Lower cell number, lateral defect location and milder grade are associated with improved autologous chondrocyte implantation outcome.

Purpose: To investigate patient demographic, injury and surgery/treatment-associated factors that can influence the patient-reported outcome (Lysholm score), following autologous chondrocyte implantation (ACI) in a large, 'real-world', nonuniform, prospective data examined retrospectively.

Methods: Knee patients treated at the Robert Jones and Agnes Hunt Orthopaedic Hospital, UK, using ACI between 1996 and 2020 were eligible. All longitudinal postoperative Lysholm scores collected between 1 and 23 years after ACI treatment and before any second major procedure (e.

Proteomic Analyses of Autologous Chondrocyte Implantation Plasma Highlight Cartilage Acidic Protein 1 as a Candidate for Preclinical Screening.

Background: Stratification is required to ensure that only patients likely to benefit receive autologous chondrocyte implantation (ACI). It would be advantageous to identify biomarkers to predict ACI outcome that are measurable in blood, avoiding the need for an invasive synovial fluid harvest.

Purpose: To assess if proteomic analyses can be used to identify novel candidate blood biomarkers in individuals who respond well or poorly to ACI.

Relationship Between Activity Level and Knee Function Is Influenced by Negative Affect in Patients Undergoing Cell Therapy for Articular Cartilage Defects in the Knee.

Background: Increased activity level is generally reported to be positively related to improved knee function after knee surgery. However, little research has been conducted into this relationship on an individual patient basis, or the influence of demographic and psychosocial factors such as patient affect-the subjective experience of emotion.

Hypothesis: The relationship between postoperative activity level and knee function will vary between patients and will be influenced by the patients' affect and demographic characteristics.

Identification of Candidate Synovial Fluid Biomarkers for the Prediction of Patient Outcome After Microfracture or Osteotomy.

Background: Biomarkers are needed to predict clinical outcomes for microfracture and osteotomy surgeries to ensure patients can be better stratified to receive the most appropriate treatment.

Purpose: To identify novel biomarker candidates and to investigate the potential of a panel of protein biomarkers for the prediction of clinical outcome after treatment with microfracture or osteotomy.

Study Design: Descriptive laboratory study.

Two independent proteomic approaches provide a comprehensive analysis of the synovial fluid proteome response to Autologous Chondrocyte Implantation.

Background: Autologous chondrocyte implantation (ACI) has a failure rate of approximately 20%, but it is yet to be fully understood why. Biomarkers are needed that can pre-operatively predict in which patients it is likely to fail, so that alternative or individualised therapies can be offered. We previously used label-free quantitation (LF) with a dynamic range compression proteomic approach to assess the synovial fluid (SF) of ACI responders and non-responders.

Autologous chondrocyte implantation-derived synovial fluids display distinct responder and non-responder proteomic profiles.

Background: Autologous chondrocyte implantation (ACI) can be used in the treatment of focal cartilage injuries to prevent the onset of osteoarthritis (OA). However, we are yet to understand fully why some individuals do not respond well to this intervention. Identification of a reliable and accurate biomarker panel that can predict which patients are likely to respond well to ACI is needed in order to assign the patient to the most appropriate therapy.

The Absence of Detectable ADAMTS-4 (Aggrecanase-1) Activity in Synovial Fluid Is a Predictive Indicator of Autologous Chondrocyte Implantation Success.

Background: Autologous chondrocyte implantation (ACI) is used worldwide in the treatment of cartilage defects in the knee. Several demographic and injury-specific risk factors have been identified that can affect the success of ACI treatment. However, the discovery of predictive biomarkers in this field has thus far been overlooked.