Development and Validation of a Predicting Model of All-Cause Mortality in Patients With Type 2 Diabetes

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Abstract

OBJECTIVE

To develop and validate a parsimonious model for predicting short-term all-cause mortality in patients with type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample. GMS and FMS cohorts were prospectively followed up for 7.40 ±2.15 and 4.51 ±1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected.

RESULTS

Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82–0.94) in the GMS and 0.82 (0.76–0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed.

CONCLUSIONS

We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.

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