Automatable end-of-life screening for older adults in the emergency department using electronic health records
© 2023 The American Geriatrics Society..
BACKGROUND: Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices.
METHODS: This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata.
RESULTS: A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores.
CONCLUSIONS: Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.
| Errataetall: |
CommentIn: J Am Geriatr Soc. 2023 Jun;71(6):1694-1697. - PMID 36949615 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:71 |
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| Enthalten in: |
Journal of the American Geriatrics Society - 71(2023), 6 vom: 01. Juni, Seite 1829-1839 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Haimovich, Adrian D [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 12.06.2023 Date Revised 10.03.2024 published: Print-Electronic CommentIn: J Am Geriatr Soc. 2023 Jun;71(6):1694-1697. - PMID 36949615 Citation Status MEDLINE |
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| doi: |
10.1111/jgs.18262 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM352545534 |
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| 245 | 1 | 0 | |a Automatable end-of-life screening for older adults in the emergency department using electronic health records |
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| 500 | |a Date Revised 10.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a CommentIn: J Am Geriatr Soc. 2023 Jun;71(6):1694-1697. - PMID 36949615 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The American Geriatrics Society. | ||
| 520 | |a BACKGROUND: Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices | ||
| 520 | |a METHODS: This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata | ||
| 520 | |a RESULTS: A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores | ||
| 520 | |a CONCLUSIONS: Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED | ||
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a emergency medicine | |
| 650 | 4 | |a end-of-life | |
| 650 | 4 | |a goals of care | |
| 650 | 4 | |a prognostication | |
| 700 | 1 | |a Xu, Wenxin |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Schonberg, Mara A |e verfasserin |4 aut | |
| 700 | 1 | |a Hwang, Ula |e verfasserin |4 aut | |
| 700 | 1 | |a Taylor, R Andrew |e verfasserin |4 aut | |
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