Determinants of Early Mortality Among Deaths at Adult Emergency Departments in Southern Ethiopia.

BACKGROUND: In Ethiopia, the first 3 days (72 h) after admission to the emergency department (ED) account for more than half (59.8%) of all deaths. However, little is known about the prevalence of early mortality and its associated factors in southern Ethiopia. OBJECTIVE: The main objective of this study is to assess the prevalence and associated factors of early mortality among deaths in adult EDs at selected public hospitals in Hawassa, southern Ethiopia. METHODS: An institutional-based retrospective cross-sectional study design was applied. A systematic random sampling technique was used to select 369 charts of patients who died in the adult EDs of selected public hospitals in the past 2 years. The data were collected using a standardized and pretested data abstraction tool using the Kobo Toolbox data collection tool. Logistic regression analyses were carried out to assess the strength of the association. Statistical significance was declared at p-value < 0.05, and an adjusted odds ratio (AOR) with a 95% confidence interval was used to report the strength of the association. RESULT: According to the results of the current study, 288 patients, or 78% of the total, passed away within 72 h of admission to the ED. The following variables were significantly associated with early mortality: delayed initial intervention (AOR 2.338), red triage categories (AOR 3.9), lack of investigation (AOR 3.4), comorbid illness (AOR 3.2), absence of prehospital treatment (AOR 4.2), and road traffic accidents (AOR 4.1). CONCLUSION: There was an increased early mortality rate seen in this investigation. The following factors were significantly associated with an early death in the ED: comorbidity, delayed intervention, red warning score, road traffic accidents, absence of prehospital treatment, and lack of diagnostic testing. By addressing the variables that are strongly linked to an early mortality, every intervention should be undertaken to reduce the risk of an early death.

Diagnostic application of the ColonFlag AI tool in combination with faecal immunochemical test in patients on an urgent lower gastrointestinal cancer pathway.

OBJECTIVE: Colorectal cancer (CRC) is the fourth most common cancer in the UK. Patients with symptoms suggestive of CRC should be referred for urgent investigation. However, gastrointestinal symptoms are often non-specific and there is a need for suitable triage tools to enable prioritisation of investigations. In this study, the performance of the faecal immunochemical test (FIT), anaemia and the artificial intelligence algorithm ColonFlag were retrospectively examined and evaluated for their potential clinical benefits in patients who had been referred on an urgent lower gastrointestinal cancer pathway. DESIGN: All patients aged over 40 years referred in a 12-month period were included. After 6 months, clinical outcomes were determined and the performance of the triage tests was evaluated. RESULTS: A total of 3822 patients completed investigations and received a diagnosis. 143 had CRC, 126 high-risk adenomas (HRA). ColonFlag would have missed 27 CRC and 29 HRA. Faecal haemoglobin (f-Hb) at a cut-off of 10 µg/g would have missed 10 CRC and 26 HRA; f-Hb in combination with anaemia would have missed 2 CRC and 14 HRA. Using f-Hb in combination with ColonFlag would have missed only 1 CRC and 5 HRA and would have reduced the need for urgent referral by over 400 patients. CONCLUSION: ColonFlag has potential to assist detection of CRC and HRA, alone where no faecal sample is present and in combination with FIT and to reduce the need for urgent referral.

Prehospital Pandemic Respiratory Infection Emergency System Triage score can effectively predict the 30-day mortality of COVID-19 patients with pneumonia.

BACKGROUND: Coronavirus disease 2019 (COVID-19) patients with pneumonia should receive the guidance of initial risk stratification and early warning as soon as possible. Whether the prehospital Pandemic Respiratory Infection Emergency System Triage (PRIEST) score can accurately predict the short-term prognosis of them remains unknown. Accordingly, we aimed to assess the performance of prehospital PRIEST in predicting the 30-day mortality of patients. METHODS: This retrospective study evaluated the accuracy of five physiological parameters scores commonly used in prehospital disposal for mortality prediction using receiver operating characteristic curves and decision curve analysis. Cox proportional hazard regression analysis was conducted to evaluate independent predictors associated with the 30-day mortality. RESULTS: A total of 231 patients were included in this study, among which 23 cases (10.0%) died within 30 days after admission. Compared with survivor patients, non-survivor patients had greater numbers of comorbidities, signs and symptoms, complications, and physiological parameters scores and required greater prehospital care (p < 0.05). When the PRIEST score was >12, the sensitivity was 91.3%, and the specificity was 77.4%. We found that the area under the curve of the PRIEST score (0.887, p < 0.05) for mortality prediction was greater than that of the quick Sequential Organ Failure Assessment (0.724), CRB-65 (0.780), Rapid Emergency Medicine Score (0.809), and National Early Warning Score 2 (0.838). Moreover, prehospital PRIEST scores were positively correlated with numbers of comorbidities and numbers of prehospital treatment measures. The 30-day survival rate of patients with PRIEST scores ≤12 (98.8%) significantly exceeded that of patients with PRIEST scores >12 (69.1%) (p < 0.001). Prehospital PRIEST scores >12 (HR = 7.409) was one of the independent predictors of the 30-day mortality. CONCLUSIONS: The PRIEST can accurately, quickly, and conveniently predict the 30-day mortality of COVID-19 patients with pneumonia in the prehospital phase and can guide their initial risk stratification and treatment.

