Evidence map›Paper›PMID 36494707›Full record

ArticleBMC public health2022

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.

Chao Tong, Yumei Han, Shan Zhang, Qiang Li, Jingbo Zhang, Xiuhua Guo, Lixin Tao, Deqiang Zheng, Xinghua Yang

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Chao TongSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China.
Yumei HanBeijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China.
Shan ZhangSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China.
Qiang LiBeijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China.
Jingbo ZhangBeijing Physical Examination Center, No. 59, Beiwei Road, Xicheng District, Beijing, China.
Xiuhua GuoSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China.
Lixin TaoSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China.
Deqiang ZhengSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China.
Xinghua YangSchool of Public Health, Capital Medical University, NO.10 Xitoutiao, Youanmen, Beijing, 100069, China. xinghuayang@ccmu.edu.cn.
Beijing YouAn Hospital · CNJingdong (China) · CNCapital Medical University · CN

Funding

Capital's Funds for Health Improvement and Research 2018-2-2242National Key Research and Development Program of China 2016YFC0900603Natural Science Foundation of Beijing Municipality 7202011
6 · The paper itself

Abstract

backgroundHealth interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups.

methodsParticipants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model.

resultsOf the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses.

conclusionsThe T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

Indexed as

Diabetes Mellitus, Type 2HumansMaleNomogramsReproducibility of ResultsRetrospective StudiesRisk FactorsDiabetes mellitusDynamic nomogramReal world studyRisk score

Identifiers

PMID36494707
PMCPMC9733342
OpenAlexW4311678654

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