Evidence map›Paper›PMID 34963444›Full record

ArticleBMC medical informatics and decision making2021

Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity.

Zhi Li, Kevin M Wheelock, Sangeeta Lathkar-Pradhan, Hakan Oral, Daniel J Clauw, Pujitha Gunaratne, Jonathan Gryak, Kayvan Najarian, Brahmajee K Nallamothu, Hamid Ghanbari

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Machine learning in the detection and management of atrial fibrillation.Clinical research in cardiology : official journal of the German Cardiac Society · 2022
    Review
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

10 authors at 3 institutions in 1 country.

Zhi LiDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Kevin M WheelockDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Sangeeta Lathkar-PradhanDepartment of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA.
Hakan OralDepartment of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA.
Daniel J ClauwDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA.
Pujitha GunaratneToyota Motor North America, Ann Arbor, MI, USA.
Jonathan GryakDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Kayvan NajarianDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Brahmajee K NallamothuDepartment of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA.
Hamid GhanbariDepartment of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA. ghhamid@med.umich.edu.
University of Michigan–Ann Arbor · USToyota Motor Corporation (United States) · USYale University · US

Funding

Novel Techniques for Evaluating and Assessing Symptoms, Affect. Heart Rhythm and Functional Status in Patients with Atrial Fibrillation: miAfib ProjectK23HL135397 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHANBARI, HAMID · 2018 to 2022
$983k
NHLBI NIH HHS K23 HL135397
6 · The paper itself

Abstract

backgroundRapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.

methodsThis study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.

resultsAmong the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67-0.78.

conclusionThe DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

Indexed as

Atrial FibrillationAlgorithmsElectrocardiographyHeart RateHeart VentriclesHumansArrhythmia predictionAtrial fibrillationMachine learningProbabilistic finite-state automataSignal processing

Identifiers

PMID34963444
PMCPMC8714444
OpenAlexW4200304308

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.