Division of General Surgery, Michael E. DeBakey Veterans Affairs Medical Center, Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, USA.
Surg Infect (Larchmt). 2009 Dec;10(6):517-22. doi: 10.1089/sur.2008.112.
Necrotizing soft tissue infections (NSTIs) are associated with a high mortality rate; however, there is no uniform way to categorize the severity of this disease early in its course. The goal of this study was to develop a clinical score based on data available at the time of initial assessment to aid in stratifying patients according to their risk of death.
A cohort of all 350 patients admitted with NSTI to two institutions over a nine-year period was examined retrospectively. Using random split sampling, two datasets were created: Prediction (PD) and validation (VD). Multivariable stepwise regression analysis of the PD identified independent predictors of death using data available at the time of admission. Model performance was evaluated for accuracy, discrimination, and calibration. A clinical score to predict death was created, and using the Trauma and Injury Severity Score (TRISS) methodology, the score was validated on the VD (z-statistic).
Six admission parameters independently predicted death: Age > 50 years, heart rate > 110 beats/min, temperature <36 degrees C, white blood cell count > 40,000/mcL, serum creatinine concentration > 1.5 mg/dL, and hematocrit > 50%. The accuracy of this model was 86.8%; the area under the receiver-operating characteristic curve was 0.81, and the Hosmer-Lemeshow statistic was 11.8. Additionally, the score had excellent performance in evaluation on the VD (z-score/statistic 0.23 to - 0.83).
A clinical score that categorizes patients with NSTI according to the risk of death was created. It uses simple variables, all available at the time of first assessment. It stratifies patients according to disease severity and can guide the use of aggressive or novel therapeutic strategies and selection of patients for clinical trials.
坏死性软组织感染（NSTIs）与高死亡率相关；然而，目前尚无一种统一的方法来对疾病早期的严重程度进行分类。本研究的目的是开发一种基于初始评估时可用数据的临床评分，以帮助根据患者的死亡风险对其进行分层。
回顾性分析了 9 年来两家机构收治的 350 例 NSTI 患者的队列。使用随机拆分抽样，创建了两个数据集：预测（PD）和验证（VD）。使用 PD 中的多元逐步回归分析，根据入院时可用的数据确定死亡的独立预测因素。评估模型的准确性、区分度和校准度。创建了一种预测死亡的临床评分，并使用创伤和损伤严重程度评分（TRISS）方法，根据 VD 对评分进行验证（z 统计量）。
6 个入院参数独立预测死亡：年龄>50 岁、心率>110 次/分、体温<36℃、白细胞计数>40,000/μL、血清肌酐浓度>1.5mg/dL 和血细胞比容>50%。该模型的准确性为 86.8%；接受者操作特征曲线下的面积为 0.81，Hosmer-Lemeshow 统计量为 11.8。此外，该评分在 VD 评估中表现出色（z 分数/统计量为 0.23 至-0.83）。
创建了一种根据死亡风险对 NSTI 患者进行分类的临床评分。它使用简单的变量，均在首次评估时可用。它根据疾病严重程度对患者进行分层，并可指导使用积极或新颖的治疗策略以及选择患者进行临床试验。
