Evidence map›Paper›PMID 39639341›Full record

ReviewCritical care (London, England)2024

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools.

Andrew J E Seely, Kimberley Newman, Rashi Ramchandani, Christophe Herry, Nathan Scales, Natasha Hudek, Jamie Brehaut, Daniel Jones, Tim Ramsay, Doug Barnaby and 4 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

14 authors.

Andrew J E SeelyFaculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada. aseely@ohri.ca.
Kimberley NewmanOttawa Hospital Research Institute, Ottawa, ON, Canada.
Rashi RamchandaniFaculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada.
Christophe HerryOttawa Hospital Research Institute, Ottawa, ON, Canada.
Nathan ScalesOttawa Hospital Research Institute, Ottawa, ON, Canada.
Natasha HudekFaculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada.
Jamie BrehautOttawa Hospital Research Institute, Ottawa, ON, Canada.
Daniel JonesFaculty of Medicine Ottawa, University of Ottawa, Ottawa, ON, Canada.
Tim RamsayOttawa Hospital Research Institute, Ottawa, ON, Canada.
Doug BarnabyDepartment of Emergency Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
Shannon FernandoDepartment of Emergency Medicine, Lakeridge Hospital, Oshawa, ON, Canada.
Jeffrey PerryDepartment of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada.
Sonny DhananiCritical Care, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada.
Karen E A BurnsInterdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.

methodsWe perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring.

resultsDevelopment and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial).

conclusionsThe proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalHumansMachine LearningMonitoring, PhysiologicSoftwareArtificial intelligenceClinical decision supportCritical illnessHeart rate variabilityRespiratory rate variabilityVariability analysisWaveform monitoring

Identifiers

PMID39639341
PMCPMC11619131

What OpenQuestion holds

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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.