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Derivation and Validation of Hemodynamic Phenotypes of Cardiac Surgery

NCT07085208 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 10847

Last updated 2025-07-25

No results posted yet for this study

Summary

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.

Conditions

  • Phenotyping
  • Machine Learning
  • Cardiac Surgery
  • Hemodynamic Parameters

Interventions

PROCEDURE

Unsupervised Machine Learning for Clinical Phenotyping

This is a data-driven study that uses an unsupervised machine learning algorithm to perform clustering on patient multimodal features. These features include: preoperative demographics, comorbidities, and laboratory data; surgical information; and high-resolution intraoperative data, most notably continuous vital sign trajectories.

Sponsors & Collaborators

  • Nanjing First Hospital, Nanjing Medical University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-04-01
Primary Completion
2024-08-31
Completion
2024-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07085208 on ClinicalTrials.gov