Meniscal tear outcome Study (METRO Study): a study protocol for a multicentre prospective cohort study exploring the factors which affect outcomes in patients with a meniscal tear

Ahmed, Imran; Bowes, M.A.; Hutchinson, Charles E.; Parsons, Nicholas R.; Staniszewska, Sophie; Price, Andrew; Metcalfe, Andrew

doi:10.1136/bmjopen-2020-038681

Cited by 6 publications

(4 citation statements)

References 35 publications

(48 reference statements)

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“…Knee injuries account for the largest percentage of sport-related, severe injuries (i.e., injuries that cause more than 21 days of missed sport participation) [ 1 , 2 , 3 , 4 ]. Anterior cruciate ligament (ACL) ruptures represent more than 50% of the cases, affecting 200,000 individuals in the United States each year [ 1 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Knee cartilage lesions affect around 900,000 individuals in the United States every year, resulting in over 200,000 surgical procedures [ 5 , 6 , 7 , 8 ]. Menisci injuries are the second most common knee impairment, with an incidence of 12–14% [ 9 ] and a prevalence of 60–70 cases per 100,000 in the United Kingdom [ 2 ]. ACL injuries alone account for an expenditure of more than $7 billion in the United States [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Siouras

Moustakidis²,

Giannakidis

et al. 2022

Diagnostics

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The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Siouras

Moustakidis²,

Giannakidis

et al. 2022

Diagnostics

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“…Recent studies have highlighted the need for research to identify a specific subset of patients where surgery may be beneficial and to undertake a study in this population [8][9][10][11]. However, before this work can be carried out more evidence is required to understand the current population of patients with meniscal tears.…”

Section: Introductionmentioning

confidence: 99%

Meniscal tears are more common than previously identified, however, less than a quarter of people with a tear undergo arthroscopy

Ahmed

Radhakrishnan

Khatri

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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Purpose The management of meniscal tears is a widely researched and evolving field. Previous studies reporting the incidence of meniscal tears are outdated and not representative of current practice. The aim of this study was to report the current incidence of MRI confirmed meniscal tears in patients with a symptomatic knee and the current intervention rate in a large NHS trust. Methods Radiology reports from 13,358 consecutive magnetic resonance imaging scans between 2015 and 2017, performed at a large UK hospital serving a population of 470,000, were assessed to identify patients with meniscal tears. The hospital database was interrogated to explore the subsequent treatment undertaken by the patient. A linear regression model was used to identify if any factors predicted subsequent arthroscopy. Results 1737 patients with isolated meniscal tears were identified in patients undergoing an MRI for knee pain, suggesting a rate of 222 MRI confirmed tears per 100,000 of the population aged 18 to 55 years old. 47% attended outpatient appointments and 22% underwent arthroscopy. Root tears [odds ratio (95% CI) 2.24 (1.0, 4.49); p = 0.049] and bucket handle tears were significantly associated with subsequent surgery, with no difference between the other types of tears. The presence of chondral changes did not significantly affect the rate of surgery [0.81 (0.60, 1.08); n.s]. Conclusion Meniscal tears were found to be more common than previously described. However, less than half present to secondary care and only 22% undergo arthroscopy. These findings should inform future study design and recruitment strategies. In agreement with previous literature, bucket handle tears and root tears were significant predictors of subsequent surgery. Level of evidence III.

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“…Meniscal tears have been identified in a high proportion of asymptomatic individuals [ 17 ], therefore it is important to explore the significance patients place on the MRI results. Therefore, there is a clear need to understand the patient experiences of living with a meniscal tear [ 18 ] in order to aid current treatment decision through increased awareness of the treatment pathway.…”

Section: Introductionmentioning

confidence: 99%

Patient beliefs and perceptions play a crucial role in the decision-making process when managing a meniscal tear. A qualitative systematic review of the literature

Ahmed

Dhaif

Abram

et al. 2021

Eur J Orthop Surg Traumatol

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Introduction There has been an increase in research on the effectiveness of treatment options for the management of meniscal tears. However, there is very little evidence about the patient experiences of meniscal tears. Aim To summarise the available qualitative evidence on patients’ experiences and expectations of meniscal tears. Method A search of EMBASE, Medline, Sociofile and Web of Science up to November 2020 was performed to identify studies reporting patient experiences of meniscal tears. Studies were critically appraised using the CASP (Critical Appraisal Skills Program) checklist, and a meta-synthesis was performed to generate third-order constructs (new themes). Results Two studies reporting semi-structured interviews from 34 participants (24 male; 10 female) were included. The mean interview length ranged from 16 to 45 min. Five themes were generated: (1) the imaging (MRI) results are a key driver in the decision-making process, (2) surgery is perceived to be the definitive and quicker approach, (3) physiotherapy and exercise is a slower approach which brought success over time, (4) patient perceptions and preferences are important in the clinical decision-making process and, (5) the impact on patient lives is a huge driver in seeking care and treatment decisions. Conclusion This is the first study to summarise the qualitative evidence on patient experiences with meniscal tears. The themes generated demonstrate the importance of patient perceptions of MRI findings and timing of treatment success as important factors in the decision-making process. This study demonstrates the need to strengthen our understanding of patients’ experiences of meniscal tears.

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Meniscal tear outcome Study (METRO Study): a study protocol for a multicentre prospective cohort study exploring the factors which affect outcomes in patients with a meniscal tear

Cited by 6 publications

References 35 publications

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Meniscal tears are more common than previously identified, however, less than a quarter of people with a tear undergo arthroscopy

Patient beliefs and perceptions play a crucial role in the decision-making process when managing a meniscal tear. A qualitative systematic review of the literature

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