Validation of a New Risk Measure for Chronic Obstructive Pulmonary Disease Exacerbation Using Health Insurance Claims Data
Bibliographic record
Abstract
RATIONALE: Current chronic obstructive pulmonary disease (COPD) exacerbation risk prediction models are based on clinical data not easily accessible to national quality-of-care organizations and payers. Models developed from data sources available to these organizations are needed. OBJECTIVES: This study aimed to validate a risk measure constructed using pharmacy claims in patients with COPD. Administrative claims data were used to construct a risk model to test and validate the ratio of controller (maintenance) medications to total COPD medications (CTR) as an independent risk measure for COPD exacerbations. The ability of the CTR to predict the risk of COPD exacerbations was also assessed. METHODS: This was a retrospective study using health insurance claims data from the Truven MarketScan database (2006-2011), whereby exacerbation risk factors of patients with COPD were observed over a 12-month period and exacerbations monitored in the following year. Exacerbations were defined as moderate (emergency department or outpatient treatment with oral corticosteroid dispensings within 7 d) or severe (hospital admission) on the basis of diagnosis codes. Models were developed and validated using split-sample data from the MarketScan database and further validated using the Reliant Medical Group database. The performance of prediction models was evaluated using C-statistics. MEASUREMENTS AND MAIN RESULTS: A total of 258,668 patients with COPD from the MarketScan database were included. A CTR of greater than or equal to 0.3 was significantly associated with a reduced risk for any (adjusted odds ratio [OR], 0.91; 95% confidence interval [CI], 0.85-0.97); moderate (OR, 0.93; 95% CI, 0.87-1.00), or severe (OR, 0.87; 95% CI, 0.80-0.95) exacerbation. The CTR, at a ratio of greater than or equal to 0.3, was predictive in various subpopulations, including those without a history of asthma and those with or without a history of moderate/severe exacerbations. The C-statistics ranged from 0.750 to 0.761 for the development set and 0.714 to 0.761 in the validation sets, indicating the CTR performed well in predicting exacerbation risk. CONCLUSIONS: The ratio of controller to total medications dispensed for COPD is a measure that can easily be calculated using only pharmacy claims data. A CTR of greater than or equal to 0.3 can potentially be used as a quality-of-care measurement for prevention of exacerbations.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".