BACKGROUND: 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. METHODS: 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). RESULTS: 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). CONCLUSION: 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.
BACKGROUND: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. METHODS: 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). RESULTS: 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). CONCLUSION: 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.
Authors: Robert A Tessler; Steve Vanhoy; Katherine Bergus; Christine Fong; Eileen M Bulger; Frederick P Rivara; Monica S Vavilala Journal: J Surg Res Date: 2019-02-13 Impact factor: 2.192
Authors: Lillian S Kao; Debbie F Lew; Samer N Arab; S Rob Todd; Samir S Awad; Matthew M Carrick; Michael G Corneille; Kevin P Lally Journal: Am J Surg Date: 2011-05-04 Impact factor: 2.565