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- Publisher Website: 10.1016/j.jdiacomp.2017.01.017
- Scopus: eid_2-s2.0-85013661014
- PMID: 28238555
- WOS: WOS:000402582900003
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Article: Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus – A population-based retrospective cohort study
Title | Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus – A population-based retrospective cohort study |
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Authors | |
Keywords | Type 2 diabetes mellitus Prediction Risk Mortality Primary care |
Issue Date | 2017 |
Publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jdiacomp |
Citation | Journal of Diabetes and its Complications, 2017, v. 31 n. 6, p. 939-944 How to Cite? |
Abstract | Aims:
This study aimed to develop and validate an all-cause mortality risk prediction model for Chinese primary care patients with type 2 diabetes mellitus(T2DM) in Hong Kong.
Methods:
A population-based retrospective cohort study was conducted on 132,462 Chinese patients who had received public primary care services during 2010. Each gender sample was randomly split on a 2:1 basis into derivation and validation cohorts and was followed-up for a median period of 5 years. Gender-specific mortality risk prediction models showing the interaction effect between predictors and age were derived using Cox proportional hazards regression with forward stepwise approach. Developed models were compared with pre-existing models by Harrell's C-statistic and calibration plot using validation cohort.
Results:
Common predictors of increased mortality risk in both genders included: age; smoking habit; diabetes duration; use of anti-hypertensive agents, insulin and lipid-lowering drugs; body mass index; hemoglobin A1c; systolic blood pressure(BP); total cholesterol to high-density lipoprotein-cholesterol ratio; urine albumin to creatinine ratio(urine ACR); and estimated glomerular filtration rate(eGFR). Prediction models showed better discrimination with Harrell”'s C-statistics of 0.768(males) and 0.782(females) and calibration power from the plots than previously established models.
Conclusions:
Our newly developed gender-specific models provide a more accurate predicted 5-year mortality risk for Chinese diabetic patients than other established models. |
Persistent Identifier | http://hdl.handle.net/10722/245100 |
ISSN | 2023 Impact Factor: 2.9 2023 SCImago Journal Rankings: 1.018 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wan, YF | - |
dc.contributor.author | Fong, DYT | - |
dc.contributor.author | Fung, SCC | - |
dc.contributor.author | Yu, YTE | - |
dc.contributor.author | Chin, WY | - |
dc.contributor.author | Chan, KC | - |
dc.contributor.author | Lam, CLK | - |
dc.date.accessioned | 2017-09-18T02:04:36Z | - |
dc.date.available | 2017-09-18T02:04:36Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Journal of Diabetes and its Complications, 2017, v. 31 n. 6, p. 939-944 | - |
dc.identifier.issn | 1056-8727 | - |
dc.identifier.uri | http://hdl.handle.net/10722/245100 | - |
dc.description.abstract | Aims: This study aimed to develop and validate an all-cause mortality risk prediction model for Chinese primary care patients with type 2 diabetes mellitus(T2DM) in Hong Kong. Methods: A population-based retrospective cohort study was conducted on 132,462 Chinese patients who had received public primary care services during 2010. Each gender sample was randomly split on a 2:1 basis into derivation and validation cohorts and was followed-up for a median period of 5 years. Gender-specific mortality risk prediction models showing the interaction effect between predictors and age were derived using Cox proportional hazards regression with forward stepwise approach. Developed models were compared with pre-existing models by Harrell's C-statistic and calibration plot using validation cohort. Results: Common predictors of increased mortality risk in both genders included: age; smoking habit; diabetes duration; use of anti-hypertensive agents, insulin and lipid-lowering drugs; body mass index; hemoglobin A1c; systolic blood pressure(BP); total cholesterol to high-density lipoprotein-cholesterol ratio; urine albumin to creatinine ratio(urine ACR); and estimated glomerular filtration rate(eGFR). Prediction models showed better discrimination with Harrell”'s C-statistics of 0.768(males) and 0.782(females) and calibration power from the plots than previously established models. Conclusions: Our newly developed gender-specific models provide a more accurate predicted 5-year mortality risk for Chinese diabetic patients than other established models. | - |
dc.language | eng | - |
dc.publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jdiacomp | - |
dc.relation.ispartof | Journal of Diabetes and its Complications | - |
dc.subject | Type 2 diabetes mellitus | - |
dc.subject | Prediction | - |
dc.subject | Risk | - |
dc.subject | Mortality | - |
dc.subject | Primary care | - |
dc.title | Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus – A population-based retrospective cohort study | - |
dc.type | Article | - |
dc.identifier.email | Wan, YF: yfwan@hku.hk | - |
dc.identifier.email | Fong, DYT: dytfong@hku.hk | - |
dc.identifier.email | Fung, SCC: cfsc@hku.hk | - |
dc.identifier.email | Yu, YTE: ytyu@hku.hk | - |
dc.identifier.email | Chin, WY: chinwy@hku.hk | - |
dc.identifier.email | Chan, KC: kcchanae@hku.hk | - |
dc.identifier.email | Lam, CLK: clklam@hku.hk | - |
dc.identifier.authority | Wan, YF=rp02518 | - |
dc.identifier.authority | Fong, DYT=rp00253 | - |
dc.identifier.authority | Fung, SCC=rp01330 | - |
dc.identifier.authority | Yu, YTE=rp01693 | - |
dc.identifier.authority | Chin, WY=rp00290 | - |
dc.identifier.authority | Lam, CLK=rp00350 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jdiacomp.2017.01.017 | - |
dc.identifier.pmid | 28238555 | - |
dc.identifier.scopus | eid_2-s2.0-85013661014 | - |
dc.identifier.hkuros | 278744 | - |
dc.identifier.volume | 31 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 939 | - |
dc.identifier.epage | 944 | - |
dc.identifier.isi | WOS:000402582900003 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1056-8727 | - |