File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Development of a cardiovascular diseases risk prediction model and tools for Chinese patients with type 2 diabetes mellitus: A population-based retrospective cohort study

TitleDevelopment of a cardiovascular diseases risk prediction model and tools for Chinese patients with type 2 diabetes mellitus: A population-based retrospective cohort study
Authors
KeywordsCardiovascular diseases
Prediction
Primary care
Risk
Type 2 diabetes mellitus
Issue Date2018
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/DOM
Citation
Diabetes, Obesity and Metabolism, 2018, v. 20 n. 2, p. 309-318 How to Cite?
AbstractAims: Evidence‐based cardiovascular diseases (CVD) risk prediction models and tools specific for Chinese patients with type 2 diabetes mellitus (T2DM) are currently unavailable. This study aimed to develop and validate a CVD risk prediction model for Chinese T2DM patients. Methods: A retrospective cohort study was conducted with 137 935 Chinese patients aged 18 to 79 years with T2DM and without prior history of CVD, who had received public primary care services between January 1, 2010 and December 31, 2010. Using the derivation cohort over a median follow‐up of 5 years, the interaction effect between predictors and age were derived using Cox proportional hazards regression with a forward stepwise approach. Harrell's C statistic and calibration plot were used on the validation cohort to assess the discrimination and calibration of the models. The web calculator and chart were developed based on the developed models. Results: For both genders, predictors for higher risk of CVD were older age, smoking, longer diabetes duration, usage of anti‐hypertensive drug and insulin, higher body mass index, haemoglobin A1c (HbA1c), systolic and diastolic blood pressure, a total cholesterol to high‐density lipoprotein‐cholesterol (TC/HDL‐C) ratio and urine albumin to creatinine ratio, and lower estimated glomerular filtration rate. Interaction factors with age demonstrated a greater weighting of TC/HDL‐C ratio in both younger females and males, and smoking status and HbA1c in younger males. Conclusion: The developed models, translated into a web calculator and color‐coded chart, served as evidence‐based visual aids that facilitate clinicians to estimate quickly the 5‐year CVD risk for Chinese T2DM patients and to guide intervention.
Persistent Identifierhttp://hdl.handle.net/10722/247504
ISSN
2017 Impact Factor: 5.98
2015 SCImago Journal Rankings: 2.729
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWan, YF-
dc.contributor.authorFong, DYT-
dc.contributor.authorFung, SCC-
dc.contributor.authorYu, YTE-
dc.contributor.authorChin, WY-
dc.contributor.authorChan, KC-
dc.contributor.authorLam, CLK-
dc.date.accessioned2017-10-18T08:28:18Z-
dc.date.available2017-10-18T08:28:18Z-
dc.date.issued2018-
dc.identifier.citationDiabetes, Obesity and Metabolism, 2018, v. 20 n. 2, p. 309-318-
dc.identifier.issn1462-8902-
dc.identifier.urihttp://hdl.handle.net/10722/247504-
dc.description.abstractAims: Evidence‐based cardiovascular diseases (CVD) risk prediction models and tools specific for Chinese patients with type 2 diabetes mellitus (T2DM) are currently unavailable. This study aimed to develop and validate a CVD risk prediction model for Chinese T2DM patients. Methods: A retrospective cohort study was conducted with 137 935 Chinese patients aged 18 to 79 years with T2DM and without prior history of CVD, who had received public primary care services between January 1, 2010 and December 31, 2010. Using the derivation cohort over a median follow‐up of 5 years, the interaction effect between predictors and age were derived using Cox proportional hazards regression with a forward stepwise approach. Harrell's C statistic and calibration plot were used on the validation cohort to assess the discrimination and calibration of the models. The web calculator and chart were developed based on the developed models. Results: For both genders, predictors for higher risk of CVD were older age, smoking, longer diabetes duration, usage of anti‐hypertensive drug and insulin, higher body mass index, haemoglobin A1c (HbA1c), systolic and diastolic blood pressure, a total cholesterol to high‐density lipoprotein‐cholesterol (TC/HDL‐C) ratio and urine albumin to creatinine ratio, and lower estimated glomerular filtration rate. Interaction factors with age demonstrated a greater weighting of TC/HDL‐C ratio in both younger females and males, and smoking status and HbA1c in younger males. Conclusion: The developed models, translated into a web calculator and color‐coded chart, served as evidence‐based visual aids that facilitate clinicians to estimate quickly the 5‐year CVD risk for Chinese T2DM patients and to guide intervention.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/DOM-
dc.relation.ispartofDiabetes, Obesity and Metabolism-
dc.rightsThis is the accepted version of the article, which has been published in final form at http://dx.doi.org/10.1111/dom.13066.-
dc.subjectCardiovascular diseases-
dc.subjectPrediction-
dc.subjectPrimary care-
dc.subjectRisk-
dc.subjectType 2 diabetes mellitus-
dc.titleDevelopment of a cardiovascular diseases risk prediction model and tools for Chinese patients with type 2 diabetes mellitus: A population-based retrospective cohort study-
dc.typeArticle-
dc.identifier.emailWan, YF: yfwan@hku.hk-
dc.identifier.emailFong, DYT: dytfong@hku.hk-
dc.identifier.emailFung, SCC: cfsc@hku.hk-
dc.identifier.emailYu, YTE: ytyu@hku.hk-
dc.identifier.emailChin, WY: chinwy@hku.hk-
dc.identifier.emailChan, KC: kcchanae@hku.hk-
dc.identifier.emailLam, CLK: clklam@hku.hk-
dc.identifier.authorityWan, YF=rp02518-
dc.identifier.authorityFong, DYT=rp00253-
dc.identifier.authorityFung, SCC=rp01330-
dc.identifier.authorityYu, YTE=rp01693-
dc.identifier.authorityChin, WY=rp00290-
dc.identifier.authorityLam, CLK=rp00350-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/dom.13066-
dc.identifier.pmid28722290-
dc.identifier.scopuseid_2-s2.0-85028357957-
dc.identifier.hkuros281363-
dc.identifier.volume20-
dc.identifier.issue2-
dc.identifier.spage309-
dc.identifier.epage318-
dc.identifier.isiWOS:000422678600009-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats