Pilot study assessing pathophysiology and healing of digital ulcers in patients with systemic sclerosis using laser Doppler imaging and thermography.

OBJECTIVES: Systemic sclerosis (SSc)-related digital ulcers (DU) cause significant pain and disability and are often a primary endpoint in clinical trials. However, their pathophysiology has been little studied. The objectives of this prospective study were to determine whether laser Doppler imaging (LDI) and thermography can identify ischaemic components in both fingertip and extensor surface DU and assess ulcer healing. METHODS: Patients prospectively reported new DU over a year. Patients' DU underwent imaging until the ulcer had healed. Ischaemia was defined as lower blood flow or skin temperature (and inflammation as higher) within the ulcer, compared to a non-affected site. RESULTS: 53 ulcers (19 fingertip, 18 extensor, 16 'other' sites) in 17 patients were imaged (53 with LDI, 52 with thermography). For LDI data 32 (60%) ulcers were ischaemic; median perfusion ulcer/unaffected area; 0.79 (range 0.11-2.9). For thermography data 35 (66%) were ischaemic; 0.98 (0.89 to 1.1). Inflammation in the surrounding area was identified for all ulcers by LDI but not thermography. In the 36 ulcers with repeat imaging, LDI showed trends (with healing) towards increased ulcer perfusion (p=0.23) and decreased hyperaemia in adjacent areas (p=0.59). Skin temperature at the ulcer site showed no significant change (p=0.13) but adjacent area showed decreased temperature (p=0.04 signifying decreased blood flow). CONCLUSIONS: LDI and thermography are sufficiently sensitive to measure ischaemia in both fingertip and extensor ulcers. LDI was better suited to monitoring change in perfusion with healing (due to higher imaging resolution, or vascular changes occurring in more superficial skin layers).

Generating normative data from web-based administration of the Cambridge Neuropsychological Test Automated Battery using a Bayesian framework.

Introduction: Normative cognitive data can distinguish impairment from healthy cognitive function and pathological decline from normal ageing. Traditional methods for deriving normative data typically require extremely large samples of healthy participants, stratifying test variation by pre-specified age groups and key demographic features (age, sex, education). Linear regression approaches can provide normative data from more sparsely sampled datasets, but non-normal distributions of many cognitive test results may lead to violation of model assumptions, limiting generalisability. Method: The current study proposes a novel Bayesian framework for normative data generation. Participants (n = 728; 368 male and 360 female, age 18-75 years), completed the Cambridge Neuropsychological Test Automated Battery via the research crowdsourcing website Prolific.ac. Participants completed tests of visuospatial recognition memory (Spatial Working Memory test), visual episodic memory (Paired Associate Learning test) and sustained attention (Rapid Visual Information Processing test). Test outcomes were modelled as a function of age using Bayesian Generalised Linear Models, which were able to derive posterior distributions of the authentic data, drawing from a wide family of distributions. Markov Chain Monte Carlo algorithms generated a large synthetic dataset from posterior distributions for each outcome measure, capturing normative distributions of cognition as a function of age, sex and education. Results: Comparison with stratified and linear regression methods showed converging results, with the Bayesian approach producing similar age, sex and education trends in the data, and similar categorisation of individual performance levels. Conclusion: This study documents a novel, reproducible and robust method for describing normative cognitive performance with ageing using a large dataset.