Benefits of bariatric surgery in patients with acute ischemic stroke—a national population-based study

Surgery for Obesity and Related Diseases - (2019) 1–9

Original article

Benefits of bariatric surgery in patients with acute ischemic stroke—a national population-based study Hedong Han, Ph.D.a, Lihong Chen, Ph.D.a,b, Meng Wang, M.S.a, Xin Wei, M.D.c, Yiming Ruan, M.S.a, Jia He, M.D., Ph.D.a,d,* b

a Department of Health Statistics, Second Military Medical University, Shanghai, China Department of Hospital Administration, Shanghai Jiao Tong University School of Medicine, Shanghai, China c Mount Sinai St. Luke’s and West Medical Center, New York, New York d Tongji University School of Medicine, Shanghai, China

Received 8 July 2019; accepted 17 August 2019

Abstract

Background: To explore the effects of prior bariatric surgery (prior BS) on clinical outcomes in hospitalized patients with acute ischemic stroke (AIS). Setting: Inpatient hospital admissions from the Nationwide Inpatient Sample. Methods: We identified hospitalized patients with a primary diagnosis of AIS between 2006 and 2014. The primary endpoint was in-hospital mortality. Secondary endpoints included disability status, poststroke complications, and healthcare utilization indicators, including length of hospital stay and total cost. Multivariate regression analyses were performed after adjusting for potential confounders to compare outcomes between patients with prior BS and those with morbid obesity. Results: Of 24,534 (weighted 121,578) patients with AIS, 1654 (weighted 8154) reported a history of BS, and the rest were diagnosed with morbid obesity. Rates of prior BS and morbid obesity in AIS have significantly increased over the study period. Patients with prior BS were younger and more likely to be white, female, with fewer co-morbidities and poststroke complications, and higher rates of thrombolysis treatment. Multivariate regression analyses revealed that prior BS with body mass index ,35 kg/m2 was associated with lower mortality (odds ratio [OR] .58, 95% confidence interval [CI] .37–.90), lower odds of moderate-to-severe disability (OR .64, 95% CI .56–.73), acute respiratory failure (OR .63, 95%CI .45–.87), sepsis (OR .50, 95% CI .26–.96), acute kidney failure (OR .67, 95% CI .52–.87), 13% shorter hospitalization, and 6% lower total hospital costs. Conclusions: Among hospitalized patients with AIS, prior BS with body mass index ,35 kg/m2 is associated with lower in-hospital mortality, fewer poststroke complications, improved disability status, and better healthcare utilization. (Surg Obes Relat Dis 2019;-:1–9.) Ó 2019 Published by Elsevier Inc. on behalf of American Society for Bariatric Surgery.

Key words:

Bariatric surgery; Ischemic stroke; Mortality; Obesity

Hedong Han, Lihong Chen, and Meng Wang contributed equally to the study. This study was funded by a grant from the Fourth Round of Shanghai Three-year Action Plan on Public Health Discipline and Talent Program: Evidence-based Public Health and Health Economics (No. 15 GWZK0901).

* Correspondence: Professor Jia He, M.D., Ph.D., Department of Health Statistics, Second Military Medical University, No. 800 Xiangyin Road, Shanghai 200433, China; Tongji University School of Medicine, Shanghai 200092, China. E-mail address: [email protected] (J. He).

https://doi.org/10.1016/j.soard.2019.08.020 1550-7289/Ó 2019 Published by Elsevier Inc. on behalf of American Society for Bariatric Surgery.

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Obesity (defined as body mass index [BMI] 30 kg/m2) is a major global health challenge [1,2]. Approximately 70.7% of the population in the United States is overweight or diagnosed with obesity [3]. Obesity is associated with co-morbidities including type 2 diabetes, dyslipidemia, and hypertension—all these being risk factors for cardiovascular and cerebrovascular morbidity and mortality4. Stroke is the fifth leading cause of death and a major cause of disability in the elderly population in the US [4,5]. Previous studies evaluating the association between obesity and stroke-related mortality risk show inconsistent results. A few studies report that higher BMI offers no survival advantage [6,7], whereas others show a better functional status in overweight patients or those with obesity than in individuals with normal BMI [6,8–16]. This contradictory phenomenon (the obesity paradox) occurs in acute myocardial infarction [17], heart failure [18], and in those undergoing coronary artery bypass grafting [19]; however, its occurrence in patients with AIS remains unclear and needs to be weighed against prior research showing improved metabolic profiles and decreased cardiovascular events in individuals with obesity who undergo a successful weight reduction strategy [20]. Morbid obesity remains refractory to conservative weight-reduction strategies, including lifestyle modification and behavioral and pharmocotherapeutic intervention [21]. Bariatric surgery (BS) has demonstrated good efficacy and reliability as a weight-reduction strategy that could significantly reduce cardiovascular morbidity and mortality in individuals with morbid obesity [22]. BS-related benefits occurred in 61.7% of patients with hypertension, 70.0% with hyperlipidemia, and 86.0% with diabetes [23]. Previous reports have demonstrated an increased mortality risk in AIS patients with pre- or poststroke weight loss [24,25], which raises questions regarding the relationship between BS-induced weight loss and prognosis in AIS patients. To date, no population-based study has demonstrated the relationship between prior BS-induced weight loss and clinical outcomes in patients with AIS. With growing interest in the cardiovascular benefits of BS and its increasing rate of application, we investigated the BS-related outcomes in patients with AIS using the National Inpatient Sample (NIS). Methods Data source This analysis was based on inpatient discharge data obtained from the NIS of the Healthcare Cost and Utilization Project between 2006 and 2014 [26]. This database contains demographic data (age, sex, and race), socioeconomic data (type of admission, type of insurance, and income), up to 30 diagnoses, 15 inpatient procedures, hospital-related information (hospital setting, location, and bed size), and clinical outcomes (death, length of stay [LOS], and total charges).

Patients and outcomes Adults (aged 18 yr) with a primary diagnosis of AIS were identified using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.01, 434.11, 434.91, and 436. Additionally, secondary diagnoses were used to define prior BS (V45.86) and morbid obesity (V85.4, V85.41, V85.42, V85.43, V85.44, and 278.01). The exposed group was defined as AIS patients with prior BS and the unexposed group was defined as AIS patients with morbid obesity. Medical complications in hospitalized patients with AIS were defined based on ICD-9-CM secondary diagnosis codes, including pulmonary embolism (415.1), pneumonia (481, 482.8, 482.3 and 486), acute respiratory failure (518.81), sepsis (995.91, 996.64, 038, 995.92, and 999.3), acute kidney failure (584), acute myocardial infarction (410), gastrointestinal hemorrhage (578), and hemorrhagic stroke (431, 432.0, 432.1 and 432.9). We further identified in-hospital procedures using ICD-9-CM procedure codes, such as cerebral angiography (88.41), craniotomy and craniectomy (01.2), mechanical ventilation (96.72), transfusion (99.04), and thrombolysis (99.10). The Charlson Comorbidity Index was calculated for each admission to assess the severity of co-morbidities, and higher Charlson Comorbidity Index scores corresponded to a greater burden of comorbid diseases. The primary outcome of interest was all-cause in-hospital mortality. Secondary outcomes included temporal trends for rates of prior BS and morbid obesity in AIS patients, degree of disability after AIS onset, incidence of medical complications, and resource utilization measures (LOS and total cost). Based on a previous study by Qureshi et al. [27], we categorized discharge status in the NIS database into 2 groups: Routine discharge was defined as patients who were discharged with minimal-to-no disability, and any other discharge status was defined as moderate-to-severe disability. Total charges in the database were adjusted to cost using the cost-to-charge ratio and the Consumer Price Index to account for inflation. Exclusion criteria were as follows: (1) age ,18 years, (2) AIS patients who were electively admitted, (3) pregnancy, (4) enrollment in clinical trials, (5) patients transferred from other hospitals (to prevent repeated counting of the same patient), and (6) concomitant diagnosis of traumatic brain injury or undergoing rehabilitation. Statistical analysis We calculated the median, upper, and lower quartiles for continuous variables showing a skewed distribution and used the rank sum test to compare differences in median values. The c2 test was used to compare differences between categorical variables. To test temporal trends of changing rates in prior BS and morbid obesity among patients with

Hedong Han et al. / Surgery for Obesity and Related Diseases - (2019) 1–9

AIS between 2006 and 2014, we used Joinpoint model to calculate the annual percent change and average annual percent change (AAPC) and examined the statistical significance. We constructed 3 logistic models for dichotomous outcomes, including in-hospital mortality and disability status. Model 1 included all patients and all the baseline variables recorded upon admission to investigate the association between prior BS and the risk of in-hospital mortality in patients with AIS. Model 2 included patients who were discharged alive; logistic regression was used to identify the association between prior BS and the odds of moderate-to-severe disability in patients with AIS. Model 3 included all patients, all baseline variables recorded upon admission, and additional in-hospital procedures that may have affected the risk of all-cause in-hospital mortality. Variables that showed P , .05 in univariate analysis were subjected to multivariate analysis. Total cost and LOS showed a right skewed distribution, and we used logtransformations for continuous dependent variables before performing multivariable linear models. Additionally, unadjusted and age- and sex-adjusted effects were recorded for all the aforementioned analyses. We repeated the analyses based on BMI categories (,35 and 35 kg/m2) after BS to examine whether the effects varied between patients who underwent prior BS with and without successful weight loss. To reduce potential bias caused by BMI inequalities between patients with and without prior BS, we conducted sensitivity analysis adjusting for BMI category (no obesity, mild/moderate obesity, and morbid obesity).

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Survey design and weighting were applied in all analyses to obtain national-level estimates. Statistical significance was defined as a P value , .05. The Joinpoint software version 4.5.0.1 developed by the National Cancer Institute (Bethesda, MD, USA) was used for trend analyses. All statistical analyses were performed using the SAS software version 9.4 (SAS Institute, Cary, NC, USA). Results A total of 26,461 patients with a primary diagnosis of AIS were identified between 2006 and 2014. After reviewing the exclusion criteria, 1927 patients were excluded, and 24,534 patients (weighted 121,578) were included in the final analysis (Fig. 1), among whom 1654 (weighted 8154) underwent BS previously; the rest were diagnosed with morbid obesity. In patients with AIS, the rates of prior BS (from .02% in 2006 to .39% in 2014) and morbid obesity (from 1.34% in 2006 to 4.84% in 2014) significantly increased over the study period. One joinpoint was identified for prior BS, and the AAPC was 36.2% (95% confidence interval [CI] 21.6%–52.5%; Fig. 2). Similarly, 1 joinpoint was detected for morbid obesity, and the AAPC was 18.2% (95% CI 14.5%–22.1%). Univariate analysis indicated that patients with prior BS were younger and more likely to be white, female, with fewer co-morbidities and poststroke complications, and a higher rate of thrombolysis treatment (8.12% versus 6.32%, P 5 .0109; Table 1). Statistically significant intergroup differences were also observed in type of insurance,

National Inpatient Sample Database (2006-2014)

Primary diagnosis of admission: Acute ischemic stroke (N=26461)

Exclusions 854 patients died during hospitalization

Acute ischemic stroke (N=24534) Prior-BS (exposed: n=1654); Morbid obesity (unexposed: n=22880)

23680 alive patients in the disability analysis

Stratified analysis based on BMI after bariatric surgery

Exclusions: 1) Age<18 years: 10 2) Admitted electively: 1047 3) Transfer from other hospitals 4) Pregnancy: 3 5) Clinical trials 24 6) Traumatic brain injury: 24 7) Rehabilitation care: 0

Prior-BS with BMI≤35kg/m2: n=1241 Prior-BS with BMI>35kg/m2: n=413

Fig. 1. Diagram of design in the study.

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Fig. 2. Trend analysis for rates of prior bariatric surgery and morbid obesity in patients with a primary diagnosis of acute ischemic stroke.

income, hospital region, and rate of mechanical ventilation. In-hospital mortality was 1.61% in the prior BS and 3.63% in the morbid obesity group. Total cost (median cost $8345 versus $8781) and LOS (median LOS, 3 versus 3 d) were significantly lower in patients with prior BS. Multivariate logistic regression analysis after adjusting for potential confounders revealed that compared with morbid obesity, prior BS was associated with decreased mortality (odds ratio [OR] .53, 95% CI .36–.78, P 5 .0016; Table 2), lower odds of moderate-to-severe disability (OR .69, 95% CI .61–.77, P , .0001), and lower incidence of acute respiratory failure (OR .63, 95% CI .47–.84, P 5 .0017), sepsis (OR .53, 95% CI .31–.92, P 5 .0252), and acute kidney failure (OR .67, 95% CI .53–.85, P 5 .0007; Table 3). No statistically significant intergroup differences were detected in rates of pulmonary embolism, pneumonia, acute myocardial infarction, gastrointestinal hemorrhage, and hemorrhagic stroke. Secondary outcomes with regard to health resource utilization showed that prior BS was associated with 5% lower hospitalization cost (coefficient 5 2.05, 95% CI 2.08 to 2.01, P 5 .0082) and 12% shorter LOS (coefficient 5 2.12, 95% CI 2.15 to 2.08, P , .0001). Results of unadjusted, age- and sex-adjusted, and model 3 analyses in consideration of inpatient procedures, including mechanical ventilation and thrombolysis treatment, were similar to results in the primary analyses (Supplemental Table 1). Sensitivity analysis adjusting for BMI category yielded comparable results (Supplemental Table 2).

According to subgroup analysis based on BMI after BS, 1241 (weighted 6129) patients had BMI ,35 kg/m2 and 413 (weighted 2026) had BMI 35 kg/m2. Multivariate models revealed that prior BS with BMI ,35 kg/m2 was associated with lower risk of in-hospital mortality (OR .58, 95% CI .37–.90, P 5 .0144) and moderate-to-severe disability (OR .64, 95% CI .56–.73, P , .0001); a lower incidence of acute respiratory failure (OR .63, 95% CI .45–.87, P 5 .0057), sepsis (OR .50, 95% CI .26–.96, P 5 .0379), and acute kidney failure (OR .67, 95% CI .52–.87, P 5 .0026); 6% lower hospitalization cost (coefficient 5 2.05, 95% CI 2.10 to 2.02, P 5 .0024); and 13% shorter LOS (coefficient 5 2.13, 95% CI 2.18 to 2.09, P , .0001). However, among prior BS patients with BMI 35 kg/m2, only in-hospital mortality was lower (OR .39, 95% CI .16–.95, P 5 .0385). Discussion The present study was the first to demonstrate that prior BS was related to decreased mortality, lower odds of moderate-to-severe disability, and lower incidence of poststroke complications in patients with AIS using the largest inpatient database. Additionally, improved healthcare resource utilization, including lower total hospitalization cost and shorter LOS, was observed in the prior BS group. Unadjusted and age- and sex-adjusted analyses and analysis using a model that takes inpatient procedures into consideration validated the robustness of results in the primary

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Table 1 Univariate analysis of characteristics comparing acute ischemic stroke patients with prior BS and morbid obesity: analysis of National Inpatient Sample from 2006 to 2014 Variables Mean age (SE) Age, yr 18–64 65–79 80 Female Race White Black Hispanic Other Missing Type of insurance Medicare Medicaid Private Self-pay/other Income quartile 0w25% 25%w50% 50%w75% 75%w100% Hospital setting Rural Urban nonteaching Urban teaching Hospital region Northeast Midwest South West Hospital bed size Small Medium Large Weekend admission Charlson index score 0–1 2 3 4 5 Medical complications Pulmonary embolism Pneumonia Acute respiratory failure Sepsis Acute kidney failure Acute myocardial infarction Gastrointestinal hemorrhage Hemorrhage stroke In-hospital procedures Cerebral angiography Craniotomy and craniectomy Mechanical ventilation Transfusion Thrombolysis

Acute ischemic stroke (N 5 24,534) Prior BS (N 5 1654, %)

Morbid obesity (N 5 22,880, %)

P value

58.98 (.28)

60.94 (.10)

,.0001

1129 (68.32) 475 (28.64) 50 (3.04) 1204 (72.89)

13566 (59.25) 7693 (33.67) 1621 (7.08) 14046 (61.41)

,.0001 ,.0001

1125 (67.96) 226 (13.79) 83 (4.98) 52 (3.11) 168 (10.16)

13320 (58.29) 5130 (22.46) 1558 (6.77) 704 (3.08) 2168 (9.40)

,.0001

747 (45.29) 115 (7.00) 674 (40.63) 117 (7.08)

11313 (49.60) 2838 (12.39) 6304 (27.56) 2387 (10.44)

,.0001

422 (25.91) 448 (27.47) 417 (25.41) 347 (21.22)

7760 (34.63) 6256 (27.88) 5091 (22.64) 3337 (14.85)

,.0001

148 (8.98) 645 (38.94) 851 (52.08)

2172 (9.56) 9218 (40.49) 11365 (49.95)

.2987

276 (16.98) 389 (23.69) 631 (38.10) 358 (21.23)

3312 (14.70) 5423 (23.83) 10278 (44.80) 3867 (16.67)

,.0001

179 (10.62) 385 (23.47) 1080 (65.91) 438 (26.52)

2454 (10.51) 5783 (25.50) 14518 (64.00) 6032 (26.38)

.2245 .8963

429 (25.91) 442 (26.74) 339 (20.53) 223 (13.46) 221 (13.36)

3018 (13.17) 5043 (22.00) 4713 (20.60) 4348 (19.03) 5758 (25.20)

,.0001

7 (.43) 23 (1.43) 49 (2.97) 13 (.78) 90 (5.51) 19 (1.16) 13 (.80) 35 (2.17)

166 (.73) 577 (2.53) 1305 (5.74) 426 (1.87) 2532 (11.11) 449 (1.96) 147 (.64) 478 (2.09)

.0788 .0004 ,.0001 ,.0001 ,.0001 .0044 .4916 .8513

129 (7.95) 6 (.37) 15 (.89) 43 (2.57) 134 (8.12)

1521 (6.67) 104 (.46) 599 (2.64) 412 (1.80) 1442 (6.32)

.0655 .5954 ,.0001 .0530 .0109 (continued on next page )

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Table 1 (continued ) Acute ischemic stroke (N 5 24,534)

Variables

Discharge disposition None to minimal disability Moderate to severe disability In-hospital mortality Healthcare utilization Total cost Length of stay, d

Prior BS (N 5 1654, %)

Morbid obesity (N 5 22,880, %)

P value

918 (55.33) 710 (43.06) 26 (1.61)

9248 (40.32) 12804 (56.05) 828 (3.63)

.0001

8345 (5741–12,888) 3 (2–5)

8781 (5969–14,389) 3 (2–6)

.0002 ,.0001

BS 5 bariatric surgery; SE 5 standard error. Total cost and length of stay were expressed as median (interquartile).

analysis. Subgroup analyses revealed that the beneficial effects of prior BS in AIS patients were BMI independent and more pronounced in those with BMI ,35 kg/m2, which supported the finding of improved clinical outcomes observed in patients with BS-induced weight loss. Leading this discussion are the rapidly increasing prevalence of morbid obesity and controversies regarding the obesity paradox in AIS patients. Over the last decade, the rate of clinically severe or morbid obesity has increased by .70% in the United States, with an estimated 15.5 million adults diagnosed with morbid obesity in 2010 [28]. The AAPC for the rate of morbid obesity in hospitalized patients with AIS is 18.2% over a 9-year period from 2006 to 2014 in the present study, which indicates the significant increasing prevalence of morbid obesity in AIS patients. No previous research has reported this trend. Although obesity has long been established as a modifiable factor for AIS incidence independent of age, hypertension, diabetes, and other cardiovascular risk factors [29,30], the effects of higher BMI on prognosis after AIS produce a counterintuitive phenomenon termed the obesity paradox, which could be attributed to a higher metabolic reserve for recovery or a better nutritional status in the obesity group [30]. Several studies since 2008 have suggested better prognosis in overweight or obese patients than in their leaner counterparts [15]. However, other studies disagreed with these findings [6,7]. This inconsistency leads to uncertainty regarding secondary prevention for AIS

patients diagnosed with obesity. The obesity paradox and diverse conclusions regarding this phenomenon across studies continue to lack a precise interpretation. Sex disparities, age dependency, inaccurate results owing to selection bias, and diverse outcomes among patients with different types of stroke might be contributors to this lack of clarity [7,8,10,31]. Additionally, the existing evidence is limited and too varied to conclusively establish an association between severe or morbid obesity and the risk of all-cause mortality in AIS. Skolarus et al. [9] identified 1791 patients with AIS and concluded that morbid obesity was associated with increased poststroke mortality in middle-aged and older adults during a median follow-up of 660 days [9]. However, results from the Virtual International Stroke Trials Archive database showed that compared with normal weight, morbid obesity was not related to mortality in those with AIS [10]. Similarly, Olsen et al. [32] did not detect any significant results in hospitalized patients with AIS in a Danish cohort. Of note, the findings in the latter 2 studies were based on a small number of patients with AIS in the morbidly obese category; thus, these findings require cautious interpretation. As a recommended strategy for the treatment of morbid obesity, BS can induce substantial weight loss based on evidence from randomized controlled trials [33]. Considering the economic and health dilemmas caused by severe obesity, weight reduction may be an effective intervention to relieve the burden of cardiovascular disease, including AIS [34].

