How do I manage functional visual loss.

Functional visual loss is a subtype of functional neurological disorder (FND) and is a common cause of visual impairment seen in both general and neuro-ophthalmological practice. Ophthalmologists can generally diagnose functional visual loss reasonably confidently but often find it harder to know what to say to the patient, how to approach, or even whether to attempt, treatment. There is little evidence-based treatment despite studies showing up to 60% of adults having impactful symptoms on long-term follow-up. The last 20 years has seen large changes in how we understand, approach, and manage FND more widely. In this article, we set out our practical approach to managing functional visual loss which includes : 1) Make a positive diagnosis based on investigations that demonstrate normal vision in the presence of subjectively impaired vision, not just because tests or ocular exam is normal; 2) Explain and label the condition with an emphasis on these positive diagnostic features, not reassurance; 3) Consider eye or brain comorbidities such as migraine, idiopathic intracranial hypertension or amblyopia; 4) Consider working with an orthoptist using diagnostic tests in a positive way to highlight the possibility of better vision; 5) Develop simple treatment strategies for photophobia; 6) Consider psychological factors and comorbidity as part of assessment and therapy, but keep a broader view of aetiology and don't use this to make a diagnosis; 7) Other treatment modalities including hypnotherapy, transcranial magnetic stimulation and more advanced forms of visual feedback are promising candidates for functional visual loss treatment in the future.

Functional neurological disorder: A common reason for a neurology inpatient referral.

BACKGROUND AND PURPOSE: In 2021, the European Academy of Neurology's training requirements were updated to include functional neurological disorder (FND) as a core topic for the first time. To reinforce these changes, we aimed to understand the proportion of inpatients (in non-neurology settings) who are diagnosed with FND. METHODS: We prospectively collected data on diagnoses made after inpatient ward reviews from neurology trainees at three tertiary neurology centres in Scotland from April to September 2021. We assessed healthcare utilization data for patients with a diagnosis of FND, epilepsy and epileptic seizures, or a neuroinflammatory disorder over the preceding 12 months. RESULTS: There were 437 inpatient reviews for 424 patients by 13 trainees. The largest single diagnosis was FND (n = 80, 18%), followed by epilepsy (n = 64, 14%), primary headache disorder (n = 40, 9%) and neuroinflammatory disorders (n = 28, 6%). There was an uncertain diagnosis for 48 reviews (11%). Compared to patients with epilepsy or neuroinflammatory disorders, patients with FND had a similar number of admissions (2 vs. 2 vs. 1) and brain/spine imaging studies (2 vs. 1 vs. 2). CONCLUSIONS: In Scotland, FND was the most common diagnosis made after a request for an inpatient review by a neurologist from another department in the hospital. Patients with FND have similar health resource needs to those with other common neurological disorders when they present to hospitals with tertiary neurology centres. This data supports the inclusion of FND as a core curriculum topic in neurology training.

Validation of a UPDRS-/MDS-UPDRS-based definition of functional dependency for Parkinson's disease.

INTRODUCTION: Functional dependency in basic activities of daily living (ADLs) is a key outcome in Parkinson's disease (PD). We aimed to define dependency in PD, using the original and MDS versions of the Unified Parkinson's Disease Rating Scale (UPDRS). METHODS: We developed two algorithms to define dependency from items of UPDRS Part 2 and MDS-UPDRS Part 2 relating to basic ADLs (feeding, dressing, hygiene and walking, and getting out of a chair). We validated both algorithms using data from 1110 patients from six community-based PD incidence cohorts, testing concurrent validity, convergent validity, and predictive validity. RESULTS: Our optimal algorithm showed high specificity and moderate to high sensitivity versus Schwab & England <80% (specificity 95% [95% confidence interval (CI) 93-97] and sensitivity 65% [95% CI 55-73] at baseline; 88% [95% CI 85-91] and 85% [95% CI 79-97] respectively at five-years follow-up). Convergent validity was demonstrated by strong associations between dependency defined by the algorithm and cognition (MMSE), quality of life (PDQ39), and impairment (UPDRS part 3) (all p < 0.001). Algorithm-defined dependency status also predicted mortality: HR for mortality in those dependent vs independent at baseline was 1.6 (95%CI 1.2-2.1) and in those dependent vs independent at five-years' follow-up was 2.2 (1.6-3.0). DISCUSSION: We have demonstrated the concurrent validity, convergent validity, and predictive validity of a UPDRS-/MDS-UPDRS-based algorithm to define functional dependency in PD. This can be used for studying dependency in any study where UPDRS or MDS-UPDRS part 2 data have been collected.

Does a multidisciplinary approach have a beneficial effect on the development of a structured patient handover process between acute surgical wards in one of Scotland's largest teaching hospitals?

BACKGROUND: Effective handover is key in preventing harm.1 In the Acute Surgical Receiving Unit of Ninewells Hospital, Dundee, large numbers of patients are transferred daily. However, lack of medical handover during transfer means important tasks are missed. Our aim was to understand and reflect on the current system and test changes to improve medical handover. AIM: Our aim was to ensure that 95% of patients being transferred from the Acute Surgical Receiving Unit receive a basic medical handover within 2 months. METHODS: Initially, we collated issues that were missed when patients were transferred. These data coupled with questionnaire data from members of the team fed into the creation of a handover tool. We proposed to link our tool with the nursing handover, hence creating one unified handover tool. We completed six full Plan-Do-Study-Act (PDSA) cycles (two on communication to aide handover and four on the tool itself) to assess and develop our tool. RESULTS: By our final PDSA cycle, 84% (33/39) of the patients had a handover, meaning no tasks were missed during transfer. After 4 months, 9 out of 10 staff felt that the introduction of the handover sheet made the handover process smoother and 8 out of 10 felt that the handover sheet improved patient safety and quality of care. CONCLUSIONS: Improving handover can be challenging. However, we have shown that a relatively simple intervention can help promote better practice. Challenges are still present as uptake was only 84%, so work still has to be done to improve this. A wider cultural change involving communication and education would be required to implement this tool more widely.