Evidence map›Paper›PMID 39290816›Full record

ArticleJACC. Advances2024

Impact of the Noninvasive Diagnostic Algorithm on Clinical Presentation and Prognosis in Cardiac Amyloidosis.

Giacomo Tini, Ernesto Cristiano, Mattia Zampieri, Alberto Ponziani, Aldostefano Porcari, Margherita Zanoletti, Carlotta Mazzoni, Matteo Sclafani, Giulia Saturi, Andrea Lalario and 11 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
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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

21 authors.

Giacomo TiniDepartment of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.
Ernesto CristianoDepartment of Electrophysiology, Humanitas Gavazzeni, Bergamo, Italy.
Mattia ZampieriCardiomyopathy Unit, Careggi University Hospital, Florence, Italy.
Alberto PonzianiCardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero- Universitaria di Bologna, Bologna, Italy.
Aldostefano PorcariEuropean Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart.
Margherita ZanolettiCardiovascular Unit, Department of Internal Medicine, University of Genova, Genova, Italy.
Carlotta MazzoniCardiomyopathy Unit, Careggi University Hospital, Florence, Italy.
Matteo SclafaniDepartment of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.
Giulia SaturiCardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero- Universitaria di Bologna, Bologna, Italy.
Andrea LalarioEuropean Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart.
Marianna Eleonora LabateCardiovascular Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Camillo AutoreDepartment of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.
Emanuele BarbatoDepartment of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.
Federico PerfettoTuscan Regional Amyloid Center, Careggi University Hospital, Florence, Italy.
Elena BiaginiCardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero- Universitaria di Bologna, Bologna, Italy.
Gianfranco SinagraEuropean Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart.
Marco CanepaCardiovascular Unit, Department of Internal Medicine, University of Genova, Genova, Italy.
Marco MerloEuropean Reference Network for Rare, Low Prevalence and Complex Diseases of the Heart-ERN GUARD-Heart.
Simone LonghiCardiology Unit, Cardiac Thoracic and Vascular Department, IRCCS Azienda Ospedaliero- Universitaria di Bologna, Bologna, Italy.
Francesco CappelliCardiomyopathy Unit, Careggi University Hospital, Florence, Italy.
Beatrice MusumeciDepartment of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The introduction of a noninvasive diagnostic algorithm in 2016 led to increased awareness and recognition of cardiac amyloidosis (CA). Objectives: The purpose of this study was to analyze the impact of the introduction of the noninvasive diagnostic algorithm on diagnosis and prognosis in a multicenter Italian CA cohort. Methods: This was a retrospective analysis of 887 CA patients from 5 Italian Cardiomyopathies Referral Centers: 311 light-chain CA, 87 variant transthyretin (TTR)-related CA, 489 wild-type TTR-related CA. Clinical characteristics and outcomes (all-cause mortality and heart failure [HF] hospitalizations) were compared overall and for each CA subtype between patients diagnosed before versus after 2016. Outcomes were further compared by propensity score weighted Kaplan-Meier analysis and Cox regression analysis. Results: CA diagnoses increased after 2016, in particular for wild-type TTR-related CA. Patients diagnosed after versus before 2016 were older, had less frequently a history of HF prior to diagnosis, and NYHA functional class III-IV at diagnosis. Over a median follow-up of 18 months, 172 (86%) patients diagnosed before 2016 died or had an HF hospitalization, versus 300 (44%) diagnosed after 2016. Propensity score weighted Kaplan-Meier analysis showed worse outcomes ( Conclusions: CA patients diagnosed after 2016 showed a less severe phenotype and a better prognosis. The impact of the noninvasive diagnostic algorithm on outcomes was particularly relevant in TTR-related CA.

Indexed as

AL cardiac amyloidosiscardiac amyloidosisheart failurenoninvasive diagnosistransthyretin cardiac amyloidosis

Identifiers

PMID39290816
PMCPMC11405895

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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.