An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study

The Accuracy of PUMA Questionnaire in Combination With Peak Expiratory Flow Rate to Identify At-risk, Undiagnosed COPD Patients

INTRODUCTION

The English PUMA questionnaire emerges as an effective COPD case-finding tool. We aimed to use the PUMA questionnaire in combination with peak expiratory flow rate (PEFR) to improve case-finding efficacy in Chinese population.

METHODS

This cross-sectional, observational study included two stages: translating English to Chinese PUMA (C-PUMA) questionnaire with linguistic validation and psychometric evaluation, followed by clinical validation. Eligible participants (with age ≥40 years, respiratory symptoms, smoking history ≥10 pack-years) were enrolled and subjected to three questionnaires (C-PUMA, COPD assessment test [CAT], and generic health survey [SF-12V2]), PEFR measurement, and confirmatory spirometry. The C-PUMA score and PEFR were incorporated into a PUMA-PEFR prediction model applying binary logistic regression coefficients to estimate the probability of COPD (PCOPD).

RESULTS

C-PUMA was finalized through standard forward-backward translation processes and confirmation of good readability, comprehensibility, and reliability. In clinical validation, 240 participants completed the study. 78/240 (32.5%) were diagnosed with COPD. C-PUMA exhibited significant validity (correlated with CAT or physical component scores of SF-12V2, both P<0.05, respectively). PUMA-PEFR model had higher diagnostic accuracy than C-PUMA alone (area under ROC curve, 0.893 vs. 0.749, P<0.05). The best cutoff values of C-PUMA and PUMA-PEFR model (PCOPD) were ≥6 and ≥0.39, accounting for a sensitivity/specificity/numbers needed to screen of 77%/64%/3 and 79%/88%/2, respectively. C-PUMA ≥5 detected more underdiagnosed patients, up to 11.5% (vs. C-PUMA ≥6).

CONCLUSION

C-PUMA is well-validated. The PUMA-PEFR model provides more accurate and cost-effective case-finding efficacy than C-PUMA alone in at-risk, undiagnosed COPD patients. These tools can be useful to detect COPD early.

How to Enhance the Diagnosis of Early Stages of Chronic Obstructive Pulmonary Disease (COPD)? The Role of Mobile Spirometry in COPD Screening and Diagnosis-A Systematic Review

COPD is the third leading cause of death worldwide. Its diagnosis can be made with spirometry, which is underused due to its limited accessibility. Portable spirometry holds promise for enhancing the efficacy of COPD diagnoses. The study aimed to estimate COPD prevalence diagnosed with a portable spirometer in high-risk patients and compare it with COPD prevalence based on data from conventional, on-site spirometry. We also evaluated the strategy of a proactive approach to identify COPD in high-risk individuals. We conducted a systematic review of original studies on COPD targeted screening and diagnosis with portable and conventional spirometers selected from 8496 publications initially found in three databases: Cochrane, PubMed, and Embase. The inclusion criteria were met by 28 studies. COPD prevalence evaluated with the use of portable spirometers reached 20.27% and was lower compared to that estimated with the use of conventional spirometers (24.67%). In 11 included studies, postbronchodilator tests were performed with portable spirometers, which enabled a bedside COPD diagnosis. Portable spirometers can be successfully used in COPD targeted screening and diagnosis and thus enhance the detection of COPD at early stages.

Current Progress of COPD Early Detection: Key Points and Novel Strategies

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approximately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients.

Screening for impaired pulmonary function using peak expiratory flow: Performance of different interpretation strategies

BACKGROUND

Spirometry is the gold standard for diagnosis of impaired pulmonary function, but is often unavailable in resource-constrained settings. Some authors have suggested using peak expiratory flow (PEF) to screen for impaired pulmonary function when spirometry is unavailable, but with no consensus on how to define abnormally low PEF. Strategies have included cutoffs based on absolute value of PEF, PEF in percent predicted, PEF Z-score, PEF × height^-2, and gender-specific cutoffs of absolute PEF. The objective of this paper is to determine the PEF interpretation strategy with the highest predictive ability for low pulmonary function, with spirometry as the gold standard.

METHODS

We analyzed data on individuals aged 40-79 years in the United States National Health and Nutrition Examination Survey 2007-2012. 6,144 individuals fulfilled inclusion criteria for the main analysis. For each PEF interpretation strategy, we calculated the area under the receiver operating curve (AUC) for the detection of low pulmonary function (defined by FEV₁ Z-score < -1.645, < -2, < -2.5 or < -3).

