Predictors of Death in Necrotizing Skin and Soft Tissue Infection

Background 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. Material and methods 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. Results 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. Conclusion 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.