Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients
Introduction. Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Cardiology Research and Practice |
| Online Access: | http://dx.doi.org/10.1155/2021/3180987 |
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| author | Dominique J. Monlezun Alfred T. Samura Ritesh S. Patel Tariq E. Thannoun Prakash Balan |
| author_facet | Dominique J. Monlezun Alfred T. Samura Ritesh S. Patel Tariq E. Thannoun Prakash Balan |
| author_sort | Dominique J. Monlezun |
| collection | DOAJ |
| description | Introduction. Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition). Method. We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. Results. Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p<0.001). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25–3.92; p=0.006) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05–1.85; p=0.023). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p<0.001). 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 had 14.62 fewer arrests (p<0.001). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences. Conclusion. This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes. |
| format | Article |
| id | doaj-art-421a35d51599433baa68a3f2ba53f642 |
| institution | Kabale University |
| issn | 2090-0597 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cardiology Research and Practice |
| spelling | doaj-art-421a35d51599433baa68a3f2ba53f6422025-02-03T05:53:26ZengWileyCardiology Research and Practice2090-05972021-01-01202110.1155/2021/3180987Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 PatientsDominique J. Monlezun0Alfred T. Samura1Ritesh S. Patel2Tariq E. Thannoun3Prakash Balan4Department of CardiologyDepartment of CardiologyDivision of Cardiovascular SciencesSection of CardiologyUniversity of Arizona College of Medicine at PhoenixIntroduction. Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition). Method. We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed. Results. Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p<0.001). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25–3.92; p=0.006) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05–1.85; p=0.023). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p<0.001). 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 had 14.62 fewer arrests (p<0.001). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences. Conclusion. This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes.http://dx.doi.org/10.1155/2021/3180987 |
| spellingShingle | Dominique J. Monlezun Alfred T. Samura Ritesh S. Patel Tariq E. Thannoun Prakash Balan Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients Cardiology Research and Practice |
| title | Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients |
| title_full | Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients |
| title_fullStr | Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients |
| title_full_unstemmed | Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients |
| title_short | Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients |
| title_sort | racial and socioeconomic disparities in out of hospital cardiac arrest outcomes artificial intelligence augmented propensity score and geospatial cohort analysis of 3 952 patients |
| url | http://dx.doi.org/10.1155/2021/3180987 |
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