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Conference Paper: 10-year Risk Prediction Models of Stroke in Chinese Patients with Type2 Diabetes Mellitus

Title10-year Risk Prediction Models of Stroke in Chinese Patients with Type2 Diabetes Mellitus
Authors
Issue Date2020
PublisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.in/journals/Journal202429
Citation
The Joint European Stroke Organisation and World Stroke Organization Conference (ESO-WSO 2020), Virtual Conference, Vienna, Austria, 7-9 November 2020. ESO-WSO 2020 Joint Meeting Abstracts in International Journal of Stroke, 2020, v. 15 n. Suppl. 1, p. 394, article no. 01487 / #3129 How to Cite?
AbstractGroup Name: Background And Aims: Stroke is a major complication of T2DM. An accurate stroke prediction model can identify high-risk patients for more intensive medical and psychosocial interventions. Chinese are more prone to stroke than Caucasians but no model for the prediction of 10-year stroke risk in Chinese T2DM patients exist. The aim of this study was to develop a 10-year stroke prediction model among Chinese T2DM patients. Methods: 10-year retrospective cohort study of 141,516 T2DM Chinese patients from 2008 to 2017. The cohort was randomly split to development and validation samples at 2:1 ratio. Potential risk factors identified from systematic review included demographics, disease characteristics, clinical parameters and treatments. Variabilities measured by standard deviation of SBP and HbA1c, non-linear terms and interaction terms were also considered in model development. Cox proportional hazards regression was used to develop the models. Results: The 10-year cumulative incidence of stroke was 11.5% (Male: 11.7%, Female: 11.3%). Age, smoking, DM duration, SBP, variability SBP, TC/HDL-C ratio, HbA1c, variability of HbA1c, Urine ACR and eGFR were statistically significant factors as a main, transformation or interaction term. Age, DM duration, Urine ACR and eGFR were the most important risk factors. The models showed good prediction power (Female: Harrell’s-C ¼ 0.739, AUC ¼ 0.750; Male: Harrell’s-C ¼ 0.728, AUC ¼ 0.741) in the validation sample. Conclusions: This is the first 10-year stroke risk prediction model developed from and for Chinese T2DM patients. The inclusion of variabilities of SBP and HbA1c, transformation and interaction terms of relevant factors improved prediction accuracy significantly. The models can be converted to normograms and charts for clinical use.
DescriptionJointly Organised by the European Stroke Organisation & the World Stroke Organization
E-Poster Viewing: AS17. Epidemiology of Stroke - no. 01487 / #3129
Persistent Identifierhttp://hdl.handle.net/10722/299766
ISSN
2020 Impact Factor: 5.266
2020 SCImago Journal Rankings: 2.375

 

DC FieldValueLanguage
dc.contributor.authorDong, DW-
dc.contributor.authorWan, YFE-
dc.contributor.authorTang, EHM-
dc.contributor.authorFong, DYT-
dc.contributor.authorKwok, RLP-
dc.contributor.authorChao, DVK-
dc.contributor.authorTan, KCB-
dc.contributor.authorHui, EMT-
dc.contributor.authorTsui, WWS-
dc.contributor.authorChan, KH-
dc.contributor.authorFung, SCC-
dc.contributor.authorLam, CLK-
dc.date.accessioned2021-05-26T03:28:46Z-
dc.date.available2021-05-26T03:28:46Z-
dc.date.issued2020-
dc.identifier.citationThe Joint European Stroke Organisation and World Stroke Organization Conference (ESO-WSO 2020), Virtual Conference, Vienna, Austria, 7-9 November 2020. ESO-WSO 2020 Joint Meeting Abstracts in International Journal of Stroke, 2020, v. 15 n. Suppl. 1, p. 394, article no. 01487 / #3129-
dc.identifier.issn1747-4930-
dc.identifier.urihttp://hdl.handle.net/10722/299766-
dc.descriptionJointly Organised by the European Stroke Organisation & the World Stroke Organization-
dc.descriptionE-Poster Viewing: AS17. Epidemiology of Stroke - no. 01487 / #3129-
dc.description.abstractGroup Name: Background And Aims: Stroke is a major complication of T2DM. An accurate stroke prediction model can identify high-risk patients for more intensive medical and psychosocial interventions. Chinese are more prone to stroke than Caucasians but no model for the prediction of 10-year stroke risk in Chinese T2DM patients exist. The aim of this study was to develop a 10-year stroke prediction model among Chinese T2DM patients. Methods: 10-year retrospective cohort study of 141,516 T2DM Chinese patients from 2008 to 2017. The cohort was randomly split to development and validation samples at 2:1 ratio. Potential risk factors identified from systematic review included demographics, disease characteristics, clinical parameters and treatments. Variabilities measured by standard deviation of SBP and HbA1c, non-linear terms and interaction terms were also considered in model development. Cox proportional hazards regression was used to develop the models. Results: The 10-year cumulative incidence of stroke was 11.5% (Male: 11.7%, Female: 11.3%). Age, smoking, DM duration, SBP, variability SBP, TC/HDL-C ratio, HbA1c, variability of HbA1c, Urine ACR and eGFR were statistically significant factors as a main, transformation or interaction term. Age, DM duration, Urine ACR and eGFR were the most important risk factors. The models showed good prediction power (Female: Harrell’s-C ¼ 0.739, AUC ¼ 0.750; Male: Harrell’s-C ¼ 0.728, AUC ¼ 0.741) in the validation sample. Conclusions: This is the first 10-year stroke risk prediction model developed from and for Chinese T2DM patients. The inclusion of variabilities of SBP and HbA1c, transformation and interaction terms of relevant factors improved prediction accuracy significantly. The models can be converted to normograms and charts for clinical use.-
dc.languageeng-
dc.publisherSage Publications Ltd. The Journal's web site is located at http://www.sagepub.in/journals/Journal202429-
dc.relation.ispartofInternational Journal of Stroke-
dc.relation.ispartofThe Joint European Stroke Organisation and World Stroke Organization Conference (ESO-WSO 2020)-
dc.title10-year Risk Prediction Models of Stroke in Chinese Patients with Type2 Diabetes Mellitus-
dc.typeConference_Paper-
dc.identifier.emailWan, YFE: yfwan@hku.hk-
dc.identifier.emailTang, EHM: erichm@hku.hk-
dc.identifier.emailFong, DYT: dytfong@hku.hk-
dc.identifier.emailTan, KCB: kcbtan@hkucc.hku.hk-
dc.identifier.emailChan, KH: koonho@hku.hk-
dc.identifier.emailFung, SCC: cfsc@hku.hk-
dc.identifier.emailLam, CLK: clklam@hku.hk-
dc.identifier.authorityWan, YFE=rp02518-
dc.identifier.authorityFong, DYT=rp00253-
dc.identifier.authorityTan, KCB=rp00402-
dc.identifier.authorityChan, KH=rp00537-
dc.identifier.authorityFung, SCC=rp01330-
dc.identifier.authorityLam, CLK=rp00350-
dc.description.natureabstract-
dc.identifier.hkuros322460-
dc.identifier.volume15-
dc.identifier.issueSuppl. 1-
dc.identifier.spage394-
dc.identifier.epage394-
dc.publisher.placeUnited Kingdom-
dc.identifier.partofdoi10.1177/1747493020963387-

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