Table 2 Multivariate regression analysis of outcomes between acute ischemic stroke patients with prior BS and morbid obesity Outcomes

Model 1 including all patients In hospital mortality Total cost Length of stay Model 2 including alive patients Moderate-to-severe disability

Overall prior BS* (n 5 1654)

Prior BS with BMI 35 kg/m2 (n 5 413)

Coefficients/adjusted OR (95% CI)

P value

Coefficients/adjusted OR (95% CI)

P value

Coefficients/adjusted OR (95% CI)

P value

.53 (.36–.78) 2.05 (2.08 to 2.01) 2.12 (2.15 to 2.08)

.0016 00082 ,.0001

.39 (.16–.95) 0 (2.07 to .07) .06 (2.13 to .01)

.0385 .0580 .0979

.58 (.37–.90) 2.06 (2.10 to 2.02) 2.13 (2.18 to 2.09)

.0144 .0024 ,.0001

.69 (.61–.77)

,.0001

.82 (.66–1.01)

.0656

.64 (.56–.73)

,.0001

Prior BS with BMI ,35 kg/m2 (n 5 1241)

BS 5 bariatric surgery; BMI 5 body mass index; OR 5 odds ratio; CI 5 confidence interval; BMI 5 body mass index. * Adjusted for age, sex, race, Charlson index score, type of admission, type of insurance, income, hospital setting, hospital location, and hospital bed size.

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Table 3 Multivariate regression analysis of poststroke complications between AIS patients with prior BS and morbid obesity Outcomes*

Pulmonary embolism Pneumonia Acute respiratory failure Sepsis Acute kidney failure Acute myocardial infarction Gastrointestinal hemorrhage Hemorrhage stroke

Overall prior BS (n 5 1654)

Prior BS with BMI 35 kg/m2 (n 5 413)

Prior BS with BMI ,35 kg/m2 (n 5 1241)

Adjusted OR (95% CI)

P value

Adjusted OR (95% CI)

P value

Adjusted OR (95% CI)

P value

.61 (.28–1.32) .68 (.45–1.05) .63 (.47–.84) .53 (.31–.92) .67 (.53–.85) .87 (.54–1.38) 1.63 (.90–2.98) 1.21 (.84–1.73)

.2081 .0823 .0017 .0252 .0007 .5513 .1099 .3081

.69 (.17–2.84) .82 (.39–1.76) .63 (.36–1.10) .63 (.23–1.71) 0.67 (.44–1.02) 0.49 (.16–1.53) .95 (.23–3.83) 1.20 (.57–2.50)

.6090 .6157 .1039 .3647 .0632 .2215 .9385 .6296

.58 (.24–1.43) .63 (.38–1.06) .63 (.45–.87) .50 (.26–.96) .67 (.52–.87) 1.01 (.60–1.68) 1.83 (.96–3.50) 1.19 (.80–1.79)

.2360 .0810 .0057 .0379 .0026 .9808 .0666 .3922

BS 5 bariatric surgery; BMI 5 body mass index; OR 5 odds ratio; CI 5 confidence interval. * Adjusted for age, sex, race, Charlson index score, type of admission, type of insurance, income, hospital setting, hospital location, and hospital bed size.

Nevertheless, limited evidence is available regarding the potential benefits of weight reduction on prognosis after AIS. In the present study, we found that prior BS was associated with lower mortality compared with morbid obesity in hospitalized patients with AIS, especially in those with s BMI ,35 kg/m2. However, Bell et al. [25] reported increased poststroke mortality with prestroke weight loss in all types of stroke. Moreover, a study performed by Wohlfahrt et al. [24], suggested that weight loss after AIS was independently associated with increased mortality. These conflicting results might reflect the difference in the effects between intentional weight loss and unintentional weight loss for obese patients, which is crucial for evaluating the relationship between weight loss and poststroke mortality. Unintentional weight loss secondary to obesity-related co-morbidities before stroke onset might result in lean muscle wasting, reduction of metabolic reserve, and consequently increased mortality in patients with AIS [25]. However, intentional BS-induced weight loss was associated with significant reductions in weight, glycated hemoglobin, and plasma low-density lipoprotein cholesterol levels; elevation in plasma high-density lipoprotein levels; and improvements in echocardiographic parameters [33,35,36]; these alterations probably lead to lower mortality in hospitalized AIS patients. Weight loss in the prior-BS group was likely intentional weight loss, and as previously described, BS can cause substantial and sustained weight reduction. The relationship between poststroke weight loss and mortality needs more reliable evidence from further research to clarify the effects of intentional BS-induced weight loss and unintentional weight loss after AIS on the prognosis of patients with AIS. Other important findings are the independent association of intentional BS-induced weight loss with lower odds of moderate-to-severe disability and the lower incidence of poststroke complications in hospitalized patients with AIS, including acute respiratory failure, sepsis, and acute kidney

