Campus Bio-Medico University Polyclinic Foundation
Humble Giuseppe Longo
Goal: The goal of this interventional study is to understand how multimodal preoperative data can predict outcomes after Total Knee Arthroplasty (TKA) and improve personalized medicine practices. Participant Population: The study will enroll 197 patients suffering from symptomatic, end-stage knee osteoarthritis, who are above 18 years old and have functionally intact ligaments. Main Questions: * Can multimodal preoperative data, genetic predisposition, and psycho-behavioral characteristics predict outcomes after TKA? * Can AI models effectively use this data to customize prostheses and surgical interventions, and predict patient outcomes? Comparison Group Information (If applicable): Not specified in the provided details. Participant Tasks: * Undergo TKA as per the normal clinical routine. * Participate in pre- and post-surgical follow-ups including: * Clinical-functional assessments. * Administration of clinical scores. * Collection of biological samples. * Biomechanical analysis using a stereophotogrammetric system. * Provide data for the comprehensive multimodal indexed database.
Knee Osteoarthritis
Total Knee Arthroplasty
Multifaceted diagnostic assessments
Follow-ups
Genetic screening
NA
Osteoarthritis is one of the most common causes of knee disorders, leading to pain, reduced mobility, and a decline in quality of life. Total knee arthroplasty (TKA) is one of the most established treatments for end-stage osteoarthritis. Despite advancements in surgical techniques, patient dissatisfaction remains high. After surgery, patients often experience swelling, pain, and difficulty with daily activities. Revision surgery is a major challenge, with aseptic loosening occurring in 15-20% of cases. Given the high disability rates and healthcare costs associated with TKA, optimizing patient care is crucial. Artificial intelligence (AI) offers the potential to identify new care profiles. For the first time, AI can integrate multimodal datasets. This approach could lead to personalized treatment for knee osteoarthritis patients, in line with precision medicine principles. This study takes a multidisciplinary approach to better understand the causes of failure and dissatisfaction following TKA. The primary aim of this study is is to create a multimodal database. This database will include structural, genetic, biomechanical, clinical, psychological, biological, stress-related, inflammatory, and demographic data. Using AI, the study aims to build predictive models for post-TKA outcomes. Insights from this research could improve patient management and lead to new therapeutic approaches. Patients suffering from knee osteoarthritis at Fondazione Policlinico Universitario Campus Bio-Medico will be enrolled in this study if they meet the inclusion/exclusion criteria described above. There are no risks for the patients recruited in the study. The total duration of the study is 5 years. The enrolment of patients will start on the 01/10/2024 and will last 12 months for each patient. The Italian Ministry of Health and the Fondazione Policlinico Universitario Campus Bio-Medico supported this study. The PI and also the main contact of this study is professor Umile Giuseppe Longo.
| Study Type : | INTERVENTIONAL |
| Estimated Enrollment : | 197 participants |
| Masking : | NONE |
| Primary Purpose : | TREATMENT |
| Official Title : | An Artificial Intelligence-based Approach in Total Knee Arthroplasty: From Inflammatory Responses to Personalized Medicine |
| Actual Study Start Date : | 2024-10-14 |
| Estimated Primary Completion Date : | 2025-12 |
| Estimated Study Completion Date : | 2029-12 |
Information not available for Arms and Intervention/treatment
| Ages Eligible for Study: | 18 Years |
| Sexes Eligible for Study: | ALL |
| Accepts Healthy Volunteers: |
Want to participate in this study, select a site at your convenience, send yourself email to get contact details and prescreening steps.
RECRUITING
Campus Bio-Medico University Polyclinic Foundation
Rome, Italy, Italy, 00128