scholarly journals DIAGNOSTIC ACCURACY OF RAPID ANTIGEN TEST FOR COVID-19 IN AN EMERGENCY DEPARTMENT

2022 ◽  
pp. 115635
Author(s):  
Silvia Elli ◽  
Francesco Blasi ◽  
Barbara Brignolo ◽  
Ferruccio Ceriotti ◽  
Andrea Gori ◽  
...  
Keyword(s):  
Emergency Department ◽  
Diagnostic Accuracy ◽  
Antigen Test

Development and Performance Assessment of a Digital Serious Game to Assess Multi-Patient Care Skills in a Simulated Pediatric Emergency Department

2020 ◽  
Vol 51 (4) ◽  
pp. 550-570
Author(s):  
Cindy Luu ◽  
Thomas B. Talbot ◽  
Cha Chi Fung ◽  
Eyal Ben-Isaac ◽  
Juan Espinoza ◽  
...  
Keyword(s):  
Emergency Department ◽  
Diagnostic Accuracy ◽  
Patient Care ◽  
Skill Acquisition ◽  
Video Game ◽  
Rank Correlation ◽  
Fixed Time ◽  
Serious Game ◽  
Cross Sectional ◽  
Number Of Patients

Objective. Multi-patient care is important among medical trainees in an emergency department (ED). While resident efficiency is a typically measured metric, multi-patient care involves both efficiency and diagnostic / treatment accuracy. Multi-patient care ability is difficult to assess, though simulation is a potential alternative. Our objective was to generate validity evidence for a serious game in assessing multi-patient care skills among a variety of learners. Methods. This was a cross-sectional validation study using a digital serious game VitalSignsTM simulating multi-patient care within a pediatric ED. Subjects completed 5 virtual “shifts,” triaging, stabilizing, and discharging or admitting patients within a fixed time period; patients arrived at cascading intervals with pre-programmed deterioration if neglected. Predictor variables included generic multi-tasking ability, video game experience, medical knowledge, and clinical efficiency with real patients. Outcome metrics in 3 domains measured diagnostic accuracy (i.e. critical orders, diagnoses), efficiency (i.e. number of patients, time-to-order) and critical thinking (number of differential diagnoses); MANOVA determined differences between novice learners and expected expert physicians. Spearman Rank correlation determined associations between levels of expertise. Results. Ninety-five subjects’ gameplays were analyzed. Diagnostic accuracy and efficiency distinguished skill level between residency trained (residents, fellows and attendings) and pre-residency trained (medical students and undergraduate) subjects, particularly for critical orders, patients seen, and correct diagnoses (p < 0.003). There were moderate to strong correlations between the game’s diagnostic accuracy and efficiency metrics compared to level of training, including patients seen (rho = 0.47, p < 0.001); critical orders (rho = 0.80, p < 0.001); time-to-order (rho = −0.24, p = 0.025); and correct diagnoses (rho = 0.69, p < 0.001). Video game experience also correlated with patients seen (rho = 0.24, p = 0.003). Conclusion. A digital serious game depicting a busy virtual ED can distinguish between expected experts in multi-patient care at the pre- vs. post-residency level. Further study can focus on whether the game appropriately assesses skill acquisition during residency.

scholarly journals Independent Validation of Sepsis Index for Sepsis Screening in the Emergency Department

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1292
Author(s):  
Luisa Agnello ◽  
Alessandro Iacona ◽  
Salvatore Maestri ◽  
Bruna Lo Sasso ◽  
Rosaria Vincenza Giglio ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Early Detection ◽  
Diagnostic Accuracy ◽  
Positive Likelihood Ratio ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Distribution Width ◽  
Independent Validation ◽  
Sepsis Screening

