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Published on: February 16, 2016
Estimating heart mass from heart volume as measured from post-mortem computed tomography
Hamish M Aitken-Buck1, Matthew Moore2, Gillian A Whalley2
1Department of Physiology, HeartOtago, School of Biomedical Sciences, University of Otago, Dunedin, 9054, New Zealand. hamish.aitken-buck@otago.ac.nz.
Insights
Estimating heart mass from post-mortem CT scans is more accurate when including age, sex, and body dimensions alongside heart volume. This combined approach improves prediction accuracy for both male and female cases.
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.
Abstract:
Heart mass can be predicted from heart volume as measured from post-mortem computed tomography (PMCT), but with limited accuracy. Although related to heart mass, age, sex, and body dimensions have not been included in previous studies using heart volume to estimate heart mass. This study aimed to determine whether heart mass estimation can be improved when age, sex, and body dimensions are used as well as heart volume. Eighty-seven (24 female) adult post-mortem cases were investigated. Univariable predictors of heart mass were determined by Spearman correlation and simple linear regression. Stepwise linear regression was used to generate heart mass prediction equations. Heart mass estimate performance was tested using median mass comparison, linear regression, and Bland-Altman plots. Median heart mass (P = 0.0008) and heart volume (P = 0.008) were significantly greater in male relative to female cases. Alongside female sex and body surface area (BSA), heart mass was univariably associated with heart volume in all cases (R2 = 0.72) and in male (R2 = 0.70) and female cases (R2 = 0.64) when segregated. In multivariable regression, heart mass was independently associated with age and BSA (R2 adjusted = 0.46-0.54). Addition of heart volume improved multivariable heart mass prediction in the total cohort (R2 adjusted = 0.78), and in male (R2 adjusted = 0.74) and female (R2 adjusted = 0.74) cases. Heart mass estimated from multivariable models incorporating heart volume, age, sex, and BSA was more predictive of actual heart mass (R2 = 0.75-0.79) than models incorporating either age, sex, and BSA only (R2 = 0.48-0.57) or heart volume only (R2 = 0.64-0.73). Heart mass can be more accurately predicted from heart volume measured from PMCT when combined with the classical predictors, age, sex, and BSA.
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