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: Objectives: Alcohol is the most widely consumed psychoactive substance in Australia and the consequences of alcohol consumption have enormous personal and social impacts. This study aimed to describe the principal diagnoses of emergency department (ED) presentations involving alcohol use in the previous 12 hours at eight hospitals in Victoria and the Australian Capital Territory, Australia. Methods: Twelve months’ data (1 July 2018 – 30 June 2019) were collected from eight EDs, including demographics, ICD‐10 codes, hospital location and self‐reported drinking in the preceding 12 hours. The ten most common ICD‐10 discharge codes were analysed based on age, sex and hospital geographic area. Results: ICD codes pertaining to mental and behavioural disorders due to alcohol use accounted for the highest proportion in most EDs. Suicide ideation/attempt was in the five highest ICD codes for all but one hospital. It was the second most common alcohol‐related presentation for both males and females. Conclusions: Alcohol plays a major role in a range of presentations, especially in relation to mental health and suicide. Implications for public health: The collection of alcohol involvement in ED presentations represents a major step forward in informing the community about the burden of alcohol on their health resources., Alcohol is the most commonly used psychoactive substance consumed in Australia and almost 80% of people aged 14 years and over have reported alcohol consumption in the past 12 months.[sup.1] [...]

Background. A noninvasive and accurate method of identifying fluid responsiveness in hemodynamically unstable patients has long been sought by physicians. Carotid ultrasound (US) is one such modality previously canvassed for this purpose. The aim of this novel systematic review and meta-analysis is to investigate whether critically unwell patients who are requiring intravenous (IV) fluid resuscitation (fluid responders) can be identified accurately with carotid US. Methods. The protocol was registered with PROSPERO on the 30/11/2022 (ID number: CRD42022380284). Studies investigating carotid ultrasound accuracy in assessing fluid responsiveness in hemodynamically unstable patients were included. Studies were identified through searches of six databases, all run on 4 November 2022, Medline, Embase, Emcare, APA PsycInfo, CINAHL, and Cochrane Library. Risk of bias was assessed using the QUADAS-2 and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) guidelines. Results were pooled, meta-analysis was conducted where amenable, and hierarchical summary receiver operating characteristic models were established to compare carotid ultrasound measures. Results. Seventeen studies were included (n = 842), with 1048 fluid challenges. 441 (42.1%) were fluid responsive. Four different carotid US measures were investigated, including change in carotid doppler peak velocity (∆CDPV), carotid blood flow (CBF), change in carotid artery velocity time integral (∆CAVTI), and carotid flow time (CFT). Pooled carotid US had a pooled sensitivity, specificity, and AUROC with 95% confidence intervals (CI) of 0.73 (0.66–0.78), 0.82 (0.72–0.90), and 0.81 (0.78–0.85), respectively. ∆CDPV had sensitivity, specificity, and AUROC with 95% CI of 0.72 (0.64–0.80), 0.87 (0.73–0.94), and 0.82 (0.78–0.85), respectively. CBF had sensitivity, specificity, and AUROC with 95% CI of 0.70 (0.56–0.80), 0.80 (0.50–0.94), and 0.77 (0.78–0.85), respectively. Risk of bias and assessment was undertaken using the QUADAS-2 and GRADE tools. The QUADAS-2 found that studies generally had an unclear or high risk of bias but with low applicability concerns. The GRADE assessment showed that ∆CDPV and CBF had low accuracy for sensitivity and specificity. Conclusion. It appears that carotid US has a limited ability to predict fluid responsiveness in critically unwell patients. ∆CDPV demonstrates the greatest accuracy of all measures analyzed. Further high-quality studies using consistent study design would help confirm this. [ABSTRACT FROM AUTHOR]

Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine. Methods: We undertook a qualitative interview‐based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented. Results: Twenty‐five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years. Conclusions: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations. This qualitative study found that emergency clinicians in Australia and New Zealand were generally optimistic about the use of artificial intelligence (AI)‐enabled decision support tools in EDs. A number of issues were raised that require attention before full acceptance. AI‐enabled tools must operate as decision support only, be locally validated, be effectively integrated into existing workflows and take ethical concerns into consideration. [ABSTRACT FROM AUTHOR]

