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Early Prediction of Post-ACLR Arthrofibrosis Using Integrated Proteomic, Spectroscopic, and Machine-Learning Biomarker Profiling

Specific Aims: Arthrofibrosis (AF) is an aberrant structural change in the joint’s fibrous connective tissue that is a sequela to inflammation due to trauma, surgery, or infection. Approximately ~5-15% of young adults develop AF within 12 weeks of anterior cruciate ligament reconstructive surgery (ACLR), resulting in knee pain, reduced range of motion, potential permanent impairment, and more than 2x the risk for developing posttraumatic osteoarthritis, even after surgical restoration of motion.^17,20,39,47 Preserving long-term joint mobility is critical for patient wellness,^7,24,37 yet there is a clinical gap in research for identifying patients most at risk of developing AF early post-ACLR, limiting opportunities for timely intervention.^3,7,30,41 Clinical data on risk factors for AF development post-ACLR are relatively scarce. However, decreased knee range of motion has been correlated with an elevated T cell-mediated immune response in the synovium (e.g., elevated T cells, recruiters, and cadherins).³⁵ These molecular changes at the joint level may be potential targets for not only elucidating prognostic biomarkers but also for developing minimally invasive therapeutic interventions. Yet, most diagnostic modalities are based on a small number of inflammatory factors, whereas we propose to utilize liquid chromatography mass spectrometry (LC-MS) to identify the maximum number of proteins that correlate with early AF-associated inflammation. We will also combine this quantitative analytical approach with Fourier-transform infrared (FTIR) spectroscopy, a qualitative disease pattern recognition tool that has been successfully used to predict fracture-related infections^5,15,27 and osteoarthritis^25,26,28 This proposed quantitative/qualitative approach towards addressing the clinical gap in early AF diagnosis can be a first step in developing a low-cost bedside clinical test. Our overall hypothesis is that computationally-derived profiles of localized and peripheral biomarkers can accurately and consistently classify patients most at risk for developing post-ACLR AF.

Aim 1: Identify synovial fluid and/or peripheral blood biomarkers that predict AF risk. Knee synovial fluid, peripheral blood, and patient clinical measures of enrolled young adult ACLR patients are currently being banked (~50 patients by 7/2026). Synovial fluid from both knees and peripheral blood are drawn prior to surgery. At routine postoperative follow-ups (2, 6, 12 weeks), clinical measures and subsequent blood samples are obtained. With Showalter funding we aim to enroll an additional 50-60 patients and batch process case-control matched (1:2) samples for LC-MS and FTIR analyses to independently identify specific proteins and mid-infrared (MIR) spectral patterns, respectively, at baseline and across postoperative intervals. Multivariate data analysis will evaluate the ability of both methods to independently discriminate between patients with decreased knee ROM and capsular lesions, and those that do not at each time point.

Aim 2: Employ a machine-learning/chemometric approach to predict AF development post-ACLR. Predictive models based on variables obtained from Aim 1 will be developed that integrate quantitative proteomic data and qualitative MIR spectral patterns to identify patients most at risk for AF post-ACLR. Feature selection will refine key variables, and machine-learning approaches will be used to construct and evaluate models across postoperative timepoints.

This prospective pilot study is intended to demonstrate feasibility in predicting post-ACLR AF risk from readily accessible biofluids. Developed models will serve as the foundation for future external funding (NIH R01) to increase patient cohorts for multi-center studies, and ultimately aid in the development of rapid, clinically deployable bedside screening tools for early risk stratification and intervention. Moreover, we aim to use banked patient samples to screen potential therapeutics with additional funding. Success would represent a paradigm shift in postoperative ACLR care and heighten the search for effective minimally invasive treatments personalized to the patient‘s condition.

For additional details, eligibility criteria, or questions regarding participation in this study, please contact Dr. Saltzman.

Dr. Saltzman is part of the STAR trial.

This Integrated Clinical Trial will investigate the effects of timing of surgery and timing of post-op rehabilitation for treatment of multiple ligament knee injuries.

To know more click here.

What Postoperative Pain Relief Medications Can You Use Apart from Opioids?

The aim of this study is to find alternative postoperative pain regimens that reduce the use of opioids by patients. So, if you have been recommended shoulder arthroplasty surgery or an arthroscopic knee surgery, you may enroll into this randomized controlled trial.

To know more, you can discuss with Dr. Bryan Saltzman.

You can also read his research on the use of opioids in this article “Elective Shoulder Surgery in the Opioid Naïve: Rates of and Risk Factors for Long-term Postoperative Opioid Use.”

Is Formal Physical Therapy Beneficial After Reverse Shoulder Arthroplasty Surgery?

Dr. Bryan Saltzman may enroll his patients, those who are interested, into a randomized controlled trial for which he is one of the principal investigators. The randomized controlled trial is conducted with the purpose to determine the benefits associated with formal physical therapy after a reverse shoulder arthroplasty surgery.

To know more about the Randomized Controlled Trial, click here.

You can also read about the Home Physical Therapy Guide created by Dr. Bryan Saltzman and his team to understand the importance of physical therapy after your shoulder surgery.

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