Population trends in human rib cross-sectional shapes.

computational models cortical bone cross-section population ribs shape

Journal

Journal of anatomy
ISSN: 1469-7580
Titre abrégé: J Anat
Pays: England
ID NLM: 0137162

Informations de publication

Date de publication:
10 Jan 2024
Historique:
revised: 05 12 2023
received: 25 08 2023
accepted: 12 12 2023
medline: 11 1 2024
pubmed: 11 1 2024
entrez: 11 1 2024
Statut: aheadofprint

Résumé

Rib fractures remain the most frequent thoracic injury in motor vehicle crashes. Computational human body models (HBMs) can be used to simulate these injuries and design mitigation strategies, but they require adequately detailed geometry to replicate such fractures. Due to a lack of rib cross-sectional shape data availability, most commercial HBMs use highly simplified rib sections extracted from a single individual during original HBM development. This study provides human rib shape data collected from chest CT scans of 240 females and males across the full adult age range. A cortical bone mapping algorithm extracted cross-sectional geometry from scans in terms of local periosteal position with respect to the central rib axis and local cortex thickness. Principal component analysis was used to reduce the dimensionality of these cross-sectional shape data. Linear regression found significant associations between principal component scores and subject demographics (sex, age, height, and weight) at all rib levels, and predicted scores were used to explore the expected rib cross-sectional shapes across a wide range of subject demographics. The resulting detailed rib cross-sectional shapes were quantified in terms of their total cross-sectional area and their cortical bone cross-sectional area. Average-sized female ribs were smaller in total cross-sectional area than average-sized male ribs by between 20% and 36% across the rib cage, with the greatest differences seen in the central portions of rib 6. This trend persisted although to smaller differences of 14%-29% when comparing females and males of equal intermediate weight and stature. Cortical bone cross-sectional areas were up to 18% smaller in females than males of equivalent height and weight but also reached parity in certain regions of the rib cage. Increased age from 25 to 80 years was associated with reductions in cortical bone cross-sectional area (up to 37% in females and 26% in males at mid-rib levels). Total cross-sectional area was also seen to reduce with age in females but to a lesser degree (of up to 17% in mid-rib regions). Similar regions saw marginal increases in total cross-sectional area for male ribs, indicating age affects rib cortex thickness moreso than overall rib cross-sectional size. Increased subject height was associated with increased rib total and cortical bone cross-sectional areas by approximately 25% and 15% increases, respectively, in mid-rib sections for a given 30 cm increase in height, although the magnitudes of these associations varied by sex and rib location. Increased weight was associated with approximately equal changes in both cortical bone and total cross-sectional areas in males. These effects were most prominent (around 25% increases for an addition of 50 kg) toward lower ribs in the rib cage and had only modest effects (less than 12% change) in ribs 2-4. Females saw greater increases with weight in total rib area compared to cortical bone area, of up to 21% at the eighth rib level. Results from this study show the expected shapes of rib cross-sections across the adult rib cage and across a broad range of demographics. This detailed geometry can be used to produce accurate rib models representing widely varying populations.

Identifiants

pubmed: 38200705
doi: 10.1111/joa.13999
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society.

