Nature and extent of selection bias resulting from convenience sampling in the emergency department.

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.

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.

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.