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Analytic morphomics, core muscle size, and surgical outcomes
Michael J Englesbe1, Jay S Lee, Kevin He
1Analytic Morphomics Group, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI 48109-5331, USA. englesbe@med.umich.edu
Annals of Surgery
|July 14, 2012
Summary
Lean core muscle size measured on preoperative CT scans is a significant predictor of surgical outcomes. Smaller muscle size correlates with increased mortality, complications, and costs, highlighting its importance as a preoperative risk factor.
Area of Science:
- Surgical outcomes research
- Medical imaging analysis
- Patient risk stratification
Background:
- Novel preoperative risk assessment tools are needed.
- Analytic morphomics can extract valuable patient-specific data from diagnostic images.
- Clinicians currently do not utilize this data.
Purpose of the Study:
- To assess the relationship between preoperative lean core muscle size and surgical outcomes.
- To determine if analytic morphomics can identify preoperative risk factors.
Main Methods:
- Utilized data from 1453 patients in the Michigan Surgical Quality Collaborative database who had preoperative CT scans before major elective surgery.
- Calculated lean core muscle size using analytic morphomic techniques.
- Assessed survival, surgical complications, and costs using Kaplan-Meier analysis, Cox regression, logistic regression, and generalized estimating equations.
Main Results:
- A decrease in lean core muscle area by 1000 mm² was associated with a 45% increase in mortality (HR=1.45, P=0.028).
- Patients in the smallest tertile of muscle size had significantly lower 1- and 3-year survival rates (87% vs 95% and 75% vs 91%, respectively).
- Lower muscle mass correlated with higher complication rates (20.9% vs 12.3%) and increased costs (+$10,110 per 1000 mm² decrease).
Conclusions:
- Lean core muscle size is an independent and significant preoperative risk factor.
- Analytic morphomics applied to preoperative CT scans offers a novel method for assessing patient risk.
- This approach can improve preoperative risk assessment and potentially surgical outcomes.
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