Dataset Information

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

ABSTRACT:

Aims/hypothesis

Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes.

Methods

The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow ?(2) statistic.

Results

The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor.

Conclusions/interpretation

We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be updated.

SUBMITTER: van Dieren S

PROVIDER: S-EPMC3017299 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

van Dieren S S Peelen L M LM Nöthlings U U van der Schouw Y T YT Rutten G E H M GE Spijkerman A M W AM van der A D L DL Sluik D D Boeing H H Moons K G M KG Beulens J W J JW

Diabetologia 20101114 2

<h4>Aims/hypothesis</h4>Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes.<h4>Met ...[more]

PMID: 21076956

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Project description:ObjectiveTo identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort.Data sourcesSystematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes.DesignPerformance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort.SettingModels were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL).Participants38,379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort.Outcome measureIncident type 2 diabetes.ResultsThe review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably.ConclusionsMost basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Project description:AimTo identify predictive factors for surgical site infection (SSI) in patients with type 2 diabetes and develop a prediction tool.Materials and methodsWe retrospectively analyzed the perioperative blood glucose management of 105 patients with type 2 diabetes treated from 2016 to 2018 at Chiba University Hospital. The primary outcome was SSI onset within 30 postoperative days; moreover, predictive factors were identified using univariate analysis. Principal component analysis and logistic regression analysis were performed to prepare SSI predictive model using the identified predictive factors. The area under the receiver operating characteristic curve (AUC) was evaluated. Based on the predictive model, we developed a risk engine for SSI prediction.ResultsCompared with patients without SSI (n = 70), those with SSI (n = 35) had significantly higher fasting blood glucose levels at referral (169.1 ± 61.8 mg/dL vs. 140.1 ± 56.6, P = 0.036), preoperative mean blood glucose levels (178.3 ± 48.4 mg/dL vs. 155.2 ± 39.7, P = 0.009), preoperative maximum blood glucose levels (280.4 ± 87.3 mg/dL vs. 230.3 ± 92.4, P = 0.009), preoperative blood glucose fluctuations (54.9 ± 24.1 mg/dL vs. 37.7 ± 23.1, P = 0.001), percentage of hospitalization at referral (54.3% vs. 20.0, P < 0.001); longer operation time (432.5 ± 179.6 min vs. 282.5 ± 178.3, P < 0.001); and greater bleeding volume (972.3 ± 920.1 mg/dL vs. 436.4 ± 795.8, P < 0.001). Logistic regression analysis revealed preoperative blood glucose fluctuation and operation time as the most reliable predictive factors. The predictive model had high prediction accuracy (AUC of 0.801). The risk engine prototype for SSI prediction can be accessed at https://www.dm-ope-riskengine.org/.ConclusionsThe predictive model developed in this study could screen high-risk patients. It may be useful to prevent SSI in such patients.Supplementary informationThe online version contains supplementary material available at 10.1007/s13340-022-00587-w.

Dataset Information

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

Aims/hypothesis

Methods

Results

Conclusions/interpretation

Publications

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes.

Similar Datasets

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