Utilizing Consumer Health Posts to Identify Underlying Factors Associated with Patients� Attitudes towards Antidepressants (Preprint)

BackgroundNonadherence to antidepressants is a major obstacle to deriving antidepressants’ therapeutic benefits, resulting in significant burdens on the individuals and the health care system. Several studies have shown that nonadherence is weakly associated with personal and clinical variables but strongly associated with patients’ beliefs and attitudes toward medications. Patients’ drug review posts in online health care communities might provide a significant insight into patients’ attitude toward antidepressants and could be used to address the challenges of self-report methods such as patients’ recruitment.ObjectiveThe aim of this study was to use patient-generated data to identify factors affecting the patient’s attitude toward 4 antidepressants drugs (sertraline [Zoloft], escitalopram [Lexapro], duloxetine [Cymbalta], and venlafaxine [Effexor XR]), which in turn, is a strong determinant of treatment nonadherence. We hypothesized that clinical variables (drug effectiveness; adverse drug reactions, ADRs; perceived distress from ADRs, ADR-PD; and duration of treatment) and personal variables (age, gender, and patients’ knowledge about medications) are associated with patients’ attitude toward antidepressants, and experience of ADRs and drug ineffectiveness are strongly associated with negative attitude.MethodsWe used both qualitative and quantitative methods to analyze the dataset. Patients’ drug reviews were randomly selected from a health care forum called askapatient. The Framework method was used to build the analytical framework containing the themes for developing structured data from the qualitative drug reviews. Then, 4 annotators coded the drug reviews at the sentence level using the analytical framework. After managing missing values, we used chi-square and ordinal logistic regression to test and model the association between variables and attitude.ResultsA total of 892 reviews posted between February 2001 and September 2016 were analyzed. Most of the patients were females (680/892, 76.2%) and aged less than 40 years (540/892, 60.5%). Patient attitude was significantly (P<.001) associated with experience of ADRs, ADR-PD, drug effectiveness, perceived lack of knowledge, experience of withdrawal, and duration of usage, whereas oth age (F4,874=0.72, P=.58) and gender (χ24=2.7, P=.21) were not found to be associated with patient attitudes. Moreover, modeling the relationship between variables and attitudes showed that drug effectiveness and perceived distress from adverse drug reactions were the 2 most significant factors affecting patients’ attitude toward antidepressants.ConclusionsPatients’ self-report experiences of medications in online health care communities can provide a direct insight into the underlying factors associated with patients’ perceptions and attitudes toward antidepressants. However, it cannot be used as a replacement for self-report methods because of the lack of information for some of the variables, colloquial language, and the unstructured format of the reports.

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“…Table 1 shows the categories of the themes and the themes in each category. The details of the themes are available in [21].…”

Section: Methodsmentioning

confidence: 99%

Identifying the Underlying Factors Associated With Patients’ Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews

et al. 2018

Self Cite

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“…Also, machine learning-based NLP systems often underperform due to a limited annotated corpus of patient-narrative data (Sarker and Gonzalez, 2015). These systems use annotated data to learn patterns, make predictions on known outcomes, and apply these inferences to unseen data Zolnoori, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Zolnour,

Eldredge,

Faiola

et al. 2023

Front. Artif. Intell.

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PurposeBetween 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.MethodsWe 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.ResultsFrom 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.ConclusionThe 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.

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A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications

Zolnoori

Fung

Patrick

et al. 2019

Journal of Biomedical Informatics

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Utilizing Consumer Health Posts to Identify Underlying Factors Associated with Patients� Attitudes towards Antidepressants (Preprint)

Cited by 3 publications

References 25 publications

Identifying the Underlying Factors Associated With Patients’ Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews

Identifying the Underlying Factors Associated With Patients’ Attitudes Toward Antidepressants: Qualitative and Quantitative Analysis of Patient Drug Reviews

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

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications

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