Development and validation of a necrotizing soft-tissue infection mortality risk calculator using nsqip

Background: Necrotizing soft-tissue infections (NSTI) are a group of uncommon, rapidly progressive infections requiring prompt surgical debridement and systemic support. A previous attempt to define risk factors for mortality from NSTI had multiple limitations. The objective of this study was to develop and validate a 30-day postoperative mortality risk calculator for patients with NSTI using NSQIP. Study Design: The NSQIP Participant Use Files (2005-2010) were used as the primary data source. Patients diagnosed with NSTI were identified by ICD-9 codes. Multiple logistic regression analysis identified key preoperative variables predicting mortality. Bootstrap analysis was used to validate the model. Results: In 1,392 identified NSTI cases, demographics were as follows: 42% were female, median age was 55 years (interquartile range 46 to 63 years), and median body mass index was 32 kg/m² (interquartile range 26 to 40 kg/m²). Thirty-day mortality was 13%. Seven independent variables were identified that correlated with mortality: age older than 60 years (odds ratio [OR] = 2.5; 95% CI 1.7-3.6), functional status (partially dependent: OR = 1.6; 95% CI 1.0-2.7; totally dependent: OR = 2.3; 95% CI 1.4-3.8), requiring dialysis (OR = 1.9; 95% CI 1.2-3.1), American Society of Anesthesiologists class 4 or higher (OR = 3.6; 95% CI 2.3-5.6), emergent surgery (OR = 1.6; 95% CI 1.0-2.3), septic shock (OR = 2.4; 95% CI 1.6-3.6), and low platelet count (<50K/μL: OR = 3.5; 95% CI 1.6-7.4; <150K/μL but >50K/μL: OR = 1.9; 95% CI 1.2-2.9). The receiver operating characteristic area was 0.85 (95% CI 0.82-0.87), which indicated a strong predictive model. Using bootstrap validation, the optimism-corrected receiver operating characteristic area was 0.83 (95% CI 0.81-0.86), which represents the model performance in future patients. The model was used to develop an interactive risk calculator. Conclusions: This risk calculator has excellent predictive ability for mortality in patients with NSTI. This simple interactive tool can aid physicians and patients in the decision-making process.