Department of Surgery, Burn Shock Trauma Research Institute, Loyola University Medical Center, 2160 S. 1st Avenue, Maywood, IL, 60153, USA.
Department of Surgery, University of South Florida, Tampa, FL, USA.
World J Surg. 2019 Nov;43(11):2734-2739. doi: 10.1007/s00268-019-05087-8.
Necrotizing skin and soft tissue infection (NSTI) is a surgical emergency that is associated with high morbidity and mortality. This study aims to identify predictors of in-hospital death following a NSTI.
We queried the Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) for California between 2006 and 2011. We used conventional and advanced statistical methods to identify predictors of in-hospital mortality, which included: logistic regression, stepwise logistic regression, decision trees, and K-nearest neighbor (KNN) algorithms.
A total of 10,158 patients had a NSTI. The full and stepwise logistic regression models had a ROC AUC in the validation dataset of 0.83 (95% CI [0.80, 0.86]) and 0.81 (95% CI [0.78, 0.83]), respectively. The KNN and decision tree model had a ROC AUC of 0.84 (95% CI [0.81, 0.85]) and 0.69 (95% CI [0.65, 0.72]), respectively. The top predictors of in-hospital mortality in the KNN and stepwise logistic model included: (1) the presence of in-hospital coagulopathy, (2) having an infectious or parasitic diagnoses, (3) electrolyte disturbances, (4) advanced age, and (5) the total number of beds in a hospital.
Patients with a NSTI have high rates of in-hospital mortality. This study highlights the important factors in managing patients with a NSTI which include: correcting coagulopathy and electrolyte imbalances, treating underlying infectious processes, providing adequate resources to the elderly population, and managing patients in high-volume centers.
坏死性皮肤和软组织感染（NSTI）是一种与高发病率和死亡率相关的外科急症。本研究旨在确定 NSTI 患者住院期间死亡的预测因素。
我们在 2006 年至 2011 年期间从医疗保健成本和利用项目（HCUP）州住院患者数据库（SID）中查询了加利福尼亚州的数据。我们使用传统和先进的统计方法来确定住院死亡率的预测因素，包括：逻辑回归、逐步逻辑回归、决策树和 K 最近邻（KNN）算法。
共有 10158 例患者患有 NSTI。全和逐步逻辑回归模型在验证数据集中的 ROC AUC 分别为 0.83（95% CI [0.80, 0.86]）和 0.81（95% CI [0.78, 0.83]）。KNN 和决策树模型的 ROC AUC 分别为 0.84（95% CI [0.81, 0.85]）和 0.69（95% CI [0.65, 0.72]）。KNN 和逐步逻辑模型中住院死亡率的主要预测因素包括：（1）存在院内凝血功能障碍，（2）有传染性或寄生虫性诊断，（3）电解质紊乱，（4）年龄较大，（5）医院的总床位数。
患有 NSTI 的患者住院期间死亡率较高。本研究强调了管理 NSTI 患者的重要因素，包括：纠正凝血功能障碍和电解质失衡、治疗潜在的感染过程、为老年人口提供充足的资源以及在高容量中心管理患者。