Factors affecting the accuracy of prehospital triage application and prehospital scene time in simulated mass casualty incidents.

BACKGROUND: The contemporary management of mass casualty incidents (MCIs) relies on the effective application of predetermined, dedicated response plans based on current best evidence. Currently, there is limited evidence regarding the factors influencing the accuracy of first responders (FRs) in applying the START protocol and the associated prehospital times during the response to MCIs. The objective of this study was to investigate factors affecting FRs' accuracy in performing prehospital triage in a series of simulated mass casualty exercises. Secondly, we assessed factors affecting triage-to-scene exit time in the same series of exercises. METHODS: This retrospective study focused on simulated casualties in a series of simulated MCIs Full Scale Exercises. START triage was the triage method of choice. For each Full-Scale Exercise (FSEx), collected data included exercise and casualty-related information, simulated casualty vital parameters, simulated casualty anatomic lesions, scenario management times, and responder experience. RESULTS: Among the 1090 casualties included in the primary analysis, 912 (83.6%) were correctly triaged, 137 (12.6%) were overtriaged, and 41 (3.7%) were undertriaged. The multinomial regression model indicated that increasing heart rate (RRR = 1.012, p = 0.008), H-AIS (RRR = 1.532, p < 0.001), and thorax AIS (T-AIS) (RRR = 1.344, p = 0.007), and lower ISS (RRR = 0.957, p = 0.042) were independently associated with overtriage. Undertriage was significantly associated with increasing systolic blood pressure (RRR = 1.013, p = 0.005), AVPU class (RRR = 3.104 per class increase), and A-AIS (RRR = 1.290, p = 0.035). The model investigating the factors associated with triage-to-scene departure time showed that the assigned prehospital triage code red (TR = 0.841, p = 0.002), expert providers (TR = 0.909, p = 0.015), and higher peripheral oxygen saturation (TR = 0.998, p < 0.001) were associated with a reduction in triage-to-scene departure time. Conversely, increasing ISS was associated with a longer triage-to-scene departure time (TR = 1.004, 0.017). CONCLUSIONS: Understanding the predictors influencing triage and scene management decision-making by healthcare professionals responding to a mass casualty may facilitate the development of tailored training pathways regarding mass casualty triage and scene management.

Triage Accuracy and the Safety of User-Initiated Symptom Assessment With an Electronic Symptom Checker in a Real-Life Setting: Instrument Validation Study.

