Article Text

PDF

O-042 Functional Outcome Prediction Model for Carotid Stenting Patients Using Admission Profiles: 29,453 patients using NIS data 2005 to 2013
  1. S Park1,
  2. M Alexander2,
  3. A Rosengart3
  1. 1Neuroscience, Albany Medical Center, Albany, NY
  2. 2Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA
  3. 3Neurology, Cedars-Sinai Medical Center, Los Angeles, CA

Abstract

Introduction Carotid stenting (CAS) has been widely used since the seminal CREST trial, demonstrating equivalent results to carotid endarterectomy (CEA). With normal surgical risk patients having symptomatic or asymptomatic carotid artery disease, CAS and CEA, in qualified hands, have comparable outcomes, but patient-specific risk factors to estimate functional outcome were poorly studied. In practice, the choice between CEA and CAS is based on lesion anatomy and preference of patient and surgeon rather than considering defined risk factors associated with unfavorable outcome. We aimed to determine medical risk factors for poor functional outcome after CAS using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database (NIS) and formulate outcome prediction model to estimate unfavorable functional outcome.

Methods We included 29,453 patients (age > ≥18) with symptomatic and asymptomatic carotid artery disease from 2005 to 2013, who underwent CAS using ICD-9 procedure code and diagnosis code for chronic and acute comorbidity as well as administration data including social economic and demographic profiles. Applying the non-parametric Jonckheere-Terpstra test, Multiple logistic regression, and multiple linear regression analyzes (SAS 9.4; SPSS 22) to formulate outcome prediction model using covariates for (1) Socio-economic status; age, sex and race, (2) chronic comorbidities; hypertension, diabetes mellitus, hyperlipidemia, coronary artery disease, congestive heart failure, atrial fibrillation/flutter, COPD, chronic kidney disease, aortic/visceral/peripheral atherosclerosis, tobacco/alcohol dependence and morbid obesity, (3) care complexity; number of comorbidities, numbers of inpatient diagnosis/ procedures and (4) Acute comorbidities; acute myocardial infarct, acute kidney failure, pneumonia, acute respiratory failure and sepsis. We compared outcome markers which were defined as (1) procedure related stroke, (2) mortality, and (3) functional outcome based on discharge disposition; long term facility or not. Care complexity and acute comorbidities were also considered for outcome analysis without utilization for then outcome prediction formula because these variables are not predictable on admission.

Results Mean age of symptomatic and asymptomatic CAS patients was 70.3 years (SD ± 10.6 years) with 20% > 80 years old; 39.5% females; 71.8% Whites, 4.5% Blacks and 4.1% Hispanics. 16.2% had symptomatic carotid stenosis. As chronic comorbidities, 75.7% had HTN, 32.1% DM, 58.1% HLD, 54.3% CAD, 0.6% CHF, 10.4% Atrial fibrillation/flutter 12.9% COPD, 11.4% chronic renal failure, 16.4% tobacco dependence, 0.4% alcohol dependence and 1.4% morbid obesity. As outcome makers, in-patient mortality was 1.1% (symptomatic 4.6%/asymptomatic 0.3%), 2% of patient had procedure related stroke (symptomatic 9.6%/asymptomatic 0.5%), discharge disposition to long term facility 9.4% (symptomatic 39.5%/asymptomatic 4.1%). Multiple outcome models developed to predict defined outcome markers to select the best predictable model. Several variables identified Age (greater than 80), presence of symptoms including TIA (p < 0.001), CHF (p < 0.001), COPD (p < 0.001) and CRF (p < 0.001) to formulate the best model. Long term facility disposition possibility = 0.012 +0.067(1, if age > 80, 0 if age < 80) +0.014(1, if DM) +0.084(1, if CHF) +0.03(1, if CRF) +0.030(1, if COPD) +0.351(1, if symptomatic) (Multiple linear regression, R2 0.205)

Conclusions Age (greater than 80), presence of symptoms including TIA, DM, CHF, COPD and CRF were identified to predict worse functional outcome after CAS.

Disclosures S. Park: None. M. Alexander: 1; C; : Investigator in CREST2 Trial. A. Rosengart: None.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Statistics from Altmetric.com

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.