RESUMO
Background: Online technology could potentially revolutionise how patients are cognitively assessed and monitored. However, it remains unclear whether assessments conducted remotely can match established pen-and-paper neuropsychological tests in terms of sensitivity and specificity. Methods: This observational study aimed to optimise an online cognitive assessment for use in traumatic brain injury (TBI) clinics. The tertiary referral clinic in which this tool has been clinically implemented typically sees patients a minimum of 6 months post-injury in the chronic phase. Between March and August 2019, we conducted a cross-group, cross-device and factor analyses at the St. Mary's Hospital TBI clinic and major trauma wards at Imperial College NHS trust and St. George's Hospital in London (UK), to identify a battery of tasks that assess aspects of cognition affected by TBI. Between September 2019 and February 2020, we evaluated the online battery against standard face-to-face neuropsychological tests at the Imperial College London research centre. Canonical Correlation Analysis (CCA) determined the shared variance between the online battery and standard neuropsychological tests. Finally, between October 2020 and December 2021, the tests were integrated into a framework that automatically generates a results report where patients' performance is compared to a large normative dataset. We piloted this as a practical tool to be used under supervised and unsupervised conditions at the St. Mary's Hospital TBI clinic in London (UK). Findings: The online assessment discriminated processing-speed, visual-attention, working-memory, and executive-function deficits in TBI. CCA identified two significant modes indicating shared variance with standard neuropsychological tests (r = 0.86, p < 0.001 and r = 0.81, p = 0.02). Sensitivity to cognitive deficits after TBI was evident in the TBI clinic setting under supervised and unsupervised conditions (F (15,555) = 3.99; p < 0.001). Interpretation: Online cognitive assessment of TBI patients is feasible, sensitive, and efficient. When combined with normative sociodemographic models and autogenerated reports, it has the potential to transform cognitive assessment in the healthcare setting. Funding: This work was funded by a National Institute for Health Research (NIHR) Invention for Innovation (i4i) grant awarded to DJS and AH (II-LB-0715-20006).