Estimating rate of lung function change using clinical spirometry data.

Abstract

Rationale: In chronic obstructive pulmonary disease (COPD), accurately estimating lung function from electronic health record (EHR) data would be beneficial but requires addressing complexities in clinically obtained testing. This study compared analytic methods for estimating rate of forced expiratory volume in one second (FEV₁) change from EHR data.

Methods: We estimated rate of FEV₁ change in patients with COPD from a single centre who had ≥3 outpatient tests spanning at least 1 year. Estimates were calculated as both an absolute mL/year and a relative %/year using non-regressive (Total Change, Average Change) and regressive (Quantile, RANSAC, Huber) methods. We compared distributions of the estimates across methods focusing on extreme values. Univariate zero-inflated negative binomial regressions tested associations between estimates and all-cause or COPD hospitalisations. Results were validated in an external cohort.

Results: Among 1417 participants, median rate of change was approximately -30 mL/year or -2%/year. Non-regressive methods frequently generated erroneous estimates due to outlier first measurements or short intervals between tests. Average change yielded the most extreme estimates (minimum=-3761 mL/year), while regressive methods, and Huber specifically, minimised extreme estimates. Huber, Total Change and Quantile FEV₁ slope estimates were associated with all-cause hospitalisations (Huber incidence rate ratio 0.98, 95% CI 0.97 to 0.99, p<0.001). Huber estimates were also associated with smoking status, comorbidities and prior hospitalisations. Similar results were identified in an external validation cohort.

Conclusions: Using EHR data to estimate FEV₁ rate of change is clinically applicable but sensitive to challenges intrinsic to clinically obtained data. While no analytic method will fully overcome these complexities, we identified Huber regression as useful in defining an individual's lung function change using EHR data.