The purpose of this study is to assess if a zoledronic acid injection can alter the trajectory of joint degeneration following an acute anterior cruciate ligament (ACL) injury.
After being informed about the study and potential risks and all participants giving written informed consent, this project will establish a cohort of young men and women who within six weeks have sustained an acute rupture of the ACL. The cohort is randomized into a control and treatment group, where the treatment group receives a zoledronic acid injection at baseline. The cohort will be followed radiographically with high resolution peripheral quantitative computed tomography (HR-pQCT), dual-energy computed tomography (DECT), digital radiography (X-Ray), bi-planar X-ray (EOS) and magnetic resonance imaging (MRI) for eighteen months to monitor the progression of joint changes and the effects of zoledronic acid.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Enrollment
4
5 mg / 100 mL intravenous infusion
100 mL intravenous infusion
University of Calgary
Calgary, Alberta, Canada
Bone microarchitecture changes at 6 months as assessed by high resolution peripheral quantitative computed tomography (HR-pQCT)
To determine morphological parameters from HR-pQCT scans, the trabecular portion must be isolated from the cortical shell of the bone in order to analyse the components separately. This is accomplished with an already developed auto-segmentation algorithm. In addition, the raw HR-pQCT images must be converted to binary images, wherein each voxel (3D pixel) is either labelled 'bone' or 'not bone.' This segmentation is performed by an algorithm which applies either a Gaussian or Laplace-Hamming filter in addition to a threshold to the grey-scale images. The binary images can then be analysed and morphological parameters can be determined. The changes in bone microarchitecture will be assessed at 6 months in comparison to baseline.
Time frame: Baseline, 6 months
Bone microarchitecture changes at 18 months as assessed by high resolution peripheral quantitative computed tomography (HR-pQCT)
To determine morphological parameters from HR-pQCT scans, the trabecular portion must be isolated from the cortical shell of the bone in order to analyse the components separately. This is accomplished with an already developed auto-segmentation algorithm. In addition, the raw HR-pQCT images must be converted to binary images, wherein each voxel (3D pixel) is either labelled 'bone' or 'not bone.' This segmentation is performed by an algorithm which applies either a Gaussian or Laplace-Hamming filter in addition to a threshold to the grey-scale images. The binary images can then be analysed and morphological parameters can be determined. The changes in bone microarchitecture will be assessed at 18 months in comparison to baseline.
Time frame: Baseline, 18 months
Bone marrow lesions (BML) and soft tissue injury changes at 2 months as assessed by Magnetic Resonance Imaging (MRI)
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using a threshold-based approach BMLs will be identified, and their location and volume will be recorded in cubic millimetres (mm\^3). This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in the location and volume of the BMLs will be assessed at 2 months comparison to baseline.
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
Time frame: Baseline, 2 months
Bone marrow lesions (BML) and soft tissue injury changes at 2 months as assessed by Magnetic Resonance Imaging (MRI)
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular thickness which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular thickness will be assessed at 2 months comparison to baseline.
Time frame: Baseline, 2 months
Bone marrow lesions (BML) and soft tissue injury changes at 2 months as assessed by Magnetic Resonance Imaging (MRI)
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular separation which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular separation will be assessed at 2 months comparison to baseline.
Time frame: Baseline, 2 months
Bone marrow lesions (BML) and soft tissue injury changes at 2 months as assessed by Magnetic Resonance Imaging (MRI)
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of bone mineral density which will be recorded in milligrams of hydroxyapatite per cubic centimeter (mg HA/cm\^3) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in bone mineral density will be assessed at 2 months comparison to baseline.
Time frame: Baseline, 2 months
Bone marrow lesions (BML) and soft tissue injury changes at 6 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using a threshold-based approach BMLs will be identified, and their location and volume will be recorded in cubic millimetres (mm\^3). This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in the location and volume of the BMLs will be assessed at 6 months comparison to baseline.
Time frame: Baseline, 6 months
Bone marrow lesions (BML) and soft tissue injury changes at 6 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular thickness which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular thickness will be assessed at 6 months comparison to baseline.
Time frame: Baseline, 6 months
Bone marrow lesions (BML) and soft tissue injury changes at 6 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular separation which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular separation will be assessed at 6 months comparison to baseline.
Time frame: Baseline, 6 months
Bone marrow lesions (BML) and soft tissue injury changes at 6 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of bone mineral density which will be recorded in milligrams of hydroxyapatite per cubic centimeter (mg HA/cm\^3) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in bone mineral density will be assessed at 6 months comparison to baseline.
