Reporting errors in plain radiographs for lower limb trauma-a systematic review and meta-analysis

INTRODUCTION

Plain radiographs are a globally ubiquitous means of investigation for injuries to the musculoskeletal system. Despite this, initial interpretation remains a challenge and inaccuracies give rise to adverse sequelae for patients and healthcare providers alike. This study sought to address the limited, existing meta-analytic research on the initial reporting of radiographs for skeletal trauma, with specific regard to diagnostic accuracy of the most commonly injured region of the appendicular skeleton, the lower limb.

METHOD

A prospectively registered, systematic review and meta-analysis was performed using published research from the major clinical-science databases. Studies identified as appropriate for inclusion underwent methodological quality and risk of bias analysis. Meta-analysis was then performed to establish summary rates for specificity and sensitivity of diagnostic accuracy, including covariates by anatomical site, using HSROC and bivariate models.

RESULTS

A total of 3887 articles were screened, with 10 identified as suitable for analysis based on the eligibility criteria. Sensitivity and specificity across the studies were 93.5% and 89.7% respectively. Compared with other anatomical subdivisions, interpretation of ankle radiographs yielded the highest sensitivity and specificity, with values of 98.1% and 94.6% respectively, and a diagnostic odds ratio of 929.97.

CONCLUSION

Interpretation of lower limb skeletal radiographs operates at a reasonably high degree of sensitivity and specificity. However, one in twenty true positives is missed on initial radiographic interpretation and safety netting systems need to be established to address this. Virtual fracture clinic reviews and teleradiology services in conjunction with novel technology will likely be crucial in these circumstances.

EP.TU.19Are virtual journal clubs a viable alternative in the Covid-19 era?

Introduction

During the COVID-19 pandemic, face to face journal clubs have been curtailed due to Governmental guidance for social distancing. We present an example of a virtual journal club set up for Orthopaedic senior house officers (SHOs) & trainees in the North West Thames (NWT) Deanery from April to August 2020.

Methods

Our aims were to establish a regular online journal club for orthopaedic trainees in NWT that successfully met the objectives of the more established face to face meetings. Assessment of each session was evaluated using a post attendance survey created using an established online generator.

Results

64.4% (16/25 participants) had not attended a virtual journal club before, with 100% (33/33) reporting that they would attend another virtual journal club. 39.4% (13/33) of attendees agreed or strongly agreed that they could participate in sessions over more conventional meetings. 24.2% (8/33) of participants reported experiencing technical issues during the sessions. 90.9% (30/33) of respondents reporting the virtual model to be more convenient than standard journal clubs.

Conclusion

The potential benefits of significantly increased accessibility, increased visiting expert contribution whilst still adhering to government guidelines; weighed against marginally less effective teaching nonetheless results in an overall benefit

Clinical Characteristics and Predictors of Mortality in Patients with COVID-19 Infection Outside Intensive Care

BACKGROUND/INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic has affected all aspects of inpatient hospital medicine with patients admitted from level 1 (general medical wards) to level 3 (intensive care). Often, there are subtle physiological differences in these cohorts of patients. In particular, in intensive care, patients tend to be younger and have increased disease severity. Data, to date, has combined outcomes from medical and intensive care cohorts, or looked exclusively at intensive care. We looked solely at the level 1 (medical) cohort to identify their clinical characteristics and predictors of outcome.

PATIENTS AND METHODS

This was a retrospective study of adult patients admitted to a central London teaching hospital with a diagnosis of COVID-19 from 23rd March to 7th April 2020 identified from the hospital electronic database. Any patients who required level 2 or 3 care were excluded.

RESULTS

A total of 229 patients were included for analysis. Increased age and frailty scores were associated with increased 30-day mortality. Reduced renal function and elevated troponin blood levels are also associated with poor outcome. Baseline observations showed that increased oxygen requirement was predictive for mortality. A trend of increased mortality with lower diastolic blood pressure was noted. Lymphopenia was not shown to be related to mortality.

CONCLUSION

Urea and creatinine are the best predictors of mortality in the level 1 cohort. Unlike previous intensive care data, lymphopenia is not predictive of mortality. We suggest that these factors be considered when prognosticating and for resource allocation for the treatment and escalation of care for patients with COVID-19 infection.

Clinician and computer: a study on patient perceptions of artificial intelligence in skeletal radiography

BACKGROUND

Up to half of all musculoskeletal injuries are investigated with plain radiographs. However, high rates of image interpretation error mean that novel solutions such as artificial intelligence (AI) are being explored.

OBJECTIVES

To determine patient confidence in clinician-led radiograph interpretation, the perception of AI-assisted interpretation and management, and to identify factors which might influence these views.

METHODS

A novel questionnaire was distributed to patients attending fracture clinic in a large inner-city teaching hospital. Categorical and Likert scale questions were used to assess participant demographics, daily electronics use, pain score and perceptions towards AI used to assist in interpretation of their radiographs, and guide management.

RESULTS

216 questionnaires were included (M=126, F=90). Significantly higher confidence in clinician rather than AI-assisted interpretation was observed (clinician=9.20, SD=1.27 vs AI=7.06, SD=2.13), 95.4% reported favouring clinician over AI-performed interpretation in the event of disagreement.Small positive correlations were observed between younger age/educational achievement and confidence in AI-assistance. Students demonstrated similarly increased confidence (8.43, SD 1.80), and were over-represented in the minority who indicated a preference for AI-assessment over their clinicians (50%).

CONCLUSIONS

Participant's held the clinician's assessment in the highest regard and expressed a clear preference for it over the hypothetical AI assessment. However, robust confidence scores for the role of AI-assistance in interpreting skeletal imaging suggest patients view the technology favourably.Findings indicate that younger, more educated patients are potentially more comfortable with a role for AI-assistance however further research is needed to overcome the small number of responses on which these observations are based.