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Objectives The underlying structural brain correlates of neuropsychiatric involvement in systemic lupus erythematosus (NPSLE) remain unclear, thus hindering correct diagnosis. We compared brain tissue volumes between a clinically well-defined cohort of patients with NPSLE and SLE patients with neuropsychiatric syndromes not attributed to SLE (non-NPSLE). Within the NPSLE patients, we also examined differences between patients with two distinct disease phenotypes: ischemic and inflammatory. Methods In this prospective (May 2007 to April 2015) cohort study, we included 38 NPSLE patients (26 inflammatory and 12 ischemic) and 117 non-NPSLE patients. All patients underwent a 3-T brain MRI scan that was used to automatically determine white matter, grey matter, white matter hyperintensities (WMH) and total brain volumes. Group differences in brain tissue volumes were studied with linear regression analyses corrected for age, gender, and total intracranial volume and expressed as B values and 95% confidence intervals. Results NPSLE patients showed higher WMH volume compared to non-NPSLE patients (p = 0.004). NPSLE inflammatory patients showed lower total brain (p = 0.014) and white matter volumes (p = 0.020), and higher WMH volume (p = 0.002) compared to non-NPSLE patients. Additionally, NPSLE inflammatory patients showed lower white matter (p = 0.020) and total brain volumes (p = 0.038) compared to NPSLE ischemic patients. Conclusion We showed that different phenotypes of NPSLE were related to distinct patterns of underlying structural brain MRI changes. Especially the inflammatory phenotype of NPSLE was associated with the most pronounced brain volume changes, which might facilitate the diagnostic process in SLE patients with neuropsychiatric symptoms. Key Points • Neuropsychiatric systemic lupus erythematosus (NPSLE) patients showed a higher WMH volume compared to SLE patients with neuropsychiatric syndromes not attributed to SLE (non-NPSLE). • NPSLE patients with inflammatory phenotype showed a lower total brain and white matter volume, and a higher volume of white matter hyperintensities, compared to non-NPSLE patients. • NPSLE patients with inflammatory phenotype showed lower white matter and total brain volumes compared to NPSLE patients with ischemic phenotype.

The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a “digital scribe”. We reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research. We performed a literature search of four scientific databases (Medline, Web of Science, ACL, and Arxiv) and requested several companies that offer digital scribes to provide performance data. We included articles that described the use of models on clinical conversational data, either automatically or manually transcribed, to automate clinical documentation. Of 20 included articles, three described ASR models for clinical conversations. The other 17 articles presented models for entity extraction, classification, or summarization of clinical conversations. Two studies examined the system’s clinical validity and usability, while the other 18 studies only assessed their model’s technical validity on the specific NLP task. One company provided performance data. The most promising models use context-sensitive word embeddings in combination with attention-based neural networks. However, the studies on digital scribes only focus on technical validity, while companies offering digital scribes do not publish information on any of the research phases. Future research should focus on more extensive reporting, iteratively studying technical validity and clinical validity and usability, and investigating the clinical utility of digital scribes.

Objective Delirium is a frequent complication after surgery with important negative outcomes for affected patients and society. However, it is still largely unknown why some patients have a predisposition for delirium and others not. To increase our understanding of the neural substrate of postoperative delirium, we studied the association between preoperative brain MRI features and the occurrence of delirium after major surgery. Methods A group of 413 patients without dementia (Mean 72 years, SD: 5) was included in a prospective observational two-center study design. The study was conducted at Charite Universitatsmedizin (Berlin, Germany) and the University Medical Center Utrecht (Utrecht, The Netherlands). We measured preoperative brain volumes (total brain, gray matter, white matter), white matter hyperintensity volume and shape, brain infarcts and cerebral perfusion, and used logistic regression analysis adjusted for age, sex, intracranial volume, study center and type of surgery. Results Postoperative delirium was present in a total of 70 patients (17%). Preoperative cortical brain infarcts increased the risk of postoperative delirium, although this did not reach statistical significance (OR (95%CI): 1.63 (0.84–3.18). Furthermore, we found a trend for an association of a more complex shape of white matter hyperintensities with occurrence of postoperative delirium (OR (95%CI): 0.97 (0.95–1.00)). Preoperative brain volumes, white matter hyperintensity volume, and cerebral perfusion were not associated with occurrence of postoperative delirium. Conclusion Our study suggests that patients with preoperative cortical brain infarcts and those with a more complex white matter hyperintensity shape may have a predisposition for developing delirium after major surgery.

Highlights • Predisposing risk is not associated with delirium-related fMRI characteristics. • Older age within an elderly cohort is related to higher functional connectivity strength. • This relation is in opposite direction than hypothesized. • The onset of delirium may reflect new functional network impairments., Delirium, the clinical expression of acute encephalopathy, is a common neuropsychiatric syndrome that is related to poor outcomes, such as long-term cognitive impairment. Disturbances of functional brain networks are hypothesized to predispose for delirium. The aim of this study in non-delirious elderly individuals was to investigate whether predisposing risk factors for delirium are associated with fMRI network characteristics that have been observed during delirium. As predisposing risk factors, we studied age, alcohol misuse, cognitive impairment, depression, functional impairment, history of transient ischemic attack or stroke, and physical status. In this multicenter study, we included 554 subjects and analyzed resting-state fMRI data from 222 elderly subjects (63% male, age range: 65–85 year) after rigorous motion correction. Functional network characteristics were analyzed and based on the minimum spanning tree (MST). Global functional connectivity strength, network efficiency (MST diameter) and network integration (MST leaf fraction) were analyzed, as these measures were altered during delirium in previous studies. Linear regression analyses were used to investigate the relation between predisposing delirium risk factors and delirium-related fMRI characteristics, adjusted for confounding and multiple testing. Predisposing risk factors for delirium were not associated with delirium-related fMRI network characteristics. Older age within our elderly cohort was related to global functional connectivity strength (β = 0.182, p

Objective: Delirium is associated with increased electroencephalography (EEG) delta activity, decreased connectivity strength and decreased network integration. To improve our understanding of development of delirium, we studied whether non-delirious individuals with a predisposition for delirium also show these EEG abnormalities. Methods: Elderly subjects (N = 206) underwent resting-state EEG measurements and were assessed on predisposing delirium risk factors, i.e. older age, alcohol misuse, cognitive impairment, depression, functional impairment, history of stroke and physical status. Delirium-related EEG characteristics of interest were relative delta power, alpha connectivity strength (phase lag index) and network integration (minimum spanning tree leaf fraction). Linear regression analyses were used to investigate the relation between predisposing delirium risk factors and EEG characteristics that are associated with delirium, adjusting for confounding and multiple testing. Results: Functional impairment was related to a decrease in connectivity strength (adjusted R2 = 0.071, β = 0.201, p < 0.05). None of the other risk factors had significant influence on EEG delta power, connectivity strength or network integration. Conclusions: Functional impairment seems to be associated with decreased alpha connectivity strength. Other predisposing risk factors for delirium had no effect on the studied EEG characteristics. Significance: Predisposition for delirium is not consistently related to EEG characteristics that can be found during delirium.