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Rethinking Cardiopulmonary Bypass Management

Rethinking Cardiopulmonary Bypass Management in the Digital Health Era

March 7, 2026 No Comments

Minimally invasive and robotic cardiac surgery reduce surgical trauma and speed recovery but often require longer cardiopulmonary bypass (CPB) times, increasing risks such as renal injury, neurological complications, and systemic inflammation. This review explores how digital health tools—including continuous physiologic monitoring, machine learning analytics, and digital twin simulations—can transform CPB from a static procedural metric into a dynamically optimized variable, enabling personalized perfusion strategies that improve safety and outcomes in modern cardiac surgery.

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AI-Powered ECMO Digital Twin in Virtual Reality Training

Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An Artificial Intelligence Model for Virtual Reality Simulation 

February 22, 2026 No Comments

In this multicentre study of 335 ECMO patients, high-resolution device and electronic health record data were integrated to develop a two-stage artificial intelligence model capable of simulating ECMO circuit behavior and patient physiological responses. The digital twin was deployed in a virtual reality platform with real-time inference. Expert evaluation confirmed clinically coherent responses, supporting scalable, high-fidelity ECMO training without dedicated hardware.

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Revolutionizing Cardiovascular Interventions With Artificial Intelligence

May 5, 2025 No Comments

This editorial outlines how artificial intelligence (AI) is transforming cardiovascular interventions by enhancing procedural planning, democratizing medical expertise, and expediting device development. AI-driven tools improve diagnostic accuracy, support training via simulation, and enable the creation of digital twins for personalized treatment planning. The article emphasizes AI’s potential to foster global equity in healthcare access and improve patient outcomes.

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Impact of Inflammation After Cardiac Surgery

Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction

April 5, 2025 No Comments

This study investigates the effects of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develops machine learning models to predict SIRS. Analyzing data from 1,908 patients, researchers found SIRS significantly raised mortality risk. Key predictors included preoperative anemia and intraoperative lactate peaks. Predictive models using random forest achieved high accuracy, offering insights for tailored interventions.

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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database

April 3, 2025 No Comments

This study developed machine learning (ML) models to predict in-hospital mortality among ICU patients with acute heart failure (AHF) using data from the MIMIC-IV database. Among five tested algorithms, XGBoost showed the highest predictive accuracy (AUC: 0.82) and outperformed traditional clinical scoring systems. The model incorporated 18 clinical variables from the first 24 hours of ICU admission to aid early intervention strategies.

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AI TEE

Continuous Monitoring of Left Ventricular Function in Postoperative Intensive Care Patients Using Artificial Intelligence and Transesophageal Echocardiography

June 13, 2024 No Comments

This study explores the efficacy of using artificial intelligence (autoMAPSE) with transesophageal echocardiography (TEE) to continuously monitor left ventricular (LV) function in postoperative intensive care patients. The prospective observational study involved 50 patients, monitored for 120 minutes post-cardiac surgery. Results showed that autoMAPSE provided precise, low-bias, and concordant measurements compared to manual methods, demonstrating excellent feasibility and trending ability.

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Myocardial Injury

Hybrid Feature Selection in a Machine Learning Predictive Model for Perioperative Myocardial Injury in Noncoronary Cardiac Surgery with Cardiopulmonary Bypass

May 12, 2024 No Comments

This study developed a predictive model for perioperative myocardial injury (PMI) using hybrid feature selection (FS) methods in patients undergoing noncoronary cardiac surgery with cardiopulmonary bypass (CPB). Conducted at Fuwai Hospital, China, the retrospective study included 1130 patients, with an overall PMI incidence of 20.3%. Various machine learning models were evaluated, with the Naïve Bayes model achieving the highest AUC. The study highlighted the importance of factors like prolonged CPB, aortic clamp time, and preoperative low platelet count in predicting PMI risk.

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Predication Models

A Systematic Review of Cardiac Surgery Clinical Prediction Models That Include Intra-operative Variables

April 26, 2024 No Comments

This systematic review assesses clinical prediction models (CPMs) that incorporate intra-operative variables to predict outcomes following adult cardiac surgery. It highlights the identification of 24 CPMs, predominantly predicting acute kidney injury and peri-operative mortality, using common variables like cardiopulmonary bypass time. Despite acceptable discrimination in internally validated models, poor calibration and high bias risk limit their practical use. The review suggests potential improvement in model accuracy with intra-operative data, advocating for more robust studies.

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