A discriminant function model as an alternative method to spirometry for COPD screening in primary care settings in China

Development of a prediction model to identify undiagnosed chronic obstructive pulmonary disease patients in primary care settings in China

BACKGROUND

At present, a large number of chronic obstructive pulmonary disease (COPD) patients are undiagnosed in China. Thus, this study aimed to develop a simple prediction model as a screening tool to identify patients at risk for COPD.

METHODS

The study was based on the data of 22,943 subjects aged 30 to 79 years and enrolled in the second resurvey of China Kadoorie Biobank during 2012 and 2013 in China. We stepwisely selected the predictors using logistic regression model. Then we tested the model validity through P-P graph, area under the receiver operating characteristic curve (AUROC), ten-fold cross validation and an external validation in a sample of 3492 individuals from the Enjoying Breathing Program in China.

RESULTS

The final prediction model involved 14 independent variables, including age, sex, location (urban/rural), region, educational background, smoking status, smoking amount (pack-years), years of exposure to air pollution by cooking fuel, family history of COPD, history of tuberculosis, body mass index, shortness of breath, sputum and wheeze. The model showed an area under curve (AUC) of 0.72 (95% confidence interval [CI]: 0.72-0.73) for detecting undiagnosed COPD patients, with the cutoff of predicted probability of COPD=0.22, presenting a sensitivity of 70.13% and a specificity of 62.25%. The AUROC value for screening undiagnosed patients with clinically significant COPD was 0.68 (95% CI: 0.66-0.69). Moreover, the ten-fold cross validation reported an AUC of 0.72 (95% CI: 0.71-0.73), and the external validation presented an AUC of 0.69 (95% CI: 0.68-0.71).

CONCLUSION

This prediction model can serve as a first-stage screening tool for undiagnosed COPD patients in primary care settings.

Influences of Two FEV1 Reference Equations (GLI-2012 and GIRH-2017) on Airflow Limitation Classification Among COPD Patients

Objective

Methods

Results

Conclusion

The Burden of COPD in China and Its Provinces: Findings From the Global Burden of Disease Study 2019

In China, chronic obstructive pulmonary disease (COPD) was accounted for a quarter of the global COPD population and has become a large economic burden. However, the comprehensive picture of the COPD burden, which could inform health policy, is not readily available for all of the provinces of China. Here, we aimed to describe the burden of COPD in China, providing an up-to-date and comprehensive analysis at the national and provincial levels, and time trends from 1990 to 2019. Following the methodology framework and general analytical strategies used in the GBD 2019, we analyzed the incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years with life lost (YLLs) attributable to COPD across China and the corresponding time trends from 1990 to 2019, stratified by age and province. In order to quantify the secular trends of the burden of COPD, the estimated annual percentage changes were calculated by the linear regression model of age-standardized rates (ASRs) and calendar years. We also presented the contribution of risk factors to COPD-related mortality and DALYs. The association between COPD burden and socio-demographic index (SDI) were also evaluated. From 1990 to 2019, the incidence and prevalence numbers of COPD increased by 61.2 and 67.8%, respectively, whereas the number of deaths and DALYs owing to COPD decreased. The ASRs of COPD burden, including incidence, prevalence, mortality, DALYs, YLDs, and YLLs continuously decreased from 1990 to 2019. The crude rates of COPD burden dramatically increased with age and reached a peak in the older than 95 years age group. In 2019, the leading risk factor for COPD mortality and DALYs was tobacco use in the whole population, but ambient particulate matter pollution was the most significant risk factor in females. At the provincial level, the ASRs of COPD burden was significantly associated with the SDIs, with the highest ASRs in the western provinces with low SDIs. Collectively, our study indicated that COPD remains an important public health problem in China. Geographically targeted considerations should be developed to enhance COPD health and reduce the COPD burden throughout China and in specific provinces.

The Construction of Primary Screening Model and Discriminant Model for Chronic Obstructive Pulmonary Disease in Northeast China

Objective

The diagnosis of chronic obstructive pulmonary disease (COPD) is challenging, especially in the primary institution which lacks spirometer. To reduce the rate of COPD missed diagnoses in Northeast China, which has a higher prevalence of COPD, this study aimed to establish efficient primary screening and discriminant models of COPD in this region.

Patients and Methods

Subjects from Northeast China were enrolled from December 2017 to April 2019 from The First Hospital of China Medical University. Pulmonary function tests and questionnaire were given to all participants. Using illness or no illness as the goal for screening models and disease severity as the goal for discriminant models, multivariate linear regression, logical regression, linear discriminant analysis, K-nearest neighbor, decision tree and support vector machine were constructed through R language and Python software. After comparing effectiveness among them, the most optimal primary screening and discriminant models were established.

Results

Enrolled were 232 COPD patients (124 GOLD I–II and 108 GOLD III–IV) and 218 normal controls. Eight primary screening models were established. The optimal model was Y = −1.2562–0.3891X₄ (education level) + 1.7996X₅ (dyspnea) + 0.5102X₆ (cooking fuel grade) + 1.498X₇ (smoking index) + 0.8077X₉ (family history)-0.5552X₁₁ (BMI) + 0.538X₁₃ (cough with sputum) + 2.0328X₁₄ (wheezing) + 1.3378X₁₆ (farmers) + 0.8187X₁₇ (mother’s smoking exposure history during pregnancy)-0.389X₁₈ (kitchen ventilation) + 0.6888X₁₉ (childhood heating). Six discriminant models were established. The optimal model was decision tree (the optimal variables: dyspnea (x₅), cooking fuel grade (x₆), second-hand smoking index (x₈), BMI (x₁₁), cough (x₁₂), cough with sputum (x₁₃), wheezing (x₁₄), farmer (x₁₆), kitchen ventilation (x₁₈), and childhood heating (x₁₉)). The code was established to combine the discriminant model with computer technology.

Conclusion

Many factors were related to COPD in Northeast China. Stepwise logistic regression and decision tree were the optimal screening and discriminant models for COPD in this region.

Guidelines for diagnosis and management of chronic obstructive pulmonary disease: Joint ICS/NCCP (I) recommendations

Chronic obstructive pulmonary disease (COPD) is a major public health problem in India. Although several International guidelines for diagnosis and management of COPD are available, yet there are lot of gaps in recognition and management of COPD in India due to vast differences in availability and affordability of healthcare facilities across the country. The Indian Chest Society (ICS) and the National College of Chest Physicians (NCCP) of India have joined hands to come out with these evidence-based guidelines to help the physicians at all levels of healthcare to diagnose and manage COPD in a scientific manner. Besides the International literature, the Indian studies were specifically analyzed to arrive at simple and practical recommendations. The evidence is presented under these five headings: (a) definitions, epidemiology, and disease burden; (b) disease assessment and diagnosis; (c) pharmacologic management of stable COPD; (d) management of acute exacerbations; and (e) nonpharmacologic and preventive measures. The modified grade system was used for classifying the quality of evidence as 1, 2, 3, or usual practice point (UPP). The strength of recommendation was graded as A or B depending upon the level of evidence.