Pradyumna Agasthi1, Hasan Ashraf2, Sai Harika Pujari1, Marlene E Girardo3, Andrew Tseng4, Farouk Mookadam1, Nithin R Venepally1, Matthew Buras3, Banveet K Khetarpal1, Mohamed Allam1, Mackram F Eleid4, Kevin L Greason5, Nirat Beohar6, Robert J Siegel7, John Sweeney1, Floyd D Fortuin1, David R Holmes4, Reza Arsanjani1. 1. Department of Cardiovascular Diseases, Mayo Clinic Arizona, Phoenix, AZ, United States of America. 2. Department of Cardiovascular Diseases, Mayo Clinic Arizona, Phoenix, AZ, United States of America. Electronic address: Ashraf.hasan@mayo.edu. 3. Department of Health Sciences Research, Mayo Clinic Arizona, Scottsdale, AZ, United States of America. 4. Department of Cardiovascular Diseases, Mayo Clinic Rochester, Rochester, MN, United States of America. 5. Department of Cardiovascular Surgery, Mayo Clinic Rochester, Rochester, MN, United States of America. 6. Columbia University Division of Cardiology, Mount Sinai Medical Center, Miami Beach, FL, United States of America. 7. Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States of America.
Abstract
BACKGROUND/ PURPOSE: Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. METHODS/MATERIALS: A retrospective study of 1055 patients who underwent TAVR (Jan 2014-June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. RESULTS: Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68-0.78) vs 0.56 (95%CI 0.51-0.62) and 0.53 (95% CI 0.47-0.59) respectively with p < 0.0001. CONCLUSION: The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.
BACKGROUND/ PURPOSE: Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. METHODS/MATERIALS: A retrospective study of 1055 patients who underwent TAVR (Jan 2014-June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. RESULTS: Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68-0.78) vs 0.56 (95%CI 0.51-0.62) and 0.53 (95% CI 0.47-0.59) respectively with p < 0.0001. CONCLUSION: The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.
Authors: Jonathan James Hyett Bray; Moghees Ahmad Hanif; Mohammad Alradhawi; Jacob Ibbetson; Surinder Singh Dosanjh; Sabrina Lucy Smith; Mahmood Ahmad; Dominic Pimenta Journal: Eur Heart J Open Date: 2022-03-17