Evidence map›Paper›PMID 40840757›Full record

ArticleRespiratory medicine2025

Machine learning based detection of true ventilatory restriction.

Pratim Saha, Muhammad F A Chaudhary, Akm Shahariar Azad Rabby, Sandeep Bodduluri, Arie Nakhmani, Chengcui Zhang, Surya P Bhatt

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Respiratory medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Pratim SahaCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Department of Computer Science, University of Alabama at Birmingham, AL, 35294, USA. Electronic address: psaha@uab.edu.
Muhammad F A ChaudharyCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA. Electronic address: mchaudh6@uab.edu.
Akm Shahariar Azad RabbyCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Department of Computer Science, University of Alabama at Birmingham, AL, 35294, USA. Electronic address: arabby@uab.edu.
Sandeep BodduluriCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA. Electronic address: sboddulu@uab.edu.
Arie NakhmaniCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, 35294, USA. Electronic address: anry@uab.edu.
Chengcui ZhangDepartment of Computer Science, University of Alabama at Birmingham, AL, 35294, USA. Electronic address: czhang02@uab.edu.
Surya P BhattCenter for Lung Analytics and Imaging Research, University of Alabama at Birmingham, Birmingham, AL, 35294, USA; Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA. Electronic address: sbhatt@uabmc.edu.

Funding

1/2 Video Telehealth Pulmonary Rehabilitation to Reduce Hospital Readmission in Chronic Obstructive Pulmonary Disease (Tele-COPD)UH3HL155806 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Surya P Bhatt · 2022 to 2026
$5.4M
Structural Determinants of Disease Progression in COPDR01HL151421 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BHATT, SURYA P., NAKHMANI, ARIE · 2020 to 2024
$2.6M
Silent Zones of Lung Disease in COPDK01HL163249 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Sandeep Bodduluri · 2023 to 2026
$674k
NHLBI NIH HHS K01 HL163249NHLBI NIH HHS R01 HL151421NHLBI NIH HHS UH3 HL155806
6 · The paper itself

Abstract

rationaleSpirometry is only 50 % accurate for the detection of true ventilatory restriction, necessitating additional lung volume tests.

objectiveTo develop a detection tool for true lung restriction using spirometry and patient demographics.

methodsWe analyzed spirometry and lung volume data from 21,062 participants. Restrictive spirometric pattern (RSP) was defined by FEV

resultsOf 21,062 participants, 12,643 (60 %) had TVR, of whom 5,255 (41.6 %) had RSP. The accuracy of RSP alone in detection of TVR was 0.61 (95% CI 0.60-0.63) with sensitivity of 0.42 (95% CI 0.40-0.43) and specificity of 0.91 (95% CI 0.90-0.92). The LightGBM model outperformed RSP alone, with an accuracy of 0.78 (95% CI 0.77-0.80), area under the ROC curve (AUC) of 0.89 (95% CI 0.88-0.90), sensitivity of 0.74 (95% CI 0.72-0.75), and specificity of 0.86 (95% CI 0.84-0.87).

conclusionsA machine learning model using demographics and spirometry can accurately detect true ventilatory restriction and lower the need for additional lung volume testing.

Indexed as

Machine LearningSpirometryAdultAgedFemaleForced Expiratory VolumeHumansLung Volume MeasurementsMaleMiddle AgedROC CurveVital CapacityLung volumeMachine learningSpirometric restrictionVentilatory restriction

Identifiers

PMID40840757
PMCPMC13455152

What OpenQuestion holds

Textmetadata
LicenceTDM
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.