The e-SEPSS scoring system effectively predicted in-hospital mortality in sepsis patients, with mortality increasing from 4.1% to 100% across score groups and an AUC of 0.798 (95% CI 0.783-0.813).
Observational (n=7,546)
Does the e-SEPSS scoring system accurately predict in-hospital mortality in patients with sepsis during early hospitalization?
The e-SEPSS scoring system, utilizing seven routine laboratory parameters, provides a reliable and objective tool for predicting in-hospital mortality in septic patients during early hospitalization.
Effect estimate: ROC AUC 0.798 (95% CI 0.783-0.813)
p-value: p=<0.001
Abstract Background Sepsis is a complex life-threatening condition. Early initiation of treatment is crucial in reducing mortality. Current scoring systems have a lack of reliability in the emergency department (ED) or during early hospitalization (EH). Thus, a quick, reliable, and objective scoring system for assessing mortality risk during EH or ED could significantly reduce sepsis mortality. Methods Using the MIMIC-IV database, we identified 7546 patients hospitalized due to septicemia. We included 13 comorbidity groups and the first chronologically available values of 75 laboratory parameters from the ED or EH. To create and validate our scoring system for early prediction of in-hospital mortality (e-SEPSS), patients were assigned to model development (MD) ( N = 1004) or model validation (MV) group ( N = 6542), with the latter serving as internal validation of e-SEPSS. Each risk factor that contributed significantly to mortality was assigned one point. Groups with different numbers of points were compared according to mortality and hospitalization duration. Results Decreased chlorides, increased mean corpuscular hemoglobin, increased red blood cell distribution width, increased phosphates, decreased pH, increased partial thromboplastin time, and increased lactate dehydrogenase were included in e-SEPSS due to the highest reliability in predicting mortality. Patients received 1 point for each parameter, creating 8 mortality risk groups. A significant linear increase in mortality with each additional point was shown, ranging from 4.1% (0 points) to 100% (7 points) in the MD group. Similar trends were observed in the MV group. High power in discriminating patients with different mortality risks was shown (MD (ROC AUC = 0.718, CI 0.682–0.754), MV (ROC AUC = 0.798, CI 0.783–0.813)). Decreased survival time and shorter time-to-death with each additional point strengthened the validity of e-SEPSS (Mantel-Cox χ 2 (7) = 994.2, p-value < 0.001). Conclusion e-SEPSS provides a simple, objective, reliable, and accessible way of predicting mortality in septic patients in the ED or during EH.. After external and clinical validation of e-SEPSS, it could become a useful additional tool in reducing sepsis mortality.
Aranza et al. (Tue,) conducted a observational in Sepsis (n=7,546). e-SEPSS scoring system was evaluated on In-hospital mortality (ROC AUC 0.798, 95% CI 0.783-0.813, p=<0.001). The e-SEPSS scoring system effectively predicted in-hospital mortality in sepsis patients, with mortality increasing from 4.1% to 100% across score groups and an AUC of 0.798 (95% CI 0.783-0.813).