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Recruitment stopped due to funding shortage. The scope of the original study increased beyond what had been budgeted in the grant that funded it, and follow-up funding attempts were not successful.
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Zoledronic Acid Injection | Experimental | Participants will receive 1 dose of 5 mg/100 mL intravenous zoledronic acid |
|
| Placebo | Placebo Comparator | Participants will receive 1 dose 100 ml Saline. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Zoledronic Acid Injection | Drug | 5 mg / 100 mL intravenous infusion |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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. | Baseline, 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
Zoledronic acid is contraindicated for:
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| Name | Affiliation | Role |
|---|---|---|
| Steven Boyd, PhD | University of Calgary | Principal Investigator |
| Gregory Kline, MD | University of Calgary | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Calgary | Calgary | Alberta | T2N 1N4 | Canada |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25182676 | Background | Dare D, Rodeo S. Mechanisms of post-traumatic osteoarthritis after ACL injury. Curr Rheumatol Rep. 2014 Oct;16(10):448. doi: 10.1007/s11926-014-0448-1. | |
| 17761605 | Background | Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. Am J Sports Med. 2007 Oct;35(10):1756-69. doi: 10.1177/0363546507307396. Epub 2007 Aug 29. |
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The cohort is randomized into a control and treatment group, where the treatment group receives a zoledronic acid injection within 6 weeks of ACL injury.
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| Placebo | Drug | 100 mL intravenous infusion |
|
|
| 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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. | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 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) | 18 months |
| Patient reported outcomes Health History Questionnaire (HHQ) - Baseline | Patient reported outcomes will be assessed using - Health History Questionnaire (HHQ) (No scale) | Baseline |
| Patient reported outcomes Health History Questionnaire (HHQ) - 2 months | Patient reported outcomes will be assessed using - Health History Questionnaire (HHQ) (No scale) | 2 months |
| Patient reported outcomes Health History Questionnaire (HHQ) - 6 months | Patient reported outcomes will be assessed using - Health History Questionnaire (HHQ) (No scale) | 6 months |
| Patient reported outcomes Health History Questionnaire (HHQ) - 18 months | Patient reported outcomes will be assessed using - Health History Questionnaire (HHQ) (No scale) | 18 months |
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| 29441423 | Background | Mattap SM, Aitken D, Wills K, Laslett L, Ding C, Pelletier JP, Martel-Pelletier J, Graves SE, Lorimer M, Cicuttini F, Jones G. How Do MRI-Detected Subchondral Bone Marrow Lesions (BMLs) on Two Different MRI Sequences Correlate with Clinically Important Outcomes? Calcif Tissue Int. 2018 Aug;103(2):131-143. doi: 10.1007/s00223-018-0402-8. Epub 2018 Feb 13. |
| 19059024 | Background | Guermazi A, Eckstein F, Hellio Le Graverand-Gastineau MP, Conaghan PG, Burstein D, Keen H, Roemer FW. Osteoarthritis: current role of imaging. Med Clin North Am. 2009 Jan;93(1):101-26, xi. doi: 10.1016/j.mcna.2008.08.003. |
| 28039095 | Background | Kroker A, Zhu Y, Manske SL, Barber R, Mohtadi N, Boyd SK. Quantitative in vivo assessment of bone microarchitecture in the human knee using HR-pQCT. Bone. 2017 Apr;97:43-48. doi: 10.1016/j.bone.2016.12.015. Epub 2016 Dec 27. |
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| 9617395 | Background | Mohtadi N. Development and validation of the quality of life outcome measure (questionnaire) for chronic anterior cruciate ligament deficiency. Am J Sports Med. 1998 May-Jun;26(3):350-9. doi: 10.1177/03635465980260030201. |
| 9699158 | Background | Roos EM, Roos HP, Lohmander LS, Ekdahl C, Beynnon BD. Knee Injury and Osteoarthritis Outcome Score (KOOS)--development of a self-administered outcome measure. J Orthop Sports Phys Ther. 1998 Aug;28(2):88-96. doi: 10.2519/jospt.1998.28.2.88. |
| 17693147 | Background | Buie HR, Campbell GM, Klinck RJ, MacNeil JA, Boyd SK. Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. Bone. 2007 Oct;41(4):505-15. doi: 10.1016/j.bone.2007.07.007. Epub 2007 Jul 18. |
| 32430614 | Background | Kemp TD, de Bakker CMJ, Gabel L, Hanley DA, Billington EO, Burt LA, Boyd SK. Longitudinal bone microarchitectural changes are best detected using image registration. Osteoporos Int. 2020 Oct;31(10):1995-2005. doi: 10.1007/s00198-020-05449-2. Epub 2020 May 19. |
| 24614646 | Background | Ellouz R, Chapurlat R, van Rietbergen B, Christen P, Pialat JB, Boutroy S. Challenges in longitudinal measurements with HR-pQCT: evaluation of a 3D registration method to improve bone microarchitecture and strength measurement reproducibility. Bone. 2014 Jun;63:147-57. doi: 10.1016/j.bone.2014.03.001. Epub 2014 Mar 12. |
| 30997546 | Background | Arias-Moreno AJ, Hosseini HS, Bevers M, Ito K, Zysset P, van Rietbergen B. Validation of distal radius failure load predictions by homogenized- and micro-finite element analyses based on second-generation high-resolution peripheral quantitative CT images. Osteoporos Int. 2019 Jul;30(7):1433-1443. doi: 10.1007/s00198-019-04935-6. Epub 2019 Apr 17. |
| ID | Term |
|---|---|
| D000070598 | Anterior Cruciate Ligament Injuries |
| ID | Term |
|---|---|
| D007718 | Knee Injuries |
| D007869 | Leg Injuries |
| D014947 | Wounds and Injuries |
Not provided
Not provided
| ID | Term |
|---|---|
| D000077211 | Zoledronic Acid |
| D012965 | Sodium Chloride |
| ID | Term |
|---|---|
| D004164 | Diphosphonates |
| D063065 | Organophosphonates |
| D009943 | Organophosphorus Compounds |
| D009930 | Organic Chemicals |
| D007093 | Imidazoles |
| D001393 | Azoles |
| D006573 | Heterocyclic Compounds, 1-Ring |
| D006571 | Heterocyclic Compounds |
| D002712 | Chlorides |
| D006851 | Hydrochloric Acid |
| D017606 | Chlorine Compounds |
| D007287 | Inorganic Chemicals |
| D017670 | Sodium Compounds |
Not provided
Not provided