Can Orthopedic Oncologists Predict Functional Outcome in Patients with Sarcoma after Limb Salvage Surgery in the Lower Limb? A Nationwide Study. (17th November 2014)

• Record Type: Journal Article
• Authors: Kolk, Sjoerd; Cox, Kevin; Weerdesteyn, Vivian; Hannink, Gerjon; Bramer, Jos; Dijkstra, Sander; Jutte, Paul; Ploegmakers, Joris; van de Sande, Michiel; Schreuder, Hendrik; Verdonschot, Nico; van der Geest, Ingrid
• Other Names: Ferguson Peter C. Academic Editor.
• Is Part Of: Sarcoma. Volume 2014(2014)
• Publication Date: 2014-11-17
• Subjects: Sarcoma -- Periodicals; 616.994
• DOI: 10.1155/2014/436598
• Languages: English
• ISSNs: 1357-714X

Abstract

Abstract : Accurate predictions of functional outcome after limb salvage surgery (LSS) in the lower limb are important for several reasons, including informing the patient preoperatively and, in some cases, deciding between amputation and LSS. This study aimed to elucidate the correlation between surgeon-predicted and patient-reported functional outcome of LSS in the Netherlands. Twenty-three patients (between six months and ten years after surgery) and five independent orthopedic oncologists completed the Toronto Extremity Salvage Score (TESS) and the RAND-36 physical functioning subscale (RAND-36 PFS). The orthopedic oncologists made their predictions based on case descriptions (including MRI scans) that reflected the preoperative status. The correlation between patient-reported and surgeon-predicted functional outcome was "very poor" to "poor" on both scores (r 2 values ranged from 0.014 to 0.354). Patient-reported functional outcome was generally underestimated, by 8.7% on the TESS and 8.3% on the RAND-36 PFS. The most difficult and least difficult tasks on the RAND-36 PFS were also the most difficult and least difficult to predict, respectively. Most questions had a "poor" intersurgeon agreement. It was difficult to accurately predict the patient-reported functional outcome of LSS. Surgeons' ability to predict functional scores can be improved the most by focusing on accurately predicting more demanding tasks.