Evidence map›Paper›PMID 39545086›Full record

ArticleBioengineering & translational medicine2024

Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation.

Daniel P Howsmon, Matthew F Mikulski, Nikhil Kabra, Joyce Northrup, Daniel Stromberg, Charles D Fraser, Carlos M Mery, Richard P Lion

Abstract read
In one paragraph

Article in Bioengineering & translational medicine, 2024. 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
–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

2 citing papers in PubMed.

  1. Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025
    Review
  2. 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

8 authors.

Daniel P HowsmonDepartment of Chemical and Biomolecular Engineering Tulane University New Orleans Louisiana USA.ORCID https://orcid.org/0000-0002-7177-1342
Matthew F MikulskiTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.
Nikhil KabraChandra Department of Electrical and Computer Engineering the University of Texas at Austin Austin Texas USA.
Joyce NorthrupTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.
Daniel StrombergTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.
Charles D FraserTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.
Carlos M MeryTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.
Richard P LionTexas Center for Pediatric and Congenital Heart Disease University of Texas Health Austin and Dell Children's Medical Center Austin Texas USA.ORCID https://orcid.org/0000-0003-2715-6665

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician-determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high-frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d-transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

Indexed as

computational modelingcongenital heart diseasemedical devicespostoperative monitoringstatistical process monitoring

Identifiers

PMID39545086
PMCPMC11558195

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.