Table 2 Ongoing trials on the application of big data and derived analysis techniques to cardiac surgery
From: Big Data in cardiac surgery: real world and perspectives
Title | Location | Status | Description |
|---|---|---|---|
Effects of AI Assisted Follow-up Strategy on Secondary Prevention in CABG Patients ClinicalTrials.gov Identifier: NCT04636996 | Cardiovascular Institute and Fuwai Hospital, Beijing, Beijing, China | Not yet recruiting | Assess if AI assisted follow-up strategy will improve secondary prevention in CABG patients |
Artificial Intelligence Guided Patient Selection for Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial (AI-PAFA Trial) ClinicalTrials.gov Identifier: NCT04997824 | Severance Hospital, Yonsei University Health System Seoul, Korea, Republic of | Not yet recruiting | Prediction of AF catheter ablation (AFCA) efficacy using artificial intelligence (AI) |
Effect of Artificial Intelligence on Nutritional Status of Children Post Cardiac Surgery ClinicalTrials.gov Identifier: NCT04782635 | Armed Forces Institute of Cardiology and National Institute of Heart Disease Rawalpindi, Punjab, Pakistan Maryam Zahid Rawalpindi, Punjab, Pakistan | Completed | Assess the effect of artificial intelligence on nutritional status of children post cardiac surgery in comparison to usual care group |
Cloud-based ECG Monitoring and Healthcare Model Building on the Population With Coronary Artery Revascularization ClinicalTrials.gov Identifier: NCT04485143 | Â | Not yet recruiting | All subjects tracked the occurrence of adverse medical events within one year after discharge from the hospital. Based on the home-based remote personal care model for patients with CABG, a risk prediction model for heart failure and vascular restenosis was established to effectively reduce medical treatment, adverse events, and medical expenditure |
Machine Learning Predict Acute Kidney Injury in Patients Following Cardiac Surgery ClinicalTrials.gov Identifier: NCT04966598 | Chinese PLA General hospital Beijing, Beijing, China | Completed | Several prediction models based on machine learning technique are developed to allow early identification of patients who at the high risk of unfavorable kidney outcomes |
Machine Learning-Based Prediction of Major Perioperative Allogeneic Blood Requirements in Cardiac Surgery (PREMATRICS) ClinicalTrials.gov Identifier: NCT04856618 | Kepler University Hospital Linz, Upper Austria, Austria | Recruiting | If an accurate prediction model based on a few features could be created and those patients particularly at risk of massive transfusion of allogeneic blood could be identified, it would subsequently be possible to develop an adapted clinical pathway that would allow patient care to be improved and individualized interventions adapted to the situation to be implemented |
Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery ClinicalTrials.gov Identifier: NCT03724123 | Â | Completed | The investigators investigate the benefit of modern machine learning methods in personalized risk prediction in patients undergoing elective heart valve surgery |
Remote Monitoring to Improve Physician Monitoring, Patient Satisfaction, and Predict Readmissions Following Surgery ClinicalTrials.gov Identifier: NCT03800329 | Mayo Clinic in Rochester Rochester, Minnesota, United States | Completed | Measure data collected via machine learning algorithms to predict readmission following cardiac surgery |