Time frame: Baseline, 6 months
Bone marrow lesions (BML) and soft tissue injury changes at 18 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using a threshold-based approach BMLs will be identified, and their location and volume will be recorded in cubic millimetres (mm\^3). This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in the location and volume of the BMLs will be assessed at 18 months comparison to baseline.
Time frame: Baseline, 18 months
Bone marrow lesions (BML) and soft tissue injury changes at 18 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular thickness which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular thickness will be assessed at 18 months comparison to baseline.
Time frame: Baseline, 18 months
Bone marrow lesions (BML) and soft tissue injury changes at 18 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of trabecular separation which will be recorded in millimeters (mm) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in trabecular separation will be assessed at 18 months comparison to baseline.
Time frame: Baseline, 18 months
Bone marrow lesions (BML) and soft tissue injury changes at 18 months as assessed by MRI
MRI data will be segmented to identify the bone surface in a similar fashion as described for HR-pQCT data. Using rigid body registration, the MRI data will be transformed to the HR-pQCT data, which allows for the analysis of bone mineral density which will be recorded in milligrams of hydroxyapatite per cubic centimeter (mg HA/cm\^3) exclusively within the volume of BMLs. This analysis will be performed using custom algorithms in Python and the visualization toolkit. The changes in bone mineral density will be assessed at 18 months comparison to baseline.
Time frame: Baseline, 18 months
Knee alignment as assessed by bi-planar x-ray
Joint alignment by bi-planar x-ray (EOS) In a standing position, the baseline study visit will capture the alignment of the tibia and femur bones bilaterally so that alignment of the knee joint can be assessed. This is a standard clinical imaging device, and the software for measurement of knee alignment is built into the system.
Time frame: Baseline
Patient reported outcomes using ACL Quality of Life Questionnaire - Baseline
Patient reported outcomes at baseline will be assessed using \- ACL Quality of Life Questionnaire Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: Baseline
Patient reported outcomes using ACL Quality of Life Questionnaire - 2 Months
Patient reported outcomes at baseline will be assessed using \- ACL Quality of Life Questionnaire Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 2 Months
Patient reported outcomes using ACL Quality of Life Questionnaire - 6 Months
Patient reported outcomes at baseline will be assessed using \- ACL Quality of Life Questionnaire Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 6 Months
Patient reported outcomes using ACL Quality of Life Questionnaire - 18 Months
Patient reported outcomes at baseline will be assessed using \- ACL Quality of Life Questionnaire Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 18 Months
Patient reported outcomes using Knee injury and Osteoarthritis Outcome Score (KOOS) - Questionnaire - Baseline
Patient reported outcomes will be assessed using \- Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire Minimum Value: 1 (best outcome); Maximum Value: 5 (worse outcome)
Time frame: Baseline
Patient reported outcomes using Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire - 2 Months
Patient reported outcomes will be assessed using \- Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire Minimum Value: 1 (best outcome); Maximum Value: 5 (worse outcome)
Time frame: 2 months
Patient reported outcomes using Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire - 6 Months
Patient reported outcomes will be assessed using \- Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire Minimum Value: 1 (best outcome); Maximum Value: 5 (worse outcome)
Time frame: 6 months
Patient reported outcomes using Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire - 18 Months
Patient reported outcomes will be assessed using \- Knee injury and Osteoarthritis Outcome Score (KOOS) Questionnaire Minimum Value: 1 (best outcome); Maximum Value: 5 (worse outcome)
Time frame: 18 months
Patient reported outcomes using 36-Item Short Form Survey (SF-36) Questionnaire - Baseline
