Racial disparities in mitral clipping: artificial intelligence-supported cohort analysis of 219 patients

Thannoun, Tariq

Racial disparities in mitral clipping: artificial intelligence-supported cohort analysis of 219 patients

Monday, May 20, 2019

Belmont Ballroom 2-3 (The Cosmopolitan of Las Vegas)

Tariq E Thannoun, MD , UT Houston, Houston, TX

Dominique J Monlezun, MD, PHD, MPH , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Krishna Pabba, MD , UT Medical School At Houston

Fisayomi Shobayo, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Anish Patnaik , University of Texas Health McGovern Medical School Houston, Houston, TX

Robin Jacob, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Logan Hostetter, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Danyi Zheng, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Ritesh Patel , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Jeffrey Chen, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Nadia Abelhad, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Cullen Grable, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Ali Agha, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Alfred Thunty Samura, M.D. , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Jordan Graham, MD , Department of Internal Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX

Enrique Garcia-Sayan, MD , University of Texas Health McGovern Medical School Houston, Houston, TX

Prakash Balan, M.D., FSCAI , University of Texas Health Science Center at Houston, Houston, TX

Background:
Racial disparities are prevalent and preventable contributors to cardiac readmission which face mounting Medicare penalties. This is the first machine learning-augmented analysis of racial disparities for guideline-directed therapy and outcomes for mitral clipping.

Methods:
Mitral clip patients from a single high-volume quaternary academic medical center in Houston, Texas, USA, were prospectively enrolled in this cohort analysis from 11/8/11-10/29/18. Machine learning (neural network)-guided multivariable regression assessed outcomes in addition to admission and discharge guideline-directed medications. Forward and backward stepwise regression aided covariate selection.

Results:
The mean age for the 219 subjects meeting criteria was 75.67 (SD 9.90), 116 (48.74%) were female, 181 (75.73%) were white, 128 (53.56%) had commercial insurance and 91 (38.08%) had Medicare, and 100 (42.02%) had HFrEF. After fully adjusting for BMI, prior revascularization, and atrial fibrillation among HFrEF patients, non-white race had significantly lower odds of being discharged on beta blockers (OR 0.20, p=0.009). Adjusting for the same variables in addition to HFrEF among atrial fibrillation patients, non-white race had significantly higher odds of being discharged on DAPT with anti-coagulation (OR 4.65, p=0.021). There were no other disparities in outcomes with equivalent rates of device failure, discharge destination other than home, length of stay, and mortality.

Conclusions:
This AI-augmented large cohort analysis suggests significant racial disparities exist in guideline-directed therapy after mitral clipping but it appears not to impact short-term outcomes. This study potentially provides a robust example of an AI-driven approach to quickly target actionable points for outcome and equity improvement.