A predictive model for the development of chronic obstructive pulmonary disease

Guo, Yi; Qian, Yanrong; Gong, Yi; Pan, Chun-Ming; Shi, Guochao; Wan, Huanying

doi:10.3892/br.2015.503

Cited by 7 publications

(40 citation statements)

References 38 publications

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“…Only four of these were selected for inclusion, data extraction, and synthesis. The four models developed by these studies differed on many characteristics including the derivation cohort (age, nationality, at-risk), predictors used, statistical methods, and COPD definitions [17][18][19][20] (Table 2).…”

Section: Overview Of the Studies Included In The Reviewmentioning

confidence: 99%

“…Of the four studies, one was from a general population cohort, 18 two were from electronic medical records databases (one from primary care data 20 and one from hospital record data of asthma patients), 19 and one was a hospital-based case-control study with participants selected from the respiratory medicine department. 17 One study did not report any specific method for the selection of the initial model predictors, 18 two studies used predictors previously α reported in the literature, 17,20 and the remaining study used a Bayesian network approach to identify predictors. 19 Predictors included in all models…”

Section: Study Type and Populationmentioning

confidence: 99%

“…All four studies included smoking as a predictor (Table 3). [17][18][19][20] The studies by Guo et al, 17 Kotz et al, 20 and Himes et al 19 all defined smoking as a binary variable; either ever or never smoker. Higgins et al 18 reported smoking in three ways: 1) any cigarette smoking (non-smoker, current smoker, ex-smoker), 2) cigarettes per day (categories of none; 1-19; 20+), and 3) change in cigarette smoking between the baseline and follow-up study.…”

Section: Study Type and Populationmentioning

confidence: 99%

“…[18][19][20] One used lung function (% predicted FEV 1 ) 18 and two studies used comorbidities (ie, "acute bronchitis and bronchiolitis", "pneumonia, organism unspecified", "shortness of breath", "respiratory distress or insufficiency", "diabetes mellitus", "acute upper respiratory infection", "viral and chlamydial infections", and "heart failure") or having respiratory infections as predictors (respiratory infections in childhood). 17,19 Included in the final model of Guo et al 17 were five single nucleotide polymorphisms (SNPs) (rs2070600, rs10947233, rs1800629, rs2241712, and rs1205) chosen out of a possible 76 identified in a preliminary genome wide association study (GWAS). Two studies developed models separately for men and women.…”

Section: Predictors Included In Some Modelsmentioning

confidence: 99%

“…The outcome definition of COPD varied between the different studies (Supplementary material). Two studies used spirometry to define COPD: one used the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criterion of post-bronchodilator FEV 1 /FVC ratio ,0.70 as a cut-off 17 and one defined COPD as an FEV 1 ,65% of the predicted value in combination with an FEV 1 /FVC ratio ,80%. 18 Two studies used disease classifications given in the electronic database systems which were based on clinical diagnosis with no further details provided.…”

Section: Predictors Included In Some Modelsmentioning

confidence: 99%

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Prediction models for the development of COPD: a systematic review

et al. 2017

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Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts.

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Section: Overview Of the Studies Included In The Reviewmentioning

confidence: 99%

Section: Study Type and Populationmentioning

confidence: 99%

Section: Study Type and Populationmentioning

confidence: 99%

Section: Predictors Included In Some Modelsmentioning

confidence: 99%

Section: Predictors Included In Some Modelsmentioning

confidence: 99%

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Prediction models for the development of COPD: a systematic review

et al. 2017

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Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors

He,

Qian,

Diao

et al. 2023

Nat Commun

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Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We perform a longitudinal analysis of COPD in the UK Biobank to derive and validate the Socioeconomic and Environmental Risk Score which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. The Socioeconomic and Environmental Risk Score is more predictive of COPD than smoking status and pack-years. Individuals in the highest decile of the risk score have a greater risk for incident COPD compared to the remaining population. Never smokers in the highest decile of exposure risk are more likely to develop COPD than previous and current smokers in the lowest decile. In general, the prediction accuracy of the Social and Environmental Risk Score is lower in non-European populations. While smoking status is often considered in screening COPD, our finding highlights the importance of other non-smoking environmental and socioeconomic variables.

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Machine Learning Prediction of Progression in FEV₁in the COPDGene Study

Boueiz

Chang

Masoomi

et al. 2022

Preprint

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Background: The heterogeneous nature of COPD complicates the identification of the predictors of disease progression and consequently the development of effective therapies. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4,496 smokers with available data from their enrollment and 5-year follow-up visits in the Genetic Epidemiology of COPD (COPDGene) study. We constructed supervised random forest models to predict 5-year progression in FEV1 from 46 baseline demographic, clinical, physiologic, and imaging features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. Nevertheless, the area under the ROC curves for the prediction of subjects in the top quartile of observed disease progression was 0.70 in the 10-year follow-up data. The model performance accuracy was best for GOLD1-2 subjects and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD grade. Conclusion: This state-of-the art approach along with deep phenotyping predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials.

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A predictive model for the development of chronic obstructive pulmonary disease

Cited by 7 publications

References 38 publications

Prediction models for the development of COPD: a systematic review

Prediction models for the development of COPD: a systematic review

Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors

Machine Learning Prediction of Progression in FEV₁in the COPDGene Study

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A predictive model for the development of chronic obstructive pulmonary disease

Cited by 7 publications

References 38 publications

Prediction models for the development of COPD: a systematic review

Prediction models for the development of COPD: a systematic review

Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors

Machine Learning Prediction of Progression in FEV1in the COPDGene Study

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Product

Resources

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Machine Learning Prediction of Progression in FEV₁in the COPDGene Study