BACKGROUND: Previous studies have evaluated the accuracy of the diagnostics of electronic symptom checkers (ESCs) and triage using clinical case vignettes. National Omaolo digital services (Omaolo) in Finland consist of an ESC for various symptoms. Omaolo is a medical device with a Conformité Européenne marking (risk class: IIa), based on Duodecim Clinical Decision Support, EBMEDS. OBJECTIVE: This study investigates how well triage performed by the ESC nurse triage within the chief symptom list available in Omaolo (anal region symptoms, cough, diarrhea, discharge from the eye or watery or reddish eye, headache, heartburn, knee symptom or injury, lower back pain or injury, oral health, painful or blocked ear, respiratory tract infection, sexually transmitted disease, shoulder pain or stiffness or injury, sore throat or throat symptom, and urinary tract infection). In addition, the accuracy, specificity, sensitivity, and safety of the Omaolo ESC were assessed. METHODS: This is a clinical validation study in a real-life setting performed at multiple primary health care (PHC) centers across Finland. The included units were of the walk-in model of primary care, where no previous phone call or contact was required. Upon arriving at the PHC center, users (patients) answered the ESC questions and received a triage recommendation; a nurse then assessed their triage. Findings on 877 patients were analyzed by matching the ESC recommendations with triage by the triage nurse. RESULTS: Safe assessments by the ESC accounted for 97.6% (856/877; 95% CI 95.6%-98.0%) of all assessments made. The mean of the exact match for all symptom assessments was 53.7% (471/877; 95% CI 49.2%-55.9%). The mean value of the exact match or overly conservative but suitable for all (ESC's assessment was 1 triage level higher than the nurse's triage) symptom assessments was 66.6% (584/877; 95% CI 63.4%-69.7%). When the nurse concluded that urgent treatment was needed, the ESC's exactly matched accuracy was 70.9% (244/344; 95% CI 65.8%-75.7%). Sensitivity for the Omaolo ESC was 62.6% and specificity of 69.2%. A total of 21 critical assessments were identified for further analysis: there was no indication of compromised patient safety. CONCLUSIONS: The primary objectives of this study were to evaluate the safety and to explore the accuracy, specificity, and sensitivity of the Omaolo ESC. The results indicate that the ESC is safe in a real-life setting when appraised with assessments conducted by triage nurses. Furthermore, the Omaolo ESC exhibits the potential to guide patients to appropriate triage destinations effectively, helping them to receive timely and suitable care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41423.

The effectiveness of a modified Manchester Triage System for geriatric patients: A retrospective quantitative study.

AIM: Geriatric patients are increasingly dominating the daily routine in emergency department (ED). The atypical clinical presentation of disease, multimorbidity, frailty and cognitive impairment of geriatric patients pose particular challenges for triage in the ED. Efficient and accurate emergency triage plays a key role in differentiating between geriatric patients who need timely treatment and those who can wait safely. The purpose of this study was to evaluate the performance of the modified Manchester Triage System (mMTS) in classifying geriatric patients. DESIGN: An observational retrospective study. METHODS: A retrospective study of 18,796 geriatric patients (≥65 years) attending the ED of a tertiary care hospital in Zhejiang province between 1 June 2020 and 30 June 2022. Baseline information on patients was collected and divided into two different study groups according to triage level: high priority (red/orange) and low priority (yellow/green). The sensitivity and specificity of the mMTS were estimated by verifying the triage classification received by the emergency geriatric patients and their survival at 7 days or the need for acute surgery within 72 h. RESULTS: The study included a total of 17,764 geriatric patients with a median age of 72 years in ED. 10.7% (1896/17,764) of the geriatric patients were assigned to the high priority code group (red/orange) and 89.3% (15,868/17,764) were in the low priority code group (yellow/green). The sensitivity of the mMTS associated with death within 7 days was 85.7% (77.5-91.4), specificity was 89.8% (89.3-90.2), and accuracy was 89.8% (89.3-90.2). 1.8% of patients required surgery within 72 h. The sensitivity was 62.6% (57.0-67.9), specificity was 90.3% (89.8-90.7), and negative predictive value was 99.2% (99.0-99.4). CONCLUSIONS: The mMTS has good specificity, accuracy and negative predictive value for geriatric patients. However, its incorrect prediction of triage in high-priority code patients results in lower sensitivity, which may serve as a protective strategy for these individuals. The current emergency triage system does not completely screen geriatric patients with severe acute illness who present to the ED, and it is necessary to add comprehensive assessment tools that match the characteristics of geriatric patients to improve triage outcomes.

Age-specific performance of human papillomavirus E6/E7 mRNA assay versus cytology for primary cervical cancer screening and triage: community-based screening in China.

