Evidence map›Paper›PMID 39719582›Full record

ArticleRespiratory research2024

Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology.

Michihiro Uyama, Tomohiro Handa, Ryuji Uozumi, Seishu Hashimoto, Yoshio Taguchi, Kohei Ikezoe, Kiminobu Tanizawa, Naoya Tanabe, Tsuyoshi Oguma, Atsushi Matsunashi and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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

  1. Pooled it
  2. 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

22 authors.

Michihiro UyamaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Tomohiro HandaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan. hanta@kuhp.kyoto-u.ac.jp.
Ryuji UozumiDepartment of Industrial Engineering and Economics, Tokyo Institute of Technology, Tokyo, Japan.
Seishu HashimotoDepartment of Respiratory Medicine, Tenri Hospital, Tenri, Japan.
Yoshio TaguchiDepartment of Respiratory Medicine, Tenri Hospital, Tenri, Japan.
Kohei IkezoeDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Kiminobu TanizawaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Naoya TanabeDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Tsuyoshi OgumaDepartment of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan.
Atsushi MatsunashiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takafumi NiwamotoDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Hiroshi ShimaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Ryobu MoriDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Tomoki MaetaniDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yusuke ShiraishiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Tomomi W NobashiDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Ryo SakamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Takeshi KuboDepartment of Radiology, Tenri Hospital, Tenri, Japan.
Akihiko YoshizawaDepartment of Diagnostic Pathology, Nara Medical University, Nara, Japan.
Kazuhiro TeradaDepartment of Diagnostic Pathology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Yuji NakamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Toyohiro HiraiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Funding

Japan Agency for Medical Research and Development 23bm1423004h0001Japan Society for the Promotion of Science KAKENHI 20H04147Japan Society for the Promotion of Science KAKENHI 22K08281Japan Society for the Promotion of Science KAKENHI 23K07652Research Program on Rare and Intractable Diseases, Ministry of Health, Labor, Welfare of Japan JPMH23FC1015
6 · The paper itself

Abstract

backgroundThe composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF. The aim of this study was to develop AIQCT-derived CPI formulas to quantify CT abnormalities in IPF and to investigate the associations of these CPI formulas with survival.

methodsThe first cohort included 116 patients with IPF. In this cohort, ILD, bronchial, and hyperlucent volumes on CT were quantified using AIQCT. New CPI formulas were developed based on PFTs to estimate the volume fraction of ILD (ILD-CPI), the sum of the ILD and bronchial volume fractions (ILDB-CPI), and the sum of the ILD, bronchial and hyperlucent volume fractions (ILDBH-CPI). The associations of the original CPI, the CALIPER-CPI and the AIQCT-derived CPIs with survival were analyzed in the first cohort and in a second cohort of patients with IPF (n = 72).

resultsIn the first cohort, over a median observation time of 92.8 months, 79 patients (68.1%) died, and one patient (0.9%) underwent living-donor lung transplantation. The original CPI, the CALIPER-CPI, and all AIQCT-derived CPIs were associated with overall survival (hazard ratios: 1.07-1.22). The C-index of the ILDB-CPI (0.759) was the highest among all AIQCT-derived CPIs and was comparable to that of the original CPI (0.765) and the CALIPER-CPI (0.749). The C-index of the ILDBH-CPI (0.729) was lower than that of the other CPI variables. The second cohort yielded similar C-indices as the first cohort for the original CPI (0.738), CALIPER-CPI (0.757) and ILDB-CPI (0.749).

conclusionsThe ILDB-CPI can predict the outcomes of IPF patients with a similar performance to that of the original CPI and the CALIPER-CPI. Adding the hyperlucent volume to the CPI formula did not improve its predictive accuracy for mortality.

trial registrationNone (no health care interventions were performed).

Indexed as

Artificial IntelligenceBronchiTomography, X-Ray ComputedAgedCohort StudiesFemaleHumansIdiopathic Pulmonary FibrosisLung Diseases, InterstitialMaleMiddle AgedPrognosisRespiratory Function TestsRetrospective StudiesArtificial intelligence-based quantitative computed tomographic image analysis softwareBronchial volumeComposite physiologic indexHyperlucent volumeIdiopathic pulmonary fibrosisInterstitial lung disease

Identifiers

PMID39719582
PMCPMC11669200

What OpenQuestion holds

Textmetadata
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.