Evidence map›Paper›PMID 41310765›Full record

Observational studyEuropean journal of medical research2025

Development and validation of a prediction model for acute kidney injury following cardiopulmonary bypass surgery.

Jianfeng Zhao, Shi Fu, Mingjie Fu, Nannan Cui, Dabing Huang, Qinghui Fu, Zhitao Li, Shuiqiao Fu

Abstract readObservational StudyValidation Study
In one paragraph

Observational study in European journal of medical research, 2025. 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. Observational
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

8 authors.

Jianfeng ZhaoThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China.
Shi FuThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China.
Mingjie FuThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China.
Nannan CuiThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China.
Dabing HuangThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China.
Qinghui FuThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China. 2513107@zju.edu.cn.
Zhitao LiThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China. lizhitao402@163.com.
Shuiqiao FuThe Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Qingchun Street 79Th, Hangzhou, 310003, Zhejiang Province, China. 2200048@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute renal impairment following cardiopulmonary bypass (CPB) is a serious perioperative complication with sustained occurrence. Proactive risk assessment and preventive strategies are crucial for enhancing surgical outcomes.

methodsThis prospective observational cohort study was conducted at a provincial tertiary hospital in Zhejiang Province, China, and involved 211 patients with heart disease who underwent CPB. Least absolute shrinkage and selection operator (LASSO) regression and best-subsets regression (BSR) were used to identify predictive variables for post-CPB acute kidney injury (AKI). A nomogram was constructed based on the selected predictors, and the model performance was evaluated using receiver operating characteristic curves, calibration plots, decision curve analysis (DCA), and internal validation via bootstrap resampling.

resultsAmong the 211 patients, 20.4% (43/211) developed AKI. LASSO and BSR identified body mass index, intrarenal venous flow pattern, ventilation time, and CPB time as significant predictors. These four prognostic factors were integrated into a web-based nomogram. The model yielded an area under the curve of 0.825 (95% confidence interval: 0.748-0.902). DCA demonstrated clinical utility across threshold probabilities ranging from 2 to 98%, confirming robust predictive accuracy.

conclusionsWe developed and validated a nomogram-based model for predicting post-CPB AKI and demonstrated its strong discriminative performance and clinical applicability. This tool may assist clinicians in risk stratification and early intervention.

Indexed as

Acute Kidney InjuryCardiopulmonary BypassPostoperative ComplicationsAgedChinaFemaleHumansMaleMiddle AgedNomogramsProspective StudiesRisk AssessmentRisk FactorsROC CurveAcute kidney injuryCardiopulmonary bypassNomogramPrediction

Identifiers

PMID41310765
PMCPMC12751627

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.