Evidence map›Paper›PMID 41873485›Full record

ArticleTechnology in cancer research & treatment

Prediction Models and Prognostic Analysis of Immune-Related Acute Kidney Injury in Lung Cancer Patients.

Suying Qian, Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang, Kanan Chen, Jing Wang, Xiaoyan Lu, Kedan Cai

Abstract read
In one paragraph

Article in Technology in cancer research & treatment. 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

9 authors.

Suying QianDepartment of Oncology, Ningbo No. 2 Hospital, Ningbo, China.
Ningjie XuDepartment of Nephrology, Ningbo No. 2 Hospital, Ningbo, China.ORCID 0009-0006-4085-6032
Yihui QuDepartment of Nephrology, Ningbo No. 2 Hospital, Ningbo, China.
Rongrong ZhuDepartment of Pharmacy, Ningbo No. 2 Hospital, Ningbo, China.
Minqiao ZhangDepartment of Nephrology, the First People's Hospital of Xiangshan, Ningbo, China.
Kanan ChenDepartment of Nephrology, Ningbo No. 2 Hospital, Ningbo, China.
Jing WangDepartment of Nephrology, Ningbo No. 2 Hospital, Ningbo, China.
Xiaoyan LuDepartment of Pharmacy, Ningbo No. 2 Hospital, Ningbo, China.
Kedan CaiDepartment of Nephrology, Ningbo No. 2 Hospital, Ningbo, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionImmune checkpoint inhibitors (ICIs) are extensively utilized in lung cancer patients, with documented instances of ICIs-associated acute kidney injury (ICIs-AKI). This study aims to explore a model for early recognition of ICIs-AKI.MethodsThe study involved 413 adult lung cancer patients treated with ICIs at Ningbo No.2 Hospital between Sept. 1, 2021, and June 30, 2023. Patients were followed until death or Dec. 31, 2023, and categorized into ICIs-AKI or non-AKI groups. We employed univariate and multivariate logistic regression to identify risk factors, developed both logistic regression and Multilayer Perceptron (MLP) prediction models, and used Kaplan-Meier survival analysis to assess prognosis.ResultsThe study included 381 lung cancer patients receiving ICIs treatment after excluding 32 patients. ICIs-AKI occurred in 13.39% of cases, with a median onset time of [123 (63, 303)] days. Multivariable logistic analysis identified diabetes, proteinuria, extrarenal irAEs, diuretic use, and chemotherapy as significant risk factors (all

Indexed as

Acute Kidney InjuryImmune Checkpoint InhibitorsLung NeoplasmsAgedFemaleHumansMaleMiddle AgedPrediction AlgorithmsPrognosisRisk FactorsImmune Checkpoint Inhibitorsacute kidney injuryimmune checkpoint inhibitorslung cancerprediction modelsprognosis

Identifiers

PMID41873485
PMCPMC13013984

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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.