Evidence map›Paper›PMID 35077583›Full record

ArticleClinical cardiology2022

Tailored risk assessment of 90-day acute heart failure readmission or all-cause death to heart failure with preserved versus reduced ejection fraction.

Jaeyoung Park, Xiang Zhong, Farnaz Babaie Sarijaloo, Anita Wokhlu

Abstract read
In one paragraph

Article in Clinical cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2023
    Article
  5. Article
  6. 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

4 authors.

Jaeyoung ParkDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA.
Xiang ZhongDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA.
Farnaz Babaie SarijalooDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA.
Anita WokhluDivision of Cardiovascular Medicine, University of Florida, Gainesville, Florida, USA.ORCID http://orcid.org/0000-0003-2001-5747

Funding

University of Florida Informatics Institute (UFII)
6 · The paper itself

Abstract

backgroundAfter incident heart failure (HF) admission, patients are vulnerable to readmission or death in the 90-day post-discharge. Although risk models for readmission or death incorporate ejection fraction (EF), patients with HF with preserved EF (HFpEF) and those with HF with reduced EF (HFrEF) represent distinct cohorts. To better assess risk, this study developed machine learning models and identified risk factors for the 90-day acute HF readmission or death by HF subtype. METHODS AND

resultsApproximately 1965 patients with HFpEF and 1124 with HFrEF underwent an index admission. Acute HF rehospitalization or death occurred in 23% of HFpEF and 28% of HFrEF groups. Of the 101 variables considered, multistep variable selection identified 24 and 25 significant factors associated with 90-day events in HFpEF and HFrEF, respectively. In addition to risk factors common to both groups, factors unique to HFpEF patients included cognitive dysfunction, low-pulse pressure, β-blocker, and diuretic use, and right ventricular dysfunction. In contrast, factors unique to HFrEF patients included a history of arrhythmia, acute HF on presentation, and echocardiographic characteristics like left atrial dilatation or elevated mitral E/A ratio. Furthermore, the model tailored to HFpEF (area under the curve [AUC] = 0.770; 95% confidence interval [CI] 0.767-0.774) outperformed a model for the combined groups (AUC = 0.759; 95% CI 0.756-0.763).

conclusionThe UF 90-day post-discharge acute HF Re admission or Death Risk Assessment (UF90-RADRA) models help identify HFpEF and HFrEF patients at higher risk who may require proactive outpatient management.

Indexed as

Heart FailureVentricular Dysfunction, LeftAftercareHumansPatient DischargePatient ReadmissionPrognosisRisk AssessmentStroke Volumecongestive heart failureHFpEFmachine learningreadmission

Identifiers

PMID35077583
PMCPMC9019897

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