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Background/Objectives: Persons with disorders of consciousness (DoCs) may perceive pain without being able to communicate their discomfort. The Nociception Coma Scale (NCS) and its revised form (NCS-R) have been proposed to assess nociception in persons with DoCs. The main aim of this international multicenter study was to confirm (or not) our preliminary results and compare the NCS-R scores of standard stimulus (NCS-R-SS) to scores of personalized painful stimuli (NCS-R-PS). A secondary aim of the study was to verify possible correlations between the NCS-R-PS and Coma Recovery Scale-Revised (CRS-R) and to estimate convergent validity. Methods: Sixty-one patients with prolonged DoCs (pDoCs) were enrolled from seven European post-acute rehabilitation centers. Responsiveness and pain perception were assessed by CRS-R and NCS-R with standard stimulus (NCS-R-SS) and personalized stimulation (NCS-R-PS). ClinicalTrials.gov Identifier: NCT06012357. Results: our results support our prior findings on the superiority and the validity of the personalized painful stimulus approach in assessment of pain in persons with DoCs in comparison with the standardized pain assessment methodology. Conclusions : A more in-depth and tailored assessment of pain perception in persons with a DoC may lead to better acknowledgment of its presence and by extension an objective foundation for more aggressive and appropriate pain management.

The COVID-19 pandemic and the associated restrictions caused great psychological suffering to the general population and psychiatric patients. We aimed to explore the course of depression and anxiety symptoms in obsessive compulsive disorder (OCD) patients, adjustment disorder (AD) patients, and participants without psychiatric disorders (control group, CG) across the different phases of the pandemic: the first lockdown, a temporary interruption of restrictions, and the second lockdown. Out of the 158 patients screened, we enrolled 46 OCD and 19 AD patients as well as 29 CG participants. The Beck Depression Inventory-II and the State-Trait Anxiety Inventory-Y were administered to all participants at each time point. The results showed different symptom severities among the groups throughout the whole study, with OCD patients always scoring higher than AD patients and the CG, and the AD patients always scoring higher than the CG. The symptom course within each group was different. OCD patients' symptoms sharply worsened during the first lockdown and then remained stable irrespective of the subsequent pandemic phases. In the AD and CG groups, symptoms waxed and waned following the fluctuations of the restriction provisions, with a complete return to the baseline when the restrictions were stopped only in the CG. These findings suggest that the influence of the pandemic and of the associated restrictions on depression and anxiety manifestations may vary depending on the particular pre-existing mental health status.

During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions.