Evidence map›Paper›PMID 36269491›Full record

ArticleJournal of nephrology2023

Post-hoc analysis of a tool to predict kidney failure in patients with IgA nephropathy.

Francesco Paolo Schena, Vito Walter Anelli, Tommaso Di Noia, Giovanni Tripepi, Daniela Isabel Abbrescia, Maria Stangou, Aikaterini Papagianni, Maria Luisa Russo, Graziella D'Arrigo, Carlo Manno

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In one paragraph

Article in Journal of nephrology, 2023. 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
0.1field-weighted citation impact, top 50% of its field
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 citations in OpenAlex.

  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

10 authors at 6 institutions in 2 countries.

Francesco Paolo SchenaDepartment of Emergency and Organ Transplantation, Nephrology, University of Bari "Aldo Moro", Bari, Italy. paolo.schena@uniba.it.
Vito Walter AnelliDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Tommaso Di NoiaDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Giovanni TripepiCNR-IFC, Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Renal Unit, General Hospital, Reggio Calabria, Italy.
Daniela Isabel AbbresciaSchena Foundation, Polyclinic, Bari, Italy.
Maria StangouDepartment of Nephrology, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloníki, Greece.
Aikaterini PapagianniDepartment of Nephrology, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloníki, Greece.
Maria Luisa RussoFondazione Ricerca Molinette, Turin, Italy.
Graziella D'ArrigoCNR-IFC, Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Renal Unit, General Hospital, Reggio Calabria, Italy.
Carlo MannoDepartment of Emergency and Organ Transplantation, Nephrology, University of Bari "Aldo Moro", Bari, Italy.
Aristotle University of Thessaloniki · GRIstituto di Fisiologia Clinica · ITPolyclinic General Hospital · GRPolytechnic University of Bari · ITFondazione Ricerca Molinette · ITUniversity of Bari Aldo Moro · IT

Funding

Ministero dell'Università e della Ricerca ARS01_0087602
6 · The paper itself

Abstract

backgroundRecently, a tool based on two different artificial neural networks has been developed. The first network predicts kidney failure (KF) development while the second predicts the time frame to reach this outcome. In this study, we conducted a post-hoc analysis to evaluate the discordant results obtained by the tool.

methodsThe tool performance was analyzed in a retrospective cohort of 1116 adult IgAN patients, as were the causes of discordance between the predicted and observed cases of KF.

resultsThere was discordance between the predicted and observed KF in 216 IgAN patients (19.35%) all of whom were elderly, hypertensive, had high serum creatinine levels, reduced renal function and moderate or severe renal lesions. Many of these patients did not receive therapy or were non-responders to therapy. In other IgAN patients the tool predicted KF but the outcome was not reached because patients responded to therapy. Therefore, in the discordant group (prediction did not match the observed outcome) the proportion of patients having or not having KF was strongly associated with treatment (P < 0.0001).

conclusionsThe post-hoc analysis shows that discordance in a low number of patients is not an error, but rather the effect of positive response to therapy. Thus, the tool could both help physicians to determine the prognosis of the disease and help patients to plan for their future.

Indexed as

Glomerulonephritis, IGAKidney Failure, ChronicRenal InsufficiencyAdultAgedHumansKidneyPrognosisRetrospective StudiesChronic kidney diseaseClinical decision support systemImmunoglobulin A nephropathyKidney biopsyKidney failure

Identifiers

PMID36269491
OpenAlexW4307036361

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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.