failure. BS-induced weight loss could improve lung function by significantly increasing arterial blood gas values, functional residual capacity, residual volume, total lung capacity, and expiratory reserve volume [37,38]. Moreover, improvements in glomerular hemodynamics and insulin sensitivity and decreased urinary albumin excretion after intentional weight loss can improve renal function [39,40]. In our study, LOS and total cost were significantly lower in the prior-BS group after adjusting for potential confounders. Along with this finding, the rates of in-hospital procedures such as mechanical ventilation and poststroke complications, including pneumonia, acute respiratory failure, sepsis, acute kidney failure, and acute myocardial infarction, were significantly higher in AIS patients concomitant with morbid obesity. These higher rates might also reflect greater disease severity and thus cause longer LOS and higher total cost. From another point of view, prior BS with BMI ,35 kg/m2 could ameliorate the healthcare burden associated with longer LOS and greater cost secondary to morbid obesity and consequent co-morbidities [41]. The strength of the present study lies in a relatively large sample size from the largest inpatient database in the United States. Protective effects of several outcomes together confirmed the clinical benefits of prior BS compared with morbid obesity. Additionally, the study was designed with morbid obesity being the nonexposed group, thereby ensuring better comparability between groups because those with morbid obesity were recommended as suitable candidates for BS. There are several limitations of our study. First, this study was a retrospective analysis based on the NIS database. As with other administrative databases, unmeasured confounders linked to both prior BS and clinical outcomes might have affected our results. We constructed several models to test the robustness of our primary findings, and all models had similar results. Second, miscoding and undercoding would serve as a potential limitation. However, ICD-9-CM codes used to identify patients with AIS have

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shown a true positive rate of .84% in previous reports [42]. Other codes indicating diagnoses and procedures, such as intracerebral hemorrhage and thrombolysis, also showed adequate accuracy [43]. Third, cause-specific death and severity of AIS could not be considered because data including National Institutes of Health Stroke Scale score were not available in the NIS database. Fourth, exact BMI values are not available in the NIS database, and we could only classify weight status according to cut-off points using ICD-9-CM codes. Fifth, the BMI used as a measure of overall obesity did not consider body fat distribution. Other methods of measurement accounting for differences among fat mass, fat-free mass, and lean mass may provide a better understanding of the association between prior BS and AIS outcomes [44]. Sixth, it was difficult to quantitatively distinguish intentional weight loss from unintentional weight loss after BS. However, BS could theoretically result in substantial and sustained weight loss; thus, the effect of this phenomenon was expected to be marginal to our main conclusions. Conclusion Prior BS with BMI ,35 kg/m2 could benefit AIS-related clinical outcomes and healthcare utilization, including inhospital mortality, poststroke disability status, complications, LOS, and total hospitalization cost. With the increasing prevalence of morbid obesity and popularity of BS, further prospective studies are required to investigate the effects of poststroke BS-induced weight loss on outcomes in patients with AIS. Disclosures The authors have no commercial associations that might be a conflict of interest in relation to this article. Supplementary materials Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.1016/ j.soard.2019.08.020. References [1] Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among us adults, 19992010. JAMA 2012;307(5):491–7. [2] York DA, Rossner S, Caterson I, et al. for the American Heart Association. Prevention Conference VII: obesity, a worldwide epidemic related to heart disease and stroke: Group I: worldwide demographics of obesity. Circulation 2004;110(18):e463–70. [3] Gostin LO. Tackling obesity and disease: the culprit is sugar; the response is legal regulation. Hastings Cent Rep 2018;48(1):5–7. [4] Mozaffarian D, Benjamin EJ, Go AS, et al. for the American Heart Association Statistics Committee; Stroke Statistics Subcommittee. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation 2016;133(4):e38–360.

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