Evidence map›Paper›PMID 32642182›Full record

ReviewJournal of thoracic disease2020

Path to precision: prevention of post-operative atrial fibrillation.

Rinku Skaria, Saman Parvaneh, Sophia Zhou, James Kim, Santana Wanjiru, Genoveffa Devers, John Konhilas, Zain Khalpey

Open access · diamondAbstract readReview
In one paragraph

Review in Journal of thoracic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
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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

8 authors at 2 institutions in 1 country.

Rinku SkariaUniversity of Arizona, College of Medicine, Tucson, AZ, USA.
Saman ParvanehPhilips Research North America, Cambridge, MA, USA.
Sophia ZhouPhilips Research North America, Cambridge, MA, USA.
James KimUniversity of Arizona, College of Medicine, Tucson, AZ, USA.
Santana WanjiruUniversity of Arizona, College of Medicine, Tucson, AZ, USA.
Genoveffa DeversNihon Kohden America, Washington, DC, USA.
John KonhilasUniversity of Arizona, College of Medicine, Tucson, AZ, USA.
Zain KhalpeyNorthwest Allied Physicians, Tucson, AZ, USA.
University of Arizona · USPhilips (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25-40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF.

Indexed as

deep learningmachine learning (ML)Post-operative atrial fibrillation (POAF)

Identifiers

PMID32642182
PMCPMC7330352
OpenAlexW3025255426

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.