Références

Agnew, A.M., Murach, M.M., Dominguez, V.M., Sreedhar, A., Misicka, E., Harden, A. et al. (2018) Sources of variability in structural bending response of pediatric and adult human ribs in dynamic frontal impacts. Stapp Car Crash Journal, 62, 119-192.
Alswat, K.A. (2017) Gender disparities in osteoporosis. Journal of Clinical Medicine Research, 9, 382-387. Available from: https://doi.org/10.14740/jocmr2970w
Chen, H., Poulard, D., Forman, J., Crandall, J. & Panzer, M.B. (2018) Evaluation of geometrically personalized THUMS pedestrian model response against sedan-pedestrian PMHS impact test data. Traffic Injury Prevention, 19, 542-548. Available from: https://doi.org/10.1080/15389588.2018.1450979
Dogrul, B.N., Kiliccalan, I., Asci, E.S. & Peker, S.C. (2020) Blunt trauma related chest wall and pulmonary injuries: an overview. Chinese Journal of Traumatology, 23, 125-138. Available from: https://doi.org/10.1016/j.cjtee.2020.04.003
Folch-Fortuny, A., Arteaga, F. & Ferrer, A. (2016) Missing data imputation toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems, 154, 93-100. Available from: https://doi.org/10.1016/j.chemolab.2016.03.019
Forman, J., Poplin, G.S., Shaw, C.G., McMurry, T.L., Schmidt, K., Ash, J. et al. (2019) Automobile injury trends in the contemporary fleet: belted occupants in frontal collisions. Traffic Injury Prevention, 20, 607-612. Available from: https://doi.org/10.1080/15389588.2019.1630825
García-Martínez, D., Gil, O.G., Cambra-Moo, O., Canillas, M., Rodríguez, M.A., Bastir, M. et al. (2017) External and internal ontogenetic changes in the first rib. American Journal of Physical Anthropology, 164, 750-762. Available from: https://doi.org/10.1002/ajpa.23313
García-Martínez, D., López-Rey, J.M., García Gil, O., Cambra-Moo, O., Notario, B., Torres-Sánchez, I. et al. (2023) How accurate are medical CT and micro-CT techniques compared to classical histology when addressing the growth of the internal rib parameters? Anthropologischer Anzeiger, 80, 307-316. Available from: https://doi.org/10.1127/anthranz/2023/1617
Holcombe, S. & Huang, Y. (2023) Cross-sectional properties of rib geometry from an adult population. Frontiers in Bioengineering and Biotechnology, 11, 1158242. Available from: https://doi.org/10.3389/fbioe.2023.1158242
Holcombe, S.A., Agnew, A.M., Derstine, B. & Wang, S.C. (2020) Comparing FE human body model rib geometry to population data. Biomechanics and Modeling in Mechanobiology, 19, 2227-2239. Available from: https://doi.org/10.1007/s10237-020-01335-2
Holcombe, S.A. & Derstine, B.A. (2022) Rib cortical bone thickness variation in adults by age and sex. Journal of Anatomy, 241, 1344-1356. Available from: https://doi.org/10.1111/joa.13751
Holcombe, S.A., Kang, Y.-S.S., Wang, S.C. & Agnew, A.M. (2019) The accuracy of local rib bone geometry measurement using full body CT images. International Research Council on the Biomechanics of Injury, 19, 64.
Holcombe, S.A., Wang, S.C. & Grotberg, J.B. (2017) The effect of age and demographics on rib shape. Journal of Anatomy, 231, 229-247. Available from: https://doi.org/10.1111/joa.12632
Hwang, E., Hu, J. & Reed, M.P. (2020) Validating diverse human body models against side impact tests with post-mortem human subjects. Journal of Biomechanics, 98, 109444. Available from: https://doi.org/10.1016/j.jbiomech.2019.109444
Iraeus, J., Brolin, K. & Pipkorn, B. (2020) Generic finite element models of human ribs, developed and validated for stiffness and strain prediction-to be used in rib fracture risk evaluation for the human population in vehicle crashes. Journal of the Mechanical Behavior of Biomedical Materials, 106, 103742. Available from: https://doi.org/10.1016/j.jmbbm.2020.103742
Kemper, A.R., Stitzel, J.D., McNally, C., Gabler, H.C. & Duma, S.M. (2009) Biomechanical response of the human clavicle: the effects of loading direction on bending properties. Journal of Applied Biomechanics, 25, 165-174. Available from: https://doi.org/10.1123/jab.25.2.165
Larsson, K.-J., Iraeus, J., Holcombe, S. & Pipkorn, B. (2023) Influences of human thorax variability on population rib fracture risk prediction using human body models. Frontiers in Bioengineering and Biotechnology, 11, 1154272. Available from: https://doi.org/10.3389/fbioe.2023.1154272
