Evidence map›Paper›PMID 34757048›Full record

ArticleUrology2022

Machine Learning for Urodynamic Detection of Detrusor Overactivity.

Kevin T Hobbs, Nathaniel Choe, Leonid I Aksenov, Lourdes Reyes, Wilkins Aquino, Jonathan C Routh, James A Hokanson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed, 1 synthesis or guideline 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

7 authors.

Kevin T HobbsDivision of Urologic Surgery, Duke University Medical Center, Durham, NC.
Nathaniel ChoeDepartment of Electrical and Computer Engineering, Duke University, Durham, NC.
Leonid I AksenovDivision of Urologic Surgery, Duke University Medical Center, Durham, NC.
Lourdes ReyesDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, NC.
Wilkins AquinoDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, NC.
Jonathan C RouthDivision of Urologic Surgery, Duke University Medical Center, Durham, NC.
James A HokansonDepartment of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI. Electronic address: jhokanson@mcw.edu.

Funding

Predicting Urinary Continence Status with Sacral Neuromodulation and Botulinum Toxin TreatmentsK01DK121866 · NIDDK · MEDICAL COLLEGE OF WISCONSIN · PI HOKANSON, JAMES ARTHUR · 2020 to 2022
$392k
NIDDK NIH HHS K01 DK121866
6 · The paper itself

Abstract

objectiveTo develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation. MATERIALS AND

methodsPatients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity.

resultsData were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%).

conclusionWe developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.

Indexed as

Machine LearningUrodynamicsAdolescentAdultChildChild, PreschoolHumansInfantSpinal DysraphismUrinary Bladder, OveractiveYoung Adult

Identifiers

PMID34757048
PMCPMC8865755

What OpenQuestion holds

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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.