Evidence map›Paper›PMID 36695300›Full record

ArticleJournal of the American Heart Association2023

Phenotyping of Elderly Patients With Heart Failure Focused on Noncardiac Conditions: A Latent Class Analysis From a Multicenter Registry of Patients Hospitalized With Heart Failure.

Ryo Nakamaru, Yasuyuki Shiraishi, Nozomi Niimi, Takashi Kohno, Yuji Nagatomo, Makoto Takei, Takenori Ikoma, Kei Nishikawa, Munehisa Sakamoto, Shintaro Nakano and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of the American Heart Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–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

17 citing papers in PubMed.

  1. Observational
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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

12 authors.

Ryo NakamaruDepartment of Cardiology Keio University School of Medicine Tokyo Japan.ORCID 0000-0002-0129-7219
Yasuyuki ShiraishiDepartment of Cardiology Keio University School of Medicine Tokyo Japan.
Nozomi NiimiDepartment of Cardiology Keio University School of Medicine Tokyo Japan.
Takashi KohnoDepartment of Cardiovascular Medicine Kyorin University Faculty of Medicine Tokyo Japan.ORCID 0000-0003-2604-1284
Yuji NagatomoDepartment of Cardiology, National Defense Medical College Tokorozawa Japan.ORCID 0000-0002-3430-4614
Makoto TakeiDepartment of Cardiology Saiseikai Central Hospital Tokyo Japan.ORCID 0000-0002-9851-4522
Takenori IkomaDivision of Cardiology, Internal Medicine III Hamamatsu University School of Medicine Hamamatsu Japan.ORCID 0000-0001-5521-5189
Kei NishikawaDepartment of Cardiology Sakakibara Heart Institute Tokyo Japan.
Munehisa SakamotoDepartment of Cardiology National Hospital Organization Tokyo Medical Center Tokyo Japan.ORCID 0000-0001-5190-856X
Shintaro NakanoDepartment of Cardiology Saitama Medical University, International Medical Center Hidaka Japan.ORCID 0000-0001-9104-4786
Shun KohsakaDepartment of Cardiology Keio University School of Medicine Tokyo Japan.ORCID 0000-0003-3779-2972
Tsutomu YoshikawaDepartment of Cardiology Sakakibara Heart Institute Tokyo Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background The burden of noncardiovascular conditions is becoming increasingly prevalent in patients with heart failure (HF). We aimed to identify novel phenogroups incorporating noncardiovascular conditions to facilitate understanding and risk stratification in elderly patients with HF. Methods and Results Data from a total of 1881 (61.2%) patients aged ≥65 years were extracted from a prospective multicenter registry of patients hospitalized for acute HF (N=3072). We constructed subgroups of patients with HF with preserved ejection fraction (HFpEF; N=826, 43.9%) and those with non-HFpEF (N=1055, 56.1%). Latent class analysis was performed in each subgroup using 17 variables focused on noncardiovascular conditions (including comorbidities, Clinical Frailty Scale, and Geriatric Nutritional Risk Index). The latent class analysis revealed 3 distinct clinical phenogroups in both HFpEF and non-HFpEF subgroups: (1) robust physical and nutritional status (Group 1: HFpEF, 41.2%; non-HFpEF, 46.0%); (2) multimorbid patients with renal impairment (Group 2: HFpEF, 40.8%; non-HFpEF, 41.9%); and (3) malnourished patients (Group 3: HFpEF, 18.0%; non-HFpEF, 12.1%). After multivariable adjustment, compared with Group 1, patients in Groups 2 and 3 had a higher risk for all-cause death over the 1-year postdischarge period (hazard ratio [HR], 2.79 [95% CI, 1.64-4.81] and HR, 2.73 [95% CI, 1.39-5.35] in HFpEF; HR, 1.96 [95% CI, 1.22-3.14] and HR, 2.97 [95% CI, 1.64-5.38] in non-HFpEF; respectively). Conclusions In elderly patients with HF, the phenomapping focused on incorporating noncardiovascular conditions identified 3 phenogroups, each representing distinct clinical outcomes, and the discrimination pattern was similar for both patients with HFpEF and non-HFpEF. This classification provides novel risk stratification and may aid in clinical decision making.

Indexed as

AftercareHeart FailureAgedHumansLatent Class AnalysisPatient DischargePrognosisProspective StudiesRegistriesStroke Volumeelderly patientsheart failurenoncardiovascular conditionsphenotyping

Identifiers

PMID36695300
PMCPMC9973643

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Registered trials

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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.