Evidence map›Paper›PMID 37737164›Full record

ArticleBMC anesthesiology2023

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study.

Bryce Benson, Ashwin Belle, Sooin Lee, Benjamin S Bassin, Richard P Medlin, Michael W Sjoding, Kevin R Ward

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

Article in BMC anesthesiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 13% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. 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

7 authors at 1 institution in 1 country.

Bryce BensonFifth Eye Inc, 110 Miller Avenue, Suite 300, Ann Arbor, MI, 48104, USA.
Ashwin BelleFifth Eye Inc, 110 Miller Avenue, Suite 300, Ann Arbor, MI, 48104, USA.
Sooin LeeFifth Eye Inc, 110 Miller Avenue, Suite 300, Ann Arbor, MI, 48104, USA.
Benjamin S BassinDepartment of Emergency Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109-5301, USA.
Richard P MedlinDepartment of Emergency Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109-5301, USA.
Michael W SjodingMax Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, NCRC 10-A103 2800 Plymouth Road, Ann Arbor, MI, 48109, USA.
Kevin R WardDepartment of Emergency Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI, 48109-5301, USA. keward@umich.edu.
University of Michigan–Ann Arbor · US

Funding

Optimizing Clinician and AI Collaborations to Support Timely Management of Acute DyspneaR01HL158626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Michael William Sjoding, Jenna Wiens · 2021 to 2026
$3.8M
NHLBI NIH HHS R01 HL158626
6 · The paper itself

Abstract

backgroundPredicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

methodsRetrospective cohort study at a quaternary care academic health system using data from hospitalized adult patients between August 2019 and April 2020 undergoing continuous ECG monitoring with intermittent noninvasive blood pressure (NIBP) or with continuous intraarterial pressure (IAP) monitoring.

resultsAHI-PI's low and high-risk indications were compared with the presence of EHI in the future as indicated by vital signs (heart rate > 100 beats/min with a systolic blood pressure < 90 mmHg or a mean arterial blood pressure of < 70 mmHg). 4,633 patients were analyzed (3,961 undergoing NIBP monitoring, 672 with continuous IAP monitoring). 692 patients had an EHI (380 undergoing NIBP, 312 undergoing IAP). For IAP patients, the sensitivity and specificity of AHI-PI to predict EHI was 89.7% and 78.3% with a positive and negative predictive value of 33.7% and 98.4% respectively. For NIBP patients, AHI-PI had a sensitivity and specificity of 86.3% and 80.5% with a positive and negative predictive value of 11.7% and 99.5% respectively. Both groups performed with an AUC of 0.87. AHI-PI predicted EHI in both groups with a median lead time of 1.1 h (average lead time of 3.7 h for IAP group, 2.9 h for NIBP group).

conclusionsAHI-PI predicted EHIs with high sensitivity and specificity and within clinically significant time windows that may allow for intervention. Performance was similar in patients undergoing NIBP and IAP monitoring.

Indexed as

ElectrocardiographyAdultHeart RateHumansRetrospective StudiesBlood pressureCritical careHeart rate variabilityHemodynamic instabilityHemodynamic monitoringHypotensionPredictive analytic

Identifiers

PMID37737164
PMCPMC10515416
OpenAlexW4386945156

What OpenQuestion holds

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LicenceCC BY
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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.