Evidence map›Paper›PMID 29713202›Full record

ArticleClinical epidemiology2018

Derivation and validation of a multivariable model to predict when primary care physicians prescribe antidepressants for indications other than depression.

Jenna Wong, Michal Abrahamowicz, David L Buckeridge, Robyn Tamblyn

Abstract read
In one paragraph

Article in Clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jenna WongDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
Michal AbrahamowiczDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
David L BuckeridgeDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
Robyn TamblynDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePhysicians commonly prescribe antidepressants for indications other than depression that are not evidence-based and need further evaluation. However, lack of routinely documented treatment indications for medications in administrative and medical databases creates a major barrier to evaluating antidepressant use for indications besides depression. Thus, the aim of this study was to derive a model to predict when primary care physicians prescribe antidepressants for indications other than depression and to identify important determinants of this prescribing practice.

methodsPrediction study using antidepressant prescriptions from January 2003-December 2012 in an indication-based electronic prescribing system in Quebec, Canada. Patients were linked to demographic files, medical billings data, and hospital discharge summary data to create over 370 candidate predictors. The final prediction model was derived on a random 75% sample of the data using 3-fold cross-validation integrated within a score-based forward stepwise selection procedure. The performance of the final model was assessed in the remaining 25% of the data.

resultsAmong 73,576 antidepressant prescriptions, 32,405 (44.0%) were written for indications other than depression. Among 40 predictors in the final model, the most important covariates included the molecule name, the patient's education level, the physician's workload, the prescribed dose, and diagnostic codes for plausible indications recorded in the past year. The final model had good discrimination (concordance (

conclusionIn the absence of documented treatment indications, researchers may be able to use health services data to accurately predict when primary care physicians prescribe antidepressants for indications other than depression. Our prediction model represents a valuable tool for enabling researchers to differentiate between antidepressant use for depression versus other indications, thus addressing a major barrier to performing pharmacovigilance research on antidepressants.

Indexed as

antidepressantindicationspharmacovigilancepredictive studiespredictorsprimary care

Identifiers

PMID29713202
PMCPMC5912382

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.