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Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, Bouzid Z, Faramand Z, Alrawashdeh MO, Gregg RE, Helman S, Riek NT, Kraevsky-Phillips K, Clermont G, Akcakaya M, Sereika SM, Van Dam P, Smith SW, Birnbaum Y, Saba S, Sejdic E, Callaway CW. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med 2023; 29:1804-1813. [PMID: 37386246 PMCID: PMC10353937 DOI: 10.1038/s41591-023-02396-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023]
Abstract
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
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Affiliation(s)
- Salah S Al-Zaiti
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Christian Martin-Gill
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Zeineb Bouzid
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziad Faramand
- Department of Emergency Medicine, Northeast Georgia Health System, Gainesville, GA, USA
| | - Mohammad O Alrawashdeh
- School of Nursing, Jordan University of Science and Technology, Irbid, Jordan
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Richard E Gregg
- Advanced Algorithm Development Center, Philips Healthcare, Cambridge, MA, USA
| | - Stephanie Helman
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nathan T Riek
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Murat Akcakaya
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan M Sereika
- Department of Acute & Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Van Dam
- Division of Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stephen W Smith
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN, USA
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Yochai Birnbaum
- Division of Cardiology, Baylor College of Medicine, Houston, TX, USA
| | - Samir Saba
- Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ervin Sejdic
- Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
- Artificial Intelligence for Health Outcomes at Research & Innovation, North York General Hospital, Toronto, ON, Canada
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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Riek NT, Susam BT, Hudac CM, Conner CM, Akcakaya M, Yun J, White SW, Mazefsky CA, Gable PA. Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents. J Autism Dev Disord 2023:10.1007/s10803-023-06038-y. [PMID: 37393370 DOI: 10.1007/s10803-023-06038-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2023] [Indexed: 07/03/2023]
Abstract
The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12-21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.
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Affiliation(s)
- Nathan T Riek
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Busra T Susam
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Caitlin M Hudac
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
- Department of Psychology and Carolina Autism and Neurodevelopment (CAN) Research Center, University of South Carolina, Colombia, SC, USA
| | - Caitlin M Conner
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Murat Akcakaya
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jane Yun
- Chemical and Petroleum Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan W White
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
| | - Carla A Mazefsky
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Philip A Gable
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
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Susam BT, Riek NT, Beck K, Eldeeb S, Hudac CM, Gable PA, Conner C, Akcakaya M, White S, Mazefsky C. Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2395-2405. [PMID: 35976834 PMCID: PMC9979338 DOI: 10.1109/tnsre.2022.3199151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.
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Affiliation(s)
- Busra T. Susam
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261
| | - Nathan T. Riek
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA 15261 USA
| | - Kelly Beck
- School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - Safaa Eldeeb
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA 15261 USA
| | - Caitlin M. Hudac
- Department of Psychology, Carolina Autism and Neurodevelopment (CAN) Research Center, University of South Carolina, Columbia, SC 29208 USA
| | - Philip A. Gable
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716 USA
| | - Caitlin Conner
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213 USA
| | - Murat Akcakaya
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA 15261 USA
| | - Susan White
- Department of Psychology, The University of Alabama, Tuscaloosa, AL 35401 USA
| | - Carla Mazefsky
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213 USA
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