COPD Study Fails to Find Model That Can Predict Exacerbations

If a health system could identify which patients with chronic obstructive pulmonary disease (COPD) were at risk for exacerbations of disease, interventions aimed at improving health outcomes could be developed. A study to develop and evaluate predictive models that could be used to identify such high-risk patients was unable to do so, however.

The study, published in the Journal of Managed Care & Specialty Pharmacy, noted that outcomes-based payment models have used COPD exacerbations as a quality metric to determine reimbursement rates, but value-based payment models require quality metrics that are measurable and actionable. The researchers noted that identification of appropriate indicators of quality care is a challenge. In addition, the data that are needed may not be routinely available in existing systems.

Previous studies have identified factors that are predictive of COPD exacerbations, including history of exacerbation, COPD disease severity, and COPD treatment. These studies generally included all patients with COPD, regardless of whether they were treated according to clinical guidelines.

Researchers sought to identify factors predictive of exacerbations among patients being treated for COPD with bronchodilator-based combination therapy. They compared patients treated with comparable regimens, but were unable to develop a model that accurately predicted exacerbations.

Using health insurance claims data, researchers obtained demographics, enrollment information, comorbidities, medication use, and healthcare resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient, outpatient, and emergency department exacerbations.

The cohort was split into training (75%) and validation (25%) sets. There were 478,772 patients included in the analytic sample, of whom 40.5% had exacerbations during the outcome period.

Patients with more severe COPD had slightly more comorbidities, medication use, and healthcare resource utilization compared with patients without exacerbations. In the base model, sensitivity was 41.6% and specificity was 85.5%. Positive and negative predictive values were 66.2% and 68.2%, respectively. Other models that were evaluated resulted in similar test characteristics as the base model.

Researchers were not able to predict exacerbations with a high level of accuracy using health insurance claims data from patients with COPD treated with bronchodilator-based combination therapy. The failure to identify predictive factors could be because exacerbations cannot be predicted based on measurable indicators using available technology.

Previous studies have focused on identifying predictors of COPD exacerbations, but none have found a single variable or subset of variables that consistently predict patients who will have an exacerbation among a subset of the COPD patient population managed according to the guidelines. The poor ability to predict exacerbations from a large number of variables throws into doubt whether COPD exacerbations are an outcome that can be consistently predicted using claims data alone among patients treated according to guidelines, the authors wrote.

Other information may be needed to identify high-risk patients, such as clinical measures of lung function and symptoms. Low socioeconomic status, poor access to healthcare, and social stressors also correlate to poorer outcomes, but if that information is not obtainable, it will be difficult to design successful interventions. If providers and healthcare systems are unable to predict and care for high-risk patients, the authors said they question whether reimbursement tied to COPD exacerbations is appropriate. Future studies are needed to explore predictive models for exacerbations, they wrote.

Reference

Samp JC, Joo MJ, Schumock GT, et al. Predicting acute exacerbations in chronic obstructive pulmonary disease. J Manag Care Spec Pharm. 2018;24(3):265-279. doi: 10.18553/jmcp.2018.24.3.265.