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Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data

Rahman et al., Interactive Journal of Medical Research, doi:10.2196/39455
Rahman et al., Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record.., Interactive Journal of Medical Research, doi:10.2196/39455
Apr 2023   Source   PDF  
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N3C retrospective reporting that common antidepressants may increase the risk of COVID-19 complications, while certain antidepressants were associated with a lower risk of worse outcomes. Machine learning methods as used in this study can improve accuracy, however they add complexity and can also reduce accuracy. In this case, the results of the two methods are conflicting. The first method raises concern for overfitting - authors use an 18,584 dimension covariate vector for each person, which is larger than the number of patients for each medication analyzed. The second 128 dimension embedding should improve results but authors report more extreme effects including biased results for the control outcomes. It's also not clear why the control outcomes includes items like bone fracture as opposed to common COVID-19 confounding factors.
Rahman et al., 11 Apr 2023, retrospective, France, peer-reviewed, 4 authors.
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Effects of Antidepressants on COVID-19 Outcomes: Retrospective Study on Large-Scale Electronic Health Record Data
Md Mahmudur Rahman, Atqiya Munawara Mahi, BSc Rachel Melamed, Mohammad Arif Ul Alam
Interactive Journal of Medical Research, doi:10.2196/39455
Background: Antidepressants exert an anticholinergic effect in varying degrees, and various classes of antidepressants can produce a different effect on immune function. While the early use of antidepressants has a notional effect on COVID-19 outcomes, the relationship between the risk of COVID-19 severity and the use of antidepressants has not been properly investigated previously owing to the high costs involved with clinical trials. Large-scale observational data and recent advancements in statistical analysis provide ample opportunity to virtualize a clinical trial to discover the detrimental effects of the early use of antidepressants. Objective: We primarily aimed to investigate electronic health records for causal effect estimation and use the data for discovering the causal effects of early antidepressant use on COVID-19 outcomes. As a secondary aim, we developed methods for validating our causal effect estimation pipeline. Methods: We used the National COVID Cohort Collaborative (N3C), a database aggregating health history for over 12 million people in the United States, including over 5 million with a positive COVID-19 test. We selected 241,952 COVID-19-positive patients (age >13 years) with at least 1 year of medical history. The study included a 18,584-dimensional covariate vector for each person and 16 different antidepressants. We used propensity score weighting based on the logistic regression method to estimate causal effects on the entire data. Then, we used the Node2Vec embedding method to encode SNOMED-CT (Systematized Nomenclature of Medicine-Clinical Terms) medical codes and applied random forest regression to estimate causal effects. We used both methods to estimate causal effects of antidepressants on COVID-19 outcomes. We also selected few negatively effective conditions for COVID-19 outcomes and estimated their effects using our proposed methods to validate their efficacy. Results: The average treatment effect (ATE) of using any one of the antidepressants was −0.076 (95% CI −0.082 to −0.069; P<.001) with the propensity score weighting method. For the method using SNOMED-CT medical embedding, the ATE of using any one of the antidepressants was −0.423 (95% CI −0.382 to −0.463; P<.001). Conclusions: We applied multiple causal inference methods with novel application of health embeddings to investigate the effects of antidepressants on COVID-19 outcomes. Additionally, we proposed a novel drug effect analysis-based evaluation technique to justify the efficacy of the proposed method. This study offers causal inference methods on large-scale electronic health record data to discover the effects of common antidepressants on COVID-19 hospitalization or a worse outcome. We found that common antidepressants may increase the risk of COVID-19 complications and uncovered a pattern where certain antidepressants were associated with a lower risk of hospitalization. While discovering the detrimental effects of these drugs on..
Conflicts of Interest None declared. Abbreviations
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©md Mahmudur Rahman, Munawara Mahi, Melamed, Arif, Alam, Originally published in the Interactive Journal of Medical Research
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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