title: Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.
abstract: OBJECTIVE
  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.
authors.affiliation: Referral Centres for Systemic Autoimmune Diseases, Fondazione IRCCS Ospedale Maggiore Policlinico and University of Milan, Italy. lorberimm@hotmail.com
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Beretta
authors.forename: Alessandro
authors.initials: A
authors.lastname: Santaniello
authors.forename: Francesca
authors.initials: F
authors.lastname: Cappiello
authors.forename: Nitesh V
authors.initials: NV
authors.lastname: Chawla
authors.forename: Madelon C
authors.initials: MC
authors.lastname: Vonk
authors.forename: Patricia E
authors.initials: PE
authors.lastname: Carreira
authors.forename: Yannick
authors.initials: Y
authors.lastname: Allanore
authors.forename: D A
authors.initials: DA
authors.lastname: Popa-Diaconu
authors.forename: Marta
authors.initials: M
authors.lastname: Cossu
authors.forename: Francesca
authors.initials: F
authors.lastname: Bertolotti
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Ferraccioli
authors.forename: Antonino
authors.initials: A
authors.lastname: Mazzone
authors.forename: Raffaella
authors.initials: R
authors.lastname: Scorza
mesh_terms: D000328:Adult; D057225:Data Mining; D005260:Female; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D045743:Scleroderma, Diffuse / Q000175:diagnosis / Q000401:mortality*; D045745:Scleroderma, Limited / Q000175:diagnosis / Q000401:mortality*; D015996:Survival Rate