Evidence map›Paper›PMID 38692206›Full record

ArticleThe journal of nutrition, health & aging2024

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

Seulgi Erica Kim, Mehrnaz Azarian, Aanand D Naik, Catherine Park, Molly J Horstman, Salim S Virani, Orna Intrator, Christopher I Amos, Ariela Orkaby, Javad Razjouyan

Open access · goldAbstract read
In one paragraph

Article in The journal of nutrition, health & aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 4 institutions in 2 countries.

Seulgi Erica KimVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA. Electronic address: seulgi.kim@bcm.edu.
Mehrnaz AzarianVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA. Electronic address: mehrnaz.azarian@bcm.edu.
Aanand D NaikVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, USA; University of Texas School of Public Health and UTHealth Consortium on Aging, Houston, TX, USA. Electronic address: aanand.naik@uth.tmc.edu.
Catherine ParkVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, USA; Division of Digital Healthcare, Yonsei University, Wonju, 26493, South Korea. Electronic address: catherine.park@bcm.edu.
Molly J HorstmanVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, USA. Electronic address: Molly.Horstman@bcm.edu.
Salim S ViraniVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA.
Orna IntratorGeriatrics & Extended Care Data Analysis Center (GECDAC), Canandaigua VA Medical Center, Canandaigua, NY, USA; Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, USA. Electronic address: Orna_Intrator@URMC.Rochester.edu.
Christopher I AmosBaylor College of Medicine, Houston, TX, USA. Electronic address: Chris.Amos@bcm.edu.
Ariela OrkabyNew England Geriatrics Research, Education, and Clinical Center, Boston VA Health Care System, Boston, MA, USA; Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: aorkaby@bwh.harvard.edu.
Javad RazjouyanVA HSR&D, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA; Baylor College of Medicine, Houston, TX, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, USA. Electronic address: javad.razjouyan@bcm.edu.
Michael E. DeBakey VA Medical Center · USBaylor College of Medicine · USBrigham and Women's Hospital · USCanandaigua VA Medical Center · US

Funding

Using Large Electronic Health Records and Advanced Analytics to Develop Predictive Frailty Trajectories in Patients with Heart FailureK25HL152006 · NHLBI · BAYLOR COLLEGE OF MEDICINE · PI RAZJOUYAN, JAVAD · 2020 to 2024
$719k
CSRD VA IK2 CX001800NHLBI NIH HHS K25 HL152006
6 · The paper itself

Abstract

objectivesTo assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure.

methodsA retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (>0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32

resultsWe identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m

conclusionAdding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF.

Indexed as

FrailtyHeart FailureMalnutritionNutrition AssessmentVeteransAgedAged, 80 and overFemaleGeriatric AssessmentHospitalizationHumansMaleMiddle AgedNutritional StatusPrognosisRetrospective StudiesAll-cause mortalityFrailty indexHeart failureMalnutrition

Identifiers

PMID38692206
PMCPMC12433797
OpenAlexW4396527478

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.