Artificial Intelligence (AI), CABG, CABG-patients, Cardiovascular Surgery, Surgeons
Conditions
Brief summary
The goal of this study aims to investigate the use of artificial intelligence to analyze and evaluate the characteristics and proficiency of surgeons during vascular anastomosis in coronary artery bypass grafting (CABG) procedures. The main question it aims to answer is: Consistency assessment between AI evaluation scores and human expert evaluation scores for surgeons during left anterior descending (LAD) artery anastomosis.
Interventions
None listed
Sponsors
China National Center for Cardiovascular Diseases
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years * Undergoing internal mammary artery-to-left anterior descending artery bypass grafting * First-time recipient of isolated CABG surgery * Signed written informed consent
Exclusion criteria
* Patients with acute coronary syndrome * Patients with contraindications to coronary CT angiography or coronary angiography * Patients with renal insufficiency or active liver disease, including those with persistently elevated serum transaminases of unknown cause or any serum transaminase levels exceeding three times the upper limit of normal.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Consistency assessment between AI surgical evaluation scores and human expert scores. | At the end of enrollment |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Intraoperative measurement of graft flow and flow resistance. | After enrollment | — |
| Characteristics of the surgeon's motion trajectory. | After enrollment | — |
| Consistency assessment between AI surgical evaluation scores levels and human expert scores levels. | After enrollment | AI surgical evaluation scores levels were divided into the following 3 score levels: low, middle, high; human expert scores levels were divided into the following 3 score levels: low, middle, high. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Surgeon-level subgroup: sex | Baseline | Sex subgroup is stratified into: female, male |
| Surgeon-level subgroup: professional title | Baseline | Professional title subgroup is stratified into: junior, associate, senior |
| Surgeon-level subgroup: annual surgery volume | Baseline | Annual surgery volume subgroup is stratified by tertiles |
| Surgeon-level subgroup: total surgery volume | Baseline | Total surgery volume subgroup is stratified by tertiles |
| Graft patency at 1 year postoperatively. | At 1 year | — |
| Surgeon-level subgroup: total CABG volume | Baseline | Total CABG volume subgroup is stratified by tertiles |
| Patient-level subgroup: age | Baseline | Age subgroup is stratified into: \<65, ≥65 years old |
| Patient-level subgroup: sex | Baseline | Sex subgroup is stratified into: female, male |
| Patient-level subgroup: the severity of coronary artery disease | Baseline | The severity of coronary artery disease is measured by SYNTAX score and is stratified into: \<23, 23-32, \>32 |
| Surgeon-level subgroup: annual CABG volume | Baseline | Annual CABG volume subgroup is stratified by tertiles |
| Adverse cardiovascular events, such as myocardial infarction and stroke, within 6 months postoperatively. | At 6 months | — |
| Adverse cardiovascular events, such as myocardial infarction and stroke, within 12 months postoperatively. | At 12 months | — |
| Surgeon-level subgroup: age | Baseline | Age subgroup is stratified into: \<45, ≥45 years old |
Countries
China
Contacts
Primary ContactLihua Zhang, M.D, Ph.D
Outcome results
None listed