Study characteristics of the included studies summarized for three exposures.

<p>Data are presented as number or range.</p>a<p>Three studies did not report follow-up time.</p>b<p>Two studies did not report the number of participants who encountered the outcome of interest.</p>c<p>One study did not report the number of participants who encountered the outcome of interest.</p><p>HOMA-IR, Homeostasis Model Assessment Insulin Resistance; CHD, coronary heart disease; CVD, cardiovascular disease.</p

Summary of search results.

<p>^aOne publication consisted of two studies. HOMA-IR, Homeostasis Model Assessment insulin resistance.</p

Results of random-effect meta-analyses comparing cardiovascular disease risk for an increase of one standard deviation.

<p>^a1 study did not specify sex-specific numbers. SD, standard deviation; 95% CI, 95% confidence interval; I², measure of heterogeneity; CHD, coronary heart disease and is defined as fatal or non-fatal myocardial infarction or angina pectoris; CVD, cardiovascular disease and is defined as myocardial infarction, angina pectoris, hemorrhagic stroke, ischemic stroke, arrhythmias, congestive heart failure or sudden cardiac death; HOMA-IR, Homeostasis Model Assessment Insulin Resistance.</p

Predicting Mortality in Patients with Diabetes Starting Dialysis

<div><p>Background</p><p>While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model.</p><p>Methods</p><p>Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping.</p><p>Results</p><p>A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results.</p><p>Conclusions</p><p>A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary.</p></div

One-year mortality according to risk quartiles.

<p>Grey bars represent predicted 1-year mortality risk and black bars represent observed 1-year mortality risk.</p

Predictive variables for 1-year mortality based on multivariate regression analysis.

<p>Abbreviations: B, estimated coefficient; S.E., standard error of estimate; B_adj, estimated coefficient adjusted for overfitting.</p><p>The intercept of the model, which is necessary for computing predicted mortality risks, was 1.692 (1.610), and 1.427 when adjusted for overfitting.</p

Risk factors for the metabolic syndrome, according to the NCEP-ATPIII criteria, in NFMA patients using four logistic regression models.

<p>Data represent odds ratio (95% confidence interval). RT, radiotherapy; VFD, visual field defects at presentation; Hypopit, hypopituitarism (number of deficient pituitary axes). OR₁: regression model 1 (age and gender). OR₂: regression model 2 (age, gender, BMI). OR₃: regression model 3 (age, gender, VFD, RT, hypopit). OR₄: regression model 4 (age, gender, VFD, RT, Hypopit, BMI). ^†according to the NCEP-ATP III <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090602#pone.0090602-Grundy1" target="_blank">[10]</a>. *difference statistically significant (P ≤ 0.05).</p

Receiver operating characteristic curve for the logistic regression model.

<p>The c-statistic was 0.810.</p

Baseline characteristics of the study population.

<p>Age and duration of DM are presented as median (interquartile range). Other continuous predictors are presented as means (SD); categorical variables are presented as %.</p><p>Abbreviations: BMI, body mass index; BP, blood pressure; DM, diabetes mellitus; HD, hemodialysis; rGFR, residual glomerular filtration rate.</p

Comparison of metabolic syndrome prevalence, according to the NCEP-ATP III criteria, between NFMA patients and the general population.

<p>O, observed; E, expected. ^†according to the NCEP-ATP III <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090602#pone.0090602-Grundy1" target="_blank">[10]</a>.</p