PurposeResearch 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’.ParticipantsClinical 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. A deferred consent procedure (by proxy) is in place to allow collecting data and biomaterials prior to obtaining written consent. The digital infrastructure used ensures automated capturing of all bed-side monitoring data (ie, vital parameters, electrophysiological waveforms) and securely importing data from other sources, such as the electronic health records of the hospital, ambulance and general practitioner, municipal registration and pharmacy. Data are collected from all included participants during the first 72 hours of their hospitalisation, while follow-up data are collected at 3 months, 1 year, 2 years and 5 years after their ED visit.Findings to dateEnrolment of the first participant occurred on 1 September 2020. During the first month, 653 participants were screened for eligibility, of which 180 were approached as potential participants. In total, 151 (84%) provided consent for participation of which 89 participants fulfilled criteria for collection of biomaterials.Future plansThe main aim of Acutelines is to facilitate research in acute medicine by providing the framework for novel studies and issuing data, images and biomaterials for future research. The protocol will be extended by connecting with central registries to obtain long-term follow-up data, for which we already request permission from the participant.Trial registration numberNCT04615065.

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. Design Post hoc analysis of prospectively collected data. Settings and participants This study was performed in the emergency department (ED) of a tertiary-care teaching hospital. Adult medical patients with (potential) sepsis were included. Outcome measures and analysis The primary outcome was clinical deterioration within 72 h after admission, defined as organ failure development, the composite outcome of ICU-admission and death. Secondary outcomes were the composite of ICU-admission/death and a rise in SOFA at least 2. Scores were calculated at the ED with 30-min intervals. ROC analyses were constructed to compare the prognostic accuracy of the scores. Results In total, 1750 patients were included, of which 360 (20.6%) deteriorated and 79 (4.5%) went to the ICU or died within 72 h. The NEWS at triage (AUC, 0.62; 95% CI, 0.59–0.65) had a higher accuracy than qSOFA (AUC, 0.60; 95% CI, 0.56–0.63) and SIRS (AUC, 0.59; 95% CI, 0.56–0.63) for predicting deterioration. The AUC of the NEWS at 1 h (0.65; 95% CI, 0.63–0.69) and 150 min after triage (0.64; 95% CI, 0.61–0.68) was higher than the AUC of the NEWS at triage. The qSOFA had the highest AUC at 90 min after triage (0.62; 95% CI, 0.58–0.65), whereas the SIRS had the highest AUC at 60 min after triage (0.60; 95% CI, 0.56–0.63); both are not significantly different from triage. The NEWS had a better accuracy to predict ICU-admission/death <72 h compared with qSOFA (AUC difference, 0.092) and SIRS (AUC difference, 0.137). No differences were found for the prediction of a rise in SOFA at least 2 within 72 h between the scores. Patients with the largest improvement in any of the scores were more prone to deteriorate. Conclusion NEWS had a higher prognostic accuracy to predict deterioration compared with SIRS and qSOFA; the highest accuracy was reached at 1 h after triage.

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. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. Methods Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. Results In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. Conclusions We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017.