Identifying emergency department patients with l<scp>ife‐limiting</scp> illnesses may be getting a little easier

Goldberg, Elizabeth M.; Kwan, Bethany M.; Lum, Hillary D.

doi:10.1111/jgs.18328

Cited by 2 publications

(5 citation statements)

References 15 publications

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“…32 Once locally validated, we envision 3 core clinical care pathways that can be implemented using GEST. 13 First, GEST can be used to identify patients planned for admission for inpatient palliative care consultation 33 or team-based serious illness discussions. 34 Second, for patients planned for discharge, 35 GEST can trigger communication interventions between the ED and primary care or direct engagement of outpatient palliative care.…”

Section: Discussionmentioning

confidence: 99%

“…Once locally validated, we envision 3 core clinical care pathways that can be implemented using GEST . First, GEST can be used to identify patients planned for admission for inpatient palliative care consultation or team-based serious illness discussions .…”

Section: Discussionmentioning

confidence: 99%

“…As a result, only approximately 10% of ED clinicians report using an successful strategy to screen and refer patients to palliative care. 13,14 The use of diagnosis codes to identify patients with life-limiting illnesses is increasingly common. 15,16 While efforts to operationalize these definitions for the purposes of prospective patient identification have yielded variable patient inclusion criteria between studies, commonly used diagnoses include metastatic cancers, dementia, severe chronic obstructive pulmonary disease, diabetes with complications, congestive heart failure, and neurodegenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous screening instruments to identify at-risk older adults have been proposed, including the Identification of Seniors at Risk, the Triage Risk Screening Tool, the Screening Tool Risk Score Assessment, and the Surprise Question, but all require data often unavailable in the electronic health record (EHR) (eg, specific survey responses from the patients or clinicians), limiting their applicability. As a result, only approximately 10% of ED clinicians report using an successful strategy to screen and refer patients to palliative care …”

Section: Introductionmentioning

confidence: 99%

“…As a result, only approximately 10% of ED clinicians report using an successful strategy to screen and refer patients to palliative care. 13 , 14 …”

Section: Introductionmentioning

confidence: 99%

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Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients

Haimovich,

Burke,

Nathanson

et al. 2024

JAMA Netw Open

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ImportanceEmergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs.ObjectivesTo externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria.Design, Setting, and ParticipantsThis prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024.ExposureGEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others.Main Outcomes and MeasuresThe primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves.ResultsThis external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%.Conclusions and RelevanceThe findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…As a result, only approximately 10% of ED clinicians report using an successful strategy to screen and refer patients to palliative care. 13 , 14 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients

Haimovich,

Burke,

Nathanson

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

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UCHealth’s virtual health center: How Colorado’s largest health system creates and integrates technology into patient care

Goldberg,

Kao,

Kwan

et al. 2024

npj Digit. Med.

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Identifying emergency department patients with life‐limiting illnesses may be getting a little easier

Abstract: This editorial comments on the article by https://doi.org/10.1111/jgs.18262. in this issue.

Cited by 2 publications

References 15 publications

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients

UCHealth’s virtual health center: How Colorado’s largest health system creates and integrates technology into patient care

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