Prognosis in functional and recognised pathophysiological neurological disorders - a shared basis

To compare self-reported outcomes, clinical trajectory and utility of baseline questionnaire responses in predicting prognosis in functional and recognised pathophysiological neurological disorders.

Baseline data on 2581 patients included health-related quality of life, psychological and physical symptoms, illness perceptions, consultation satisfaction and demographics. The prospective cohort included neurology outpatients classified with a functional (reporting symptoms 'not at all' or 'somewhat explained' by 'organic disease'; n = 716) or recognised pathophysiological disorder ('largely' or 'completely explained'; n = 1865). Logistic regression and deep neural network models were used to predict self-reported global clinical improvement (CGI) at 12-months.

Patients with functional and recognised pathophysiological disorders reported near identical outcomes at 12-months with 67% and 66% respectively reporting unchanged or worse CGI. In multivariable modelling 'negative expectation of recovery' and 'disagreement with psychological attribution' predicted same or worse outcome in both groups. Receipt of disability-related state benefit predicted same or worse CGI outcome in the functional disorder group only (OR = 2.28 (95%-CI: 1.36-3.84) in a group-stratified model) and was not related to a measure of economic deprivation. Deep neural network models trained on all 92 baseline features predicted poor outcome with area under the receiver-operator curve of 0.67 in both groups.

Those with functional and recognised pathophysiological neurological disorder share similar outcomes, clinical trajectories, and poor prognostic markers in multivariable models. Prediction of outcome at a patient level was not possible using the baseline data in this study.