Dataset Information

Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China.

ABSTRACT: PURPOSE:To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China. METHODS:A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort. RESULTS:In the derivation cohort, 257 patients were identified from a total of 4498 cases. In the validation cohort, 92 patients were identified from a total of 1525 cases. Four models performed nicely for both calibration and discrimination. The AUC in the derivation cohort for models A, B, C and D were 0.788 (0.761-0.816), 0.807 (0.780-0.834), 0.905 (0.879-0.932) and 0.882 (0.853-0.912), respectively. The Youden index for models A, B, C and D were 1.46, 1.48, 1.67 and 1.65, respectively. Model C showed the highest sensitivity and model D showed the highest specificity. CONCLUSION:Models A and B were non-invasive and can be used to identify high-risk patients for broad screening. Models C and D may be used to provide more accurate assessments of diabetes risk. Furthermore, model C showed the best performance for predicting T2DM risk and identifying individuals who are in need of interventions, current approach improvement and additional follow-up.

SUBMITTER: Shao X

PROVIDER: S-EPMC7470416 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China.

Shao Xian X Wang Yao Y Huang Shuai S Liu Hongyan H Zhou Saijun S Zhang Rui R Yu Pei P

PloS one 20200903 9

<h4>Purpose</h4>To derive and validate a concise prediction model estimating the 10-year risk for type 2 diabetes (T2DM) in China.<h4>Methods</h4>A total of 11494 subjects from the China Health and Nutrition Survey recorded from 2004 to 2015 were analyzed and only 6023 participants were enrolled in this study. Four logistic models were analyzed using the derivation cohort. Methods of calibration and discrimination were used for the validation cohort.<h4>Results</h4>In the derivation cohort, 257 ...[more]

PMID: 32881911

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Project description:Aims/hypothesisWe aimed to report current rates of CVD in type 1 diabetes and to develop a CVD risk prediction tool for type 1 diabetes.MethodsA cohort of 27,527 people with type 1 diabetes without prior CVD was derived from the national register in Scotland. Incident CVD events during 199,552 person-years of follow-up were ascertained using hospital admissions and death registers. A Poisson regression model of CVD was developed and then validated in the Swedish National Diabetes Register (n = 33,183). We compared the percentage with a high 10 year CVD risk (i.e., ≥10%) using the model with the percentage eligible for statins using current guidelines by age.ResultsThe age-standardised rate of CVD per 100,000 person-years was 4070 and 3429 in men and women, respectively, with type 1 diabetes in Scotland, and 4014 and 3956 in men and women in Sweden. The final model was well calibrated (Hosmer-Lemeshow test p > 0.05) and included a further 22 terms over a base model of age, sex and diabetes duration (C statistic 0.82; 95% CI 0.81, 0.83). The model increased the base model C statistic from 0.66 to 0.80, from 0.60 to 0.75 and from 0.62 to 0.68 in those aged <40, 40-59 and ≥ 60 years, respectively (all p values <0.005). The model required minimal calibration in Sweden and had a C statistic of 0.85. Under current guidelines, >90% of those aged 20-39 years and 100% of those ≥40 years with type 1 diabetes were eligible for statins, but it was not until age 65 upwards that 100% had a modelled risk of CVD ≥10% in 10 years.Conclusions/interpretationA prediction tool such as that developed here can provide individualised risk predictions. This 10 year CVD risk prediction tool could facilitate patient discussions regarding appropriate statin prescribing. Apart from 10 year risk, such discussions may also consider longer-term CVD risk, the potential for greater benefits from early vs later statin intervention, the potential impact on quality of life of an early CVD event and evidence on safety, all of which could influence treatment decisions, particularly in younger people with type 1 diabetes.

Project description:BackgroundIn increasingly ageing populations, there is an emergent need to develop a robust prediction model for estimating an individual absolute risk for all-cause mortality, so that relevant assessments and interventions can be targeted appropriately. The objective of the study was to derive, evaluate and validate (internally and externally) a risk prediction model allowing rapid estimations of an absolute risk of all-cause mortality in the following 10?years.MethodsFor the model development, data came from English Longitudinal Study of Ageing study, which comprised 9154 population-representative individuals aged 50-75?years, 1240 (13.5%) of whom died during the 10-year follow-up. Internal validation was carried out using Harrell's optimism-correction procedure; external validation was carried out using Health and Retirement Study (HRS), which is a nationally representative longitudinal survey of adults aged ?50?years residing in the United States. Cox proportional hazards model with regularisation by the least absolute shrinkage and selection operator, where optimisation parameters were chosen based on repeated cross-validation, was employed for variable selection and model fitting. Measures of calibration, discrimination, sensitivity and specificity were determined in the development and validation cohorts.ResultsThe model selected 13 prognostic factors of all-cause mortality encompassing information on demographic characteristics, health comorbidity, lifestyle and cognitive functioning. The internally validated model had good discriminatory ability (c-index=0.74), specificity (72.5%) and sensitivity (73.0%). Following external validation, the model's prediction accuracy remained within a clinically acceptable range (c-index=0.69, calibration slope ?=0.80, specificity=71.5% and sensitivity=70.6%). The main limitation of our model is twofold: 1) it may not be applicable to nursing home and other institutional populations, and 2) it was developed and validated in the cohorts with predominately white ethnicity.ConclusionsA new prediction model that quantifies absolute risk of all-cause mortality in the following 10-years in the general population has been developed and externally validated. It has good prediction accuracy and is based on variables that are available in a variety of care and research settings. This model can facilitate identification of high risk for all-cause mortality older adults for further assessment or interventions.

Dataset Information

Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China.

Publications

Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China.

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