External validation of the sepsis severity score

Abstract

Introduction: Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. Mortality rates are high, exceeding 50% in patients with septic shock. The sepsis severity score (SSS) was developed to determine the severity of sepsis and as a prognostic model. The aim of this study was to externally validate the SSS model. Methods: Calibration and discrimination of the SSS were retrospectively evaluated using data from a single-center sepsis registry. Results: Data from 156 septic patients were recorded; 56% of them had septic shock, 94% of patients required mechanical ventilation. The observed hospital mortality was 60.3%. The mean SSS value was 94.4 (95% CI 90.5–98.3). The SSS presented excellent discrimination with an area under the receiver operating characteristic curve (AUC) of 0.806 (95% CI 0.734–0.866). The pairwise comparison of APACHE II (AUC = 0.789; 95% CI 0.715–0.851) with SSS and 1st day SOFA (AUC = 0.75; 95% CI 0.673–0.817) with SSS revealed no significant differences in discrimination between the models. The calibration of the SSS was good with the Hosmer-Lemeshow goodness-of-fit H test 9.59, P > 0.05. Analyses of calibration curve show absence of accurate predictions in lower deciles of lower risk (2nd and 4th). Conclusion: The SSS demonstrated excellent discrimination. The calibration evaluation gave conflicting results; the H-L test result indicated a good calibration, while the visual analysis of the calibration curve suggested the opposite. The SSS requires further evaluation before it can be safely recommended as an outcome prediction model.

A critical appraisal of the prognostic predictive models for patients with sepsis: Which model can be applied in clinical practice?

“…Of the studies found in this systematic review, only three predictive models had been subjected to external validation: those led by Osborn, 54 Sivayoham, 55 and Granja 56 . The first had four external validation studies, 64‐67 and the others just one (Table 2). 68,69 …”

“…Comparison of the discrimination of these three models 54‐56 showed that the maximum area under the ROC curve was 0.84, 56 although in all cases this value was above 70%. However, this study had only undergone one external validation process, 68 whereas another study with an area of 0.736 54 had been submitted to various validation studies 64‐67 . The third model had the lowest discrimination (0.712) and just one external validation study, 55,69 although it attained better indicators in PROBAST.…”

“…However, this study had only undergone one external validation process, 68 whereas another study with an area of 0.736 54 had been submitted to various validation studies. [64][65][66][67] The third model had the lowest discrimination (0.712) and just one external validation study, 55,69 although it attained better indicators in PROBAST. Consequently, if we had to recommend one of these three studies we would opt for that of Osborn, 54 as it has undergone external validation in different geographical areas and has a satisfactory discrimination.…”

Positive Role of Delta Neutrophil Index (DNI) as a Prodiagnostic Marker in Cecal Ligation and Puncture (CLP)-Induced Sepsis Murine Model

“…Prior to the clinical application of DNI, clinical scoring systems such as APACH score, SOFA score and SAPS score have been widely used [ 18 , 19 ]. However, such scoring systems need many parameters such as blood pH, Glasgow coma scale and vital signs to have a final score and take time to draw arterial and venous blood and test electrolytes [ 20 ]. Consequently, those complex processes may be at risk of some complications, such as subcutaneous hematoma and pseudo-aneurysm after blood sampling and time spent of at least 6 h with higher cost.…”

Explainable Artificial Intelligence Helps in Understanding the Effect of Fibronectin on Survival of Sepsis

“…It is practical for planning the allocation of resources and evaluating the performance of ICU wards. Mortality risk assessment is also used in clinical trials to characterize and compare patient groups, and is also an important part of quality assessment; risk-adjusted mortality is the most commonly used indicator of the quality of ICU care [ 3 ].…”