Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.

OBJECTIVES: Systemic sclerosis (SSc) is a multiorgan disease with high mortality rates. Several clinical features have been associated with poor survival in different populations of SSc patients, but no clear and reproducible prognostic model to assess individual survival prediction in scleroderma patients has ever been developed. METHODS: We used Cox regression and three data mining-based classifiers (Naïve Bayes Classifier [NBC], Random Forests [RND-F] and logistic regression [Log-Reg]) to develop a robust and reproducible 5-year prognostic model. All the models were built and internally validated by means of 5-fold cross-validation on a population of 558 Italian SSc patients. Their predictive ability and capability of generalisation was then tested on an independent population of 356 patients recruited from 5 external centres and finally compared to the predictions made by two SSc domain experts on the same population. RESULTS: The NBC outperformed the Cox-based classifier and the other data mining algorithms after internal cross-validation (area under receiving operator characteristic curve, AUROC: NBC=0.759; RND-F=0.736; Log-Reg=0.754 and Cox= 0.724). The NBC had also a remarkable and better trade-off between sensitivity and specificity (e.g. Balanced accuracy, BA) than the Cox-based classifier, when tested on an independent population of SSc patients (BA: NBC=0.769, Cox=0.622). The NBC was also superior to domain experts in predicting 5-year survival in this population (AUROC=0.829 vs. AUROC=0.788 and BA=0.769 vs. BA=0.67). CONCLUSIONS: We provide a model to make consistent 5-year prognostic predictions in SSc patients. Its internal validity, as well as capability of generalisation and reduced uncertainty compared to human experts support its use at bedside. Available at: http://www.nd.edu/~nchawla/survival.xls.

Disease activity as a risk factor for myocardial infarction in rheumatoid arthritis.

OBJECTIVE: Patients with rheumatoid arthritis (RA) are at greater risk of developing coronary heart disease than the general population. Systemic inflammation may contribute to this risk. This study investigated whether the level of disease activity is associated with the risk of developing myocardial infarction (MI) in patients with RA. METHODS: A case-control study was performed within a large prospective cohort of patients with RA. Cases were patients who developed their first MI after the diagnosis of RA, controls were patients with RA without MI. Cases and controls had similar RA disease duration. Traditional and disease-specific risk factors for MI were collected and a time-averaged disease activity score (DAS28) was calculated. The data were analysed using conditional logistic regression analysis. RESULTS: Cases of MI were significantly older, were more often male, with higher body mass index (BMI) and total cholesterol and lower high-density lipoprotein (HDL) serum levels than controls. Time-averaged disease activity was similar for cases and controls. The raw odds ratio for MI in patients with a "high" (>4.0) versus a "low" (