Quantifying the Economic Impact of Delayed Multiple-Inhaler Triple Therapy Initiation in Patients with COPD: A Retrospective Cohort Study of Linked Electronic Medical Record and Hospital Administrative Data in England
Bibliographic record
Abstract
PURPOSE: To assess if early multiple-inhaler triple therapy (MITT) initiation in patients with chronic obstructive pulmonary disease (COPD) reduces subsequent healthcare resource utilization (HCRU), direct medical costs, and acute exacerbations of COPD (AECOPDs). PATIENTS AND METHODS: This retrospective, longitudinal cohort study used electronic health records and linked hospital administrative data in England. COPD patients with an AECOPD between July 2012 and May 2016 (index), and who subsequently started MITT within 180 days were eligible. Patients with an AECOPD 6 months prior to index were excluded. HCRU, direct healthcare costs, and AECOPDs were assessed in the following 24-month period for early (≤30 days) and delayed (31-180 days) MITT initiators. RESULTS: A total of 934 patients were included in the analysis and categorized as early (n=367, 39%) or delayed (n=567, 61%) MITT initiators. Mean patient age was 68.5 years and 53.2% were male. A significantly higher proportion of delayed MITT initiators required ≥1 outpatient appointment (all-cause) compared with early MITT initiators (87% vs 79%; p=0.0016). A significantly higher proportion of delayed MITT initiators required ≥1 COPD‑related inpatient stay versus early MITT initiators (47% vs 40%; p=0.0262). Over the 24-month follow-up, mean all-cause and COPD-related total healthcare costs were significantly higher in delayed MITT initiators compared with early MITT initiators (all‑cause: £11,348 vs £8126; p=0.0011; COPD-related: £7307 vs £4535; p=0.0009). CONCLUSION: Delayed initiation of multiple-inhaler triple therapy was associated with higher all-cause and COPD-related costs, suggesting that earlier initiation of triple therapy in COPD patients may help reduce the economic burden on the healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".