Shipston-Sharman Oliver, Popkirov Stoyan, Hansen Christian H, Stone Jon, Carson Alan
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany.
J Psychosom Res. 2021 Nov 20;152:110681. doi: 10.1016/j.jpsychores.2021.110681.
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.
比较自我报告的结果、临床轨迹以及基线问卷调查结果在预测功能性和已确认的病理生理神经性疾病预后方面的效用。
2581例患者的基线数据包括与健康相关的生活质量、心理和身体症状、疾病认知、咨询满意度和人口统计学信息。前瞻性队列包括被分类为功能性（报告症状“完全没有”或“部分由器质性疾病解释”；n = 716）或已确认的病理生理疾病（“大部分”或“完全由器质性疾病解释”；n = 1865）的神经科门诊患者。使用逻辑回归和深度神经网络模型预测12个月时自我报告的整体临床改善（CGI）情况。
功能性和已确认的病理生理疾病患者在12个月时报告的结果几乎相同，分别有67%和66%的患者报告CGI无变化或恶化。在多变量建模中，“对康复的消极期望”和“不同意心理归因”在两组中均预测了相同或更差的结果。仅在功能性疾病组中，领取与残疾相关的国家福利预测了相同或更差的CGI结果（在分组分层模型中OR = 2.28（95%可信区间：1.36 - 3.84）），且与经济贫困程度无关。在所有92个基线特征上训练的深度神经网络模型在两组中预测不良结果的受试者工作特征曲线下面积均为0.67。
在多变量模型中，功能性和已确认的病理生理神经性疾病患者具有相似的结果、临床轨迹和不良预后标志物。本研究中使用基线数据无法在个体患者水平上预测结果。