Objective: Artificial intelligence (AI) has gradually found its way into healthcare, and its future integration into clinical practice is inevitable. In the present study, we evaluate the accuracy of a novel AI algorithm designed to predict admission based on a triage note after clinical implementation. This is the first of such studies to investigate real‐time AI performance in the emergency setting. Methods: The novel AI algorithm that predicts admission using a triage note was translated into clinical practice and integrated within St Vincent's Hospital Melbourne's electronic emergency patient management system. The data were collected from 1 January 2021 to 17 August 2022 to evaluate the diagnostic accuracy of the AI system after implementation. Results: A total of 77 125 ED presentations were included. The live AI algorithm has a sensitivity of 73.1% (95% confidence interval 72.5–73.8), specificity of 74.3% (73.9–74.7), positive predictive value of 50% (49.6–50.4) and negative predictive value of 88.7% (88.5–89) with a total accuracy of 74% (73.7–74.3). The accuracy of the system was at the lowest for admission to psychiatric units (34%) and at the highest for gastroenterology and medical admission (84% and 80%, respectively). Conclusion: Our study showed the diagnostic evaluation of a real‐time AI clinical decision‐support tool became less accurate than the original. Although real‐time sensitivity and specificity of the AI tool was still acceptable as a decision‐support tool in the ED, we propose that continuous training and evaluation of AI‐enabled clinical support tools in healthcare are conducted to ensure consistent accuracy and performance to prevent inadvertent consequences. [ABSTRACT FROM AUTHOR]

Ketamine is a fast-acting, dissociative anesthetic with a favorable adverse effect profile that is effective for managing acute agitation as a chemical restraint in the prehospital and emergency department (ED) settings. However, some previously published individual studies have reported high intubation rates when ketamine was administered prehospitally. This systematic review aims to determine the rate and settings in which intubation following prehospital administration of ketamine for agitation is occurring, as well as associated indications and adverse events. We searched PubMed, Scopus, Ovid MEDLINE, Embase, CINAHL Plus, PsycINFO, the Cochrane Library, ClinicalTrials.gov, OpenGrey, Open Access Theses and Dissertation, and Google Scholar from the earliest possible date until 13/February/2022. Inclusion criteria required studies to describe agitated patients who received ketamine in the prehospital setting as a first-line drug to control acute agitation. Reference lists of appraised studies were screened for additional relevant articles. Study quality was assessed using the Newcastle-Ottawa quality assessment scale. Synthesis of results was completed via meta-analysis, and the GRADE tool was used for certainty assessment. The search yielded 1466 unique records and abstracts, of which 50 full texts were reviewed, resulting in 18 being included in the analysis. All studies were observational in nature and 15 were from USA. There were 3476 patients in total, and the overall rate of intubation was 16% (95% confidence interval [CI] = 8%–26%). Most intubations occurred in the ED. Within the studies, the prehospital intubation rate ranged from 0% to 7.9% and the ED intubation rate ranged from 0 to 60%. The overall pooled prehospital intubation rate was 1% (95% CI = 0%–2%). The overall pooled ED intubation rate was 19% (95% CI = 11%–30%). The most common indications for intubation were for airway protection and respiratory depression/failure. There is wide variation in intubation rates between and within studies. The majority of intubations performed following prehospital administration of ketamine for agitation took place in the ED. [ABSTRACT FROM AUTHOR]

Background: Individuals with Friedreich ataxia (FRDA) can find it difficult to access specialized clinical care. To facilitate best practice in delivering healthcare for FRDA, clinical management guidelines (CMGs) were developed in 2014. However, the lack of high-certainty evidence and the inadequacy of accepted metrics to measure health status continues to present challenges in FRDA and other rare diseases. To overcome these challenges, the Grading of Recommendations Assessment and Evaluation (GRADE) framework for rare diseases developed by the RARE-Bestpractices Working Group was adopted to update the clinical guidelines for FRDA. This approach incorporates additional strategies to the GRADE framework to support the strength of recommendations, such as review of literature in similar conditions, the systematic collection of expert opinion and patient perceptions, and use of natural history data.Methods: A panel representing international clinical experts, stakeholders and consumer groups provided oversight to guideline development within the GRADE framework. Invited expert authors generated the Patient, Intervention, Comparison, Outcome (PICO) questions to guide the literature search (2014 to June 2020). Evidence profiles in tandem with feedback from individuals living with FRDA, natural history registry data and expert clinical observations contributed to the final recommendations. Authors also developed best practice statements for clinical care points that were considered self-evident or were not amenable to the GRADE process.Results: Seventy