1. IDENTIFYING FRAILTY IN SURGICAL PATIENTS Furqaan Sadiq 1, Michael S. Avidan 2, Arbi Ben Abdallah 2 1 University of Missouri – Kansas City School of Medicine, 2 Washington University School of Medicine INTRODUCTION Frailty is a decline in physical, cognitive & social functioning. 1 About 4 in every 10 surgery patients are frail. Compared to non-frail patients, frail ones are twice as likely to suffer postoperative complications and report poor quality of life Current assessment tools are cumbersome and rely on scaled scores, which inherently neglect the nuances of frailty. There’s a gap in the field regarding the optimal preoperative screening approach METHODS Exploratory, retrospective study, merging data from 3 sources: 1: Electronic medical record at Barnes-Jewish Hospital, St. Louis, MO (MetaVision) 2 & 3:Baseline & 30-Day Postoperative Outcomes. Part of SATISFY-SOS. Systematic Assessment & Targeted Improvement of Services Following Yearlong Surgical Outcomes Surveys. Operated by the Department of Anesthesiology. METHODS Step 1: Factor Analysis identified most contributive factors to frailty. Step 2: Item Response Theory eliminated risk factors that were not able to differentiate among levels of frailty. Step 3: Latent Class Analysis grouped similar patterns of frailty into classes. Step 4: Multivariate logistic regression determined if frailty phenotypes predicted complications within 30-days after surgery, controlling for potential confounders like risk and duration of surgery. RESULTS CREDITS/DISCLOSURE/REFERENCES 1.Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype..J Gerontol A Biol Sci Med Sci.; 2001;56:M146–56. Special thank you to Dr. Reem Mustafa. This work was supported by Washington University’s Institute of Clinical and Translational Sciences grant UL1TR000448, sub-award TL1TR000449, from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). RESULTS Figure 1. Step 3: Latent Class Analysis showing frailty indicator’s probability of membership to three frailty phenotypes EMR 1 Baseline Surveys 2 Post-op Surveys 2 n=2,828 Table1. Demographics n=2,828 OverallYounger a Older b p-Value %% Female Gender59.761.855.50.001 Non-white ethnicity10.711.98.30.003 < 4 METS89.186.394.50.001 BMI < 251.81.72.00.571 Disabled10.614.43.60.001 ASA * Class ≥ 336.730.548.40.001 Charlson Comorbidity # ≥ 429.521.245.00.001 Table 2. Multivariable logistic regression predicting complications within 30-days Predictor VariablesEstimateOdds Ratio95% C.I. Age0.011.011.003-1.018 Female Sex-0.0440.9570.776-1.181 No Frailty* Physical Frailty0.3871.4721.149-1.887 Physical & Mental Frailty0.7752.1711.672-2.818 Low Risk Surgery* Moderate Risk Surgery0.2361.2660.97-1.654 High Risk Surgery-0.1930.8250.64-1.062 General Anesthesia* MAC/Regional Anesthesia0.8032.2330.525-9.501 *Reference category R-squared=0.064, -2LL=2,387.95 a.) Age less than 65 years b.) Age more than 65 years *ASA = American Society of Anesthesiologists physical status classification CONCLUSION Physically & Mentally Frail patients have twice the odds for postoperative complications than Not Frail patients Clinicians can gauge clinically relevant frailty by asking patients about their perceived physical and emotional health, and their limitations in social or work activities.