Chronic obstructive pulmonary disease risk assessment tools: is one better than the others?

Wang, Jennifer M; Han, MeiLan K.; Labaki, Wassim W.

doi:10.1097/mcp.0000000000000833

Cited by 11 publications

(6 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study also sought to determine the markers of clinically significant exacerbation and hospitalization [ 34 , 35 , 36 ]. We found that most of the clinical, physiological, and PRO measurements taken at the start were significant predictors of the first exacerbation and the first hospitalization caused by it, apart from BMI and the D-12 Affective score.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD

et al. 2023

View full text Add to dashboard Cite

Background: Patient-reported outcome (PRO) measures must be evaluated for their discriminatory, evaluative, and predictive properties. However, the predictive capability remains unclear. We aimed to examine the predictive properties of several PRO measures of all-cause mortality, acute exacerbation of chronic obstructive pulmonary disease (COPD), and associated hospitalization. Methods: A total of 122 outpatients with stable COPD were prospectively recruited and completed six self-administered paper questionnaires: the COPD Assessment Test (CAT), St. George’s Respiratory Questionnaire (SGRQ), Baseline Dyspnea Index (BDI), Dyspnoea-12, Evaluating Respiratory Symptoms in COPD and Hyland Scale at baseline. Cox proportional hazards analyses were conducted to examine the relationships with future outcomes. Results: A total of 66 patients experienced exacerbation, 41 were hospitalized, and 18 died. BDI, SGRQ Total and Activity, and CAT and Hyland Scale scores were significantly related to mortality (hazard ratio = 0.777, 1.027, 1.027, 1.077, and 0.951, respectively). The Hyland Scale score had the best predictive ability for PRO measures, but the C index did not reach the level of the most commonly used FEV1. Almost all clinical, physiological, and PRO measurements obtained at baseline were significant predictors of the first exacerbation and the first hospitalization due to it, with a few exceptions. Conclusions: Measurement of health status and the global scale of quality of life as well as some tools to assess breathlessness, were significant predictors of all-cause mortality, but their predictive capacity did not reach that of FEV1. In contrast, almost all baseline measurements were unexpectedly related to exacerbation and associated hospitalization.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In this study, we collected medical information of patients and focused on the cases with around 2-year follow-up records of HRCT and respiratory function tests. We combined HRCT data with indices of lung function for evaluating progress of the disease ( 14 ). This is the first report to compare the cumulative progression rate between patients with artificial stone-associated silicosis and non-artificial stone-associated silicosis.…”

Section: Introductionmentioning

confidence: 99%

Risk Factors of Silicosis Progression: A Retrospective Cohort Study in China

Quan

Yang

et al. 2022

Front. Med.

View full text Add to dashboard Cite

BackgroundSilicosis poses a threat to workers’ health due to the irreversible lung lesions.DesignA retrospective cohort study.MethodsA total of 259 patients [80 worked with artificial stone (AS), 179 with non-artificial stone (non-AS)] with confirmed silicosis were included in this study. Forty-one of AS and 91 of non-AS had approximately 2 years’ follow-up records [lung function tests and high-resolution computer tomography (HRCT)]. Compared with the first records, increased, densified, or newly emerging lesions in lung HRCT images were judged as progression of the disease. Cox proportional hazards models were used to determine the risk factors. Kaplan–Meier survival curve and log-rank test were used to compare prognostic factors for cumulative risk of progression.ResultsIn 132 patients with median follow-up of 24.0 months (IQR, 13.8, 24.9), 66 patients showed progression, in them, 36 (87.8%) were from AS group and 30 (32.9%) from non-AS group. Working experience of AS processing (hazard ratio, 5.671; 95% CI, 3.048–10.550) and complicated silicosis in CT images (hazard ratio, 2.373; 95% CI, 1.379–4.082) were the main risk factors associated with progression. Forced vital capacity decreased after 1-year (241.5 vs. 55.2 mL) and 2-year (328.1 vs. 68.8 mL) follow-up in the two groups (AS vs. non-AS). History of anti-tuberculosis medication, chest oppression and pain, ground-glass opacity, pleural abnormalities, and restrictive pulmonary dysfunction were more frequently found on HRCT images in the AS group than non-AS group. Lung functions (DLCO, %) were lower in the current/former smokers than the non-smokers (P < 0.05) in AS patients.ConclusionPrevention and protection rules are needed to be enforced in the occupation involving AS processing; smoking may be associated with declined lung function in AS patients.

show abstract

“… 15 However, solely relying on peripheral blood eosinophils to guide personalized corticosteroid treatment may be inadequate, and all available clinical data should be taken into consideration. 16 , 17 …”

Section: Introductionmentioning

confidence: 99%

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

Feng,

Li,

Qian

et al. 2024

JIR

View full text Add to dashboard Cite

Purpose Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) exhibit heterogeneous responses to corticosteroid treatment. We aimed to determine whether combining eosinophil levels with other routine clinical indicators can enhance the predictability of corticosteroid treatment outcomes and to come up with a scoring system. Patients and Methods Consecutive patients admitted with AECOPD receiving corticosteroid treatment between July 2013 and March 2022 at Beijing Chao-Yang Hospital were retrospectively analyzed. Data on patients’ demographics, smoking status, hospitalization for AECOPD in the previous year, comorbidities, blood laboratory tests, in-hospital treatment and clinical outcomes were collected. Least absolute shrinkage and selection operator (LASSO) regression and backward logistic regression were used for predictor selection, and predictive nomograms were developed. The discrimination and calibration of the nomograms were assessed using the area under the receiver operating curve (AUC) and calibration plots. Internal validation was performed using the 500-bootstrap method, and clinical utility was evaluated using decision curve analysis (DCA). Results Among the 3254 patients included, 804 (24.7%) had treatment failure. A nomogram of eosinophils, platelets, C-reactive protein (CRP), low density lipoprotein cholesterol, prognostic nutritional index (PNI), hospitalization for AECOPD in the previous year, ischemic heart diseases and chronic hepatic disease was developed to predict treatment failure for patients with a smoking history. For patients without a smoking history, a nomogram of CRP, PNI, ischemic heart diseases and chronic hepatic disease was developed. Although the AUCs of these two nomograms were only 0.644 and 0.647 respectively, they were significantly superior to predictions based solely on blood eosinophil levels. Conclusion We developed easy-to-use comprehensive nomograms utilizing readily available clinical biomarkers related to inflammation, nutrition and immunity, offering modestly enhanced predictive value for treatment outcomes in corticosteroid-treated patients with AECOPD. Further investigations into novel biomarkers and additional patient data are imperative to optimize the predictive performance.

show abstract

Chronic obstructive pulmonary disease risk assessment tools: is one better than the others?

Cited by 11 publications

References 75 publications

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD

Risk Factors of Silicosis Progression: A Retrospective Cohort Study in China

Comprehensive Nomograms Using Routine Biomarkers Beyond Eosinophil Levels: Enhancing Predictability of Corticosteroid Treatment Outcomes in AECOPD

Contact Info

Product

Resources

About