1. Acute Renal Failure in Aneurysmal Subarachnoid Hemorrhage: Nationwide Analysis of Hospitalizations in the United States Kavelin Rumalla 1, Adithi Y. Reddy 1, Manoj K. Mittal 2 1 University of Missouri-Kansas City School of Medicine, 2 University of Kansas Medical Center, Department of Neurology ★ ~ 30,000 people a year in the U.S. suffer from aneurysmal subarachnoid hemorrhage (aSAH), the most deadly form of stroke 1 ★ Non-neurological (medical) complications occur in ~ 82% of patients and are severe in ~ 40% 1-3 ★ These medical complications (respiratory, cardiac, electrolyte, infectious, hepatic, renal) account for ~ 23% of deaths, rivaling the neurological complications ★ Acute renal failure (ARF) occurs in ~ 1-25% of critically ill patients 4 ★ We extracted data from the largest all-payer database of inpatient stays in the United States to study the 1) incidence, 2) risk factors, and 3) in- hospital outcomes associated with ARF in patients hospitalized with aSAH. ★ S tudy Population: ○Design: Retrospective administrative data analysis ○Data source: Nationwide Inpatient Sample, 2002-2011 ○Inclusion criteria: ■ aSAH diagnosis: ICD-9-CM 1º diagnosis code 430 5 ■ ARF diagnosis: ICD-9-CM 2º diagnosis codes 584.5-584.9 6-7 ■ Previous sensitivity and specificity analyses have verified the use of these ICD-9-CM codes in administrative data 5-7 ○Exclusion criteria: ■ Younger than 18 years of age ■ History of chronic kidney disease, renal transplant ★ Variables ○Patient demographics → age, gender, race, payer status ○Pre-existing comorbidity → 31 total (Elixhauser et al.) 8 ○Hospital type → location, bedsize, teaching status, admission day ○In-hospital procedures → coiling, clipping, ventriculostomy ○Discharge outcomes → length of stay (LOS), disability, mortality ★ Statistical analysis ○SPSS V23 software, alpha was set a priori <0.0001 ○The effect of patient demographics, pre-existing comorbidity, hospital type, and in-hospital procedures on ARF occurrence and the impact of in-hospital mortality were explored in both bivariate and multivariable analyses ○Only variables with P<0.01 in the bivariate analysis were entered into the adjusted multivariable analysis ★ The incidence of ARF in aSAH patients was 4.0%; despite recent advancements in the management of aSAH, the incidence doubled between 2002 and 2011 ★ The identification of possible risk factors for ARF in SAH patients may help identify high risk patients. ★ ARF in patients hospitalized for aSAH is associated with increased likelihood of moderate to severe disability and in-hospital mortality. 1.Connolly ES, et al. Guidelines for the Management of Aneurysmal Subarachnoid Hemorrhage A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2012;43(6):1711- 1737. doi:10.1161/STR.0b013e3182587839. 2.Solenski NJ, et al. Medical complications of aneurysmal subarachnoid hemorrhage: a report of the multicenter, cooperative aneurysm study. Crit Care Med. 1995;23(6):1007-1017. 3.Suarez JI et al.. Aneurysmal Subarachnoid Hemorrhage. N Engl J Med. 2006;354(4):387-396. doi:10.1056/NEJMra052732. 4.Liangos O et al.. Epidemiology and outcomes of acute renal failure in hospitalized patients: a national survey. Clin J Am Soc Nephrol. 2006;1(1):43-51. 5.Tirschwell DL, Longstreth W. Validating administrative data in stroke research. Stroke. 2002;33(10):2465-2470. 6.Waikar SS, et al. Validity of international classification of diseases, ninth revision, clinical modification codes for acute renal failure. J Am Soc Nephrol. 2006;17(6):1688-1694. 7.Goldman LE et al.. The Accuracy of Present ‐ on ‐ Admission Reporting in Administrative Data. Health Serv Res. 2011;46:1946-1962. 8.Elixhauser A et al.. Comorbidity measures for use with administrative data. Med Care. 1998;36(1). INCIDENCE AND RISK FACTORS IN-HOSPITAL OUTCOMES ★ After adjusting for confounding factors, SAH patients with ARF had increased likelihood of moderate to severe disability (OR: 2.03, 95% CI: 1.89-2.19) and in-hospital mortality (OR: 2.14, 95% CI: 2.03-2.26) INTRODUCTION METHODS REFERENCES CONCLUSION Multivariable Analysis: Risk Factors for ARF in aSAH patients Bivariate Analysis: Risk Factors for ARF in aSAH patients LIMITATIONS ★ The database relies on ICD-9-CM coding practices for identification of diagnoses and procedures, which is subject to errors and variability. ★ The database lacked pre-admission and post-discharge data. ★ The retrospective design of our study did not enable us to establish a causal relationship between aSAH, ARF, and discharge outcomes. ★ We encourage future research of prospective design to further assess the relationship between aSAH and ARF.