An Artificial Intelligence-based Approach in Total Knee Arthroplasty: From Inflammatory Responses to Personalized Medicine

Обзор

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.

Подробное описание

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.

Вмешательства

• Процедура Total Knee Arthroplasty
  Total Knee Arthroplasty is performed using conventional surgical techniques.
• Диагностический тест Multifaceted diagnostic assessments
  Multifaceted diagnostic assessments involving genetic analysis, biomechanical data collection, radiographic imaging, and psychological evaluations.
• Поведенческое Follow-ups
  Postoperative follow-up includes behavioral interventions, such as lifestyle counseling and rehabilitation programs, tailored based on AI-driven insights into individual patient recovery profiles.
• Генная терапия Genetic screening
  Genetic screening and analysis, including whole exome sequencing, are conducted to identify genetic markers that might influence the outcomes of knee arthroplasty. This data is utilized within AI models to predict patient-specific surgical outcomes and recovery processes.

Первичные конечные точки

• Change From Baseline in Knee Society Score (KSS) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change From Baseline in Oxford Knee Score (OKS) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change From Baseline in Knee Injury and Osteoarthritis Outcome Score (KOOS) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change From Baseline in Forgotten Joint Score Short Form (FJS-12) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]

Вторичные конечные точки (12)

• Change From Baseline in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in knee range of motion (ROM) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in ground reaction forces at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in center of pressure (CoP) at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in walking speed at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in cadence at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in Step and Stride Length at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in osteoarthritis (OA) severity based on the Kellgren-Lawrence score at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in joint alignment at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in lateral distal femoral angle at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]
• Change from baseline in medial proximal tibial angle at 12 months [Срок оценки: Baseline (before surgery) and 12 months postoperatively]
• Change from baseline in joint line convergence angle at 12 months [Срок оценки: Before surgery (Baseline) and at 12 months postoperatively]

Критерии участия

Критерии включения

• Symptomatic, end-stage knee osteoarthritis
• Ligaments functionally intact
• Age: older than18 years old

Критерии исключения

• Neurological or other conditions affecting patients ability to join walking trials
• Inflammatory or infectious arthritis
• Previous articular fracture or knee surgery (excluding knee arthroscopy and meniscal surgery)
• Active tumors or pregnancy.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Лечение

Центры проведения

Италия · 1 центр

• Fondazione Policlinico Universitario Campus Bio-Medico — Rome

Публикации

• Spallone G, Mancini L, Carnevale A, Campi S, Schena E, D'Hooghe P, Hirschmann MT, Papalia R, Longo UG. Joint modeling and marker set selection significantly influence functional biomechanics in end-stage knee osteoarthritis: evidence from the sit-to-stand task. Front Bioeng Biotechnol. 2025 Oct 13;13:1677244. doi: 10.3389/fbioe.2025.1677244. eCollection 2025. PMID 41158195

Идентификаторы

NCT: NCT06634654 · 179.24 CET2 cbm · PNRR-MCNT2-2023-12378237

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