This multicenter, prospective, observational study aims to address two primary objectives.
The first objective is to demonstrate that hospital-acquired pneumonia (nosocomial pneumonia) following cardiac surgery can be predicted using artificial intelligence (AI), and to develop a personalized risk calculator for its development.
The second objective is to demonstrate that hospital-acquired pneumonia can serve as a risk factor for a 1-year composite cardiovascular and pulmonary outcome after cardiac surgery, and to develop a personalized risk calculator for this composite outcome.
The composite outcome will include the occurrence of any of the following events:
This is a non-interventional study. Patient evaluation and treatment are conducted in strict accordance with approved standards of medical care for the respective conditions. No experimental or unregistered (not approved for use in the Russian Federation) medical or diagnostic procedures will be performed during this study.
Inclusion Criteria:
Exclusion Criteria:
bosh@cardio-tomsk.ru
Patients of 18 years and older, after cardiac and/or vascular surgical intervention during the current hospitalization, who signed informed consent for the use of their anonymized data for scientific purposes
kalashnikova-t@mail.ru+7 913 814 1664
Pulmonary Arterial Hypertension and Associated Cardiovascular Disease Detection Using Artificial Intelligence
Artificial Intelligence-Based Evaluation of Chest X-Rays in Ventilator-Associated Pneumonia
Pulmonary Microbiota Changes and Clinical Outcomes in Neurosurgical ICU Patients With Artificial Airways
Comparing Traditional Risk Scores and an AI-Based Multimodal Model for Predicting Cardiovascular Events After Gastrointestinal Surgery
The Predictability of the Necessity for Cardiology Consultation in Patients Scheduled for Non-Cardiac Surgery Using Artificial Intelligence Models in Preoperative Anesthesia Assessment
AI-based System for Assessing Suspected Viral Pneumonia Related Lung Changes
Predicting Severe Cardiac Arrhythmias in the Perioperative Period Using AI-ECG