Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease
Year: 2026
Type: article
Abstract: Brian J Wells,1 Amit K Saha,2 Jill A Ohar3 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston Salem, NC, USA; 2Department of Anesthesiology, Wake Forest University School of Medicine, Winston Salem, NC, USA; 3Department of Internal Medicine, Section on Pulmonary, Critical Care, Allergy and Immunologic Disease, Wake Forest University School of Medicine, Winston Salem, NC, USACorrespondence: Brian J Wells, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd, Winston Salem, NC, 27185, USA, Tel +1 336 257-7128, Email [email protected]: Chronic obstructive pulmonary disease (COPD) is frequently diagnosed and treated based on clinical suspicion alone, without spirometric confirmation of expiratory airflow obstruction (AFO, defined by a forced expiratory volume in 1 second (FEV1) to forced vital capacity (FVC) ratio of < 0.7). This can lead to overdiagnosis and unnecessary medication use, whereas underdiagnosis results in missed treatment opportunities. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends targeted case finding. This study aimed to develop and validate an automated Electronic Health Record (EHR) based algorithm to predict AFO and guide targeted spirometric testing.Patients and Methods: Our analysis included 15,065 patients who underwent pulmonary function testing between 2016– 2022. Patients were categorized as having AFO (n=4632) or not (n=10,433) based on spirometry. Patients < 45 years, with cystic fibrosis, alpha-1 antitrypsin deficiency, or prior spirometric evidence of obstruction were excluded. Logistic regression assessed 65 variables, retaining those that optimized model discrimination. The data were randomly split into training (n=10,546) and validation (n=4519) sets.Results: Key predictors of AFO included older age, male sex, lower BMI, smoking history, prior COPD diagnosis, increased emergency department utilization, fewer outpatient visits, fewer chest X-rays, and higher cumulative beta-agonist prescriptions. The final model achieved an area under the receiver operating characteristic curve (AUC) of 0.82 (95% CI: 0.81– 0.83) in the validation dataset and was well calibrated.Conclusion: We developed an EHR-based algorithm that accurately predicts AFO using routinely collected structured data. This tool provides a practical method for identifying patients for targeted COPD case finding. Future efforts will focus on external validation and integration into clinical workflows, enabling automated identification and provider or patient notification to facilitate appropriate pulmonary function testing.Plain Language Summary: Researchers at the Wake Forest University School of Medicine found patterns in computerized medical records that can help identify patients with undiagnosed chronic obstructive pulmonary disease (COPD). Finding patients with possible COPD allows for testing, earlier diagnosis, and treatment before the disease becomes severe. COPD is a group of lung diseases (such as emphysema) that make it difficult to breathe. Although many people with COPD have a history of smoking, there are other risk factors as well. It is estimated that about 60% of people with COPD do not know they have it. COPD can be diagnosed easily with a painless breathing test called spirometry, which only takes a few minutes in a doctor’s office. This new tool can automatically find patients who should be tested for COPD without needing them to fill out symptom questionnaires. It uses statistics to combine information such as age, sex, weight, height, and smoking history with data on past prescriptions, chest x-rays, and emergency department visits. The researchers hope that, in the future, patients might be notified about their risk and have the test done even before seeing their doctor. Early diagnosis allows people to get vaccines that protect their lungs and to start medicines that can help prevent serious health problems. By using this tool, doctors can help more patients get the care they need sooner.Keywords: risk assessment, chronic obstructive pulmonary disease, spirometry, screening, clinical informatics, electronic health records
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7119397226
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