Purpose In this systematic literature review, the effects of the application of a checklist during in hospital resuscitation of trauma patients on adherence to the ATLS guidelines, trauma team performance, and patient-related outcomes were integrated. Methods A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Metaanalyses checklist. The search was performed in Pubmed, Embase, CINAHL, and Cochrane inception till January 2019. Randomized controlled-or controlled before-and-after study design were included. All other forms of observational study designs, reviews, case series or case reports, animal studies, and simulation studies were excluded. The Effective Public Health Practice Project Quality Assessment Tool was applied to assess the methodological quality of the included studies. Results Three of the 625 identified articles were included, which all used a before-and-after study design. Two studies showed that Advanced Trauma Life Support (ATLS)-related tasks are significantly more frequently performed when a checklist was applied during resuscitation. [14 of 30 tasks (p < 0.05), respectively, 18 of 19 tasks (p < 0.05)]. One study showed that time to task completion (− 9 s, 95% CI = − 13.8 to − 4.8 s) and workflow improved, which was analyzed as model fitness (0.90 vs 0.96; p < 0.001); conformance frequency (26.1% vs 77.6%; p < 0.001); and frequency of unique workflow traces (31.7% vs 19.1%; p = 0.005). One study showed that the incidence of pneumonia was higher in the group where a checklist was applied [adjusted odds ratio (aOR) 1.69, 95% Confidence Interval (CI 1.03-2.80)]. No difference was found for nine other assessed complications or missed injuries. Reduced mortality rates were found in the most severely injured patient group (Injury Severity score > 25, aOR 0.51, 95% CI 0.30-0.89). Conclusions The application of a checklist may improve ATLS adherence and workflow during trauma resuscitation. Current literature is insufficient to truly define the effect of the application of a checklist during trauma resuscitation on patientrelated outcomes, although one study showed promising results as an improved chance of survival for the most severely injured patients was found.

Introduction Bacterial infections are frequently seen in the emergency department (ED), but can be difficult to distinguish from viral infections and some non-infectious diseases. Common biomarkers such as c-reactive protein (CRP) and white blood cell (WBC) counts fail to aid in the differential diagnosis. Neutrophil CD64 (nCD64), an IgG receptor, is suggested to be more specific for bacterial infections. This study investigated if nCD64 can distinguish bacterial infections from other infectious and non-infectious diseases in the ED. Methods All COVID-19 suspected patients who visited the ED and for which a definitive diagnosis was made, were included. Blood was analyzed using an automated flow cytometer within 2 h after presentation. Patients were divided into a bacterial, viral, and non-infectious disease group. We determined the diagnostic value of nCD64 and compared this to those of CRP and WBC counts. Results Of the 291 patients presented at the ED, 182 patients were included with a definitive diagnosis (bacterial infection n = 78; viral infection n = 64; non-infectious disease n = 40). ROC-curves were plotted, with AUCs of 0.71 [95%CI: 0.64–0.79], 0.77 [0.69–0.84] and 0.64 [0.55–0.73] for nCD64, WBC counts and CRP, respectively. In the bacterial group, nCD64 MFI was significantly higher compared to the other groups (p < 0.01). A cut-off of 9.4 AU MFI for nCD64 corresponded with a positive predictive value of 1.00 (sensitivity of 0.27, a specificity of 1.00, and an NPV of 0.64). Furthermore, a diagnostic algorithm was constructed which can serve as an example of what a future biomarker prediction model could look like. Conclusion For patients in the ED presenting with a suspected infection, nCD64 measured with automatic flow cytometry, has a high specificity and positive predictive value for diagnosing a bacterial infection. However, a low nCD64 cannot rule out a bacterial infection. For future purposes, nCD64 should be combined with additional tests to form an algorithm that adequately diagnoses infectious diseases.