Background: In the general population, primary human papillomavirus (HPV) testing is advocated for cervical cancer (CC) screening. HPV E6/E7 mRNA (Aptima HPV, AHPV) assays have garnered considerable traction due to their higher specificity when compared with HPV DNA assays. Here, we investigated age-specific primary AHPV screening assays and different triage strategies versus cytology to identify the best approach. Methods: Between April 2018 and December 2021, we recruited female participants from 34 communities across Liaoning province and Qingdao City, China. Primary cervical screening protocols included liquid-based cytology (LBC) and AHPV assays, with females positive for any assays undergoing colposcopy. Genotyping (AHPV-GT) was conducted on all HPV-positive samples. Our primary outcomes were the identification of age-specific detection rates, colposcopy referral rates, and sensitivity and specificity values for high-grade squamous intraepithelial lesions or worse (HSIL+). AHPV and different triage strategy performances were also examined across different age cohorts. Results: Our investigation included 9911 eligible females. Age-specific abnormal cytology rates were in the 6.1%-8.0% range, and were highest in 45-54-year olds. When compared with 35-44-or 45-54-year olds, HPV prevalence was highest in 55-64-year olds (12.2% or 11.6% vs.14.1%, P = 0.048 and P = 0.002, respectively). In 35-44-year olds, AHPV sensitivity for detecting HSIL+ was 96.6 (95% confidence interval [CI]: 89.7-100) - significantly higher than LBC sensitivity (65.5 [95% CI: 48.3-82.8], P < 0.001). When compared with LBC, HSIL+ detection rates by AHPV-GT using reflex LBC triage increased by 31.5% (9.6‰ vs. 7.3‰), and colposcopy referral rates decreased by 16.4% (5.1% vs. 6.1%). In 45-54-year olds, HSIL+ detection rates for AHPV-GT using reflex LBC triage were lower than LBC rates (6.2‰ vs. 6.6‰). In 55-64-year olds, AHPV sensitivity (97.2 [95% CI: 91.7-100.0]) was higher than LBC sensitivity (66.7 [95% CI: 50.0-80.6], P = 0.003). The area under the curve (AUC) value was not significantly different between AHPV-GT with reflex LBC triage and LBC (0.845 [95% CI: 0.771-0.920] vs. 0.812 [95% CI: 0.734-0.891], P = 0.236). Conclusions: Primary AHPV screening using different triage strategies were different across different age cohorts. Thus, AHPV may be an appropriate primary screening method for 35-44 and 55-64 year old females, while AHPV-GT with reflex LBC triage may be more apt for 35-44 year old females.

Faecal immunochemical test (FIT) based prioritisation of new patient symptomatic cases referred for colorectal investigation.

AIM: Quantitative faecal haemoglobin (fHb) measurement by faecal immunochemical test (FIT) is a powerful biomarker for colorectal cancer (CRC) and is incorporated in referral, prioritisation and triage protocols for symptomatic cases in other countries. We report our use of FIT to prioritise new patient symptomatic cases referred for colorectal investigation. METHOD: Cases referred for investigation of new colorectal symptoms who were aged ≥50 years (≥40 years Maori/Pacific peoples), who would otherwise be triaged to non-urgent colonoscopy, were asked to provide a stool sample for FIT. Following FIT testing, cases were re-triaged to either urgent colonoscopy, non-urgent colonoscopy or computed tomography colonography (CTC) depending on fHb concentration (measured in micrograms haemoglobin per gram of stool [mcg/g]) and incorporating clinical judgement. At pathway initiation, cases already waiting for colonoscopy on the non-urgent new patient waiting list were approached first, and then new patient (NP) referrals for colonoscopy could be triaged to the pathway at the discretion of the triaging consultant. RESULTS: Out of 739 cases, 715 (97%) returned FIT samples, and 691 cases completed colorectal investigations. Overall FIT positivity ≥10mcg/g was 17.1%. Fifteen colorectal cancers (CRC) were detected (2.2%). The sensitivity and specificity of FIT ≥10mcg/g for CRC were 80.0% (54.0-93.7%) and 84.3 (81.4-86.9%) respectively. A total of 432 cases (62.5%) completed the pathway without recourse to colonoscopy, and the median time to CRC diagnosis for NP from referral was 25 days. CONCLUSION: FIT based prioritisation of cases referred with symptoms concerning for CRC is feasible and reduces time to CRC diagnosis.

Adherence to sepsis protocol in a high-risk maternity reference center.

OBJECTIVE: To describe the adherence to the sepsis protocol by obstetric nurses in the obstetric triage of a high-risk maternity reference center. METHODS: This was a quantitative, documental, and retrospective study involving 105 pregnant women treated in obstetric triage under sepsis criteria. Data were collected through electronic medical records using structured forms and were organized into tables employing descriptive statistics. This research adhered to ethical principles concerning human studies. RESULTS: Of the checklists for initiating the SEPSIS protocol by obstetric nurses, 105 were identified. Regarding the protocol steps performed, lactate was collected in 97.1% of cases and blood cultures in 98.1%, antibiotic therapy was administered in 94.3%, and hydration was carried out in 51.4% of the cases. CONCLUSION: The initiation of the sepsis protocol for all women meeting the criteria was confirmed. However, the steps were not fully implemented as recommended by the institutional protocol, and the recommended broad-spectrum antibiotic was not administered.

