Evidence map›Paper›PMID 37366306›Full record

ArticleEuropean stroke journal2023

Predictive model of acute kidney injury after spontaneous intracerebral hemorrhage: A multicenter retrospective study.

Yixin Tian, Yu Zhang, Jialing He, Lvlin Chen, Pengfei Hao, Tiangui Li, Liyuan Peng, Weelic Chong, Yang Hai, Chao You and 2 more

Open access · bronzeAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European stroke journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

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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

12 authors at 3 institutions in 2 countries.

Yixin TianDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0002-4195-0710
Yu ZhangCenter for Evidence-based Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Jialing HeDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.ORCID 0000-0001-8871-0012
Lvlin ChenDepartment of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Pengfei HaoDepartment of Neurosurgery, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
Tiangui LiDepartment of Neurosurgery, Longquan Hospital, Chengdu, Sichuan, China.
Liyuan PengDepartment of Critical Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Weelic ChongDepartment of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
Yang HaiSidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA.
Chao YouDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Lu JiaDepartment of Neurosurgery, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
Fang FangDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Sichuan University · CNShanxi Medical University · CNThomas Jefferson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesAcute kidney injury is a common comorbidity in patients with intracerebral hemorrhage. Although there are predictive models to determine risk of AKI in patients in critical care or post-surgical scenarios or in general medical floors, there are no models that specifically determine the risk of AKI in patients with ICH.

methodsClinical features and laboratory tests were selected by previous studies and LASSO (least absolute shrinkage and selection operator) regression. We used multivariable logistic regression with a bidirectional stepwise method to construct ICH-AKIM (intracerebral hemorrhage-associated acute kidney injury model). The accuracy of ICH-AKIM was measured by the area under the receiver operating characteristic curve. The outcome was AKI development during hospitalization, defined as KDIGO (Kidney Disease: Improving Global Outcomes) Guidelines.

resultsFrom four independent medical centers, a total of 9649 patients with ICH were available. Overall, five clinical features (sex, systolic blood pressure, diabetes, Glasgow coma scale, mannitol infusion) and four laboratory tests at admission (serum creatinine, albumin, uric acid, neutrophils-to-lymphocyte ratio) were predictive factors and were included in the ICH-AKIM construction. The AUC of ICH-AKIM in the derivation, internal validation, and three external validation cohorts were 0.815, 0.816, 0.776, 0.780, and 0.821, respectively. Compared to the univariate forecast and pre-existing AKI models, ICH-AKIM led to significant improvements in discrimination and reclassification for predicting the incidence of AKI in all cohorts. An online interface of ICH-AKIM is freely available for use.

conclusionICH-AKIM exhibited good discriminative capabilities for the prediction of AKI after ICH and outperforms existing predictive models.

Indexed as

Acute Kidney InjuryCerebral HemorrhageHospitalizationHumansRetrospective StudiesROC Curveacute kidney injuryIntracerebral hemorrhagepredictive model

Identifiers

PMID37366306
PMCPMC10472951
OpenAlexW4382182141

What OpenQuestion holds

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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.