Derivation and validation of a prognostic model for postoperative risk stratification of critically ill patients with...

Prognostic scores and models of illness severity are useful both clinically and for research. The aim of this study was to develop two prognostic models for the prediction of long-term (6 months) and 28-day mortality of postoperative critically ill patients with faecal peritonitis (FP).

Patients admitted to intensive care units with faecal peritonitis and recruited to the European GenOSept study were divided into a derivation and a geographical validation subset; patients subsequently recruited to the UK GAinS study were used for temporal validation. Using all 50 clinical and laboratory variables available on day 1 of critical care admission, Cox proportional hazards regression was fitted to select variables for inclusion in two prognostic models, using stepwise selection and nonparametric bootstrapping sampling techniques. Using Area under the receiver operating characteristic curve (AuROC) analysis, the performance of the models was compared to SOFA and APACHE II.

Five variables (age, SOFA score, lowest temperature, highest heart rate, haematocrit) were entered into the prognostic models. The discriminatory performance of the 6-month prognostic model yielded an AuROC 0.81 (95% CI 0.76-0.86), 0.73 (95% CI 0.69-0.78) and 0.76 (95% CI 0.69-0.83) for the derivation, geographic and temporal external validation cohorts, respectively. The 28-day prognostic tool yielded an AuROC 0.82 (95% CI 0.77-0.88), 0.75 (95% CI 0.69-0.80) and 0.79 (95% CI 0.71-0.87) for the same cohorts. These AuROCs appeared consistently superior to those obtained with the SOFA and APACHE II scores alone.

The two prognostic models developed for 6-month and 28-day mortality prediction in critically ill septic patients with FP, in the postoperative phase, enhanced the day one SOFA score's predictive utility by adding a few key variables: age, lowest recorded temperature, highest recorded heart rate and haematocrit. External validation of their predictive capability in larger cohorts is needed, before introduction of the proposed scores into clinical practice to inform decision making and the design of clinical trials.