10 codes is that the ICD-10 system includes individual codes for BMI values and ranges.

| Study population and variables
We identified patients 18 years of age or older with a primary diagnosis of sudden cardiac death based on ICD-10-CM codes I46.2, I46.8, or I46.9, who had one of the BMI, Z68.x, codes, among the secondary diagnoses. Of notice, these represented only the successfully resuscitated OHSCD patients, since those who were not successfully resuscitated in the field or died in the emergency departments, were not hospitalized. To have the "cleanest" possible data on patients admitted for successfully resuscitated out of hospital cardiac arrest, we avoided including patients with a secondary diagnosis of a cardiac arrest in our analysis due to the fact that these could represent patients who underwent an in-hospital sudden cardiac death or had a prior history of cardiac arrest included as a secondary diagnosis. BMI ≥40 kg/m 2 , extremely obese group. In addition to analyzing the individual BMI subgroups mentioned above, we combined the overweigh, Obese I and Obese II groups to compare the outcomes of these patients to the combined group of all the underweight, normal weight and extremely obese patients.
The following patient demographics were collected from the database: age, sex, and race. Prior comorbidities were identified by measures from the AHRQ. For the purposes of calculating Deyo-Charlson Comorbidity Index (Deyo-CCI), additional comorbidities were identified from the database using ICD-10-CM codes. Deyo-CCI is a modification of the Charlson Comorbidity Index, containing 17 comorbidity conditions with differential weights, with a total score ranging from 0 to 33.
(Detailed information on Deyo-CCI provided in the Appendix A table).
Higher Deyo-CCI scores indicate a greater burden of comorbid diseases and are associated with mortality, 1 year after admission. 24 The index has been used extensively in studies from administrative databases, with proved validity in predicting short-and long-term outcomes. 25,26 Our primary outcome in this study was in-hospital mortality. Length of stay was the secondary outcome we analyzed.

| Statistical analysis
The chi-square (χ 2 ) test and Wilcoxon Rank Sum test were used to compare categorical variables and continuous variables, respectively.
The NIS provides discharge sample weights that are calculated within each sampling stratum as the ratio of discharges in the universe to discharges in the sample. 27 We generated a weighted logistic regression model to identify independent predictors of in-hospital mortality.

| Length of stay and mortality by BMI groups
The average LOS in the hospital for the study population was 5.51 ± 0.42 days. As shown in Figure 1; the trend of the correlation between BMI and length of stay was linear in nature with longer hospital stay in obese patients, p < .001 (Figure 1).
The overall rate of in-hospital mortality during the study period was documented at 69.3%. A U-shaped relationship between the BMI and the in-hospital mortality was documented, as described in we performed an additional statistical analysis dividing the patients into these two subgroups ( Table 2).

| Predictors of in-hospital mortality
In an unadjusted analysis, we found several parameters that significantly increased the odds of in-hospital mortality ( unequivocal evidence to support preventive strategies to reduce the prevalence of obesity.
Our study should be interpreted in the context of several limitations. First, the NIS database is a retrospective administrative database that contains discharge-level records and as such is susceptible to coding errors. Second, the lack of patient identifiers in the NIS database prevented us from using other outcome variables and mortality measures such as at 30 days. We could only capture events that occurred in the same index hospitalization. The NIS database also does not include detailed information about patients' clinical characteristics, medication, blood tests etc. Therefore, we cannot rule out residual confounding of the association we observed. These limitations are counterbalanced by the real world, nationwide nature of the data, lack of selection bias as well as absence of reporting bias introduced by selective publication of results from specialized centers.

| Conclusion
A U-shaped relationship between BMI and in-hospital mortality was documented in patients hospitalized for out of hospital sudden cardiac death in the United States in the recent years. These findings support the existence of an "obesity paradox" in OHSCD, associated with improved in-hospital survival.

DATA AVAILABILITY STATEMENT
Data availability statement The data from the national database used for this study will not be made available to other researchers for purposes of reproducing the results or replicating the procedure due to restrictions on the sharing of data in the Healthcare Cost and Utilization Project (HCUP) Data Use Agreement. The National Inpatient Sample (NIS) database is publicly available for purchase and the transparent and detailed methods that are described below make it possible for anyone who wishes to do so to reproduce our results.