Evidence map›Paper›PMID 39699030›Full record

ArticleClinical cardiology2024

Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

Luca Cumitini, Ailia Giubertoni, Lidia Rossi, Domenico D'Amario, Leonardo Grisafi, Paola Abbiati, Fabrizio D'Ascenzo, Gaetano Maria De Ferrari, Giuseppe Patti

Abstract read
In one paragraph

Article in Clinical cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

9 authors.

Luca CumitiniDepartment of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
Ailia GiubertoniDivision of Cardiology, Maggiore della Carità Hospital, Novara, Italy.
Lidia RossiDivision of Cardiology, Maggiore della Carità Hospital, Novara, Italy.
Domenico D'AmarioDepartment of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
Leonardo GrisafiDivision of Cardiology, Maggiore della Carità Hospital, Novara, Italy.
Paola AbbiatiDepartment of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
Fabrizio D'AscenzoCardiovascular and Thoracic Department, Division of Cardiology, "Città della Salute e della Scienza" Hospital, Turin, Italy.
Gaetano Maria De FerrariCardiovascular and Thoracic Department, Division of Cardiology, "Città della Salute e della Scienza" Hospital, Turin, Italy.
Giuseppe PattiDepartment of Translational Medicine, University of Eastern Piedmont, Novara, Italy.ORCID http://orcid.org/0000-0002-5404-3968

Funding

The authors received no specific funding for this work.
6 · The paper itself

Abstract

backgroundThe PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) score is a machine learning-based model for predicting 1-year adverse cardiovascular or bleeding events in patients with acute coronary syndrome (ACS). Its role in predicting arrhythmic complications in ACS remains unknown.

methodsAtrial fibrillation (AF) and ventricular arrhythmias (VA) were recorded by continuous electrocardiographic monitoring until discharge in a cohort of 365 participants with ACS prospectively enrolled. We considered two separate timeframes for VA occurrence: ≤ 48 and > 48 h. The objective was to evaluate the ability of the PRAISE score to identify ACS patients at higher risk of in-hospital arrhythmic complications.

resultsROC curve analysis indicated a significant association between PRAISE score and risk of both AF (AUC 0.89, p = 0.0001; optimal cut-off 5.77%) and VA (AUC 0.69, p = 0.0001; optimal cut-off 2.17%). Based on these thresholds, high/low AF PRAISE score groups and high/low VA PRAISE score groups were created, respectively. Patients with a high AF PRAISE score more frequently developed in-hospital AF (19% vs. 1%). Multivariate analysis showed a high AF PRAISE score risk as an independent predictor of AF (HR 4.30, p = 0.016). Patients with high VA PRAISE scores more frequently developed in-hospital VA (25% vs. 8% for VA ≤ 48 h; 33% vs. 3% for VA > 48 h). Multivariate analysis demonstrated a high VA PRAISE score risk as an independent predictor of both VA ≤ 48 h (HR 2.48, p = 0.032) and VA > 48 h (HR 4.93, p = 0.014).

conclusionThe PRAISE score has a comprehensive ability to identify with high specificity those patients at risk for arrhythmic events during hospitalization for ACS.

Indexed as

Acute Coronary SyndromeMachine LearningAgedAtrial FibrillationFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsROC CurveTime Factorsacute coronary syndromearrhythmiasmachine learningPRAISE risk score

Identifiers

PMID39699030
PMCPMC11656403

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.