File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Development and validation of a prediction model for airflow obstruction in older Chinese: Guangzhou Biobank Cohort Study

TitleDevelopment and validation of a prediction model for airflow obstruction in older Chinese: Guangzhou Biobank Cohort Study
Authors
KeywordsAirflow obstruction
Prediction model
Issue Date2020
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/rmed
Citation
Respiratory Medicine, 2020, v. 173, p. article no. 106158 How to Cite?
AbstractObjective: To develop and validate a prediction model for airflow obstruction (AO) in older Chinese. Methods. Design: Multivariable logistic regression analysis in large population cohort of Chinese aged ≥50 years. Participants: Model development: 8762 Chinese aged ≥50 years were selected from the early phase recruits to the Guangzhou Biobank Cohort Study (GBCS) (recruited from September 2003 to May 2006). Internal validation: 100 bootstrap samples drawn with replacement from the development sample. External validation: 8395 Chinese aged ≥50 years from later phase GBCS (recruited from September 2006 to January 2008). Outcomes: AO was defined by a forced expiratory volume in 1 s/forced vital capacity ratio < lower limits of normal. Results: 839 (9.6%) and 764 (9.1%) individuals had AO in the development and temporal validation samples respectively. The predictors in the prediction model included sex, age, body mass index groups, smoking status, presence of respiratory symptoms, and history of asthma. Model development and validation was stratified by sex. Model performance including calibration (calibration-in-the-large −0.017 vs. −0.157; and calibration slope 0.88 vs. 1.02), discrimination (C-statistic 0.72 vs. 0.63 with 95% confidence interval 0.69–0.75 vs. 0.62–0.73) and clinical usefulness (decision curve analysis) in the external temporal validation sample were more satisfactory in men than that in women. Prediction models with risk thresholds (13% in men and 7% in women) and easy-to-use nomograms were developed to assess the probability of AO. Conclusion: The diagnostic models based on readily available epidemiologic and clinical information with satisfactory performance can assist physicians to identify older individuals at high risk of AO and may improve the efficiency of spirometry for active case finding. Further validation beyond the Chinese population is warranted.
Persistent Identifierhttp://hdl.handle.net/10722/289526
ISSN
2019 Impact Factor: 3.095
2015 SCImago Journal Rankings: 1.396

 

DC FieldValueLanguage
dc.contributor.authorPan, J-
dc.contributor.authorAdab, P-
dc.contributor.authorCheng, KK-
dc.contributor.authorJiang, C-
dc.contributor.authorZhang, WS-
dc.contributor.authorZhu, F-
dc.contributor.authorJin, YL-
dc.contributor.authorThomas, GN-
dc.contributor.authorSteyerberg, EW-
dc.contributor.authorLam, TH-
dc.date.accessioned2020-10-22T08:13:53Z-
dc.date.available2020-10-22T08:13:53Z-
dc.date.issued2020-
dc.identifier.citationRespiratory Medicine, 2020, v. 173, p. article no. 106158-
dc.identifier.issn0954-6111-
dc.identifier.urihttp://hdl.handle.net/10722/289526-
dc.description.abstractObjective: To develop and validate a prediction model for airflow obstruction (AO) in older Chinese. Methods. Design: Multivariable logistic regression analysis in large population cohort of Chinese aged ≥50 years. Participants: Model development: 8762 Chinese aged ≥50 years were selected from the early phase recruits to the Guangzhou Biobank Cohort Study (GBCS) (recruited from September 2003 to May 2006). Internal validation: 100 bootstrap samples drawn with replacement from the development sample. External validation: 8395 Chinese aged ≥50 years from later phase GBCS (recruited from September 2006 to January 2008). Outcomes: AO was defined by a forced expiratory volume in 1 s/forced vital capacity ratio < lower limits of normal. Results: 839 (9.6%) and 764 (9.1%) individuals had AO in the development and temporal validation samples respectively. The predictors in the prediction model included sex, age, body mass index groups, smoking status, presence of respiratory symptoms, and history of asthma. Model development and validation was stratified by sex. Model performance including calibration (calibration-in-the-large −0.017 vs. −0.157; and calibration slope 0.88 vs. 1.02), discrimination (C-statistic 0.72 vs. 0.63 with 95% confidence interval 0.69–0.75 vs. 0.62–0.73) and clinical usefulness (decision curve analysis) in the external temporal validation sample were more satisfactory in men than that in women. Prediction models with risk thresholds (13% in men and 7% in women) and easy-to-use nomograms were developed to assess the probability of AO. Conclusion: The diagnostic models based on readily available epidemiologic and clinical information with satisfactory performance can assist physicians to identify older individuals at high risk of AO and may improve the efficiency of spirometry for active case finding. Further validation beyond the Chinese population is warranted.-
dc.languageeng-
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/rmed-
dc.relation.ispartofRespiratory Medicine-
dc.subjectAirflow obstruction-
dc.subjectPrediction model-
dc.titleDevelopment and validation of a prediction model for airflow obstruction in older Chinese: Guangzhou Biobank Cohort Study-
dc.typeArticle-
dc.identifier.emailCheng, KK: chengkk@hkucc.hku.hk-
dc.identifier.emailJiang, C: cqjiang@hkucc.hku.hk-
dc.identifier.emailZhang, WS: zhangws9@hku.hk-
dc.identifier.emailThomas, GN: neilt@hkucc.hku.hk-
dc.identifier.emailLam, TH: hrmrlth@hkucc.hku.hk-
dc.identifier.authorityLam, TH=rp00326-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rmed.2020.106158-
dc.identifier.pmid33011445-
dc.identifier.scopuseid_2-s2.0-85091799480-
dc.identifier.hkuros316292-
dc.identifier.volume173-
dc.identifier.spagearticle no. 106158-
dc.identifier.epagearticle no. 106158-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats