Evidence map›Paper›PMID 38017336›Full record

ArticleThe patient2024

Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis.

Axel C Mühlbacher, Andrew Sadler, Christin Juhnke

Abstract read
In one paragraph

Article in The patient, 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
–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

2 citing papers in PubMed.

  1. Trial
  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

3 authors.

Axel C MühlbacherHealth Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. igm@hs-nb.de.
Andrew SadlerHealth Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany.
Christin JuhnkeHealth Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany.ORCID 0000-0002-3949-0612

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo measure preference heterogeneity for monitoring systems among patients with a chronic heart failure.

methodsA best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred.

resultsData from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization.

conclusionUsing LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

Indexed as

Choice BehaviorHeart FailureHospitalizationHumansLatent Class AnalysisPatient PreferenceSurveys and Questionnaires

Identifiers

PMID38017336
PMCPMC10770186

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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.