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance. OBJECTIVE: Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state "value-estimation" problem outperform a commercially available deterioration score? DERIVATION COHORT: The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System. There were 330,729 total patients; 71,735 were inpatient and 258,994 were ED. Of these data, 20% were randomly sampled as a retrospective "testing set." VALIDATION COHORT: The validation cohort contained temporal patient data. There were 65,898 total patients; 13,750 were inpatient and 52,148 were ED. PREDICTION MODEL: DETERIO was developed and validated on these data, using the AIDE criteria to generate a composite score. DETERIO's architecture builds upon previous work. DETERIO's prediction performance up to 12 hours before T0 was compared against Epic Deterioration Index (EDI). RESULTS: In the retrospective testing set, DETERIO's area under the receiver operating characteristic curve (AUC) was 0.797 and 0.874 for inpatient and ED subsets, respectively. In the temporal validation cohort, the corresponding AUC were 0.775 and 0.856, respectively. DETERIO outperformed EDI in the inpatient validation cohort (AUC, 0.775 vs. 0.721; p < 0.01) while maintaining superior sensitivity and a comparable rate of false alarms (sensitivity, 45.50% vs. 30.00%; positive predictive value, 20.50% vs. 16.11%). CONCLUSIONS: DETERIO demonstrates promise in the viability of a state value-estimation approach for predicting adult physiologic deterioration. It may outperform EDI while offering additional clinical utility in triage and clinician interaction with prediction confidence and explanations. Additional studies are needed to assess generalizability and real-world clinical impact.

Identification of emergencies in the telephone queue and routing to a fast track (FAST): study protocol for a prospective, two-armed cohort study.

BACKGROUND: In Germany, the telephone patient service 116,117 for callers with non-life-threatening health issues is available 24/7. Based on structured initial assessment, urgency and placement of suitable medical care offer have been offered since 2020. The service has been in increasing demand for several years: Depending on time and residence, this can result in longer waiting times. METHODS: Prospective, two-armed cohort study with two intervention groups and one control group, alternating between blinding and unblinding for employees of 116,117 regarding prioritization status. Two interventions based on automated voice dialogues (1: Simple self-rating tool, 2: Automated brief query of emergency symptoms). In case of high level of urgency, callers are prioritized. Validation of urgency and need for care is carried out routinely based on structured initial assessment. DISCUSSION: By creating and providing a largely reproducible documentation of the implemented solutions for a waiting queue management, the developed approach would be available for comparable projects in the German health care system or in the European context. This potentially leads to a reduction in the use of resources in the development of comparable technical solutions based on automated voice dialogs. TRIAL REGISTRATION: DRKS00031235, registered on 10th November 2023, https://drks.de/search/de/trial/DRKS00031235 .

Geriatric patient at emergency department.

Emergency departments in the Czech Republic have been established in recent years. Seniors are typical patients of these departments. Emergency medicine´s approach is based on symptoms' evaluation and on deciding about the priority of the care needed. The approach to older patients is specific both in diagnostics and in therapy. The triage of geriatric patients is more accurate when we also evaluate patient´s cognition, when we use geriatric frailty scales and screening tools for detection of delirium. Comprehensive geriatric evaluation is a time demanding process and thus inadequate for emergency department however we must maintain its basic components. The therapeutical approach must be complex, and it must include biological, psychological, and social aspects and environmental risk analysis. Trauma management in seniors requires evaluation of different vital function´s values compared to common triage criteria, the influence of medication on adaptive mechanisms and the risk of low energy trauma mechanisms. Therapy of trauma must be timely and complex and the continuity of care between intensive and standard level and then rehabilitation must be ensured. Palliative approach is appropriate for terminally ill patients.

Salivary Molecular Spectroscopy with Machine Learning Algorithms for a Diagnostic Triage for Amelogenesis Imperfecta.

