Does Screening for Depressive Symptoms Help Optimize Duloxetine Use in Knee <scp>Osteoarthritis</scp> Patients With Moderate Pain? A <scp>Cost‐Effectiveness</scp> Analysis
Bibliographic record
Abstract
OBJECTIVE: Duloxetine is a treatment approved by the US Food and Drug Administration for both osteoarthritis (OA) pain and depression, though uptake of duloxetine in knee OA management varies. We examined the cost-effectiveness of adding duloxetine to knee OA care in the absence or presence of depression screening. METHODS: We used the Osteoarthritis Policy Model, a validated computer microsimulation of knee OA, to examine the value of duloxetine for patients with knee OA who have moderate pain by comparing 3 strategies: 1) usual care, 2) usual care plus duloxetine for patients who screen positive for depression on the Patient Health Questionnaire 9 (PHQ-9), and 3) usual care plus universal duloxetine. Outcome measures included quality-adjusted life years (QALYs), lifetime direct medical costs, and incremental cost-effectiveness ratios (ICERs), discounted at 3% annually. Model inputs, drawn from the published literature and national databases, included annual cost of duloxetine ($721-937); average pain reduction for duloxetine (17.5 points on the Western Ontario and McMaster Universities Osteoarthritis Index pain scale [0-100]), and likelihood of depression remission with duloxetine (27.4%). We considered 2 willingness-to-pay (WTP) thresholds of $50,000/QALY and $100,000/QALY. We varied parameters related to the PHQ-9 and the cost of duloxetine, efficacy, and toxicities to address uncertainty in model inputs. RESULTS: The screening strategy led to an additional 17 QALYs per 1,000 subjects and increased costs by $289/subject (ICER = $17,000/QALY). Universal duloxetine led to an additional 31 QALYs per 1,000 subjects and $1,205 per subject (ICER = $39,300/QALY). Under the majority of sensitivity analyses, universal duloxetine was cost-effective at the $100,000/QALY threshold. CONCLUSION: The addition of duloxetine to usual care for knee OA patients with moderate pain, regardless of depressive symptoms, is cost-effective at frequently used WTP thresholds.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".