The accuracy of the Edinburgh diplopia diagnostic algorithm

Assessing the Performance of a Novel Bayesian Algorithm at Point of Care for Red Eye Complaints

The current diagnostic aids for red eye are static flowcharts that do not provide dynamic, stepwise workups. The diagnostic accuracy of a novel dynamic Bayesian algorithm for red eye was tested. Fifty-seven patients with red eye were evaluated by an emergency medicine physician who completed a questionnaire about symptoms/findings (without requiring extensive slit lamp findings). An ophthalmologist then attributed an independent "gold-standard diagnosis". The algorithm used questionnaire data to suggest a differential diagnosis. The referrer's diagnostic accuracy was 70.2%, while the algorithm's accuracy was 68.4%, increasing to 75.4% with the algorithm's top two diagnoses included and 80.7% with the top three included. In urgent cases of red eye (n = 26), the referrer diagnostic accuracy was 76.9%, while the algorithm's top diagnosis was 73.1% accurate, increasing to 84.6% (top two included) and 88.5% (top three included). The algorithm's sensitivity for urgent cases was 76.9% (95% CI: 56-91%) using its top diagnosis, with a specificity of 93.6% (95% CI: 79-99%). This novel algorithm provides dynamic workups using clinical symptoms, and may be used as an adjunct to clinical judgement for triaging the urgency of ocular causes of red eye.

Evaluating the Diagnostic Accuracy of a Novel Bayesian Decision-Making Algorithm for Vision Loss

The current diagnostic aids for acute vision loss are static flowcharts that do not provide dynamic, stepwise workups. We tested the diagnostic accuracy of a novel dynamic Bayesian algorithm for acute vision loss. Seventy-nine “participants” with acute vision loss in Windsor, Canada were assessed by an emergency medicine or primary care provider who completed a questionnaire about ocular symptoms/findings (without requiring fundoscopy). An ophthalmologist then attributed an independent “gold-standard diagnosis”. The algorithm employed questionnaire data to produce a differential diagnosis. The referrer diagnostic accuracy was 30.4%, while the algorithm’s accuracy was 70.9%, increasing to 86.1% with the algorithm’s top two diagnoses included and 88.6% with the top three included. In urgent cases of vision loss (n = 54), the referrer diagnostic accuracy was 38.9%, while the algorithm’s top diagnosis was correct in 72.2% of cases, increasing to 85.2% (top two included) and 87.0% (top three included). The algorithm’s sensitivity for urgent cases using the top diagnosis was 94.4% (95% CI: 85–99%), with a specificity of 76.0% (95% CI: 55–91%). This novel algorithm adjusts its workup at each step using clinical symptoms. In doing so, it successfully improves diagnostic accuracy for vision loss using clinical data collected by non-ophthalmologists.

Peek Community Eye Health - mHealth system to increase access and efficiency of eye health services in Trans Nzoia County, Kenya: study protocol for a cluster randomised controlled trial

BACKGROUND

Globally, eye care provision is currently insufficient to meet the requirement for eye care services. Lack of access and awareness are key barriers to specialist services; in addition, specialist services are over-utilised by people with conditions that could be managed in the community or primary care. In combination, these lead to a large unmet need for eye health provision. We have developed a validated smartphone-based screening algorithm (Peek Community Screening App). The application (App) is part of the Peek Community Eye Health system (Peek CEH) that enables Community Volunteers (CV) to make referral decisions about patients with eye problems. It generates referrals, automated short messages service (SMS) notifications to patients or guardians and has a program dashboard for visualising service delivery. We hypothesise that a greater proportion of people with eye problems will be identified using the Peek CEH system and that there will be increased uptake of referrals, compared to those identified and referred using the current community screening approaches.

STUDY DESIGN

A single masked, cluster randomised controlled trial design will be used. The unit of randomisation will be the 'community unit', defined as a dispensary or health centre with its catchment population. The community units will be allocated to receive either the intervention (Peek CEH system) or the current care (periodic health centre-based outreach clinics with onward referral for further treatment). In both arms, a triage clinic will be held at the link health facility four weeks from sensitisation, where attendance will be ascertained. During triage, participants will be assessed and treated and, if necessary, referred onwards to Kitale Eye Unit.

DISCUSSION

We aim to evaluate a M-health system (Peek CEH) geared towards reducing avoidable blindness through early identification and improved adherence to referral for those with eye problems and reducing demand at secondary care for conditions that can be managed effectively at primary care level.

TRIAL REGISTRATION

The Pan African Clinical Trials Registry (PACTR), 201807329096632 . Registered on 8 June 2018.

Diplopia: a unique presentation of recurrence of a salivary gland carcinoma

A 79-year-old man with a history of radical excision of a left submandibular gland carcinoma ex-pleomorphic adenoma presented with a new 2 cm lump in his left submandibular region which proved to be recurrence on surgical excision. During work up for revision surgery he developed a right VI cranial nerve palsy, which was attributed to his microvascular status having had a history of three previous transient ischemic attacks (TIAs). 6 months later, his palsy had not resolved. MRI revealed new soft tissue by the cavernous segment of the internal carotid artery. The exact source of this was unclear as there was no evidence of local recurrence or nodal disease. A repeat MRI scan 16 months later revealed further growth of abnormal tissue in the cavernous sinus and the primary submandibular location, now involving multiple nerves including branches of cranial nerves IV, V, VI, VII and XII making surgical excision impossible.