Dataset Information

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease.

ABSTRACT:

Aims

The rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden.

Methods

We tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up.

Results

ENFORCE's survival C-statistic was 0.81 (95%CI: 0.72-0.89) and 0.78 (95%CI: 0.68-0.87) in both samples. Calibration was also good. Very similar results were obtained with RECODe, an alternative prediction model of all-cause mortality in type 2 diabetes.

Conclusions

In conclusion, our data show that two well-established prediction models of all-cause mortality in type 2 diabetes can also be successfully applied in the early stage of the disease, thus becoming powerful tools for educated and timely prevention strategies for high-risk patients.

SUBMITTER: Copetti M

PROVIDER: S-EPMC8164049 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease.

Copetti Massimiliano M Biancalana Edoardo E Fontana Andrea A Parolini Federico F Garofolo Monia M Lamacchia Olga O De Cosmo Salvatore S Trischitta Vincenzo V Solini Anna A

Acta diabetologica 20210529 10

<h4>Aims</h4>The rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden.<h4>Methods</h4>We tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up.<h4>Results</h4>ENFORCE's survival C ...[more]

PMID: 34050821

Similar Datasets

Project description:BackgroundThe main goal of this study was to examine how diabetes, cardiovascular calcification characteristics and other risk factors affect mortality in end-stage renal disease (ESRD) patients in the early stages of hemodialysis.MethodsA total of 285 ESRD patients in the early stages of hemodialysis were enrolled in this research, including 101 patients with diabetes. Survival time was monitored, and general data, biochemical results, cardiac ultrasound calcification of valvular tissue, and thoracic CT calcification of the coronary artery and thoracic aorta were recorded. Subgroup analysis and logistic regression were applied to investigate the association between diabetes and calcification. Cox regression analysis and survival between calcification, diabetes, and all-cause mortality. Additionally, the nomogram model was used to estimate the probability of survival for these individuals, and its performance was evaluated using risk stratification, receiver operating characteristic, decision, and calibration curves.ResultsCardiovascular calcification was found in 81.2% of diabetic patients (82/101) and 33.7% of nondiabetic patients (62/184). Diabetic patients had lower phosphorus, calcium, calcium-phosphorus product, plasma PTH levels and lower albumin levels (p < 0.001). People with diabetes were more likely to have calcification than people without diabetes (OR 5.66, 95% CI 1.96-16.36; p < 0.001). The overall mortality rate was 14.7% (42/285). The risk of death was notably greater in patients with both diabetes and calcification (29.27%, 24/82). Diabetes and calcification, along with other factors, collectively predict the risk of death in these patients. The nomogram model demonstrated excellent discriminatory power (area under the curve (AUC) = 0.975 at 5 years), outstanding calibration at low to high-risk levels and provided the greatest net benefit across a wide range of clinical decision thresholds.ConclusionsIn patients with ESRD during the early period of haemodialysis, diabetes significantly increases the risk of cardiovascular calcification, particularly multisite calcification, which is correlated with a higher mortality rate. The risk scores and nomograms developed in this study can assist clinicians in predicting the risk of death and providing individualised treatment plans to lower mortality rates in the early stages of hemodialysis.

Dataset Information

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease.

Aims

Methods

Results

Conclusions

Publications

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease.

Similar Datasets

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