Evidence map›Paper›PMID 40298901›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2025

External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.

Erica Frechman, Byron C Jaeger, Marc Kowalkowski, Jeff D Williamson, Kristin M Lenoir, Jessica A Palakshappa, Brian J Wells, Kathryn E Callahan, Nicholas M Pajewski, Jennifer L Gabbard

Abstract readValidation Study
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Erica FrechmanSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.ORCID 0000-0002-7726-347X
Byron C JaegerDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Marc KowalkowskiSection on Hospital Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.ORCID 0000-0003-2624-4209
Jeff D WilliamsonSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Kristin M LenoirDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Jessica A PalakshappaSection on Pulmonary, Critical Care, Allergy, and Immunologic Diseases, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
Brian J WellsDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.
Kathryn E CallahanSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.ORCID 0000-0001-6405-3062
Nicholas M PajewskiDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.ORCID 0000-0002-4447-6196
Jennifer L GabbardSection on Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, United States.

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI JOSEPH E. GAUGLER · 2019 to 2026
$125.9M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Wake Forest University School of Medicine Alzheimer's Disease Research CenterP30AG072947 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Sharon Letchworth · 2021 to 2026
$24.3M
CTSA UM1 Program at Wake ForestUM1TR004929 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Jamy D Ard, KRISTIE L FOLEY · 2024 to 2026
$11.9M
Implementation Strategies to Promote Advance Care Planning among Persons Living with ADRD and those with Mild Cognitive Impairment in Outpatient Primary Care PracticesK23AG070234 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Jennifer Lynn Gabbard · 2022 to 2026
$879k
Screening for Cognitive Impairment Following Critical Illness: Designing and Testing an Implementation Program to Support High Risk Older AdultsK23AG073529 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI PALAKSHAPPA, JESSICA ANN · 2021 to 2025
$801k
AD-Related Dementias Clinical Trials CollaboratoryImbedded Pragmatic Alzheimer's DiseaseNCATS NIH HHS UL1 TR001420NCATS NIH HHS UM1 TR004929NIANIA NIH HHS K23 AG070234NIA NIH HHS K23 AG073529NIA NIH HHS P30 AG072947NIA NIH HHS U54 AG063546NIH HHS K23AG070234NIH HHS K23AG073529NIH HHS U54AG063546NIH HHS UL1TR004929
6 · The paper itself

Abstract

objectiveTo examine the discrimination, calibration, and algorithmic fairness of the Epic End of Life Care Index (EOL-CI). MATERIALS AND

methodsWe assessed the EOL-CI's performance by estimating area under the receiver operating characteristic curve (AUC), sensitivity, and positive and negative predictive values in community-dwelling adults ≥65 years of age in a single health system in the Southeastern United States. Algorithmic fairness was examined by comparing the model's performance across sex, race, and ethnicity subgroups. Using a machine learning approach, we also explored local re-calibration of the EOL-CI considering additional information on past hospitalizations and frailty.

resultsAmong 215 731 patients (median age = 74 years, 57% female, 12% of Black race), 10% were classified as medium risk (15-44) and 3% as high risk (≥45) by the EOL-CI. The observed 1-year mortality rate was 3%. The EOL-CI had an AUC 0.82 for 1-year mortality, with a positive predictive value of 22%. Predictive performance was generally similar across sex and race subgroups, though the EOL-CI displayed better performance with increasing age and in older adults with 2 or more outpatient encounters in the past 24 months. Local re-calibration of the EOL-CI was required to provide absolute estimates of mortality risk, and calibration was further improved when the EOL-CI was augmented with data on inpatient hospitalizations and frailty. DISCUSSION: The EOL-CI demonstrates reasonable discrimination, albeit with better performance in older adults and in those with greater health system contact.

conclusionLocal refinement and calibration of the EOL-CI score is required to provide direct estimates of prognosis, with the goal of making the EOL-CI a more a valuable tool at the point of care for identifying patients who would benefit from targeted palliative care interventions and proactive care planning.

Indexed as

MortalityTerminal CareAgedAged, 80 and overAlgorithmsArea Under CurveFemaleHumansIndependent LivingMachine LearningMaleRisk AssessmentROC CurveSoutheastern United Statesclinical decision-makingelectronic health recordspalliative careprognosis

Identifiers

PMID40298901
PMCPMC12199354

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.