Liebsch, C., Hübner, S., Palanca, M., Cristofolini, L. & Wilke, H.-J. (2021) Experimental study exploring the factors that promote rib fragility in the elderly. Scientific Reports, 11, 9307. Available from: https://doi.org/10.1038/s41598-021-88800-9
López-Rey, J.M., Cambra-Moo, O., González Martín, A., Candelas González, N., Sánchez-Andrés, O., Tawane, M. et al. (2022) Mineral content analysis in the rib cross-sections of Homo sapiens and Pan troglodytes and its implications for the study of Sts 14 costal remains. American Journal of Biological Anthropology, 177, 784-791. Available from: https://doi.org/10.1002/ajpa.24491
López-Rey, J.M., Cambra-Moo, O., González Martín, A., Candelas González, N., Sánchez-Andrés, O., Tawane, M. et al. (2023) Covariation between the shape and mineralized tissues of the rib cross section in Homo sapiens, Pan troglodytes and Sts 14. American Journal of Biological Anthropology, 183, 157-164. Available from: https://doi.org/10.1002/ajpa.24844
Mohr, M., Abrams, E., Engel, C., Long, W.B. & Bottlang, M. (2007) Geometry of human ribs pertinent to orthopedic chest-wall reconstruction. Journal of Biomechanics, 40, 1310-1317. Available from: https://doi.org/10.1016/j.jbiomech.2006.05.017
Murach, M.M., Kang, Y.-S., Goldman, S.D., Schafman, M.A., Schlecht, S.H., Moorhouse, K. et al. (2017) Rib geometry explains variation in dynamic structural response: potential implications for frontal impact fracture risk. Annals of Biomedical Engineering, 45, 1-15. Available from: https://doi.org/10.1007/s10439-017-1850-4
Peek, J., Ochen, Y., Saillant, N., Groenwold, R.H.H., Leenen, L.P.H., Uribe-Leitz, T. et al. (2020) Traumatic rib fractures: a marker of severe injury. A nationwide study using the National Trauma Data Bank. Trauma Surgery & Acute Care Open, 5, e000441. Available from: https://doi.org/10.1136/tsaco-2020-000441
Pipkorn, B., Iraeus, J., Lindkvist, M., Puthan, P. & Bunketorp, O. (2020) Occupant injuries in light passenger vehicles-a NASS study to enable priorities for development of injury prediction capabilities of human body models. Accident Analysis & Prevention, 138, 105443. Available from: https://doi.org/10.1016/j.aap.2020.105443
Rampersadh, C., Agnew, A.M., Malcolm, S., Gierczycka, D., Iraeus, J. & Cronin, D. (2022) Factors affecting the numerical response and fracture location of the GHBMC M50 rib in dynamic anterior-posterior loading. Journal of the Mechanical Behavior of Biomedical Materials, 136, 105527. Available from: https://doi.org/10.1016/j.jmbbm.2022.105527
Robinson, A., Von Kleeck, B.W. & Gayzik, F.S. (2023) Development and preliminary validation of computationally efficient and detailed 50th percentile female human body models. Accident Analysis & Prevention, 190, 107182. Available from: https://doi.org/10.1016/j.aap.2023.107182
Schoell, S.L., Weaver, A.A., Vavalle, N.A. & Stitzel, J.D. (2015) Age- and sex-specific thorax finite element model development and simulation. Traffic Injury Prevention, 16, S57-S65. Available from: https://doi.org/10.1080/15389588.2015.1005208
Seeman, E. (2001) During aging, men lose less bone than women because they gain more periosteal bone, not because they resorb less endosteal bone. Calcified Tissue International, 69, 205-208. Available from: https://doi.org/10.1007/s00223-001-1040-z
Shi, X., Cao, L., Reed, M.P., Rupp, J.D., Hoff, C.N. & Hu, J. (2014) A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index. Journal of Biomechanics, 47, 2277-2285. Available from: https://doi.org/10.1016/j.jbiomech.2014.04.045
Vavalle, N.A., Schoell, S.L., Weaver, A.A., Stitzel, J.D. & Gayzik, F.S. (2014) Application of radial basis function methods in the development of a 95th percentile male seated FEA model. Stapp Car Crash Journal, 58, 361-384.

Auteurs

Sven A Holcombe (SA)

Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan, USA.

Yuan Huang (Y)

Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan, USA.

Brian A Derstine (BA)

Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan, USA.

Classifications MeSH