Introduction of a multidisciplinary neuro-oncology long-term follow-up clinic at GOSH during the covid-19 pandemic: experience of the past 18 months

BackgroundThe number of children and young people (CYP) surviving brain tumours is increasing annually, with 5-year survival having nearly doubled over the past 40 years. However, more than two-thirds of these survivors have multiple long-term co-morbidities (‘late effects’) resulting from their tumour and/or treatment(s) necessitating lifelong follow-up from multiple professionals.ObjectiveWe sought to establish a collaborative multidisciplinary ‘one-stop shop’ long-term follow-up clinic for CYP who were >5 years from the end of treatment of a brain tumour to reduce the need for multiple hospital appointments and to provide holistic assessment and supportive care.ResultsThe first clinic began in January 2020 as a collaborative effort between healthcare professionals in the neuro-oncology, endocrinology, psychology, neuropsychology, physiotherapy and occupational therapy departments. Clinics ran once to twice-monthly throughout the year, and apart from a 6-month period during the Covid-19 pandemic, all appointments were carried out face to face. Over 18 months, a total of 61 patients have been seen in 111 appointments. Apart from neuro-oncology, CYP also had endocrinology (97%), neuropsychology (65%), physiotherapy/occupational therapy (55%) and psychology (53%) needs. Other specialties not represented in the clinic who were still involved in the care of these CYP included ophthalmology (57%), audiology (30%), neurology (22%) and CAMHS (22%). Feedback from CYP and their families thus far has been overwhelmingly positive, with all leaving feeling very satisfied or satisfied. Major issues include continued funding, the lack of clinic room space, and the appropriateness of the whole team seeing some CYP at the same time.ConclusionsFew models of similar multidisciplinary neuro-oncology long-term follow-up clinics exist in the UK, and streamlined funding of such services is sorely needed, despite the recognition from both families and professionals about their utility.

A single-centre retrospective analysis of cinacalcet therapy in primary hyperparathyroidism.

Primary hyperparathyroidism (pHPT) is a common endocrine disorder that can be cured by parathyroidectomy; patients unsuitable for surgery can be treated with cinacalcet. Availability of surgery may be reduced during COVID-19, and cinacalcet can be used as bridging therapy. In this single-centre retrospective analysis, we investigated the utility and safety of cinacalcet in patients with pHPT receiving cinacalcet between March 2019 and July 2020, including pre-parathyroidectomy bridging. We reviewed and summarised the published literature. Cinacalcet dosages were adjusted by endocrinologists to achieve target calcium < 2.70 mmol/L. Eighty-six patients were identified, with the most achieving target calcium (79.1%) with a mean dose of 39.4 mg/day (±17.1 mg/day) for a median duration of 35 weeks (1-178 weeks). Calcium was normalised in a median time of 5 weeks. The majority of patients commenced cinacalcet of 30 mg/day (78 patients) with the remainder at 60 mg/day (8 patients). Forty-seven patients commencing lower dose cinacalcet (30 mg/day) achieved target calcium without requiring 60 mg/day. Baseline PTH was significantly higher in patients requiring higher doses of cinacalcet. 18.6% of patients reported adverse reactions and 4.7% discontinued cinacalcet. Patients treated with cinacalcet pre-parathyroidectomy required a higher dose and fewer achieved target calcium compared to medical treatment with cinacalcet alone. Post-operative calcium was similar to patients who were not given pre-parathyroidectomy cinacalcet. In summary, cinacalcet at an initial dose of 30 mg/day is safe and useful for achieving target calcium in patients with symptomatic or severe hypercalcaemia in pHPT, including those treated for pre-parathyroidectomy. We propose a PTH threshold of >30 pmol/L to initiate at a higher dose of 60 mg/day.

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras.

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the 'busyness' of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK's Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).