RESULTS

The AUC was substantially and statistically significantly higher for PEF in percent predicted and PEF Z-score than for absolute value and PEF × height^-2, including after stratification by gender. There was no difference in AUC between PEF in percent predicted and PEF Z-score.

CONCLUSION

If using PEF to screen adults aged 40 years or older for impaired pulmonary function defined by low FEV₁ Z-score, basing cutoffs on PEF in percent predicted or PEF Z-score may result in improved predictive ability. As percent predicted is a mathematically simpler term than Z-score, it may be preferable to use cutoffs based on PEF in percent predicted.

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.

Discriminative Accuracy of the CAPTURE Tool for Identifying Chronic Obstructive Pulmonary Disease in US Primary Care Settings

Importance

Chronic obstructive pulmonary disease (COPD) is underdiagnosed in primary care.

Objective

To evaluate the operating characteristics of the CAPTURE (COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk) screening tool for identifying US primary care patients with undiagnosed, clinically significant COPD.

Design, Setting, and Participants

In this cross-sectional study, 4679 primary care patients aged 45 years to 80 years without a prior COPD diagnosis were enrolled by 7 primary care practice-based research networks across the US between October 12, 2018, and April 1, 2022. The CAPTURE questionnaire responses, peak expiratory flow rate, COPD Assessment Test scores, history of acute respiratory illnesses, demographics, and spirometry results were collected.

Exposure

Undiagnosed COPD.

Main Outcomes and Measures

The primary outcome was the CAPTURE tool's sensitivity and specificity for identifying patients with undiagnosed, clinically significant COPD. The secondary outcomes included the analyses of varying thresholds for defining a positive screening result for clinically significant COPD. A positive screening result was defined as (1) a CAPTURE questionnaire score of 5 or 6 or (2) a questionnaire score of 2, 3, or 4 together with a peak expiratory flow rate of less than 250 L/min for females or less than 350 L/min for males. Clinically significant COPD was defined as spirometry-defined COPD (postbronchodilator ratio of forced expiratory volume in the first second of expiration [FEV1] to forced vital capacity [FEV1:FVC] <0.70 or prebronchodilator FEV1:FVC <0.65 if postbronchodilator spirometry was not completed) combined with either an FEV1 less than 60% of the predicted value or a self-reported history of an acute respiratory illness within the past 12 months.

Results

Of the 4325 patients who had adequate data for analysis (63.0% were women; the mean age was 61.6 years [SD, 9.1 years]), 44.6% had ever smoked cigarettes, 18.3% reported a prior asthma diagnosis or use of inhaled respiratory medications, 13.2% currently smoked cigarettes, and 10.0% reported at least 1 cardiovascular comorbidity. Among the 110 patients (2.5% of 4325) with undiagnosed, clinically significant COPD, 53 had a positive screening result with a sensitivity of 48.2% (95% CI, 38.6%-57.9%) and a specificity of 88.6% (95% CI, 87.6%-89.6%). The area under the receiver operating curve for varying positive screening thresholds was 0.81 (95% CI, 0.77-0.85).

Conclusions and Relevance

Within this US primary care population, the CAPTURE screening tool had a low sensitivity but a high specificity for identifying clinically significant COPD defined by presence of airflow obstruction that is of moderate severity or accompanied by a history of acute respiratory illness. Further research is needed to optimize performance of the screening tool and to understand whether its use affects clinical outcomes.

Associations of Clinical Characteristics and Intestinal Flora Imbalance in Stable Chronic Obstructive Pulmonary Disease (COPD) Patients and the Construction of an Early Warning Model

Objective

Establish a simple predictive model and scoring rule that is suitable for clinical medical staff in respiratory departments to assess intestinal flora imbalance occurrence in stable chronic obstructive pulmonary disease (COPD) patients.

Methods

From January 1, 2019, to December 31, 2020, COPD patients (195 cases) – who attended the Outpatient Department, Respiratory and Critical Care, Yixing Hospital, Jiangsu University – were enrolled in a cross-sectional study. Based on stool examination results, patients were divided into experimental (41 cases) and control (154 cases) groups. Single-factor and logistic regression analyses were performed with the baseline data of the two groups to obtain a new predictive model, which was further simplified.

