Related Experiment Video
Updated: Jun 24, 2026
Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
Published on: June 7, 2024
Computed Tomography-based Body Composition Assessment for Preoperative Cardiovascular Risk Prediction: A Prospective
Bing-Cheng Zhao1, Jing Zhang2, Shao-Hui Lei3
1Bing-Cheng Zhao, M.D.: Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Anesthesia and Perioperative Organ Protection, Guangzhou, China; Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, Guangzhou, China; O utcomes R esearch Consortium, Houston, Texas.
Insights
CT-derived body composition metrics, including skeletal muscle area and fat radiodensity, significantly improve prediction of postoperative cardiovascular events. These novel markers offer enhanced risk assessment beyond traditional clinical predictors for surgical patients.
Area of Science:
- Cardiology
- Radiology
- Surgical Risk Assessment
Background:
- Current preoperative cardiovascular risk prediction methods are insufficient.
- CT-derived body composition offers potential objective cardiometabolic health markers.
- The predictive value of these metrics for postoperative cardiovascular events is not well-established.
Purpose of the Study:
- To investigate the predictive value of CT-derived body composition metrics for postoperative cardiovascular events.
- To determine if body composition metrics improve risk prediction beyond established clinical guidelines.
Main Methods:
- Prospective, multicenter study (PREVENGE-CB cohort) including patients undergoing major noncardiac surgery.
- Preoperative CT scans analyzed for skeletal muscle and adipose tissue area and radiodensity.
- Logistic regression used to assess added predictive value of body composition metrics over clinical predictors.
Main Results:
- 1594 patients included; 13.2% experienced primary outcome (30-day cardiovascular events).
- Skeletal muscle area and adipose area associated with lower risk; adipose radiodensity and muscle radiodensity associated with higher risk.
- An optimal subset of three metrics (skeletal muscle area, muscle radiodensity, subcutaneous fat radiodensity) significantly improved prediction over existing risk indices in validation cohorts.
Conclusions:
- CT-derived body composition metrics enhance the prediction of postoperative cardiovascular events.
- These metrics provide valuable information beyond conventional clinical predictors.
- Body composition analysis via CT may refine risk stratification for patients undergoing surgery.
Background:
Current approaches for preoperative cardiovascular risk prediction remain suboptimal. Computed tomography (CT)-derived body composition metrics may provide objective markers of cardiometabolic health, yet their predictive value for postoperative cardiovascular events remains unclear.
Methods:
This study included patients with cardiovascular disease or risk factors undergoing major noncardiac surgery in the prospective, multicenter PREdiction of Vascular Events after Noncardiac surGEry with Cardiac Biomarkers (PREVENGE-CB) cohort and its Nanfang extension. Preoperative abdominal CT scans were analyzed to quantify the area and radiodensity of skeletal muscle and adipose tissues at the third lumbar vertebral level. The primary outcome was composite cardiovascular events within 30 days after surgery. Logistic regression models were used to evaluate the added predictive value of body composition metrics beyond guideline-recommended predictors. In the Nanfang cohort, the optimal subset of body composition metrics was selected by minimizing the Akaike Information Criterion for the primary outcome. Nested models were compared using measures of model fit, discrimination, risk reclassification, and net benefit. The findings were validated in the PREVENGE-CB cohort.
Results:
Among 1,594 patients, 211 (13.2%) had the primary outcome. Larger skeletal muscle and adipose areas were generally associated with lower risk, whereas higher adipose radiodensity and lower muscle radiodensity indicated higher risk. In the Nanfang cohort, an optimal subset of three body composition metrics-skeletal muscle area, muscle radiodensity, and subcutaneous fat radiodensity-improved discrimination of the primary outcome over the Revised Cardiac Risk Index (increase in area under the curve [ΔAUC] = 0.136; 95% CI, 0.083 to 0.188), the Gupta Myocardial Infarction and Cardiac Arrest risk calculator (ΔAUC = 0.032; 95% CI, -0.002 to 0.065), and a refitted clinical model (ΔAUC = 0.035; 95% CI, 0.008 to 0.062). These findings were validated in the PREVENGE-CB cohort.
Conclusions:
CT-derived body composition metrics improved prediction of postoperative cardiovascular events beyond conventional clinical predictors.
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