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Angarita GA, Pittman B, Nararajan A, Mayerson TF, Parate A, Marlin B, Gueorguieva RR, Potenza MN, Ganesan D, Malison RT. Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors. Drug Alcohol Depend 2023; 250:110898. [PMID: 37523916 PMCID: PMC10905422 DOI: 10.1016/j.drugalcdep.2023.110898] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/05/2023] [Accepted: 07/09/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Our group has established the feasibility of using on-body electrocardiographic (ECG) sensors to detect cocaine use in the human laboratory. The purpose of the current study was to test whether ECG sensors and features are capable of discriminating cocaine use from other non-cocaine sympathomimetics. METHODS Eleven subjects with cocaine use disorder wore the Zephyr BioHarness™ 3 chest band under six experimental (drug and non-drug) conditions, including 1) laboratory, intravenous cocaine self-administration, 2) after a single oral dose of methylphenidate, 3) during aerobic exercise, 4) during tobacco use (N=7 who smoked tobacco), and 5) during routine activities of daily inpatient living (unit activity). Three ECG-derived feature sets served as primary outcome measures, including 1) the RR interval (i.e., heart rate), 2) a group of ECG interval proxies (i.e., PR, QS, QT and QTc intervals), and 3) the full ECG waveform. Discriminatory power between cocaine and non-cocaine conditions for each of the three outcomes measures was expressed as the area under the receiver operating characteristics (AUROC) curve. RESULTS All three outcomes successfully discriminated cocaine use from unit activity, exercise, tobacco, and methylphenidate conditions with a mean AUROC values ranging from 0.66 to 0.99 and with least squares means values all statistically different/higher than 0.5 among all subjects [F(3, 99) = 3.38, p =0.02] and among those with tobacco use [F(4, 84) = 5.39, p = 0.0007]. CONCLUSIONS These preliminary results support discriminatory power of wearable ECG sensors for detecting cocaine use.
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Affiliation(s)
- Gustavo A Angarita
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
| | - Brian Pittman
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Annamalai Nararajan
- Philips Research North America, Cambridge, MA02141, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Talia F Mayerson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Abhinav Parate
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Lumme Health Inc, Boston, MA02210, USA
| | - Benjamin Marlin
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Ralitza R Gueorguieva
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT06510, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Child Study Center, Yale University School of Medicine, New Haven, CT06510, USA; Department of Neuroscience, Yale University, New Haven, CT06510, USA; Connecticut Council on Problem Gambling, Wethersfield, CT06109, USA; Wu Tsai Institute, New Haven, CT06510, USA
| | - Deepak Ganesan
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Robert T Malison
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Department of Neuroscience, Yale University, New Haven, CT06510, USA
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