(1) Background: The early detection of sepsis is still challenging, and there is an urgent need for biomarkers that could identify patients at a high risk of developing it. We recently developed an index, namely the Sepsis Index (SI), based on the combination of two CBC parameters: monocyte distribution width (MDW) and mean monocyte volume (MMV). In this study, we sought to independently validate the performance of SI as a tool for the early detection of patients at a high risk of sepsis in the Emergency Department (ED). (2) Methods: We enrolled all consecutive patients attending the ED with a request of the CBC. MDW and MMV were measured on samples collected in K3-EDTA tubes on the UniCel DxH 900 haematology analyser. SI was calculated based on the MDW and MMV. (3) Results: We enrolled a total of 703 patients stratified into four subgroups according to the Sepsis-2 criteria: control (498), infection (105), SIRS (52) and sepsis (48). The sepsis subgroup displayed the highest MDW (median 27.5, IQR 24.6–32.9) and SI (median 1.15, IQR 1.05–1.29) values. The ROC curve analysis for the prediction of sepsis showed a good and comparable diagnostic accuracy of the MDW and SI. However, the SI displayed an increased specificity, positive predictive value and positive likelihood ratio in comparison to MDW alone. (4) Conclusions: SI improves the diagnostic accuracy of MDW for sepsis screening.

scholarly journals Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

2021 ◽  
Author(s):  
Eun-Seok Choi ◽  
Jae Ang Sim ◽  
Young Gon Na ◽  
Jong- Keun Seon ◽  
Hyun Dae Shin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Confidence Interval ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Inflammatory Arthritis ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Extreme Gradient Boosting ◽  
Wbc Count

Abstract Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

scholarly journals Diagnostic accuracy of a SARS-CoV-2 rapid antigen test in real-life clinical settings

2021 ◽  
Author(s):  
Sabrina Jegerlehner ◽  
Franziska Suter-Riniker ◽  
Philipp Jent ◽  
Pascal Bittel ◽  
Michael Nagler
Keyword(s):  
Diagnostic Accuracy ◽  
Real Life ◽  
Clinical Settings ◽  
Antigen Test

scholarly journals Diagnostic Accuracy of Neurological Problems in the Emergency Department

2008 ◽  
Vol 35 (3) ◽  
pp. 335-341 ◽  
Author(s):  
Jeremy J Moeller ◽  
Joelius Kurniawan ◽  
Gordon J Gubitz ◽  
John A Ross ◽  
Virender Bhan
Keyword(s):  
Emergency Department ◽  
Diagnostic Accuracy ◽  
Tertiary Care ◽  
Retrospective Chart Review ◽  
Primary Headache ◽  
Final Diagnosis ◽  
Initial Diagnosis ◽  
Educational Strategies ◽  
Diagnostic Uncertainty ◽  
Peripheral Vertigo

Background:Previous studies describe significant rates of misdiagnosis of stroke, seizure and other neurological problems, but there are few studies examining diagnostic accuracy of all emergency referrals to a neurology service. This information could be useful in focusing the neurological education of physicians who assess and refer patients with neurological complaints in emergency departments.Methods:All neurological consultations in the emergency department at a tertiary-care teaching hospital were recorded for six months. The initial diagnosis of the requesting physician was recorded for each patient. This was compared to the initial diagnosis of the consulting neurologist and to the final diagnosis, as determined by retrospective chart review.Results:Over a six-month period, 493 neurological consultations were requested. The initial diagnosis of the requesting physician agreed with the final diagnosis in 60.4% (298/493) of cases, and disagreed or was uncertain in 35.7% of cases (19.1% and 16.6% respectively). In 3.9% of cases, the initial diagnosis of both the referring physician and the neurologist disagreed with the final diagnosis. Common misdiagnoses included neurocardiogenic syncope, peripheral vertigo, primary headache and psychogenic syndromes. Often, these were initially diagnosed as stroke or seizure.Conclusions:Our data indicate that misdiagnosis or diagnostic uncertainty occurred in over one-third of all neurological consultations in the emergency department setting. Benign neurological conditions, such as migraine, syncope and peripheral vertigo are frequently mislabeled as seizure or stroke. Educational strategies that emphasize emergent evaluation of these common conditions could improve diagnostic accuracy, and may result in better patient care.

scholarly journals Diagnostic accuracy of a new commercially available HCV-antigen test

2010 ◽  
Vol 25 (1) ◽  
Author(s):  
Marta Monari ◽  
Serenella Valaperta ◽  
Roberto Assandri ◽  
Alessandro Montanelli
Keyword(s):  
Diagnostic Accuracy ◽  
Antigen Test
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