A Point-based Mortality Prediction System for Older Adults with Diabetes

Abstract

The mortality prediction models for the general diabetic population have been well established, but the corresponding elderly-specific model is still lacking. This study aims to develop a mortality prediction model for the elderly with diabetes. The data used for model establishment were derived from the nationwide adult health screening program in Taiwan in 2007–2010, from which we applied a 10-fold cross-validation method for model construction and internal validation. The external validation was tested on the MJ health screening database collected in 2004–2007. Multivariable Cox proportional hazards models were used to predict five-year mortality for diabetic patients ≥65 years. A total of 220,832 older subjects with diabetes were selected for model construction, of whom 23,241 (10.5%) died by the end of follow-up (December 31, 2011). The significant predictors retained in the final model included age, gender, smoking status, body mass index (BMI), fasting glucose, systolic and diastolic blood pressure, leukocyte count, liver and renal function, total cholesterol, hemoglobin, albumin, and uric acid. The Harrell’s C in the development, internal-, and external-validation datasets were 0.737, 0.746, and 0.685, respectively. We established an easy-to-use point-based model that could accurately predict five-year mortality risk in older adults with diabetes.

A nomogram for predicting the mortality of patients with type 2 diabetes mellitus complicated with acute kidney injury in the intensive care unit

“…A study [ 28 ] found that a higher WBC count was a predictor of death in DM patients with heart failure. A higher WBC count was associated with an increased risk of death in T2DM patients [ 29 ]. Our model showed that WBC count was a significant independent risk prognostic factor for T2DM patients with AKI.…”

Statistical modeling of health space based on metabolic stress and oxidative stress scores

“…Our findings are consistent with the literature regarding the significance of components in the both axes for predicting lifestyle-related chronic diseases and their outcomes. It was reported that the significant predictor variables for mortality in older adults with diabetes included age, gender, smoking status, BMI, fasting glucose, WBC, and GPT [ 23 ]. A role of smoking status was also shown in predicting mortality outcomes, in particular for cardiovascular mortality [ 24 ].…”

Precision prognostics for the development of complications in diabetes

“…Several models have been developed to predict all-cause mortality as the ultimate complication of diabetes. Models that have been externally validated include the RECODe model, the model by Chang et al and the ENFORCE model [14,60,61]. The included predictors were mainly demographic, BPor blood lipid-related, or were renal variables (Table 3 and ESM Table 1).…”

“…The included predictors were mainly demographic, BPor blood lipid-related, or were renal variables (Table 3 and ESM Table 1). All three models showed acceptable to good discrimination in the external validations, with C statistics of 0.71-0.81 (RECODe), 0.75-0.82 (ENFORCE) and 0.69 (Chang et al) [14,[60][61][62][63]. For the prediction of mortality in type 1 diabetes, few models exist and are set mainly in the context of lifetime health outcome simulations [64].…”