Evidence map›Paper›PMID 33496911›Full record

ArticleMedical & biological engineering & computing2021

ManoMap: an automated system for characterization of colonic propagating contractions recorded by high-resolution manometry.

Niranchan Paskaranandavadivel, Anthony Y Lin, Leo K Cheng, Ian Bissett, Andrew Lowe, John Arkwright, Saeed Mollaee, Phil G Dinning, Gregory O'Grady

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In one paragraph

Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

9 authors.

Niranchan PaskaranandavadivelAuckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. nira.pask@auckland.ac.nz.ORCID http://orcid.org/0000-0001-7377-8524
Anthony Y LinDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Leo K ChengAuckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
Ian BissettDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Andrew LoweInstitute of Biomedical Engineering, Auckland University of Technology, Auckland, New Zealand.
John ArkwrightCollege of Science and Engineering, Flinders University, Adelaide, Australia.
Saeed MollaeeAuckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.
Phil G DinningDepartments of Gastroenterology & Surgery Flinders Medical Centre, Flinders University, Adelaide, Australia.
Gregory O'GradyAuckland Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

rationaleColonic high-resolution manometry (cHRM) is an emerging clinical tool for defining colonic function in health and disease. Current analysis methods are conducted manually, thus being inefficient and open to interpretation bias.

objectiveThe main objective of the study was to build an automated system to identify propagating contractions and compare the performance to manual marking analysis.

methodscHRM recordings were performed on 5 healthy subjects, 3 subjects with diarrhea-predominant irritable bowel syndrome, and 3 subjects with slow transit constipation. Two experts manually identified propagating contractions, from five randomly selected 10-min segments from each of the 11 subjects (72 channels per dataset, total duration 550 min). An automated signal processing and detection platform was developed to compare its effectiveness to manually identified propagating contractions. In the algorithm, individual pressure events over a threshold were identified and were then grouped into a propagating contraction. The detection platform allowed user-selectable thresholds, and a range of pressure thresholds was evaluated (2 to 20 mmHg). KEY

resultsThe automated system was found to be reliable and accurate for analyzing cHRM with a threshold of 15 mmHg, resulting in a positive predictive value of 75%. For 5-h cHRM recordings, the automated method takes 22 ± 2 s for analysis, while manual identification would take many hours.

conclusionsAn automated framework was developed to filter, detect, quantify, and visualize propagating contractions in cHRM recordings in an efficient manner that is reliable and consistent.

Indexed as

ColonConstipationAlgorithmsHumansManometrySignal Processing, Computer-AssistedAutomated methodsColonic manometryHigh-resolutionRectosigmoid brake

Identifiers

PMID33496911

What OpenQuestion holds

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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.