Ten-year prediction model for post-bronchodilator airflow obstruction and early detection of COPD: development and validation in two middle-aged population-based cohorts

Abstract

BackgroundClassifying individuals at high chronic obstructive pulmonary disease (COPD)-risk creates opportunities for early COPD detection and active intervention.ObjectiveTo develop and validate a statistical model to predict 10-year probabilities of COPD defined by post-bronchodilator airflow obstruction (post-BD-AO; forced expiratory volume in 1 s/forced vital capacity<5th percentile).SettingGeneral Caucasian populations from Australia and Europe, 10 and 27 centres, respectively.ParticipantsFor the development cohort, questionnaire data on respiratory symptoms, smoking, asthma, occupation and participant sex were from the Tasmanian Longitudinal Health Study (TAHS) participants at age 41–45 years (n=5729) who did not have self-reported COPD/emphysema at baseline but had post-BD spirometry and smoking status at age 51–55 years (n=2407). The validation cohort comprised participants from the European Community Respiratory Health Survey (ECRHS) II and III (n=5970), restricted to those of age 40–49 and 50–59 with complete questionnaire and spirometry/smoking data, respectively (n=1407).Statistical methodRisk-prediction models were developed using randomForest then externally validated.ResultsArea under the receiver operating characteristic curve (AUCROC) of the final model was 80.8% (95% CI 80.0% to 81.6%), sensitivity 80.3% (77.7% to 82.9%), specificity 69.1% (68.7% to 69.5%), positive predictive value (PPV) 11.1% (10.3% to 11.9%) and negative predictive value (NPV) 98.7% (98.5% to 98.9%). The external validation was fair (AUCROC 75.6%), with the PPV increasing to 17.9% and NPV still 97.5% for adults aged 40–49 years with ≥1 respiratory symptom. To illustrate the model output using hypothetical case scenarios, a 43-year-old female unskilled worker who smoked 20 cigarettes/day for 30 years had a 27% predicted probability for post-BD-AO at age 53 if she continued to smoke. The predicted risk was 42% if she had coexistent active asthma, but only 4.5% if she had quit after age 43.ConclusionThis novel and validated risk-prediction model could identify adults aged in their 40s at high 10-year COPD-risk in the general population with potential to facilitate active monitoring/intervention in predicted ‘COPD cases’ at a much earlier age.

Undiagnosed and ‘overdiagnosed’ COPD using postbronchodilator spirometry in primary healthcare settings: a systematic review and meta-analysis

“…This poses a serious new challenge for primary care clinicians when trying to follow clinical pathways that align with the recommendations of COPD guidelines,4–7 20 given that access to public and private respiratory laboratories can be limited by either availability or patient out-of-pocket expenses, respectively. A potential way forward is developing prediction tools that risk-stratify patients,8 84 and/or establishing diagnostic ‘hubs or hublets’ to provide a good-quality diagnostic spirometry service at a local network level in the community 81 85…”

Development and validation of immune-based biomarkers and deep learning models for Alzheimer’s disease

“…The random forest (RF) algorithm is an emerging and high precision machine learning algorithm that has been widely used in numerous fields, and of course, its role in the medical field is also exact. RF algorithms have been used for clinical diseases, such as using random forests to identify biomarkers for glioblastoma to find potential targets for treatment ( Li et al, 2021 ), building COPD risk prediction models ( Perret et al, 2021 ), and detecting and predicting type 2 diabetes ( Muneeb and Henschel, 2021 ), all with good results. An artificial neural network is a new type of algorithm derived from imitating the structure and function of the human brain, which has the characteristics of self-learning ability and high efficiency compared with the traditional machine learning algorithm.…”

Cigarette Smoking and Asthma

“…21, 93 A COPD risk-prediction model estimated that a 43-year-old female unskilled worker with asthma who smoked 20 cigarettes/day for 30 years had an estimated 42% risk of COPD in the next 10 years, but only 4.5% if she stopped smoking at age 43. 94 Cigarette smokers with asthma and COPD are no more likely to receive smoking cessation counseling and pharmacotherapy from physicians compared to the general smoking population. 95 Cigarette smoking quit rates are improved with behavioral counseling in combination with pharmacotherapies, such as nicotine replacement products, varenicline, and bupropion.…”