Work

Feasibility study of the Cambridge knee injury tool (CamKIT): 18-month analysis of acute knee injuries at an urgent treatment centre

Year: 2025

Type: article

Abstract: Background: Soft tissue knee injuries are associated with many short- and long-term consequences. Currently, obstacles in the diagnostic pathway lead to inefficiencies in the management process, resulting in suboptimal patient outcomes. Early stratification of knee injury severity is essential for guiding timely investigations and management decisions. This study aimed to prospectively evaluate how the Cambridge Knee Injury Tool (CamKIT) performs in stratifying knee injuries at initial presentation. Methods: This prospective observational study involved eighty-five participants presenting with acute knee injuries in a Major Regional Urgent Treatment Centre (UTC) over 18 months. Patients recorded information regarding patient factors, external factors, injury mechanisms and signs and symptoms at index presentation. CamKIT scores were then calculated. Patients then continued the management stream determined by the treating healthcare team. Management decisions were not influenced by the results of the CamKIT score. Patient outcomes were analysed by extracting time-to-event outcomes, injury classification, and management information. Statistical analysis involved sensitivity and specificity calculations, descriptive statistics, and Mann-Whitney Test calculations between injured and non-injured cohorts, and surgical and conservative cohorts. Results: The tool achieved a sensitivity of 100 % and specificity of 31.7 %, with a negative predictive value (NPV) of 100 % and a positive predictive value (PPV) of 34.9 %. CamKIT scores were higher in patients with injuries (median 8; IQR: 7-9) than in non-injured patients (median 6; IQR: 4-7) (p < 0.0001). Similarly, patients who proceeded to surgical intervention (n = 9) had higher scores (median 8; IQR: 7.5-10) compared to those managed conservatively (median 6; IQR: 4.25-7) (p = 0.0001). Conclusion: The CamKIT continues to show promise in guiding early triage and management of acute knee injuries. Future research involves a multicenter study as well as the integration of machine learning to develop a more robust prediction tool and create a risk-stratified management protocol.

Source: Journal of Clinical Orthopaedics and Trauma

DOI: 10.1016/j.jcot.2025.103203

Authors: Thomas Molloy, Benjamin Gompels, Simone Castagno, Andrew McCaskie, Stephen McDonnell

Institutions: The University of Queensland, University of Cambridge, Addenbrooke's Hospital

Language: English

Topic: Knee injuries and reconstruction techniques

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,000