Predictors of Death in Necrotizing Skin and Soft Tissue Infection.

Abstract

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.