Amelogenesis imperfecta (AI) is a genetic disease characterized by poor formation of tooth enamel. AI occurs due to mutations, especially in AMEL, ENAM, KLK4, MMP20, and FAM83H, associated with changes in matrix proteins, matrix proteases, cell-matrix adhesion proteins, and transport proteins of enamel. Due to the wide variety of phenotypes, the diagnosis of AI is complex, requiring a genetic test to characterize it better. Thus, there is a demand for developing low-cost, noninvasive, and accurate platforms for AI diagnostics. This case-control pilot study aimed to test salivary vibrational modes obtained in attenuated total reflection fourier-transformed infrared (ATR-FTIR) together with machine learning algorithms: linear discriminant analysis (LDA), random forest, and support vector machine (SVM) could be used to discriminate AI from control subjects due to changes in salivary components. The best-performing SVM algorithm discriminates AI better than matched-control subjects with a sensitivity of 100%, specificity of 79%, and accuracy of 88%. The five main vibrational modes with higher feature importance in the Shapley Additive Explanations (SHAP) were 1010 cm-1, 1013 cm-1, 1002 cm-1, 1004 cm-1, and 1011 cm-1 in these best-performing SVM algorithms, suggesting these vibrational modes as a pre-validated salivary infrared spectral area as a potential biomarker for AI screening. In summary, ATR-FTIR spectroscopy and machine learning algorithms can be used on saliva samples to discriminate AI and are further explored as a screening tool.

Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study.

Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI® and is being trained for use in natural language processing tasks. Our study aims to determine the accuracy of patient triage using ChatGPT according to the emergency severity index (ESI) for triage in EDs. In our cross-sectional study, 18 years and over patients who consecutively presented to our ED within 24 h were included. Age, gender, admission method, chief complaint, state of consciousness, and comorbidities were recorded on the case form, and the vital signs were detected at the triage desk. A five-member expert committee (EC) was formed from the fourth-year resident physicians. The investigators converted real-time patient information into a standardized case format. The urgency status of the patients was evaluated simultaneously by EC and ChatGPT according to ESI criteria. The median value of the EC decision was accepted as the gold standard. There was a statistically significant moderate agreement between EC and ChatGPT assessments regarding urgency status (Cohen's Kappa = 0.659; P < 0.001). The accuracy between these two assessments was calculated as 76.6%. There was a high degree of agreement between EC and ChatGPT for the prediction of ESI-1 and 2, indicating high acuity (Cohen's Kappa = 0.828). The diagnostic specificity, NPV, and accuracy of ChatGPT were determined as 95.63, 98.17 and 94.90%, respectively, for ESI high acuity categories. Our study shows that ChatGPT can successfully differentiate patients with high urgency. The findings are promising for integrating artificial intelligence-based applications such as ChatGPT into triage processes in EDs.

Timeliness and accuracy of the 7-Item Japan Urgent Stroke Triage (JUST-7) score, a prehospital stroke triage tool, assessed by emergency medical services.

The prompt initiation of stroke treatment significantly influences patient outcomes, highlighting the crucial role of prehospital triage. This study aimed to assess the implementation of the 7-Item Japan Urgent Stroke Triage (JUST-7) score by emergency medical services (EMS) in our region and its effect on emergency transportation for suspected stroke patients. Data were collected from patients suspected of having an acute stroke with a Cincinnati Prehospital Stroke Scale (CPSS) score of 1 or more who were transferred by ambulance within 24 h of symptom onset. Two prehospital stroke scales were employed during different periods: period 1 with CPSS alone (January to December 2020) and period 2 with both CPSS and JUST-7 (January 2021 to March 2023). On-scene time data were obtained from the EMS crews, and data regarding the final diagnosis of patients and their outcomes were obtained from the respective hospitals to which the patients were transferred. These data were compared between periods 1 and 2 and between the CPSS and JUST-7. The results revealed that additional evaluation with JUST-7 did not affect ambulance transport time. The CPSS+JUST-7 approach demonstrated higher specificity in identifying stroke and major artery occlusion than with the CPSS alone; however, an appropriate cut-off value needs to be considered. The JUST-7 achieved a diagnostic concordance rate of 35.9% for the most likely stroke type and 64.0% for the first two most likely types. This research emphasizes the potential of JUST-7 as a valuable addition to prehospital stroke diagnosis protocols. Its flexibility in adapting cut-off values based on regional factors and available medical resources optimizes its utility in diverse healthcare settings. The JUST-7 score is a promising tool for improving patient outcomes through prompt and accurate prehospital assessments.

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.

Triage Versus Obstetric Emergency Department and Main Emergency Department: Best Practices.

An obstetric emergency department (OBED) allows for timely, standardized and quality care by a clinician for pregnant patients presenting unscheduled to a hospital. Understanding the differences between a traditional labor and delivery triage model and an OBED are important in developing a successful, safe, and quality obstetric program that meets the needs of the community with appropriate resource allocation. The benefits in an OBED of every patient seen in a timely fashion by a clinician, and ultimately the impact on outcomes are noteworthy and should be considered when developing a labor and delivery unit.