Analgesic eluting bone cement: A novel approach for targeted pain management in total knee arthroplasty - An in-vitro study.

BACKGROUND: The average rate of patient dissatisfaction following total knee arthroplasty (TKA) is 10%. Multi-modal analgesia is the present standard of pain management after TKA. Studies show that with multi-modal analgesia, approximately 60% of patients experience severe knee pain following surgery, while around 30% experience moderate pain. To date, there is no literature available on targeted pain management using bone cement. OBJECTIVES: To investigate the feasibility of incorporating anti-inflammatory medications and identify the analgesic with the best release pharmacokinetics from bone cement for application in pain management. METHODS: In an in-vitro study, 100 mg of five drugs (aceclofenac, diclofenac, naproxen, paracetamol and methyl prednisolone) were incorporated into bone cement (Palacos). Cement cubes holding each drug were made and allowed to harden for 30 min. Each drug-containing cube was placed in a beaker with saline for 72 h. Fractions of 10 ml were collected at 0, 6, 24, 48 and 72 h and analysed using high-pressure liquid chromatography to measure the percentage release of the drug from bone cement. RESULTS: Naproxen showed superior elution from bone cement, with 10.9% at 24 h and 9.08% at 72 h. Paracetamol showed 4.9% at 24 h and 3.78% at 72 h, aceclofenac 0.2% at 24 h and 0.4% at 72 h, diclofenac 3.03% at 24 h and 1.99% at 72 h, and methylprednisolone 0.26% at 24 h and 0.32% at 72 h. CONCLUSIONS: Polymethylmethacrylate bone cement can elute analgesics in vitro. Among the five drugs studied, naproxen had the best release kinematics from polymethylmethacrylate bone cement. Analgesic eluting bone cement is a novel approach for targeted postoperative pain management in TKA.

Coronal Plane Alignment Classification of Arthritic Knees in a South Indian Population and Functional Outcome Comparison Post-mechanical Alignment Total Knee Arthroplasty.

Study Background: Mechanical alignment has always been considered as the gold standard in total knee arthroplasty (TKA), but various other coronal alignment strategies have been proposed to enhance native knee kinematics and thus elevate patient satisfaction levels. Coronal plane alignment of the knee (CPAK) classification introduced by MacDessi is a simple yet comprehensive system to classify knees based on their coronal plane alignment. It categorizes knees into nine phenotypes based on medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA). Materials and Methods: This study investigates the distribution of classification of primary arthritic knees (CPAK) types among arthritic knees in the South Indian population and compares the functional outcomes following total knee arthroplasty (TKA) using traditional mechanical alignment among various CPAK types. The research, spanning from September 2021 to August 2023, encompasses a comprehensive analysis of 324 patients with 352 knees in the first part and 48 patients with 72 knees in the second part of the study who underwent TKA, incorporating demographic data and radiological evaluations. Results: Results indicate a predominant distribution of CPAK type 1, followed by type 2 and type 4 among the South Indian population. In the functional outcomes analysis, regardless of CPAK type, patients exhibited significant improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS), Oxford Knee Score (OKS), and visual analog scale (VAS) scores post-operatively. Conclusion: CPAK distribution among the South Indian population is comparable to other Indian study and studies with an Asian population, but varies with studies among the White population. Significant improvement of functional outcome among all CPAK types signifies the robust nature of conventional mechanical alignment strategy. Thus, our study serves as an initial exploration into the knee phenotype of the South Indian population and findings contribute to ongoing research on optimal alignment strategies in knee arthroplasty, paving the way for future, more extensive studies in this dynamic field.

When to Do Selective Patellar Resurfacing in Total Knee Arthroplasty: A Decision-Making Algorithm Based on Pre-operative and Intra-operative Findings.

Background: Persistent anterior knee pain post total knee arthroplasty (TKA) is a significant cause of patient dissatisfaction. Selective patellar resurfacing is commonly practiced for primary total knee replacement (TKR) but there is a paucity in literature regarding its decision making. Study Objective: This study aims to develop a decision-making algorithm for selective patellar resurfacing using Hospital for Special Surgery Patello-femoral Assessment score (HSS PFA score), weight-bearing patellofemoral X-ray, and intraoperative cartilage wear assessment based on the Outerbridge classification. Materials and Methods: This prospective study enrolled 65 patients, assessing preoperative factors including HSS PFA score and Baldini view radiography. Intraoperative cartilage wear was categorized using the Outerbridge classification. Preoperative findings were correlated with intraoperative outcomes through statistical analysis, leading to the development of a predictive algorithm. The efficiency of algorithm was assessed at 3-year follow-up using HSS PFA score. Results: A significant negative correlation (r = - 0.272, p = 0.029) was observed between HSS PFA score and cartilage wear. However, no significant relationships were established between HSS PFA score and Baldini view observations, including radiological tilt (p = 0.517) and displacement (p = 0.277). Intraoperative cartilage wear versus patellar tilt (p = 0.65) and displacement (p = 0.837) also yielded non-significant results. Three-year follow-up examinations revealed no complications and significant HSS PFA score improvements in all patients. Conclusion: The requirement for patellar resurfacing can be predicted using a combination of preoperative parameter such as HSS PFA score and the intra-operative cartilage wear. We put forward an algorithm based on above findings to aid in the decision making.