Patient reported outcomes will be assessed using \- 36-Item Short Form Survey (SF-36) Questionnaire Questions 1, 2, 20, 22, 34, 36 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 3-12 - Minimum Value: 1 (worst outcome); Maximum Value: 3 (best outcome) Question 13-19 - Minimum Value: 1 (worst outcome); Maximum Value: 2 (best outcome) Questions 21, 23, 26, 27, 30 - Minimum Value: 1 (best outcome); Maximum Value: 6 (worst outcome) Questions 24, 25, 28, 29, 31 - Minimum Value: 1 (worst outcome); Maximum Value: 6 (best outcome) Questions 32, 33, 35 - Minimum Value: 1 (worst outcome); Maximum Value: 5 (best outcome)
Time frame: Baseline
Patient reported outcomes using 36-Item Short Form Survey (SF-36) Questionnaire - 2 months
Patient reported outcomes will be assessed using \- 36-Item Short Form Survey (SF-36) Questionnaire Questions 1, 2, 20, 22, 34, 36 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 3-12 - Minimum Value: 1 (worst outcome); Maximum Value: 3 (best outcome) Question 13-19 - Minimum Value: 1 (worst outcome); Maximum Value: 2 (best outcome) Questions 21, 23, 26, 27, 30 - Minimum Value: 1 (best outcome); Maximum Value: 6 (worst outcome) Questions 24, 25, 28, 29, 31 - Minimum Value: 1 (worst outcome); Maximum Value: 6 (best outcome) Questions 32, 33, 35 - Minimum Value: 1 (worst outcome); Maximum Value: 5 (best outcome)
Time frame: 2 months
Patient reported outcomes using 36-Item Short Form Survey (SF-36) Questionnaire - 6 months
Patient reported outcomes will be assessed using \- 36-Item Short Form Survey (SF-36) Questionnaire Questions 1, 2, 20, 22, 34, 36 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 3-12 - Minimum Value: 1 (worst outcome); Maximum Value: 3 (best outcome) Question 13-19 - Minimum Value: 1 (worst outcome); Maximum Value: 2 (best outcome) Questions 21, 23, 26, 27, 30 - Minimum Value: 1 (best outcome); Maximum Value: 6 (worst outcome) Questions 24, 25, 28, 29, 31 - Minimum Value: 1 (worst outcome); Maximum Value: 6 (best outcome) Questions 32, 33, 35 - Minimum Value: 1 (worst outcome); Maximum Value: 5 (best outcome)
Time frame: 6 months
Patient reported outcomes using 36-Item Short Form Survey (SF-36) Questionnaire - 18 months
Patient reported outcomes will be assessed using \- 36-Item Short Form Survey (SF-36) Questionnaire Questions 1, 2, 20, 22, 34, 36 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 3-12 - Minimum Value: 1 (worst outcome); Maximum Value: 3 (best outcome) Question 13-19 - Minimum Value: 1 (worst outcome); Maximum Value: 2 (best outcome) Questions 21, 23, 26, 27, 30 - Minimum Value: 1 (best outcome); Maximum Value: 6 (worst outcome) Questions 24, 25, 28, 29, 31 - Minimum Value: 1 (worst outcome); Maximum Value: 6 (best outcome) Questions 32, 33, 35 - Minimum Value: 1 (worst outcome); Maximum Value: 5 (best outcome)
Time frame: 18 months
Patient reported outcomes using EQ-5D-5L Questionnaire - Baseline
Patient reported outcomes will be assessed using \- EQ-5D-5L Questionnaire Questions 1-5 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 6 - Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: Baseline
Patient reported outcomes using EQ-5D-5L Questionnaire - 2 months
Patient reported outcomes will be assessed using \- EQ-5D-5L Questionnaire Questions 1-5 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 6 - Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 2 months
Patient reported outcomes using EQ-5D-5L Questionnaire - 6 months
Patient reported outcomes will be assessed using \- EQ-5D-5L Questionnaire Questions 1-5 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 6 - Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 6 months
Patient reported outcomes using EQ-5D-5L Questionnaire - 18 months
Patient reported outcomes will be assessed using \- EQ-5D-5L Questionnaire Questions 1-5 - Minimum Value: 1 (best outcome); Maximum Value: 5 (worst outcome) Question 6 - Minimum Value: 0 (worst outcome); Maximum Value: 100 (best outcome)
Time frame: 18 months
Patient reported outcomes Health History Questionnaire (HHQ) - Baseline
Patient reported outcomes will be assessed using \- Health History Questionnaire (HHQ) (No scale)
Time frame: Baseline
Patient reported outcomes Health History Questionnaire (HHQ) - 2 months
Patient reported outcomes will be assessed using \- Health History Questionnaire (HHQ) (No scale)
Time frame: 2 months
Patient reported outcomes Health History Questionnaire (HHQ) - 6 months
Patient reported outcomes will be assessed using \- Health History Questionnaire (HHQ) (No scale)
Time frame: 6 months
Patient reported outcomes Health History Questionnaire (HHQ) - 18 months
Patient reported outcomes will be assessed using \- Health History Questionnaire (HHQ) (No scale)
Time frame: 18 months