ArticleBMC cardiovascular disorders2024
Development and evaluation of the model for acute kidney injury in patients with cardiac arrest after successful resuscitation.
Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development of a Clinical Prediction Model for Acute Kidney Injury Among In-Hospital Cardiac Arrest Patients During Intensive Care Unit Hospitalization.Reviews in cardiovascular medicine · 2026Article
- Construction and Validation of a Nomogram Based on Preoperative Inflammatory Indicators for Predicting Acute Kidney Injury After Pancreaticoduodenectomy.Journal of inflammation research · 2026Article
- Construction and validation of a risk prediction model for extensive abdominal aortic calcification in hypertensive patients aged 40 and above: a cross-sectional study based on the 2013-2014 NHANES database.BMC cardiovascular disorders · 2025Article
- Acute Kidney Injury in Patients After Cardiac Arrest: Effects of Targeted Temperature Management.Life (Basel, Switzerland) · 2025Article
- A machine learning model for prediction of cardiac arrest-associated acute kidney injury in the ICU: an internal and external validation study.Frontiers in medicine · 2025Article
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9 authors.
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Abstract
backgroundThis study aims to construct a clinical prediction model and create a visual line chart depicting the risk of acute kidney injury (AKI) following resuscitation in cardiac arrest (CA) patients. Additionally, the study aims to validate the clinical predictive accuracy of the developed model.
methodsData were retrieved from the Dryad database, and publicly shared data were downloaded. This retrospective cohort study included 347 successfully resuscitated patients post-cardiac arrest from the Dryad database. Demographic and clinical data of patients in the database, along with their renal function during hospitalization, were included. Through data analysis, the study aimed to explore the relevant influencing factors of acute kidney injury (AKI) in patients after cardiopulmonary resuscitation. The study constructed a line chart prediction model using multivariate logistic regression analysis with post-resuscitation shock status (Post-resuscitation shock refers to the condition where, following successful cardiopulmonary resuscitation after cardiac arrest, some patients develop cardiogenic shock.), C reactive protein (CRP), Lactate dehydrogenase (LDH), and Alkaline phosphatase (ALP) identified as predictive factors. The predictive efficiency of the fitted model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
resultsMultivariate logistic regression analysis showed that post-resuscitation shock status, CRP, LDH, and PAL were the influencing factors of AKI after resuscitation in CA patients. The calibration curve test indicated that the prediction model was well-calibrated, and the results of the Decision Curve Analysis (DCA) demonstrated the clinical utility of the model constructed in this study.
conclusionPost-resuscitation shock status, CRP, LDH, and ALPare the influencing factors for AKI after resuscitation in CA patients. The clinical prediction model constructed based on the above indicators has good clinical discriminability and practicality.
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