Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach.

Background: Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint.

Prognostic value of serial score measurements of the national early warning score, the quick sequential organ failure assessment and the systemic inflammatory response syndrome to predict clinical outcome in early sepsis.

Background And Importance: Sepsis is a common and potentially lethal syndrome, and early recognition is critical to prevent deterioration. Yet, currently available scores to facilitate recognition of sepsis lack prognostic accuracy.

Objective: To identify the optimal time-point to determine NEWS, qSOFA and SIRS for the prediction of clinical deterioration in early sepsis and to determine whether the change in these scores over time improves their prognostic accuracy.

Cohort profile of Acutelines: a large data/biobank of acute and emergency medicine.

Purpose: Research in acute care faces many challenges, including enrolment challenges, legal limitations in data sharing, limited funding and lack of singular ownership of the domain of acute care. To overcome these challenges, the Center of Acute Care of the University Medical Center Groningen in the Netherlands, has established a de novo data, image and biobank named 'Acutelines'.

Participants: Clinical data, imaging data and biomaterials (ie, blood, urine, faeces, hair) are collected from patients presenting to the emergency department (ED) with a broad range of acute disease presentations.