Results

Five predictive factors composed the model: body mass index (BMI), serum albumin (ALB), Charlson’s Comorbidity Index (CCI), gastrointestinal symptom score (GSRs), and Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. The model to predict intestinal flora imbalance in stable COPD patients had an area under the ROC curve (AUC) of 0.953 [95% CI (0.924, 0.982)]. After simplifying the scoring rules, the AUC was 0.767 [95% CI (0.676, 0.858)].

Conclusion

In the current study, we obtained a model that could effectively predict intestinal flora imbalance risk in stable COPD patients, being suitable for implementation in early treatments to improve the prognosis. Moreover, all indicators can be easily and simply obtained.

Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools

BACKGROUND AND AIM

Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed because of the unavailability of spirometers, especially in resource-limited outpatient settings. This study provides real-world evidence to identify optimal approaches for COPD case finding in outpatient settings.

METHODS

This retrospective study enrolled individuals who were at risk of COPD (age ≥40 years, ≥10 pack-years, and ≥1 respiratory symptom). Eligible participants were examined using various COPD case-finding tools, namely the COPD Population Screener (COPD-PS) questionnaire, a COPD prediction (PCOPD) model, and a microspirometer, Spirobank Smart; subsequently, the participants underwent confirmatory spirometry. The definition and confirmation of COPD were based on conventional spirometry. Receiver operating characteristic curve (ROC), area under the curve (AUC), and decision curve analyses were conducted, and a clinical impact curve was constructed.

RESULTS

In total, 385 participants took part in the study [284 without COPD (73.77%) and 101 with COPD (26.23%)]. The microspirometer exhibited a higher AUC value than did the COPD-PS questionnaire and the PCOPD model. The AUC for microspirometry was 0.908 (95% confidence interval [CI] = 0.87-0.95), that for the PCOPD model was 0.788 (95% CI = 0.74-0.84), and that for the COPD-PS questionnaire was 0.726 (95% CI = 0.67-0.78). Decision and clinical impact curve analyses revealed that a microspirometry-derived FEV1/FVC ratio of <74% had superior clinical utility to the other measurement tools.

CONCLUSION

The PCOPD model and COPD-PS questionnaire were useful for identifying symptomatic patients likely to have COPD, but microspirometry was more accurate and had higher clinical utility. This study provides real-world evidence to identify optimal practices for COPD case finding; such practices ensure that physicians have convenient access to up-to-date evidence when they encounter a symptomatic patient likely to have COPD.

Novel App-Based Portable Spirometer for the Early Detection of COPD

Chronic obstructive pulmonary disease (COPD) is preventable and treatable. However, many patients remain undiagnosed and untreated due to the underutilization or unavailability of spirometers. Accordingly, we used Spirobank Smart, an app-based spirometer, for facilitating the early detection of COPD in outpatient clinics. This prospective study recruited individuals who were at risk of COPD (i.e., with age of ≥40 years, ≥10 pack-years of smoking, and at least one respiratory symptoms) but had no previous COPD diagnosis. Eligible participants were examined with Spirobank Smart and then underwent confirmatory spirometry (performed using a diagnostic spirometer), regardless of their Spirobank Smart test results. COPD was defined and confirmed using the postbronchodilator forced expiratory volume in 1 s/forced vital capacity values of <0.70 as measured by confirmatory spirometry. A total of 767 participants were enrolled and examined using Spirobank Smart; 370 participants (94.3% men, mean age of 60.9 years and mean 42.6 pack-years of smoking) underwent confirmatory spirometry. Confirmatory spirometry identified COPD in 103 participants (27.8%). At the optimal cutoff point of 0.74 that was determined using Spirobank Smart for COPD diagnosis, the area under the receiver operating characteristic was 0.903 (95% confidence interval (CI) = 0.860-0.947). Multivariate logistic regression revealed that participants who have an FEV₁/FVC ratio of <74% that was determined using Spirobank Smart (odds ratio (OR) = 58.58, 95% CI = 27.29-125.75) and old age (OR = 3.23, 95% CI = 1.04-10.07 for 60 ≤ age < 65; OR = 5.82, 95% CI = 2.22-15.27 for age ≥ 65) had a higher risk of COPD. The Spirobank Smart is a simple and adequate tool for early COPD detection in outpatient clinics. Early diagnosis and appropriate therapy based on GOLD guidelines can positively influence respiratory symptoms and quality of life.

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.