Comparison of the risk prediction systems POSSUM and P-POSSUM with the Surgical Risk Scale: A prospective cohort study of 721 patients

Autor: Sergio González-Martínez, Isidro Martí-Saurí, José M. Pueyo-Zurdo, Montserrat Martín-Baranera, Nuria Borrell-Grau
Rok vydání: 2016
Předmět:
Zdroj: International Journal of Surgery. 29:19-24
ISSN: 1743-9191
Popis: The outcomes of surgery are subject to variability and difficult to be accurately predicted. Different score systems have been developed to estimating the risk of undergoing a surgical procedure. The aim of this study was to assess the predictive ability of POSSUM and P-POSSUM scoring systems, compared to the Surgical Risk Scale (SRS), in Spanish patients undergoing general surgery.In this prospective observational study, 721 consecutive patients needing a surgical procedure were included. Observed morbidity and mortality after surgery were compared to the expected ones obtained by applying POSSUM, P-POSSUM and SRS.Mean age was 59.2 years (standard deviation (SD): 17.4 years), 43.5% were women. 616 (85.5%) patients underwent elective general surgery and 105 (14.5%) emergency surgery. The 30-day morbidity was 15.4%. The reintervention rate was 2.1% and mortality was 2.1%. The discrimination ability was excellent in predicting mortality. The Area Under the Curve (AUC) values were: POSSUM: AUC = 0.97, C.I.95%: 0.948-0.992, p 0.0001; P-POSSUM: AUC = 0.966, C.I.95%: 0.941-0.991, p 0.0001; SRS: AUC = 0.91, C.I.95%:0.853-0.967, p 0.0001. POSSUM was also discriminative in the prediction of morbidity (AUC = 0.772, C.I.95%: 0.719-0.826, p 0.0001). POSSUM predicted morbidity and mortality were higher than the observed ones (p = 0.01 and p = 0.04). Predicted and observed mortality were very similar for P-POSSUM (p = 0.93) and SRS (p = 0.37).Expected morbidity and mortality determined by POSSUM score showed values significantly above the observed ones. P-POSSUM and SRS systems were effective in predicting mortality. The SRS application is simple and may contribute to appropriate medical decision making.
Databáze: OpenAIRE