Proximal femur (Ariza 2015)¶

Validation model information

Performed by: Alexander Schubert / Nico Erlinger
Reviewed by: Corina Klug
Current Responsible:

Added to VIVA+ Validation Catalog on: 2022-10-21

Last modified: 2023-03-13

Model version (this notebook run for): 0.3.2

Available openly under under Creative Commons Attribution 4.0 International License

Reference¶

A. Schubert, N. Erlinger, C. Leo, J. Iraeus, J. John, C. Klug (2021): "Development of a 50th Percentile Female Femur Model", IRCOBI conference proceedings, http://www.ircobi.org/wordpress/downloads/irc21/pdf-files/2138.pdf

Experiments by Ariza (2014) and Ariza et al. (2015)¶

Ariza, O.; Gilchrist, S.; Widmer, R. P.; Guy, P.; Ferguson, S. J.; Cripton, P. A.; Helgason, B., Comparison of explicit finite element and mechanical simulation of the proximal femur during dynamic drop-tower testing, Journal of biomechanics, Vol. 48, 2015.

Ariza, O. A novel approach to finite element analysis of hip fractures due to sideways falls Master Thesis University of British Columbia (Vancouver), 2014.

Summary¶

14 dynamic drop tower tests on the proximal femur were replicated by prescribing the vertical motion of the upper potting.

Information on the subjects/specimens¶

Specimen	Bone density	Donor age
H1167L	normal	50
H1365R	osteoporotic	71
H1366R	normal	73
H1368R	osteoporotic	70
H1369L	osteoporotic	69
H1372R	normal	79
H1373R	osteoporotic	76
H1374R	osteoporotic	78
H1375L	normal	83
H1376L	normal	79
H1377R	osteoporotic	80
H1380R	normal	71
H1381R	osteoporotic	92
H1382L	osteoporotic	96

Loading and Boundary Conditions¶

The displacement-time histories from the diagrams published by Ariza (2014) were digitised with WebPlotDigitizer v4.4 (https://automeris.io/WebPlotDigitizer).

Experimental responses¶

The force-time histories, published by Ariza (2014) were digitised with WebPlotDigitizer v4.4.

Other notes for simulation¶

Notes on implementation and iterations during the implementation of validation simulations.

In [2]:

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simulation_list = ["H1167L", "H1365R", "H1366R", "H1368R", "H1369L", "H1372R", "H1373R", 
                   "H1374R", "H1375L", "H1376L", "H1377R", "H1380R", "H1381R", "H1382L"]

name ='Name_of_person'
date = datetime.date.today().strftime('%Y-%m-%d')
dynasaur_output_file_name = 'Dynasaur_output.csv'
simulation_list = ["H1167L", "H1365R", "H1366R", "H1368R", "H1369L", "H1372R", "H1373R", 
                   "H1374R", "H1375L", "H1376L", "H1377R", "H1380R", "H1381R", "H1382L"]

name ='Name_of_person'
date = datetime.date.today().strftime('%Y-%m-%d')
dynasaur_output_file_name = 'Dynasaur_output.csv'

Plots¶

No description has been provided for this image

Injury Risk Curves¶

--------------------------------------------------------------------------------------
MPS
--------------------------------------------------------------------------------------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 11/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha       0.0251501      0.00303181 0.0198577 0.0318531
     Beta         2.62164        0.664574   1.59514   4.30873 

Goodness of fit    Value
 Log-likelihood  35.8516
           AICc -66.2032
            BIC -66.9074
             AD  1.42996

------Lower----------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 9998/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha       0.0188615     8.70578e-05 0.0186917 0.0190329
     Beta          2.2698       0.0185938   2.23365   2.30654 

Goodness of fit    Value
 Log-likelihood  34433.1
           AICc -68862.2
            BIC -68847.8
             AD  45.4323 

------Upper----------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 9998/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha        0.033667      0.00011894 0.0334347 0.0339009
     Beta         3.00049       0.0215959   2.95846   3.04312 

Goodness of fit    Value
 Log-likelihood  31481.4
           AICc -62958.7
            BIC -62944.3
             AD  101.785

--------------------------------------------------------------------------------------
PS99
--------------------------------------------------------------------------------------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 11/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha        0.015083      0.00203068 0.0115848 0.0196376
     Beta         2.35987        0.575181    1.4636   3.80501 

Goodness of fit    Value
 Log-likelihood  40.8492
           AICc -76.1984
            BIC -76.9026
             AD  1.33966

------Lower----------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 9998/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha       0.0109873     5.63325e-05 0.0108774 0.0110983
     Beta         2.04409       0.0166948   2.01163   2.07708 

Goodness of fit    Value
 Log-likelihood  39103.2
           AICc -78202.5
            BIC -78188.1
             AD  39.3013 

------Upper----------
Results from Fit_Weibull_2P (95% CI):
Analysis method: Maximum Likelihood Estimation (MLE)
Optimizer: TNC
Failures / Right censored: 9998/0 (0% right censored) 

Parameter  Point Estimate  Standard Error  Lower CI  Upper CI
    Alpha       0.0207839     8.12327e-05 0.0206253 0.0209437
     Beta         2.71109       0.0196152   2.67292   2.74981 

Goodness of fit    Value
 Log-likelihood  35444.9
           AICc -70885.8
            BIC -70871.4
             AD  87.3722