Evidence map›Paper›PMID 40221688›Full record

ArticleBMC pulmonary medicine2025

Criteria for identifying acute respiratory events based on FEV1 decline in home spirometry for lung transplant patients.

Yoshikazu Shinohara, Miho Yamaguchi, Muhammad Wannous, Yan Luo, Kazumichi Yamamoto, Masaaki Sato

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pulmonary 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

6 authors.

Yoshikazu ShinoharaDepartment of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, 7 - 3- 1 Hongo, Bunkyo-Ku, Tokyo, 113 - 8655, Japan. shinoharay-sur@h.u-tokyo.ac.jp.
Miho YamaguchiDepartment of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, 7 - 3- 1 Hongo, Bunkyo-Ku, Tokyo, 113 - 8655, Japan.
Muhammad WannousDepartment of Computer and Information Science, Higher Colleges of Technology, Zayed Al Awwal Street, Falaj Hazzaa, P.O. Box 17258, Al Ain, Abu Dhabi, United Arab Emirates.
Yan LuoCenter for Medical Education and Internationalization, Kyoto University Graduate School of Medicine, Yoshida Konoe-Cho, Sakyo-Ku, Kyoto, 606 - 8501, Japan.
Kazumichi YamamotoDepartment of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, 7 - 3- 1 Hongo, Bunkyo-Ku, Tokyo, 113 - 8655, Japan.
Masaaki SatoDepartment of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, 7 - 3- 1 Hongo, Bunkyo-Ku, Tokyo, 113 - 8655, Japan.

Funding

Japan Society for the Promotion of Science JP24K02533Japan Society for the Promotion of Science JP24K20173
6 · The paper itself

Abstract

backgroundLung transplantation is a critical treatment for end-stage lung diseases, but long-term survival is challenged by graft rejection and infection. The detection of adverse respiratory events depends on home spirometry, which can exhibit greater fluctuations than laboratory tests and may not provide timely alerts. Our LT-FollowUp system offers an internet-based platform for daily FEV1 monitoring. This paper explores whether a new algorithm using LT-FollowUp data can detect the clinically significant FEV1 declines that predict adverse respiratory events.

methodsA retrospective cohort study of lung transplant patients from the University of Tokyo Hospital was conducted using LT-FollowUp. The accuracy of the algorithm was evaluated using a nested case-crossover study comparing FEV1 declines before acute respiratory events with control periods, and a nested case-time-control study comparing cases with matched controls to adjust for time trends and bias.

resultsOf the 95 patients included in this study, 21 experienced acute respiratory events. The odds ratios derived from conditional logistic regression in the nested case-crossover study and the conditional logistic regression in the nested case-time-control study are 5.42 × 10

conclusionThe proposed algorithm using LT-FollowUp data shows promise for the real-time detection of respiratory events in lung transplant patients, potentially facilitating early interventions that may prevent chronic lung allograft dysfunction. Further validation in larger, multi-centre studies is needed to confirm these findings and enhance clinical utility.

Indexed as

AlgorithmsLung TransplantationSpirometryAdultAgedCase-Control StudiesCross-Over StudiesFemaleForced Expiratory VolumeHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesCase-crossover studyCase-time-control studyForced expiratory volume in one second (FEV1)Home spirometryLung transplantation

Identifiers

PMID40221688
PMCPMC11994002

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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.