Mp72-12 artificial intelligence analysis of pre-operative ecgs can predict post-operative death and major cardiac complications

You have accessJournal of UrologyCME1 Apr 2023MP72-12 ARTIFICIAL INTELLIGENCE ANALYSIS OF PRE-OPERATIVE ECGS CAN PREDICT POST-OPERATIVE DEATH AND MAJOR CARDIAC COMPLICATIONS Vidit Sharma, Harrison Gottlich, Christine Lohse, Abhinav Khanna, Elizabeth Habermann, Suraj Kapa, Zachi Attia, Stephen Boorjian, and Bradley Leibovich Vidit SharmaVidit Sharma More articles by this author , Harrison GottlichHarrison Gottlich More articles by this author , Christine LohseChristine Lohse More articles by this author , Abhinav KhannaAbhinav Khanna More articles by this author , Elizabeth HabermannElizabeth Habermann More articles by this author , Suraj KapaSuraj Kapa More articles by this author , Zachi AttiaZachi Attia More articles by this author , Stephen BoorjianStephen Boorjian More articles by this author , and Bradley LeibovichBradley Leibovich More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003340.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 1% of patients undergoing non-cardiac surgery die or have a major adverse cardiac event(MACE) within 30 days. Only 20% of surgeons use online risk calculators to risk-stratify patients due to increased clinical burden. Herein, we investigate if our institution's previously created AI algorithms on pre-operative electrocardiograms(ECGs) can serve as a point-of-care risk stratification for post-operative complications. METHODS: From our institutional National Surgical Quality Improvement Program(NSQIP) data, we identified 128,992 non-cardiac surgeries performed on 116,702 distinct patients between 2006-2020. We identified pre-operative ECGs within 90 days of surgery; AI algorithms were used to derive variables in (Table 1). We evaluated the relationship of AI ECG features with the following 30-day outcomes: death and MACE (stroke, cardiac event, or death). AI ECG features were evaluated in conjunction with NSQIP Surgical Risk Calculator (SRC) variables using multivariable logistic regression models to predict these outcomes, using 500-sample bootstrap-corrected c-indexes. RESULTS: A total of 50,254(39%) surgeries(13% Urologic) had pre-operative ECGs available. The AI ECG features (Table 1) collectively had a c-index of 0.755 and 0.719, respectively, for 30-day death(n=414; 0.8%) and MACE(n=676; 1.4%). After adjusting for NSQIP SRC variables, AI ECG variables remained significantly associated with death and MACE (Table 2). The final model with AI ECG features and NSQIP SRC variables had a c-index of 0.902 death) and 0.845(MACE). CONCLUSIONS: AI features of pre-operative ECGs were significantly associated with death or MACE within 30-days of non-cardiac surgery. Clinical implementation of these algorithms to pre-operatively identify high risk surgical candidates warrants further study. Source of Funding: No sources of funding to disclose © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1029 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Vidit Sharma More articles by this author Harrison Gottlich More articles by this author Christine Lohse More articles by this author Abhinav Khanna More articles by this author Elizabeth Habermann More articles by this author Suraj Kapa More articles by this author Zachi Attia More articles by this author Stephen Boorjian More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...