Evidence map›Paper›PMID 37941080›Full record

ArticleEuropean journal of medical research2023

Acute renal injury after aortic arch reconstruction with cardiopulmonary bypass for children: prediction models by machine learning of a retrospective cohort study.

Xiangpan Kong, Lu Zhao, Zhengxia Pan, Hongbo Li, Guanghui Wei, Quan Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 3 pooled it
2.8field-weighted citation impact, top 8% 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, 3 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Observational
  6. Article
  7. Article
  8. Review
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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

6 authors at 3 institutions in 1 country.

Xiangpan Kong *Department of Urology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China.
Lu Zhao *Department of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China.
Zhengxia PanDepartment of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China.
Hongbo LiDepartment of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China.
Guanghui WeiDepartment of Urology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China.
Quan WangDepartment of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application, No.136 Zhongshan Second Road, Yuzhong District, Chongqing, 400014, China. quanwang@hospital.cqmu.edu.cn.
Chongqing Medical University · CNChildren's Hospital of Chongqing Medical University · CNChina International Science and Technology Cooperation · CN

Funding

National Children's health and disease clinical medicine research centre No. NCRCCHD-2021-YP-17the Basic Research in Key Laboratory of Ministry of Education (Youth Program) YBRP-202119
6 · The paper itself

Abstract

backgroundAcute renal injury (AKI) after aortic arch reconstruction with cardiopulmonary bypass leads to injury of multiple organs and increases perioperative mortality. The study was performed to explore risk factors for AKI. We aim to develop a prediction model that can be used to accurately predict AKI through machine learning (ML).

methodsA retrospective analysis was performed on 134 patients with aortic arch reconstruction with cardiopulmonary bypass who were treated at our hospital from January 2002 to January 2022. Risk factors for AKI were compositive and were evaluated with comprehensive analyses. Six artificial intelligence (AI) models were used for machine learning to build prediction models and to screen out the best model to predict AKI.

resultsWeight, eGFR, cyanosis, PDA, newborn birth and duration of renal ischemia were closely related to AKI. By integrating the results of the training cohort and validation cohort, we finally confirmed that the logistic regression model was the most stable model among all the models, and the logistic regression model showed good discrimination, calibration and clinical practicability. Based on 6 independent factors, the dynamic nomogram can be used as a predictive tool for clinical application.

conclusionsDHCA could be considered in aortic arch reconstruction if additional perfusion of lower body were not performed especially when renal ischemia is greater than 30 min. Machine Learning models should be developed for early recognition of AKI.

trial registrationChiCTR, ChiCTR2200060552. Registered 4 june 2022.

Indexed as

Acute Kidney InjuryAorta, ThoracicArtificial IntelligenceCardiopulmonary BypassChildHumansInfant, NewbornIschemiaMachine LearningRetrospective StudiesRisk FactorsTreatment OutcomeAcute renal injuryArtificial intelligenceCoarctation of aortaInterrupted aortic archMachine learning

Identifiers

PMID37941080
PMCPMC10631067
OpenAlexW4388498126

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.