A risk identification model for detection of patients at risk of antidepressant discontinuation

Ali Zolnour; Christina E Eldredge; Anthony Faiola; Yadollah Yaghoobzadeh; Masoud Khani; Doreen Foy; Maxim Topaz; Hadi Kharrazi; Kin Wah Fung; Paul Fontelo; Anahita Davoudi; Azade Tabaie; Scott A Breitinger; Tyler S Oesterle; Masoud Rouhizadeh; Zahra Zonnor; Hans Moen; Timothy B Patrick; Maryam Zolnoori

doi:10.3389/frai.2023.1229609

A risk identification model for detection of patients at risk of antidepressant discontinuation

Front Artif Intell. 2023 Aug 24:6:1229609. doi: 10.3389/frai.2023.1229609. eCollection 2023.

Authors

Ali Zolnour¹, Christina E Eldredge², Anthony Faiola³, Yadollah Yaghoobzadeh¹, Masoud Khani⁴, Doreen Foy⁵, Maxim Topaz^{6

7}, Hadi Kharrazi⁸, Kin Wah Fung⁹, Paul Fontelo⁹, Anahita Davoudi⁷, Azade Tabaie¹⁰, Scott A Breitinger¹¹, Tyler S Oesterle¹¹, Masoud Rouhizadeh¹², Zahra Zonnor¹³, Hans Moen¹⁴, Timothy B Patrick⁴, Maryam Zolnoori^{6

11}

Affiliations

¹ School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
² School of Information, University of South Florida, Tampa, FL, United States.
³ College of Health Sciences, University of Kentucky, Lexington, KY, United States.
⁴ Biomedical and Health Informatics, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
⁵ School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States.
⁶ School of Nursing and Data Science Institute, Columbia University, New York, NY, United States.
⁷ Center for Home Care Policy and Research, VNS Health, New York, NY, United States.
⁸ Department of Health Policy and Management, Johns Hopkins University, Baltimore, MD, United States.
⁹ Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States.
¹⁰ Center of Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, DC, United States.
¹¹ Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.
¹² Collage of Pharmacy, University of Florida, Gainesville, FL, United States.
¹³ Department of Biomechanics, Bu-Ali Sina University, Hamedan, Iran.
¹⁴ Department of Computer Science, Aalto University, Otaniemi, Finland.

Abstract

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources.

Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.com," utilizing content analysis to create PsyRisk-a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation.

Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants.

Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at risk of prematurely discontinuing their antidepressant treatment.

Keywords: adverse drug events; antidepressant discontinuation; antidepressant effectiveness; content analysis; machine learning; online healthcare forums.

Grants and funding

This research was partially supported by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM) and Lister Hill National Center for Biomedical Communications (LHNCBC).