title: Predicting severity of pathological scarring due to burn injuries: a clinical decision making tool using Bayesian networks.
abstract: It is important for clinicians to understand which are the clinical signs, the patient characteristics and the procedures that are related with the occurrence of hypertrophic burn scars in order to carry out a possible prognostic assessment. Providing clinicians with an easy-to- use tool for predicting the risk of pathological scars. A total of 703 patients with 2440 anatomical burn sites who were admitted to the Department of Plastic and Reconstructive Surgery, Burn Center of the Traumatological Hospital in Torino between January 1994 and May 2006 were included in the analysis. A Bayesian network (BN) model was implemented. The probability of developing a hypertrophic scar was evaluated on a number of scenarios. The error rate of the BN model was assessed internally and it was equal to 24·83%. While classical statistical method as logistic models can infer only which variables are related to the final outcome, the BN approach displays a set of relationships between the final outcome (scar type) and the explanatory covariates (patient's age and gender, burn surface area, full-thickness burn surface area, burn anatomical area and wound-healing time; burn treatment options such as advanced dressings, type of surgical approach, number of surgical procedures, type of skin graft, excision and coverage timing). A web-based interface to handle the BN model was developed on the website www.pubchild.org (burns header). Clinicians who registered at the website could submit their data in order to get from the BN model the predicted probability of observing a pathological scar type.
authors.affiliation: Department of Public Health and Microbiology, University of Torino, Torino, ItalyDepartment of Plastic and Reconstructive Surgery, Burn Center, Trauma Center, Torino, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiologic, Thoracic and Vascular Sciences, University of Padova, Padova, ItalyProchild ONLUS, Trieste, ItalyUnit of Cancer Epidemiology, CPO Piemonte, University of Torino, Torino, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Berchialla
authors.forename: Ezio Nicola
authors.initials: EN
authors.lastname: Gangemi
authors.forename: Francesca
authors.initials: F
authors.lastname: Foltran
authors.forename: Arber
authors.initials: A
authors.lastname: Haxhiaj
authors.forename: Alessandra
authors.initials: A
authors.lastname: Buja
authors.forename: Fulvio
authors.initials: F
authors.lastname: Lazzarato
authors.forename: Maurizio
authors.initials: M
authors.lastname: Stella
authors.forename: Dario
authors.initials: D
authors.lastname: Gregori
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D002056:Burns / Q000150:complications* / Q000473:pathology; D002648:Child; D002675:Child, Preschool; D017439:Cicatrix, Hypertrophic / Q000209:etiology* / Q000473:pathology / Q000517:prevention & control*; D015331:Cohort Studies; D003658:Decision Making, Computer-Assisted; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D020407:Internet; D007558:Italy; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D015397:Program Evaluation; D018570:Risk Assessment; D014945:Wound Healing; D055815:Young Adult