Geriatric Screening, Triage Urgency, and 30‐Day Mortality in Older Emergency Department Patients

Blomaard, Laura C.; Speksnijder, Corianne; Lucke, Jacinta A.; Gelder, Jelle de; Anten, Sander; Schuit, Stephanie C. E.; Gussekloo, Jacobijn; Groot, Bas de; Mooijaart, Simon P.

doi:10.1111/jgs.16427

Cited by 56 publications

(53 citation statements)

References 45 publications

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“…Previous studies suggested that geriatric screening tools have the potential to refine the prognostic performance of routine assessments in acute care settings [ 10 , 27 ]. Similar to what we now report, these studies have shown that the combination of measures of illness acuity and vulnerability can better explain the variability of adverse outcomes in acutely ill older patients [ 10 , 27 , 28 ]. For example, Blomaard et al .…”

Section: Discussionmentioning

confidence: 99%

“…That is unfortunate, as multiple factors possibly increase the older adults’ risk of experiencing complications from COVID-19, including physiological changes that affect vital signs (e.g. absence of fever), multimorbidity, cognitive impairment, sensory deficits and atypical presentations of disease [ 9 , 10 ]. Because conventional screening tools often fail to capture the key elements of risk in older persons (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Because conventional screening tools often fail to capture the key elements of risk in older persons (e.g. frailty, functional decline and cognitive impairment), they can misclassify illness severity in patients aged 65 years and older [ 10 , 11 ]. Such inaccuracies are particularly problematic in the context of COVID-19 since almost half of the admissions and up to 80% of deaths from the infection occur in older adults [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several geriatric screening tools have been developed to estimate the prognosis in acutely ill older patients [ 14 , 15 ]. These easy-to-use vulnerability screening tools allow the quick assessment of geriatric conditions in acute care settings [ 9 , 10 ]. Previous studies have demonstrated that tools like the Identification of Seniors at Risk (ISAR) and the PRO-AGE scoring system can provide valuable prognostic information by identifying factors that are rarely contemplated in routine medical assessments [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults

et al. 2020

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Background Although COVID-19 disproportionally affects older adults, the use of conventional triage tools in acute care settings ignores the key aspects of vulnerability. Objective This study aimed to determine the usefulness of adding a rapid vulnerability screening to an illness acuity tool to predict mortality in hospitalised COVID-19 patients. Design Cohort study. Setting Large university hospital dedicated to providing COVID-19 care. Participants Participants included are 1,428 consecutive inpatients aged ≥50 years. Methods Vulnerability was assessed using the modified version of PRO-AGE score (0–7; higher = worse), a validated and easy-to-administer tool that rates physical impairment, recent hospitalisation, acute mental change, weight loss, and fatigue. The baseline covariates included age, sex, Charlson comorbidity score, and the National Early Warning Score (NEWS), a well-known illness acuity tool. Our outcome was time-to-death within 60 days of admission. Results The patients had a median age of 66 years, and 58% were male. The incidence of 60-day mortality ranged from 22% to 69% across the quartiles of modified PRO-AGE. In adjusted analysis, compared with modified PRO-AGE scores 0–1 (“lowest quartile”), the hazard ratios (95% CI) for 60-day mortality for modified PRO-AGE scores 2–3, 4, and 5–7, were 1.4 (1.1–1.9), 2.0 (1.5–2.7), and 2.8 (2.1–3.8), respectively. The modified PRO-AGE predicted different mortality risk levels within each stratum of NEWS and improved the discrimination of mortality prediction models. Conclusions Adding vulnerability to illness acuity improved accuracy of predicting mortality in hospitalised COVID-19 patients. Combining tools such as PRO-AGE and NEWS may help stratify the risk of mortality from COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults

et al. 2020

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“…Furthermore, 30-day mortality after ED visit is more associated with frailty than triage urgency. (38) As such, caregiver burden may be a marker of system frailty (as opposed to individual frailty).…”

Section: Discussionmentioning

confidence: 99%

Prevalence and Risk Factors of Burden Among Caregivers of Older Patients in the Emergency Department The Caregiver Burden of Acutely Presenting Older Patients

Zaalberg¹,

Barten²,

Heugten³

et al. 2020

Preprint

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BACKGROUND The number of older individuals that live independently at home is rising. These older individuals often rely on caregivers who typically have a similar age and health status. Therefore, caregivers may experience a high burden. We determined the prevalence and risk factors of burden among caregivers of older patients in the emergency department (ED). Methods a prospective observational cohort study of primary caregivers of patients aged ≥ 70 years visiting the ED of a Dutch teaching hospital. Structured interviews were conducted with patients and their caregivers. Caregiver burden was measured using the caregiver strain index (CSI). Additionally, data from medical records were extracted to determine potential risk factors. Univariate and multivariate regression analyses were conducted to identify independent determinants for burden. RESULTS A primary caregiver was reported by 200 of 628 included ED patients. The mean age of patients with a caregiver was 82 (SD 6.7) years and the mean age of the caregivers was 66 (SD 12.4) years. Seventy-eight caregivers (39%) experienced a high burden. Multivariate analysis showed a significant association between high caregiver burden and patients with cognitive impairment or dependency for instrumental activities of daily living (IADL) and more self-reported hours of care per day. CONCLUSION Almost 40% of older patients in the ED have a caregiver who experiences a high burden. Formal assessment in the ED may help provide adequate care to the patients and their caregivers.

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Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

et al. 2021

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IMPORTANCE Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. OBJECTIVES To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores. DESIGN, SETTING, AND PARTICIPANTS This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. MAIN OUTCOMES AND MEASURESThree SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively.Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curve in the receiver operating characteristic analysis. RESULTSThe study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates inthe training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores. CONCLUSIONS AND RELEVANCEIn this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.

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Geriatric Screening, Triage Urgency, and 30‐Day Mortality in Older Emergency Department Patients

Cited by 56 publications

References 45 publications

A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults

A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults

Prevalence and Risk Factors of Burden Among Caregivers of Older Patients in the Emergency Department The Caregiver Burden of Acutely Presenting Older Patients

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

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