Evidence map›Paper›PMID 35570322›Full record

ArticleESC heart failure2022

Heart failure ejection fraction class conversions: impact of biomarkers, co-morbidities, and pharmacotherapy.

Christian J Lorenzo, Jorge I Conte, Ricardo J Villasmil, Qassem K Abdelal, Derek Pierce, Wilhelmine Wiese-Rometsch, Joel A Garcia-Fernandez

Abstract 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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

7 authors.

Christian J LorenzoDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.ORCID 0000-0001-8891-9713
Jorge I ConteDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.
Ricardo J VillasmilDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.
Qassem K AbdelalDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.
Derek PierceDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.
Wilhelmine Wiese-RometschDepartment of Medicine, Sarasota Memorial Health Care System, Florida State University College of Medicine, Sarasota, FL, USA.
Joel A Garcia-FernandezDepartment of Medicine, Division of Cardiology, Orlando Health, Orlando, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTemporal conversions among ejection fraction (EF) classes can occur across the heart failure (HF) spectrum reflecting amended structural and functional outcomes unaccounted for by current taxonomy. This retrospective study aims to investigate the differences in serum laboratory values, guideline-directed medical therapy (GDMT), and co-morbidity burden across EF conversion groups. METHODS AND

resultsHeart failure patients at least 18-year-old who obtained at least two echocardiograms between January 2018 and January 2020 were identified using ICD-10 codes. Analysis of variance, chi-square tests, and analysis of means for proportions were used as appropriate to identify associations with class conversion groups. A total of 874 patients who underwent 1748 echocardiograms on unique visits were categorized according to initial EF as HF with preserved EF (HFpEF) (n = 531, 61%), HF with mildly reduced or midrange EF (HFmrEF) (n = 132, 15%), or HF with reduced EF (HFrEF) (n = 211, 24%). In accordance with follow-up EF, class conversions were categorized into HF with improved EF (HFiEF) (n = 143, 16%), HF with worsened EF (HFwEF) (n = 171, 20%), or HF with stable EF (HFsEF) (n = 560, 64%). The average age was 75 ± 13 years old; 54% were male, 85% were Caucasian, 11% were African American, and 4% other. The mean time between EF assessments was 208.6 ± 170.2 days. Serum sodium levels were greater in HFwEF (139 ± 3 mmol/L) when compared with HFsEF (138 ± 4 mmol/L) (P = 0.05). Pro-BNP levels were higher in HFiEF (12 150 ± 19 554 pg/mL) versus HFsEF (6671 ± 10 525 pg/mL) (P = 0.007). Angiotensin receptor-neprilysin inhibitors (ARNI) were more frequently ordered on index visit in HFiEF (P = 0.03), but no other significant differences in GDMT were identified. Despite similar Elixhauser Co-morbidity Measure (ECM) scores, ECM categorical analysis revealed that HFwEF was more likely to have an established diagnosis of depression (P = 0.03) and a spectrum of psychiatric illnesses (P = 0.03) on preliminary visit. HFsEF was less likely to have an established diagnosis of blood loss anaemia (P = 0.04). Metastatic cancer was more likely to have been diagnosed in HFiEF and less likely in HFsEF (P = 0.002).

conclusionsDespite similar ECM scores, EF class conversion groups demonstrated salient differences in average serum sodium and pro-BNP levels. Inpatient ARNI orders, psychiatric, hematologic, and oncologic co-morbidity patterns were also significantly different. Findings demonstrate blood-based biomarker patterns and targetable co-morbid conditions which may play a role in future EF class conversion. Dedicated studies evaluating measurements related to GDMT dose-titration, quality of life, and functionality are the next steps in this field of HF.

Indexed as

Heart FailureAdolescentAgedAged, 80 and overBiomarkersFemaleHumansMaleMiddle AgedMorbidityPrognosisQuality of LifeRetrospective StudiesSodiumStroke VolumeBiomarkersSodiumEjection fraction changeGDMTHeart failureHFiEFLongitudinalMultiple chronic conditions

Identifiers

PMID35570322
PMCPMC9288751

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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.