Older Emergency Department Patients: Does Baseline Care Status Matter?

Mowbray, Fabrice; Aryal, Komal; Mercier, Éric; Heckman, George A.; Costa, Andrew P.

doi:10.5770/cgj.23.421

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…These ndings are consistent with previous literature which analyzed admission predictors of unscheduled ED visits. (22)(23)(24)(25) A plethora of research has been dedicated to examining variables predictive of admission in the older adult cohorts, yielding results consistent with our predictors. (26-29) Receiving home care was included less as an admission predictor in the literature, representing a plausible underreporting of this variables in uence on ED visit dispositions.…”

Section: Discussionsupporting

confidence: 69%

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Strum

Mowbray

Zargoush

et al. 2022

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Background Paramedics are mandated to transport emergently triaged patients to the closest emergency department (ED). The closest ED may not be the optimal transport destination if further distanced ED’s can provide specialized care or are less crowded. Machine learning may support paramedic decision-making to transport a specific subgroup of emergently triaged patients that are unlikely to require hospital admission or emergency care to a more appropriate ED. We examined whether prehospital patient characteristics known to paramedics were predictive of hospital admission. Methods We conducted a retrospective cohort study using machine learning algorithms to analyze ED visits of the National Ambulatory Care Reporting System from Jan 1, 2018 to Dec 31, 2019 in Ontario, Canada. We included all adult (≥ 18 years) paramedic transports to the ED who had an emergent Canadian Triage Acuity Scale score (CTAS 2). Eight prehospital characteristic classes known to paramedics were used. We applied four machine learning algorithms that were trained and assessed using 10-fold cross-validation to predict the ED visit disposition of admission to hospital or discharged from ED. Predictive model performance was determined using the area under the receiving operating characteristic curve (AUC) with 95% confidence intervals and probabilistic accuracy using the Brier Scaled score. Variable importance scores were computed to determine the top 10 predictors of hospital admission. We also reported sensitivity, specificity, and positive and negative predictive values to support performance interpretation. Results All machine learning algorithms performed similarly for the prediction of which ED patient visits would be admitted to hospital (AUC 0.77–0.78, Brier Scaled 0.22–0.24). The characteristics most predictive of admission included age 65 to 105 years, referral source from a residential care facility, presenting with a respiratory complaint, and receiving home care. Conclusions Machine learning algorithms performed well in predicting ED visit dispositions using a comprehensive list of prehospital patient characteristics. To the best of our knowledge, this study is the first to utilize machine learning to predict ED visit outcomes from patient characteristics known prior to paramedic transport. This study has potential to inform paramedic regulations regarding the distribution of emergently triaged patients.

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Section: Discussionsupporting

confidence: 69%

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Strum

Mowbray

Zargoush

et al. 2022

Preprint

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show abstract

“…CTAS is an ordinal scale that ranges from one to five, with a score of one indicating the most emergent (resuscitation) and five the least urgent (non-urgent) [ 23 ]. Triage acuity was condensed into three categories, similar to prior ED studies in Canada, [ 24 ] as CTAS score one (0.8%) and five (4.9%) are relatively infrequent: scores of one and two were grouped as ‘emergent’, scores of three as ‘urgent’, and scores four and five grouped as ‘non-urgent’.…”

Section: Methodsmentioning

confidence: 99%

Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study

et al. 2021

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Background Increasing hospitalization rates present unique challenges to manage limited inpatient bed capacity and services. Transport by paramedics to the emergency department (ED) may influence hospital admission decisions independent of patient need/acuity, though this relationship has not been established. We examined whether mode of transportation to the ED was independently associated with hospital admission. Methods We conducted a retrospective cohort study using the National Ambulatory Care Reporting System (NACRS) from April 1, 2015 to March 31, 2020 in Ontario, Canada. We included all adult patients (≥18 years) who received a triage score in the ED and presented via paramedic transport or self-referral (walk-in). Multivariable binary logistic regression was used to determine the association of mode of transportation between hospital admission, after adjusting for important patient and visit characteristics. Results During the study period, 21,764,640 ED visits were eligible for study inclusion. Approximately one-fifth (18.5%) of all ED visits were transported by paramedics. All-cause hospital admission incidence was greater when transported by paramedics (35.0% vs. 7.5%) and with each decreasing Canadian Triage and Acuity Scale level. Paramedic transport was independently associated with hospital admission (OR = 3.76; 95%CI = 3.74–3.77), in addition to higher medical acuity, older age, male sex, greater than two comorbidities, treatment in an urban setting and discharge diagnoses specific to the circulatory or digestive systems. Conclusions Transport by paramedics to an ED was independently associated with hospital admission as the disposition outcome, when compared against self-referred visits. Our findings highlight patient and visit characteristics associated with hospital admission, and can be used to inform proactive healthcare strategizing for in-patient bed management.

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“…This time metric is commonly used in ED research of older persons and conveniently represents the top quartile of the length‐of‐stay data. 23 , 24 , 25 , 26 ED registration time was used as the point of reference when determining ED length of stay.…”

Section: Methodsmentioning

confidence: 99%

“…Data on mortality were not collected, although prior work has demonstrated that <1% of older Canadian patients will die in the ED, 16 and ≈5% will die within 1 month of presentation. 26 , 27 We used ED registration as the starting point for length of stay, which may have increased additional health system–based confounders associated with disposition time. Missing data were systematically missing, whereby particular sites provided no data on age, and only Quebec hospitals reported on interRAI ED Screener time or ED disposition.…”

Section: Limitationsmentioning

confidence: 99%

Examining the utility and accuracy of the interRAI Emergency Department Screener in identifying high‐risk older emergency department patients: A Canadian multiprovince prospective cohort study

et al. 2023

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Objectives We set out to determine the accuracy of the interRAI Emergency Department (ED) Screener in predicting the need for detailed geriatric assessment in the ED. Our secondary objective was to determine the discriminative ability of the interRAI ED Screener for predicting the odds of discharge home and extended ED length of stay (>24 hours). Methods We conducted a multiprovince prospective cohort study in Canada. The need for detailed geriatric assessment was determined using the interRAI ED Screener and the interRAI ED Contact Assessment as the reference standard. A score of ≥5 was used to classify high‐risk patients. Assessments were conducted by emergency and research nurses. We calculated the sensitivity, positive predictive value, and false discovery rate of the interRAI ED Screener. We employed logistic regression to predict ED outcomes while adjusting for age, sex, academic status, and the province of care. Results A total of 5629 older ED patients across 11 ED sites were evaluated using the interRAI ED Screener and 1061 were evaluated with the interRAI ED Contact Assessment. Approximately one‐third of patients were discharged home or experienced an extended ED length of stay. The interRAI ED Screener had a sensitivity of 93%, a positive predictive value of 82%, and a false discovery rate of 18%. The interRAI ED Screener predicted discharge home and extended ED length of stay with fair accuracy. Conclusion The interRAI ED Screener is able to accurately and rapidly identify individuals with medical complexity. The interRAI ED Screener predicts patient‐important health outcomes in older ED patients, highlighting its value for vulnerability screening.

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Older Emergency Department Patients: Does Baseline Care Status Matter?

Cited by 10 publications

References 29 publications

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Examining the association between paramedic transport to the emergency department and hospital admission: a population-based cohort study

Examining the utility and accuracy of the interRAI Emergency Department Screener in identifying high‐risk older emergency department patients: A Canadian multiprovince prospective cohort study

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