Evidence map›Paper›PMID 39446712›Full record

ArticlePLOS digital health2024

Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.

Sooin Lee, Bryce Benson, Ashwin Belle, Richard P Medlin, David Jerkins, Foster Goss, Ashish K Khanna, Michael A DeVita, Kevin R Ward

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Sooin LeeFifth Eye, Inc, Ann Arbor, Michigan, United States of America.
Bryce BensonFifth Eye, Inc, Ann Arbor, Michigan, United States of America.
Ashwin BelleFifth Eye, Inc, Ann Arbor, Michigan, United States of America.ORCID https://orcid.org/0000-0002-8084-3563
Richard P MedlinDepartment of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.
David JerkinsMax Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, Michigan, United States of America.
Foster GossDepartment of Emergency Medicine, University of Colorado-Anschutz, Aurora, Colorado, United States of America.
Ashish K KhannaDepartment of Anesthesiology, Section on Critical Care Medicine, Perioperative Outcomes and Informatics Collaborative (POIC), Wake Forest School of Medicine, Atrium Health Wake Forest Baptist, Winston-Salem, North Carolina, United States of America.
Michael A DeVitaDepartment of Clinical Medicine, Columbia Vagelos College of Physicians and Surgeons, New York, New York, United States of America.
Kevin R WardDepartment of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID https://orcid.org/0000-0001-5939-409X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying the onset of patient deterioration is challenging despite the potential to respond to patients earlier with better vital sign monitoring and rapid response team (RRT) activation. In this study an ECG based software as a medical device, the Analytic for Hemodynamic Instability Predictive Index (AHI-PI), was compared to the vital signs of heart rate, blood pressure, and respiratory rate, evaluating how early it indicated risk before an RRT activation. A higher proportion of the events had risk indication by AHI-PI (92.71%) than by vital signs (41.67%). AHI-PI indicated risk early, with an average of over a day before RRT events. In events whose risks were indicated by both AHI-PI and vital signs, AHI-PI demonstrated earlier recognition of deterioration compared to vital signs. A case-control study showed that situations requiring RRTs were more likely to have AHI-PI risk indication than those that did not. The study derived several insights in support of AHI-PI's efficacy as a clinical decision support system. The findings demonstrated AHI-PI's potential to serve as a reliable predictor of future RRT events. It could potentially help clinicians recognize early clinical deterioration and respond to those unnoticed by vital signs, thereby helping clinicians improve clinical outcomes.

Identifiers

PMID39446712
PMCPMC11500862

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Registered trials

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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.