Lammers Richard L, Hudson Donna L, Seaman Matthew E
Department of Emergency Medicine, Michigan State University/Kalamazoo Center for Medical Studies, Kalamazoo, MI 49008, USA.
Am J Emerg Med. 2003 Jan;21(1):1-7. doi: 10.1053/ajem.2003.50026.
The objective of this study was to develop and validate a decision model, using an artificial neural network, that predicts infection in uncomplicated, traumatic, sutured wounds. The study was a prospective, cohort study of all patients presenting to the emergency department of a county teaching hospital with uncomplicated wounds that required suturing. In evaluating and treating wounds, emergency medicine (EM) faculty and residents, resident physicians in primary-care specialties, and supervised medical students on EM clerkships followed a standardized wound-management protocol. Clinicians estimated the likelihood of subsequent infection using a 5-point scale. Wound healing was followed until sutures were removed. Wound outcome data were collected by medical personnel blinded to the initial prediction. Student's t-tests and Pearson's chi-square statistic were used to identify independent predictors that served as input variables. Wound infection was the single output variable. Neural network analysis was used to assign weights to input variables and derive a decision equation. A total of 1,142 wounds were analyzed in the study. The overall infection rate was 7.2%. The most predictive factors for wound infection were wound location, wound age, depth, configuration, contamination, and patient age. To derive a decision equation for the model, the network was trained on data from half of the subjects and tested on the remainder. When used as a diagnostic test for wound infection, the decision model had a sensitivity of 70%, as compared to 54% for physicians, and a specificity of 76%, as compared to 78% for physicians. We conclude that through the use of combinations of 7 clinical variables available at the time of initial wound management, a neural network-derived decision model may be used to identify uncomplicated, traumatic wounds at higher risk for infection.
本研究的目的是开发并验证一种使用人工神经网络的决策模型，该模型可预测单纯性创伤缝合伤口的感染情况。本研究是一项前瞻性队列研究，研究对象为所有到某县教学医院急诊科就诊、有需要缝合的单纯性伤口的患者。在评估和治疗伤口时，急诊医学（EM）教员和住院医师、初级保健专科的住院医师以及在EM实习中接受监督的医学生遵循标准化的伤口管理方案。临床医生使用5分制评估后续感染的可能性。伤口愈合情况一直跟踪到缝线拆除。伤口结局数据由对初始预测不知情的医务人员收集。使用学生t检验和Pearson卡方统计量来确定作为输入变量的独立预测因素。伤口感染是唯一的输出变量。使用神经网络分析为输入变量分配权重并推导决策方程。本研究共分析了1142个伤口。总体感染率为7.2%。伤口感染的最具预测性的因素是伤口位置、伤口时长、深度、形态、污染情况和患者年龄。为了推导该模型的决策方程，该网络使用一半受试者的数据进行训练，并在其余受试者上进行测试。当用作伤口感染的诊断测试时，该决策模型的灵敏度为70%，而医生的灵敏度为54%；特异性为76%，而医生的特异性为78%。我们得出结论，通过使用伤口初始处理时可用的7个临床变量的组合，基于神经网络得出的决策模型可用于识别感染风险较高的单纯性创伤伤口。
