Berchialla Paola, Gangemi Ezio Nicola, Foltran Francesca, Haxhiaj Arber, Buja Alessandra, Lazzarato Fulvio, Stella Maurizio, Gregori Dario
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.
Int Wound J. 2014 Jun;11(3):246-52. doi: 10.1111/j.1742-481X.2012.01080.x. Epub 2012 Sep 7.
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.
临床医生了解哪些临床体征、患者特征和程序与肥厚性烧伤瘢痕的发生有关，对于进行可能的预后评估非常重要。为临床医生提供一种易于使用的工具来预测病理性瘢痕的风险。对1994年1月至2006年5月期间入住都灵创伤医院烧伤中心整形与重建外科的703例患者（共2440个解剖学烧伤部位）进行了分析。实施了一个贝叶斯网络（BN）模型。在多种情况下评估了形成肥厚性瘢痕的概率。内部评估了BN模型的错误率，其等于24.83%。虽然像逻辑模型这样的经典统计方法只能推断哪些变量与最终结果相关，但BN方法展示了最终结果（瘢痕类型）与解释性协变量（患者年龄和性别、烧伤表面积、全层烧伤表面积、烧伤解剖部位和伤口愈合时间；烧伤治疗选择，如先进敷料、手术方法类型、手术次数、皮肤移植类型、切除和覆盖时间）之间的一组关系。在网站www.pubchild.org（烧伤标题）上开发了一个基于网络的界面来处理BN模型。在该网站注册的临床医生可以提交他们的数据，以便从BN模型中获得观察到病理性瘢痕类型的预测概率。
