医院焦虑抑郁量表
医学
认知
生物心理社会模型
焦虑
物理疗法
前瞻性队列研究
心理干预
临床心理学
精神科
内科学
作者
Simona Klinkhammer,Annelien Duits,Kay Deckers,Janneke Horn,Arjen J. C. Slooter,Esmée Verwijk,Caroline van Heugten,Johanna M. A. Visser‐Meily,Marcel Aries,Bas C. T. van Bussel,Jacobus F.A. Jansen,Marcus L.F. Janssen,Susanne van Santen,Fabienne Magdelijns,Rein Posthuma,David E.J. Linden,Margaretha C. E. van der Woude,Tom Dormans,Amy Otten,Alida A. Postma,Attila Karakus,Inez Bronsveld,Karin A. H. Kaasjager,Niek Galenkamp,Gert J. Geurtsen,Matthijs C. Brouwer,Kees Brinkman,Wytske A. Kylstra,Dook W. Koch,Martijn Beudel
标识
DOI:10.1016/j.apmr.2023.12.014
摘要
Objective To evaluate whether psychological and social factors complement biomedical factors in understanding post-COVID-19 fatigue and cognitive complaints. Additionally, to incorporate objective (neuro-cognitive) and subjective (patient-reported) variables in identifying factors related to post-COVID-19 fatigue and cognitive complaints. Design Prospective, multicentre cohort study. Setting Six Dutch hospitals. Participants 205 initially hospitalized (March-June 2020), confirmed SARS-CoV-2 patients, aged ≥18 years, physically able to visit the hospital, without prior cognitive deficit, MRI contraindication, or severe neurological damage post-hospital discharge. Interventions Not applicable. Main Outcome Measures Nine months post-hospital discharge, a 3T MRI scan and cognitive testing were performed and patients completed questionnaires. Medical data were retrieved from medical dossiers. Hierarchical regression analyses were performed on fatigue severity (Fatigue Severity Scale; FSS) and cognitive complaints (Cognitive Consequences following Intensive Care Admission; CLC-IC; dichotomized into CLC-high/low). Variable blocks: 1. Demographic and premorbid factors (sex, age, education, comorbidities), 2. Illness severity (ICU/general ward, PROMIS physical functioning [PROMIS-PF]), 3. Neuro-cognitive factors (self-reported neurological symptoms, MRI abnormalities, cognitive performance), and 4. Psychological and social factors (Hospital Anxiety and Depression Scale [HADS], Utrecht Coping List, Social Support List), 5. Fatigue or cognitive complaints. Results The final models explained 60% (FSS) and 48% (CLC-IC) variance, with most blocks (except neuro-cognitive factors for FSS) significantly contributing. Psychological and social factors accounted for 5% (FSS) and 11% (CLC-IC) unique variance. Higher FSS scores were associated with younger age (p=.01), lower PROMIS-PF (p<.001), higher HADS-Depression (p=.03), and CLC-high (p=.04). Greater odds of CLC-high were observed in individuals perceiving more social support (OR=1.07, p<.05). Conclusions Results show that psychological and social factors add to biomedical factors in explaining persistent post-COVID-19 fatigue and cognitive complaints. Objective neuro-cognitive factors were not associated with symptoms. Findings highlight the importance of multidomain treatment, including psychosocial care, which may not target biologically-rooted symptoms directly but may reduce associated distress.