Improving emergency department flow by introducing a simple time out moment (The TRAFFIC LIGHT study).

BACKGROUND AND IMPORTANCE: Long waiting times in the emergency department (ED) is an increasing problem in the recent years and is expected to become an even bigger problem in the future Objective: We aimed to test the hypothesis whether increasing awareness of the time lapse with the treating physician, 2 hours after patient arrival, can reduce long patient turnaround time (TAT). METHOD: In this prospective single-center cohort study we compared and analyzed patient TAT in the ED before and after implementation of a so called 'traffic light' moment 2 hours after patient arrival. At this 'traffic light' moment a team member contacted the treating physician to increased awareness over the time lapse. Difference in percentage of patients who stayed more than 4 hours in the ED before and after intervention was the primary outcome Results: Between October 2nd 2021 and January 2nd,2022 1494 patients were included for primary outcome analysis. A total of 419 patients (n=740, 56.6%) had a TAT of less than 4 hour in the ED before intervention, compared to 497 (n=754, 65.9%) after intervention (p <0.001). Median time spent in de ED before intervention was 3:40 (IQR 2:24 - 5:04) compared to 3:15 (IQR 2:03 - 4:38) after intervention (p<0.001). CONCLUSION: This simple and low-cost intervention reduces the ED length of stay significantly. Although multiple interventions will be required to ensure less patients spending more than 4-hours in the ED, a 'traffic light' moment can be a simple and an effective tool.

Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool.

BACKGROUND: Overuse of blood cultures (BCs) in emergency departments (EDs) leads to low yields and high numbers of contaminated cultures, accompanied by increased diagnostics, antibiotic usage, prolonged hospitalization, and mortality. We aimed to simplify and validate a recently developed machine learning model to help safely withhold BC testing in low-risk patients. METHODS: We extracted data from the electronic health records (EHR) for 44.123 unique ED visits with BC sampling in the Amsterdam UMC (locations VUMC and AMC; the Netherlands), Zaans Medical Center (ZMC; the Netherlands), and Beth Israel Deaconess Medical Center (BIDMC; United States) in periods between 2011 and 2021. We trained a machine learning model on the VUMC data to predict blood culture outcomes and validated it in the AMC, ZMC, and BIDMC with subsequent real-time prospective evaluation in the VUMC. FINDINGS: The model had an Area Under the Receiver Operating Characteristics curve (AUROC) of 0.81 (95%-CI = 0.78-0.83) in the VUMC test set. The most important predictors were temperature, creatinine, and C-reactive protein. The AUROCs in the validation cohorts were 0.80 (AMC; 0.78-0.82), 0.76 (ZMC; 0.74-0.78), and 0.75 (BIDMC; 0.74-0.76). During real-time prospective evaluation in the EHR of the VUMC, it reached an AUROC of 0.76 (0.71-0.81) among 590 patients with BC draws in the ED. The prospective evaluation showed that the model can be used to safely withhold blood culture analyses in at least 30% of patients in the ED. INTERPRETATION: We developed a machine learning model to predict blood culture outcomes in the ED, which retained its performance during external validation and real-time prospective evaluation. Our model can identify patients at low risk of having a positive blood culture. Using the model in practice can significantly reduce the number of blood culture analyses and thus avoid the hidden costs of false-positive culture results. FUNDING: This research project was funded by the Amsterdam Public Health - Quality of Care program and the Dutch "Doen of Laten" project (project number: 839205002).

Optimal use of procalcitonin to rule out bacteremia in patients with possible viral infections.

Objective: During the winter, many patients present with suspected infection that could be a viral or a bacterial (co)infection. The aim of this study is to investigate whether the optimal use of procalcitonin (PCT) is different in patients with and without proven viral infections for the purpose of excluding bacteremia. We hypothesize that when a viral infection is confirmed, this lowers the probability of bacteremia and, therefore, influences the appropriate cutoff of procalcitonin. Methods: This study was conducted in the emergency department of an academic medical center in The Netherlands in the winter seasons of 2019 and 2020. Adults (>18 years) with suspected infection, in whom a blood culture and a rapid polymerase chain reaction test for influenza was performed were included. Results: A total of 546 patients were included of whom 47 (8.6%) had a positive blood culture. PCT had an area under the curve of 0.85, 95% confidence interval (95% CI) 0.80-0.91, for prediction of bacteremia. In patients with a proven viral infection (N = 212) PCT < 0.5 µg/L had a sensitivity of 100% (95% CI 63.1-100) and specificity of 81.2% (95% CI 75.1-86.3) to exclude bacteremia. In patients without a viral infection, the procalcitonin cutoff point of < 0.25 µg/L showed a sensitivity of 87.2% (95% CI 72.6-95.7) and specificity of 64.1 % (95% CI 58.3-69.6). Conclusion: In patients with a viral infection, our findings suggest that a PCT concentration of <0.50 µg/L makes bacteremia unlikely. However, this finding needs to be confirmed in a larger population of patients with viral infections, especially because the rate of coinfection in our cohort was low.

Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study.

OBJECTIVES: To develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting. DESIGN: Retrospective observational study. SETTING: ED of a large teaching hospital in the Netherlands between 1 September 2018 and 24 June 2020. PARTICIPANTS: Adult patients from whom BCs were collected in the ED. Data of demographic information, vital signs, administered medications in the ED and laboratory and radiology results were extracted from the electronic health record, if available at the end of the ED visits. MAIN OUTCOME MEASURES: The primary outcome was the performance of two models (logistic regression and gradient boosted trees) to predict bacteraemia in ED patients, defined as at least one true positive BC collected at the ED. RESULTS: In 4885 out of 51 399 ED visits (9.5%), BCs were collected. In 598/4885 (12.2%) visits, at least one of the BCs was true positive. Both a gradient boosted tree model and a logistic regression model showed good performance in predicting BC results with area under curve of the receiver operating characteristics of 0.77 (95% CI 0.73 to 0.82) and 0.78 (95% CI 0.73 to 0.82) in the test sets, respectively. In the gradient boosted tree model, the optimal threshold would predict 69% of BCs in the test set to be negative, with a negative predictive value of over 94%. CONCLUSIONS: Both models can accurately identify patients with low risk of bacteraemia at the ED in this single-centre setting and may be useful to reduce unnecessary BCs and associated healthcare costs. Further studies are necessary for validation and to investigate the potential clinical benefits and possible risks after implementation.

[A women with burning pain in her arms after a fall]. / Een vrouw met brandende pijn in de armen na een val.

BACKGROUND: The central cord syndrome is an incomplete spinal cord injury that can develop after a minor trauma to the cervical spinal column. CASE DESCRIPTION: A 73-year-old woman presented at our Emergency Department with pyelonephritis accompanied by weakness and a burning feeling in her arms and legs after a fall on her head. The weakness and pain did not improve during her hospital admission. On consultation with a neurologist, the patient was diagnosed with central cord syndrome. As the neurological loss of function did not resolve spontaneously, the patient underwent laminectomy and spondylodesis. Postoperatively her symptoms improved. After 22 days in hospital, the patient was discharged to a rehabilitation clinic. CONCLUSION: In older patients with pre-existing degenerative cervical spinal stenosis, central cord stenosis resulting from minor trauma can cause severe and invalidating symptoms. The early tracing and treatment of patients with this syndrome is essential in order to increase the chance of neurological and functional recovery.

Duration of untreated illness in schizophrenia is not associated with 5-year brain volume change.

OBJECTIVES: Evidence for an association between duration of untreated illness (DUI) with clinical and functional outcome or brain volume (change) in schizophrenia patients is inconclusive. We aimed to investigate the relationship between DUI, outcome and brain volume at illness onset or brain volume change during the first five years of the illness in first-episode patients. METHODS: Magnetic resonance images were acquired at baseline (T0) and after 5-year (T5) of 57 schizophrenia patients. Correlations were calculated in patients between brain volume (change), DUI and outcome variables. RESULTS: We found no significant correlation between DUI and brain volume (change) in schizophrenia patients. A longer DUI was significantly correlated with higher PANSS scores at T0 and T5, and with higher scores on the Camberwell Assessment of Need scale at T5. Baseline volume of the cerebrum and lateral ventricles, and cerebellum volume (change) were associated with PANSS scores at T0 and T5. CONCLUSION: Although clinical outcome is associated with both brain volume (change) and DUI, we found no evidence for a relationship between DUI and brain volume (change). DUI and baseline brain volume or 5-year brain volume (change) seem to explain different parts of the variation in clinical outcome.