Estimating heart mass from heart volume as measured from post-mortem computed tomography

Area of Science:

• Forensic Medicine
• Cardiovascular Imaging
• Biometrics

Background:

• Heart mass estimation is crucial in forensic and clinical contexts.
• Previous methods using heart volume from post-mortem computed tomography (PMCT) had limited accuracy.
• Factors like age, sex, and body dimensions were not fully integrated into prior heart mass prediction models.

Purpose of the Study:

• To enhance heart mass prediction accuracy by incorporating age, sex, and body dimensions with heart volume from PMCT.
• To develop and validate improved multivariable prediction equations for post-mortem heart mass.

Main Methods:

• Investigated 87 adult post-mortem cases (24 female).
• Utilized Spearman correlation and simple linear regression for univariable analysis.
• Employed stepwise linear regression to generate multivariable prediction equations for heart mass.
• Assessed prediction performance using median mass comparison, linear regression, and Bland-Altman plots.

Main Results:

• Heart mass and volume were significantly greater in males than females.
• Heart mass was univariably associated with heart volume, sex, and body surface area (BSA).
• Multivariable models incorporating heart volume, age, sex, and BSA achieved higher prediction accuracy (R² adjusted = 0.75–0.79) compared to models using only age, sex, BSA (R² = 0.48–0.57) or only heart volume (R² = 0.64–0.73).

Conclusions:

• Combining heart volume from PMCT with age, sex, and BSA significantly improves heart mass prediction accuracy.
• The developed multivariable models offer a more reliable method for estimating post-mortem heart mass.
• This integrated approach provides a more comprehensive tool for forensic and pathological assessments of cardiac parameters.