Prognostic accuracy of initial and 24-h maximum SOFA scores of septic shock patients in the emergency department

Estimated plasma volume status is a simple and quick tool that could help define the severity of patients with infection on arrival at the emergency department

BACKGROUND

Infectious states are subtle and rapidly evolving conditions observed daily in the emergency department (ED), and their prognostic evaluation remains a complex clinical challenge. Recently, estimated plasma volume status (ePVS) has been suggested to have a prognostic role in conditions where volemic alteration is central to the pathophysiology. The aim of this study was to verify whether ePVS recorded at ED admission can provide prognostic indications of 30-day mortality in patients with infection.

METHODS

A prospective observational study was performed between 1 January 2021 and 31 December 2021 at the ED of the Merano Hospital. All patients with infection were enrolled. ePVS values were derived from haemoglobin and haematocrit measured on the immediate arrival of patients in the ED. The predictive power of ePVS for 30-day mortality was assessed using a multivariate model adjusted for severity, comorbidity and urgency. Kaplan-Meier analysis was also performed.

RESULTS

Of the 949 patients with infection enrolled in the study (47.9%, SOFA ≥2), 8.9% (84/949) died at 30 days. The median ePVS value was higher in patients who died at 30 days than in patients who survived (5.83 vs. 4.61, p < 0.001). Multivariate analysis revealed that ePVS in both continuous and categorical form around the median was an independent risk factor for 30-day mortality even after adjusting for severity, comorbidity and urgency. Kaplan-Meier analysis confirmed an increased risk of death in patients with high ePVS values.

CONCLUSIONS

ePVS recorded on ED admission of patients with infection was an independent predictor of risk for 30-day mortality.

Utilizing predictive machine-learning modelling unveils feature-based risk assessment system for hyperinflammatory patterns and infectious outcomes in polytrauma

Purpose

Earlier research has identified several potentially predictive features including biomarkers associated with trauma, which can be used to assess the risk for harmful outcomes of polytraumatized patients. These features encompass various aspects such as the nature and severity of the injury, accompanying health conditions, immune and inflammatory markers, and blood parameters linked to organ functioning, however their applicability is limited. Numerous indicators relevant to the patients` outcome are routinely gathered in the intensive care unit (ICU) and recorded in electronic medical records, rendering them suitable predictors for risk assessment of polytraumatized patients.

Methods

317 polytraumatized patients were included, and the influence of 29 clinical and biological features on the complication patterns for systemic inflammatory response syndrome (SIRS), pneumonia and sepsis were analyzed with a machine learning workflow including clustering, classification and explainability using SHapley Additive exPlanations (SHAP) values. The predictive ability of the analyzed features within three days after admission to the hospital were compared based on patient-specific outcomes using receiver-operating characteristics.

Results

A correlation and clustering analysis revealed that distinct patterns of injury and biomarker patterns were observed for the major complication classes. A k-means clustering suggested four different clusters based on the major complications SIRS, pneumonia and sepsis as well as a patient subgroup that developed no complications. For classification of the outcome groups with no complications, pneumonia and sepsis based on boosting ensemble classification, 90% were correctly classified as low-risk group (no complications). For the high-risk groups associated with development of pneumonia and sepsis, 80% of the patients were correctly identified. The explainability analysis with SHAP values identified the top-ranking features that had the largest impact on the development of adverse outcome patterns. For both investigated risk scenarios (infectious complications and long ICU stay) the most important features are SOFA score, Glasgow Coma Scale, lactate, GGT and hemoglobin blood concentration.

Conclusion

The machine learning-based identification of prognostic feature patterns in patients with traumatic injuries may improve tailoring personalized treatment modalities to mitigate the adverse outcomes in high-risk patient clusters.