Cardiac Surgery, Hemodynamic Parameters, Machine Learning, Phenotyping
Conditions
Brief 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.
Interventions
PROCEDUREUnsupervised 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
Nanjing First Hospital, Nanjing Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Acute organ dysfunction | Within 7 days post-surgery for acute liver failure and acute kidney inkury, and 90 days for postoperative acute kidney disease | including postoparative acute liver failure and acute kidney injury (up to 7 days postoperative), and acute kidney disease(up to 90 days postoperative) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Total LOS and ICU-LOS | up to 90 days post-surgery | Length of hospital stay and length of ICU stay |
| In-hospital mortality | up to 90 days postoperative, from the end of surgery until patient discharge | All-cause in-hospital mortality |
Outcome results
None listed