Evidence map›Paper›PMID 35338613›Full record

ArticleCancer medicine2022

Prognostic value of the veterans affairs frailty index in older patients with non-small cell lung cancer.

David Cheng, Clark Dumontier, Ayesha R Sheikh, Jennifer La, Mary T Brophy, Nhan V Do, Jane A Driver, David P Tuck, Nathanael R Fillmore

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.5field-weighted citation impact, top 12% of its field
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

15 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Evaluating Frailty Index Integrity: Insights from an International Network Study.medRxiv : the preprint server for health sciences · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Observational
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
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

9 authors at 5 institutions in 1 country.

David ChengMassachusetts General Hospital, Boston, MA, United States.ORCID 0000-0002-2816-6585
Clark DumontierDepartment of Medicine, Harvard Medical School, Boston, MA, United States.
Ayesha R SheikhSaint Vincent Hospital, Worcester, MA, United States.
Jennifer LaVA Boston Healthcare System, Boston, MA, United States.
Mary T BrophyVA Boston Healthcare System, Boston, MA, United States.
Nhan V DoVA Boston Healthcare System, Boston, MA, United States.
Jane A DriverDepartment of Medicine, Harvard Medical School, Boston, MA, United States.
David P TuckVA Boston Healthcare System, Boston, MA, United States.
Nathanael R FillmoreDepartment of Medicine, Harvard Medical School, Boston, MA, United States.
Boston University · USHarvard University · USBrigham and Women's Hospital · USSaint Vincent Hospital · USVA Boston Healthcare System · US

Funding

Preparing For What Is Next With Aducanumab In Real World SettingsR33AG057806 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Christopher Barrett Bowling, Ana Roman Quinones · 2018 to 2026
$9.0M
Integration of Geriatric Measures Alongside Disease-Based Measures to Advance Precision Oncology for Older Veterans with Multiple MyelomaIK2CX002218 · VA · VA BOSTON HEALTH CARE SYSTEM · PI DUMONTIER, CLARK WILLIAM · 2021 to 2024
–
CSRD VA IK2 CX002218NIA NIH HHS R33 AG057806
6 · The paper itself

Abstract

backgroundOlder patients with non-small cell lung cancer (NSCLC) are a heterogeneous population with varying degrees of frailty. An electronic frailty index such as the Veterans Affairs Frailty Index (VA-FI) can potentially help identify vulnerable patients at high risk of poor outcomes.

methodsNSCLC patients ≥65 years old and diagnosed in 2002-2017 were identified using the VA Central Cancer Registry. The VA-FI was calculated using administrative codes from VA electronic health records data linked with Medicare and Medicaid data. We assessed associations between the VA-FI and times to mortality, hospitalization, and emergency room (ER) visit following diagnosis by Kaplan-Meier analysis and multivariable stratified Cox models. We also evaluated the change in discrimination and calibration of reference prognostic models after adding VA-FI.

resultsWe identified a cohort of 42,204 older NSCLC VA patients, in which 55.5% were classified as frail (VA-FI >0.2). After adjustment, there was a strong association between VA-FI and the risk of mortality (HR = 1.23 for an increase of four deficits or, equivalently, an increase of 0.129 on VA-FI, p < 0.001), hospitalization (HR = 1.16 for four deficits, p < 0.001), and ER visit (HR = 1.18 for four deficits, p < 0.001). Adding VA-FI to baseline prognostic models led to statistically significant improvements in time-dependent area under curves and did not have a strong impact on calibration.

conclusionOlder NSCLC patients with higher VA-FI have significantly elevated risks of mortality, hospitalizations, and ER visits following diagnosis. An electronic frailty index can serve as an accessible tool to identify patients with vulnerabilities to inform clinical care and research.

Indexed as

Carcinoma, Non-Small-Cell LungFrailtyLung NeoplasmsVeteransAgedHumansMedicarePrognosisUnited Stateselectronic health recordsfrailtynon-small cell lungprognosisveterans

Identifiers

PMID35338613
PMCPMC9359868
OpenAlexW4220772103

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.