Artefact in Physiological Data Collected from Patients with Brain Injury: Quantifying the Problem and Providing a Solution Using a Factorial Switching Linear Dynamical Systems Approach.

INTRODUCTION: High-resolution, artefact-free and accurately annotated physiological data are desirable in patients with brain injury both to inform clinical decision-making and for intelligent analysis of the data in applications such as predictive modelling. We have quantified the quality of annotation surrounding artefactual events and propose a factorial switching linear dynamical systems (FSLDS) approach to automatically detect artefact in physiological data collected in the neurological intensive care unit (NICU). METHODS: Retrospective analysis of the BrainIT data set to discover potential hypotensive events corrupted by artefact and identify the annotation of associated clinical interventions. Training of an FSLDS model on clinician-annotated artefactual events in five patients with severe traumatic brain injury. RESULTS: In a subset of 187 patients in the BrainIT database, 26.5 % of potential hypotensive events were abandoned because of artefactual data. Only 30 % of these episodes could be attributed to an annotated clinical intervention. As assessed by the area under the receiver operating characteristic curve metric, FSLDS model performance in automatically identifying the events of blood sampling, arterial line damping and patient handling was 0.978, 0.987 and 0.765, respectively. DISCUSSION: The influence of artefact on physiological data collected in the NICU is a significant problem. This pilot study using an FSLDS approach shows real promise and is under further development.

Accuracy and Feasibility of an Android-Based Digital Assessment Tool for Post Stroke Visual Disorders-The StrokeVision App.

BACKGROUND: Visual impairment affects up to 70% of stroke survivors. We designed an app (StrokeVision) to facilitate screening for common post stroke visual issues (acuity, visual fields, and visual inattention). We sought to describe the test time, feasibility, acceptability, and accuracy of our app-based digital visual assessments against (a) current methods used for bedside screening and (b) gold standard measures. METHODS: Patients were prospectively recruited from acute stroke settings. Index tests were app-based assessments of fields and inattention performed by a trained researcher. We compared against usual clinical screening practice of visual fields to confrontation, including inattention assessment (simultaneous stimuli). We also compared app to gold standard assessments of formal kinetic perimetry (Goldman or Octopus Visual Field Assessment); and pencil and paper-based tests of inattention (Albert's, Star Cancelation, and Line Bisection). Results of inattention and field tests were adjudicated by a specialist Neuro-ophthalmologist. All assessors were masked to each other's results. Participants and assessors graded acceptability using a bespoke scale that ranged from 0 (completely unacceptable) to 10 (perfect acceptability). RESULTS: Of 48 stroke survivors recruited, the complete battery of index and reference tests for fields was successfully completed in 45. Similar acceptability scores were observed for app-based [assessor median score 10 (IQR: 9-10); patient 9 (IQR: 8-10)] and traditional bedside testing [assessor 10 (IQR: 9-10); patient 10 (IQR: 9-10)]. Median test time was longer for app-based testing [combined time to completion of all digital tests 420 s (IQR: 390-588)] when compared with conventional bedside testing [70 s, (IQR: 40-70)], but shorter than gold standard testing [1,260 s, (IQR: 1005-1,620)]. Compared with gold standard assessments, usual screening practice demonstrated 79% sensitivity and 82% specificity for detection of a stroke-related field defect. This compares with 79% sensitivity and 88% specificity for StrokeVision digital assessment. CONCLUSION: StrokeVision shows promise as a screening tool for visual complications in the acute phase of stroke. The app is at least as good as usual screening and offers other functionality that may make it attractive for use in acute stroke. CLINICAL TRIAL REGISTRATION: https://ClinicalTrials.gov/ct2/show/NCT02539381.