clinical experts contributed to fifteen topic-specific chapters with clinical recommendations and/or best practice statements. New topics since 2014 include emergency medicine, digital and assistive technologies and a stand-alone section on mental health. Evidence was evaluated according to GRADE criteria and 130 new recommendations and 95 best practice statements were generated.Discussion and Conclusion: Evidence-based CMGs are required to ensure the best clinical care for people with FRDA. Adopting the GRADE rare-disease framework enabled the development of higher quality CMGs for FRDA and allows individual topics to be updated as new evidence emerges. While the primary goal of these guidelines is better outcomes for people living with FRDA, the process of developing the guidelines may also help inform the development of clinical guidelines in other rare diseases. [ABSTRACT FROM AUTHOR]

Background: To compare the clinical and demographic variables of patients who present to the ED at different times of the day in order to determine the nature and extent of potential selection bias inherent in convenience sampling METHODS: We undertook a retrospective, observational study of data routinely collected in five EDs in 2019. Adult patients (aged ≥18 years) who presented with abdominal or chest pain, headache or dyspnoea were enrolled. For each patient group, the discharge diagnoses (primary outcome) of patients who presented during the day (08:00-15:59), evening (16:00-23:59), and night (00:00-07:59) were compared. Demographics, triage category and pain score, and initial vital signs were also compared.Results: 2500 patients were enrolled in each of the four patient groups. For patients with abdominal pain, the diagnoses differed significantly across the time periods (p<0.001) with greater proportions of unspecified/unknown cause diagnoses in the evening (47.4%) compared with the morning (41.7%). For patients with chest pain, heart rate differed (p<0.001) with a mean rate higher in the evening (80 beats/minute) than at night (76). For patients with headache, mean patient age differed (p=0.004) with a greater age in the daytime (46 years) than the evening (41). For patients with dyspnoea, discharge diagnoses differed (p<0.001). Asthma diagnoses were more common at night (12.6%) than during the daytime (7.5%). For patients with dyspnoea, there were also differences in gender distribution (p=0.003), age (p<0.001) and respiratory rates (p=0.003) across the time periods. For each patient group, the departure status differed across the time periods (p<0.001).Conclusion: Patients with abdominal or chest pain, headache or dyspnoea differ in a range of clinical and demographic variables depending upon their time of presentation. These differences may potentially introduce selection bias impacting upon the internal validity of a study if convenience sampling of patients is undertaken. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to describe the characteristics and outcomes of patients presenting to Australian EDs with suspected and confirmed COVID‐19 during 2020, and to determine the predictors of in‐hospital death for SARS‐CoV‐2 positive patients. Methods: This analysis from the COVED Project presents data from 12 sites across four Australian states for the period from 1 April to 30 November 2020. All adult patients who met local criteria for suspected COVID‐19 and underwent testing for SARS‐CoV‐2 in the ED were eligible for inclusion. Study outcomes were mechanical ventilation and in‐hospital mortality. Results: Among 24 405 eligible ED presentations over the whole study period, 423 tested positive for SARS‐CoV‐2. During the 'second wave' from 1 July to 30 September 2020, 26 (6%) of 406 SARS‐CoV‐2 patients received invasive mechanical ventilation, compared to 175 (2%) of the 9024 SARS‐CoV‐2 negative patients (odds ratio [OR] 3.5; 95% confidence interval [CI] 2.3–5.2, P < 0.001), and 41 (10%) SARS‐CoV‐2 positive patients died in hospital compared to 312 (3%) SARS‐CoV‐2 negative patients (OR 3.2; 95% CI 2.2–4.4, P = 0.001). For SARS‐CoV‐2 positive patients, the strongest independent predictors of hospital death were age (OR 1.1; 95% CI 1.1–1.1, P < 0.001), higher triage category (OR 3.5; 95% CI 1.3–9.4, P = 0.012), obesity (OR 4.2; 95% CI 1.2–14.3, P = 0.024) and receiving immunosuppressive treatment (OR 8.2; 95% CI 1.8–36.7, P = 0.006). Conclusions: ED patients who tested positive for SARS‐CoV‐2 had higher odds of mechanical ventilation and death in hospital. The strongest predictors of death were age, a higher triage category, obesity and receiving immunosuppressive treatment. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the potential of machine learning and capability of natural language processing (NLP) to predict disposition of patients based on triage notes in the ED. Methods: A retrospective cohort of ED triage notes from St Vincent's Hospital (Melbourne) was used to develop a deep‐learning algorithm that predicts patient disposition. Bidirectional Encoder Representations from Transformers, a recent language representation model developed by Google, was utilised for NLP. Eighty percent of the dataset was used for training the model and 20% was used to test the algorithm performance. Ktrain library, a wrapper for TensorFlow Keras, was employed to develop the model. Results: The accuracy of the algorithm was 83% and the area under the curve was 0.88. Sensitivity, specificity, precision and F1‐score of the algorithm were 72%, 86%, 56% and 63%, respectively. Conclusion: Machine learning and NLP can be together applied to the ED triage note to predict patient disposition with a high level of accuracy. The algorithm can potentially assist ED clinicians in early identification of patients requiring admission by mitigating the cognitive load, thus optimises resource allocation in EDs. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to describe the epidemiology and clinical features of patients presenting to the ED with suspected and confirmed COVID‐19 during Australia's 'second wave'. Methods: The COVID‐19 ED (COVED) Project is an ongoing prospective cohort study in Australian EDs. This analysis presents data from 12 sites across four Australian states for the period from 1 July to 31 August 2020. All adult patients who met the criteria for 'suspected COVID‐19' and underwent testing for SARS‐CoV‐2 in the ED were eligible for inclusion. Study outcomes included a positive SARS‐CoV‐2 test result, mechanical ventilation and in‐hospital mortality. Results: There were 106 136 presentations to the participating EDs and 12 055 (11.4%; 95% confidence interval [CI] 11.2–11.6) underwent testing for SARS‐CoV‐2. Of these, 255 (2%) patients returned a positive result. Among positive cases, 13 (5%) received mechanical ventilation during their hospital admission compared to 122 (2%) of the SARS‐CoV‐2 negative patients (odds ratio 2.7; 95% CI 1.5–4.9, P = 0.001). Nineteen (7%) SARS‐CoV‐2 positive patients died in hospital compared to 212 (3%) of the SARS‐CoV‐2 negative patients (odds ratio 2.3; 95% CI 1.4–3.7, P = 0.001). Strong clinical predictors of the SARS‐CoV‐2 test result included self‐reported fever, sore throat, bilateral infiltrates on chest X‐ray, and absence of a leucocytosis on first ED blood tests (P < 0.05). Conclusions: In this prospective multi‐site study during Australia's 'second wave', a substantial proportion of ED presentations required SARS‐CoV‐2 testing and isolation. Presence of SARS‐CoV‐2 on nasopharyngeal swab was associated with an increase in the odds of death and mechanical ventilation in hospital. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to describe the epidemiology and clinical features of patients presenting to the ED with suspected and confirmed COVID‐19. Methods: The COVID‐19 ED (COVED) Project is an ongoing prospective cohort study in Australian EDs. This analysis presents data from eight sites across Victoria and Tasmania for July 2020 (during Australia's 'second wave'). All adult patients who met criteria for 'suspected COVID‐19' and underwent testing for SARS‐CoV‐2 in the ED were eligible for inclusion. Study outcomes included a positive SARS‐CoV‐2 test result and mechanical ventilation. Results: In the period 1 July to 31 July 2020, there were 30 378 presentations to the participating EDs and 2917 (9.6%; 95% confidence interval 9.3–9.9) underwent testing for SARS‐CoV‐2. Of these, 50 (2%) patients returned a positive result. Among positive cases, two (4%) received mechanical ventilation during their hospital admission compared to 45 (2%) of the SARS‐CoV‐2 negative patients (odds ratio 1.7, 95% confidence interval 0.4–7.3; P = 0.47). Two (4%) SARS‐CoV‐2 positive patients died in hospital compared to 46 (2%) of the SARS‐CoV‐2 negative patients (odds ratio 1.7, 95% confidence interval 0.4–7.1; P = 0.49). Strong clinical predictors of a positive SARS‐CoV‐2 result included self‐reported fever, non‐smoking status, bilateral infiltrates on chest X‐ray and absence of a leucocytosis on first ED blood tests (P < 0.05). Conclusion: In this prospective multi‐site study from July 2020, a substantial proportion of ED patients required SARS‐CoV‐2 testing, isolation and enhanced infection prevention and control precautions. Presence of SARS‐CoV‐2 on nasopharyngeal swab was not associated with death or mechanical ventilation. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusion To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs.Qualitative semi‐structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022.We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision‐making process. There is considerably more approval of AI tools that support clinical decision‐making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision‐making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI.Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence suggests that traumatic brain injury (TBI) is a major risk factor for developing neurodegenerative disease later in life. Quantitative susceptibility mapping (QSM) has been used by an increasing number of studies in investigations of pathophysiological changes in TBI. However, generating artefact-free quantitative susceptibility maps in brains with large focal lesions, as in the case of moderate-to-severe TBI (ms-TBI), is particularly challenging. To address this issue, we utilized a novel two-pass masking technique and reconstruction procedure (two-pass QSM) to generate quantitative susceptibility maps (QSMxT; Stewart et al., 2022, Magn Reson Med.) in combination with the recently developed virtual brain grafting (VBG) procedure for brain repair (Radwan et al., 2021, NeuroImage) to improve automated delineation of brain areas. We used QSMxT and VBG to generate personalised QSM profiles of individual patients with reference to a sample of healthy controls., Methods: Chronic ms-TBI patients (N = 8) and healthy controls (N = 12) underwent (multi-echo) GRE, and anatomical MRI (MPRAGE) on a 3T Siemens PRISMA scanner. We reconstructed the magnetic susceptibility maps using two-pass QSM from QSMxT. We then extracted values of magnetic susceptibility in grey matter (GM) regions (following brain repair via VBG) across the whole brain and determined if they deviate from a reference healthy control group [Z-score < -3.43 or > 3.43, relative to the control mean], with the aim of obtaining personalised QSM profiles., Results: Using two-pass QSM, we achieved susceptibility maps with a substantial increase in quality and reduction in artefacts irrespective of the presence of large focal lesions, compared to single-pass QSM. In addition, VBG minimised the loss of GM regions and exclusion of patients due to failures in the region delineation step. Our findings revealed deviations in magnetic susceptibility measures from the HC group that differed across individual TBI patients. These changes included both increases and decreases in magnetic susceptibility values in multiple GM regions across the brain., Conclusions: We illustrate how to obtain magnetic susceptibility values at the individual level and to build personalised QSM profiles in ms-TBI patients. Our approach opens the door for QSM investigations in more severely injured patients. Such profiles are also critical to overcome the inherent heterogeneity of clinical populations, such as ms-TBI, and to characterize the underlying mechanisms of neurodegeneration at the individual level more precisely. Moreover, this new personalised QSM profiling could in the future assist clinicians in assessing recovery and formulating a neuroscience-guided integrative rehabilitation program tailored to individual TBI patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Objective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments., Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020)., Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period., Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors., Competing Interests: Competing interests: Some authors and collaborators are emergency physicians or directors, and others work in community health (prehospital and district nursing). One collaborator is a consumer. The Australian government, Medical Research Future Fund, via Monash Partners, funded this study. Researchers contributed in-kind donations of time. The Cabrini Institute and Monash University provided research infrastructure support., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: To compare the clinical and demographic variables of patients who present to the ED at different times of the day in order to determine the nature and extent of potential selection bias inherent in convenience sampling METHODS: We undertook a retrospective, observational study of data routinely collected in five EDs in 2019. Adult patients (aged ≥18 years) who presented with abdominal or chest pain, headache or dyspnoea were enrolled. For each patient group, the discharge diagnoses (primary outcome) of patients who presented during the day (08:00-15:59), evening (16:00-23:59), and night (00:00-07:59) were compared. Demographics, triage category and pain score, and initial vital signs were also compared., Results: 2500 patients were enrolled in each of the four patient groups. For patients with abdominal pain, the diagnoses differed significantly across the time periods (p<0.001) with greater proportions of unspecified/unknown cause diagnoses in the evening (47.4%) compared with the morning (41.7%). For patients with chest pain, heart rate differed (p<0.001) with a mean rate higher in the evening (80 beats/minute) than at night (76). For patients with headache, mean patient age differed (p=0.004) with a greater age in the daytime (46 years) than the evening (41). For patients with dyspnoea, discharge diagnoses differed (p<0.001). Asthma diagnoses were more common at night (12.6%) than during the daytime (7.5%). For patients with dyspnoea, there were also differences in gender distribution (p=0.003), age (p<0.001) and respiratory rates (p=0.003) across the time periods. For each patient group, the departure status differed across the time periods (p<0.001)., Conclusion: Patients with abdominal or chest pain, headache or dyspnoea differ in a range of clinical and demographic variables depending upon their time of presentation. These differences may potentially introduce selection bias impacting upon the internal validity of a study if convenience sampling of patients is undertaken., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)