Evidence map›Paper›PMID 34779142›Full record

ArticleESC heart failure2022

Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities.

Chu Zheng, Linai Han, Jing Tian, Jing Li, Hangzhi He, Gangfei Han, Ke Wang, Hong Yang, Jingjing Yan, Bingxia Meng and 2 more

Open access · goldAbstract read
In one paragraph

Article in ESC heart failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
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  3. Review
  4. Review
  5. Article
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  7. 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

12 authors at 2 institutions in 1 country.

Chu ZhengDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Linai HanDepartment of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China.
Jing TianDepartment of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China.
Jing LiDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Hangzhi HeDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Gangfei HanDepartment of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China.
Ke WangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Hong YangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Jingjing YanDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Bingxia MengDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Qinghua HanDepartment of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China.
Yanbo ZhangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Shanxi Medical University · CNFirst Hospital of Shanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsChronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorbidities and to verify their relationship with clinical outcomes. METHODS AND

resultsElectronic health records of patients hospitalized with CHF from January 2014 to April 2019 were collected, and 12 common comorbidities were included in the latent class analysis. The Fruchterman-Reingold layout was used to draw the comorbidity network, and analysis of variance was used to compare the weighted degrees among them. The incidence of clinical outcomes among different clusters was presented on Kaplan-Meier curves and compared using the log-rank test, and the hazard ratio was calculated using the Cox proportional risk model. Sensitivity analysis was performed according to the left ventricular ejection fraction. Four different clinical clusters from 4063 total patients were identified: metabolic, ischaemic, high comorbidity burden, and elderly-atrial fibrillation. Compared with the metabolic cluster, patients in the high comorbidity burden cluster had the highest adjusted risk of combined outcome and all-cause mortality {1.67 [95% confidence interval (CI), 1.40-1.99] and 2.87 [95% CI, 2.17-3.81], respectively}, followed by the elderly-atrial fibrillation and ischaemic clusters. The adjusted readmission risk of patients with ischaemic, high comorbidity burden, and elderly-atrial fibrillation clusters were 1.35 (95% CI, 1.08-1.68), 1.39 (95% CI, 1.13-1.72), and 1.42 (95% CI, 1.14-1.77), respectively. The comorbidity network analysis found that patients in the high comorbidity burden cluster had more and higher comorbidity correlations than those in other clusters. Sensitivity analysis revealed that patients in the high comorbidity burden cluster had the highest risk of combined outcome and all-cause mortality (P < 0.05).

conclusionsThe difference in adverse outcomes among clusters confirmed the heterogeneity of CHF and the importance of hierarchical management. This study can provide a basis for personalized treatment and management of patients with CHF, and provide a new perspective for clinical decision making.

Indexed as

Atrial FibrillationHeart FailureAgedComorbidityHumansStroke VolumeVentricular Function, LeftChronic heart failureComorbidityLCAMortalityReadmission

Identifiers

PMID34779142
PMCPMC8788137
OpenAlexW3213649292

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.