Evidence map›Paper›PMID 36084046›Full record

ArticlePloS one2022

Structural modeling for Oxford histological classifications of immunoglobulin A nephropathy.

Kensuke Joh, Takashi Nakazato, Akinori Hashiguchi, Akira Shimizu, Ritsuko Katafuchi, Hideo Okonogi, Kentaro Koike, Keita Hirano, Nobuo Tsuboi, Tetsuya Kawamura and 3 more

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

13 authors.

Kensuke JohDepartment of Pathology, The Jikei University Graduate School of Medicine, Tokyo, Japan.ORCID 0000-0002-9334-3664
Takashi NakazatoDepartment of Medical Information Management, NHO Chiba Medical Center, Chiba, Japan.
Akinori HashiguchiDepartment of Pathology, Keio University School of Medicine, Tokyo, Japan.
Akira ShimizuDepartment of Pathology, Nihon Medical School, Tokyo, Japan.
Ritsuko KatafuchiKidney Unit, National Hospital Organization Fukuoka-Higashi Medical Center, Fukuoka, Japan.
Hideo OkonogiDivision of Nephrology and Hypertension, Atsugi City Hospital, Kanagawa-ken, Japan.
Kentaro KoikeDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Keita HiranoDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Nobuo TsuboiDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Tetsuya KawamuraDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Takashi YokooDivision of Nephrology and Hypertension, Department of Internal Medicine, The Jikei University School of Medicine, Tokyo, Japan.
Ichiei NaritaDivision of Clinical Nephrology and Rheumatology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Yusuke SuzukiDivision of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan.

Funding

Wellcome Trust FC001045
6 · The paper itself

Abstract

In immunoglobulin A nephropathy (IgAN), Cox regression analysis can select independent prognostic variables for renal functional decline (RFD). However, the correlation of the selected histological variables with clinical and/or treatment variables is unknown, thereby making histology-based treatment decisions unreliable. We prospectively followed 946 Japanese patients with IgAN for a median of 66 mo. and applied structural equation modeling (SEM) to identify direct and indirect effects of histological variables on RFD as a regression line of estimated glomerular filtration rate (eGFR) via clinical variables including amount of proteinuria, eGFR, mean arterial pressure (MAP) at biopsy, and treatment variables such as steroid therapy with/without tonsillectomy (ST) and renin-angiotensin system blocker (RASB). Multi-layered correlations between the variables and RFD were identified by multivariate linear regression analysis and the model's goodness of fit was confirmed. Only tubular atrophy/interstitial fibrosis (T) had an accelerative direct effect on RFD, while endocapillary hypercellularity and active crescent (C) had an attenuating indirect effect via ST. Segmental sclerosis (S) had an attenuating indirect effect via eGFR and mesangial hypercellularity (M) had accelerative indirect effect for RFD via proteinuria. Moreover, M and C had accelerative indirect effect via proteinuria, which can be controlled by ST. However, both T and S had additional indirect accelerative effects via eGFR or MAP at biopsy, which cannot be controlled by ST. SEM identified a systemic path links between histological variables and RFD via dependent clinical and/or treatment variables. These findings lead to clinically applicable novel methodologies that can contribute to predict treatment outcomes using the Oxford classifications.

Indexed as

Glomerulonephritis, IGABiopsyGlomerular Filtration RateHumansKidneyProteinuriaRetrospective Studies

Identifiers

PMID36084046
PMCPMC9462802

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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.