Derivation and Validation of Hemodynamic Phenotypes of Cardiac Surgery

Nanjing Medical University

Status: Completed

Conditions: Cardiac Surgery; Phenotyping; Machine Learning; Hemodynamic Parameters

Treatments: Procedure: Unsupervised Machine Learning for Clinical Phenotyping

Study type: Observational

Funder types: Other

Identifiers

NCT07085208

KY20250224-KS-04

Details and patient eligibility

About

Background & Objective:

Cardiac surgery patients differ significantly in their health conditions and how they react during operations. Standard risk assessments before surgery often miss the real-time changes happening inside a patient's body during the procedure, which can affect their recovery. Therefore, researchers conducted this study to find different groups (phenotypes) of patients who face varying risks for poor outcomes. They did this by using advanced computer learning techniques to analyze a lot of detailed health information collected both before and during surgery.

Methods:

This was a study that looked back at patient records from several hospitals. Researchers gathered a large amount of patient information from before surgery, including their basic health details and lab results. They also collected very detailed measurements of patients' vital signs taken during surgery, noting how these changed over time. Then, a computer program that can find patterns without being told what to look for (unsupervised hierarchical clustering) was used to sort patients into distinct groups based on this combined data.

Clinical Relevance:

This study expects to show that using data to identify patient groups can reveal differences that traditional methods miss. These new patient groups, which are based on how their blood flow and vital signs behave, offer a new way to understand risks in real-time. This could help doctors to predict problems more accurately and create personalized care plans for each patient around the time of surgery, which has great potential for practical use in hospitals.

Enrollment: 10,847 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients aged 18 years or older
• Patients who underwent cardiac surgery with cardiopulmonary bypass

Exclusion criteria

• Incomplete information on surgical procedures,
• With History of prior cardiac surgery or underwent second surgery during the same hospitalization
• Insufficient valid perioperative vital sign monitoring data