Geo-spatial analysis of racial disparities in 3,952 cardiac arrests: machine learning-augmented multi-site case-control study

Thannoun, Tariq

Geo-spatial analysis of racial disparities in 3,952 cardiac arrests: machine learning-augmented multi-site case-control study

Tuesday, May 21, 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

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

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

Robin Jacob, MD , University of Texas at Houston Program, Houston, TX

Anish Patnaik , University of Texas Health McGovern Medical School 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

Krishna Pabba, MD , UT Medical School At Houston

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

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

Pratik Doshi, 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:
There are no known artificial intelligence (AI)-guided geo-spatial analyses of cardiac arrests.

Methods:
This is a multi-site case-control study of 3,952 cardiac arrest cases from January 1, 2007 to 2016 in the Houston metro area. Geo-spatial analysis was conducted with neural network machine learning-backed multivariable regression of arrest outcomes.

Results:
By zip code there were 16.92 (SD 21.55) arrests on average. Regression analysis showed each additional $10,000 above median household income was associated with a decrease in the total number of cardiac arrests per zip code by 2.86 (95%CI -4.26, -1.46; p<0.001). Zip codes with a median income above $54,600 versus the federal poverty level lowered arrests by 14.62 (p<0.001). For each additional 10 African Americans suffering cardiac arrest in a zip code, the total number of poor neurologic outcomes increased by an average of 8.78 (p<0.001). Geo-spatial maps showed a clock-wise band from north to east to south of more poor neurologic outcomes overlapping with where more African Americans with lower incomes lived (Figure 1).

Conclusions:
This novel geo-spatial AI analysis suggests poorer minorities suffer more adverse cardiac arrest outcomes, potentially providing the opportunity to focus more resources on improving outcomes in less socio-economically advantaged areas.