Improving prescribing practices in primary care: pharmacological treatment of depression in patients with excess weight
Bibliographic record
Abstract
Background. Pharmacological treatment of depression needs individualized approaches, with consideration of patients’ characteristics amongst other factors. One of the most important factors associated with the differential response to antidepressants (AD) is a patient’s body weight. Presently, there are no guidelines to individualize the prescribing of AD for patients with excess weight. Moreover, it is not clear whether prescribing of AD with obesogenic (weight-increasing) adverse effects is associated with increased health risks in this population.Objectives. The objectives of this thesis are 1) to synthesize the evidence, by groups and types of AD, on the role of excess body weight in response to AD treatment in people with depression; 2a) to describe, in Canadian primary care, the prevalence and patterns of AD prescribing to patients with depression and obesity; 2b) to quantify the differences in prescribing AD with weight-modulating and cardiovascular adverse effects for patients in different weight groups; 3) to estimate the difference in the association between prescribing of obesogenic AD and health care utilization (hospitalizations) in patients with and without excess weight.Methods. For objective 1, a comprehensive scoping review was conducted. For objective 2a, a cohort of adult patients with depression was extracted from the national Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Electronic Medical Records database for 2011-2016. The association between AD prescribing and weight category was evaluated cross-sectionally. For objective 2b, the CPCSSN cohort was restricted to the incident users of AD. Associations between obesity and prescribing of AD known for their weight-modulating and cardiovascular adverse effects were examined in logistic and mixed effects regression models. For objective 3, the population-based cohort, “The Care Trajectories - Enriched Data” (TorSaDE), was used. Cox regression analysis and cosine similarity metrics were utilized to examine the role of excess weight in the association between exposure to obesogenic AD and all-cause hospitalizations. Results. In the scoping review, the evidence on the differential response of people with excess weight to individual AD was synthesized. The analysis for objective 2 showed that, compared with normal weight patients, patients with obesity were more likely to receive an AD prescription (adjusted Odds Ratio [aOR]=1.17; 95% Confidence Interval [CI]: 1.12-1.22). Prescribing patterns of AD with weight-modulating and cardiovascular effects were different between patients with obesity and normal weight. The adjusted hazard ratio for all-cause hospitalizations was higher in the patients jointly exposed to excess weight and obesogenic AD, compared with patients with only one of these exposures (objective 3). Conclusion. The data synthesized in the scoping review helped clarifying best practices for antidepressant prescribing for patients with obesity. Prescribing high number of different AD, including AD with obesogenic and cardiovascular side effects, to patients with obesity is concerning, as well as the trend for the increased risk for hospitalizations in patients with the joint exposure to excess weight and obesogenic AD. The risks and benefits of treatment of the excess weight patients with individual obesogenic AD need to be further studied using a large longitudinal cohort of patients with depression and repeated BMI measures
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".