ArticleAnnals of medicine2023
Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model.
Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- An LLM-enhanced AI agent for rapid diagnosis of acute aortic dissection in multi-center settings.iScience · 2026Article
- Forecasting ICU Acute Kidney Injury with Actionable Lead Time Using Interpretable Machine Learning: Development and Multi-Center Validation.Journal of clinical medicine · 2026Article
- Evaluating comorbidity scoring systems for flumatinib therapy in chronic myeloid leukemia: a machine learning and SHAP-based predictive analysis.Frontiers in medicine · 2026Article
- Dynamic machine-learning model for early prediction of intraoperative hypotension using 5-minute perioperative time-series data: a multicenter retrospective study.Frontiers in medicine · 2026Article
- Correlation between systolic blood pressure variability and in-hospital mortality in patients with acute aortic dissection.Frontiers in cardiovascular medicine · 2026Article
- Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Intraoperative hemodynamics in prediction of postoperative kidney injury after type a aortic dissection surgery: a retrospective cohort study.Annals of medicine · 2025Article
- Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.Annals of medicine · 2025Article
- Effect of intraoperative hypotension depth and duration on acute kidney injury after Type A acute aortic dissection repair: An observational cohort study based on early risk stratification models.Journal of anesthesia and translational medicine · 2025Article
- Machine learning-based prediction of gastroesophageal junction cancer using electronic medical records.Clinical and experimental medicine · 2025Article
- Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.Scientific reports · 2025Article
- Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results.Reviews in cardiovascular medicine · 2025Article
- Development and validation of a nomogram for predicting acute kidney injury risks in patients undergoing acute stanford type A aortic dissection repair surgery.BMC nephrology · 2025Article
- A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.PLoS medicine · 2025Observational
- Machine Learning Model-Based Prediction of In-Hospital Acute Kidney Injury Risk in Acute Aortic Dissection Patients.Reviews in cardiovascular medicine · 2025Article
- Incorporating Circulating Plasma Interleukin-10 Enhanced Risk Predictability of Mortality in Acute Type A Aortic Dissection Surgery.Reviews in cardiovascular medicine · 2025Article
- Multimodal analysis of TAAD pathogenesis: SHAP-enhanced interpretable models and single-cell sequencing analysis reveal immune microenvironment alterations.Frontiers in immunology · 2025Article
- Preoperative leptin levels were associated with postoperative acute kidney injury in patients with acute type A aortic dissection.Renal failure · 2024Article
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11 authors.
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Abstract
backgroundAcute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk.
methodsIndicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models.
resultsAmong the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (
conclusionWith the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.
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