Evidence map›Paper›PMID 33690706›Full record

Trial reportPloS one2021

Prediction of COPD risk accounting for time-varying smoking exposures.

Joanne T Chang, Rafael Meza, David T Levy, Douglas Arenberg, Jihyoun Jeon

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
5.3field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
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  8. Epidemiological Trends and Projections of PMInternational journal of chronic obstructive pulmonary disease · 2026
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  18. Patterns of Birth Cohort‒Specific Smoking Histories in Brazil.American journal of preventive medicine · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Joanne T ChangDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.
Rafael MezaDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.
David T LevyDepartment of Oncology, Georgetown Lombardi Comprehensive Cancer Center, Washington D.C., DC, United States of America.
Douglas ArenbergDivision of Pulmonary and Critical Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Jihyoun JeonDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID 0000-0001-7003-3412
University of Michigan · USGeorgetown Lombardi Comprehensive Cancer Center

Funding

Research Project 3: Modeling the Impact of Tobacco Control Policies on Polytobacco Use and Associated Health DisparitiesU54CA229974 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ritesh I Mistry · 2018 to 2026
$39.2M
Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
MODELING THE POLICY IMPACT OF CIGARETTE AND SMOKELESS USE ON US MORTALITYR01DA036497 · NIDA · GEORGETOWN UNIVERSITY · PI LEVY, DAVID THEODORE · 2013 to 2016
$2.5M
NCI NIH HHS U01 CA199284NCI NIH HHS U54 CA229974NIDA NIH HHS R01 DA036497
6 · The paper itself

Abstract

rationaleChronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death in the United States. Studies have primarily assessed the relationship between smoking on COPD risk focusing on summary measures, like smoking status.

objectiveDevelop a COPD risk prediction model incorporating individual time-varying smoking exposures.

methodsThe Nurses' Health Study (N = 86,711) and the Health Professionals Follow-up Study (N = 39,817) data was used to develop a COPD risk prediction model. Data was randomly split in 50-50 samples for model building and validation. Cox regression with time-varying covariates was used to assess the association between smoking duration, intensity and year-since-quit and self-reported COPD diagnosis incidence. We evaluated the model calibration as well as discriminatory accuracy via the Area Under the receiver operating characteristic Curve (AUC). We computed 6-year risk of COPD incidence given various individual smoking scenarios.

resultsSmoking duration, year-since-quit (if former smokers), sex, and interaction of sex and smoking duration are significantly associated with the incidence of diagnosed COPD. The model that incorporated time-varying smoking variables yielded higher AUCs compared to models using only pack-years. The AUCs for the model were 0.80 (95% CI: 0.74-0.86) and 0.73 (95% CI: 0.70-0.77) for males and females, respectively.

conclusionsUtilizing detailed smoking pattern information, the model predicts COPD risk with better accuracy than models based on only smoking summary measures. It might serve as a tool for early detection programs by identifying individuals at high-risk for COPD.

Indexed as

Models, BiologicalPulmonary Disease, Chronic ObstructiveSmokingAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPredictive Value of TestsRisk Factors

Identifiers

PMID33690706
PMCPMC7946316
OpenAlexW3133581524

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.