Literature DB >> 27486205

Individualized prediction of lung-function decline in chronic obstructive pulmonary disease.

Zafar Zafari1, Don D Sin2, Dirkje S Postma1, Claes-Göran Löfdahl1, Judith Vonk1, Stirling Bryan1, Stephen Lam1, C Martin Tammemagi1, Rahman Khakban1, S F Paul Man1, Donald Tashkin1, Robert A Wise1, John E Connett1, Bruce McManus1, Raymond Ng1, Zsuszanna Hollander1, Mohsen Sadatsafavi1.   

Abstract

BACKGROUND: The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD.
METHODS: Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations.
RESULTS: We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being -124 to -15 mL/yr for smokers and -83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets.
INTERPRETATION: A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD. TRIAL REGISTRATION: Lung Health Study - ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study - ClinicalTrials.gov, no. NCT00751660.
© 2016 Canadian Medical Association or its licensors.

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Year:  2016        PMID: 27486205      PMCID: PMC5047815          DOI: 10.1503/cmaj.151483

Source DB:  PubMed          Journal:  CMAJ        ISSN: 0820-3946            Impact factor:   8.262


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  13 in total

1.  Predicting individual lung-function trajectories: An opportunity for prevention?

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2.  Cost Effectiveness of Case Detection Strategies for the Early Detection of COPD.

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Review 3.  Treatment Trials in Young Patients with Chronic Obstructive Pulmonary Disease and Pre-Chronic Obstructive Pulmonary Disease Patients: Time to Move Forward.

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Journal:  Am J Respir Crit Care Med       Date:  2022-02-01       Impact factor: 21.405

4.  Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.

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

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6.  Evaluation of clinical variables according to follow-up times in COPD: results from ON-SINT cohort.

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7.  The Long-Term Burden of COPD Exacerbations During Maintenance Therapy and Lung Function Decline.

Authors:  Marjan Kerkhof; Jaco Voorham; Paul Dorinsky; Claudia Cabrera; Patrick Darken; Janwillem W H Kocks; Mohsen Sadatsafavi; Don D Sin; Victoria Carter; David B Price
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