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Objective The influence of different faecal collection methods on metagenomic analyses remains under discussion, and there is no general agreement on which collection method is preferable for gut microbiome research. We compared faecal samples collected in tubes without preservatives with those containing 10 mL of 96% ethanol for gut microbiome research when the timeframe from defecation to freezing at – 80 °C was up to 24 h. We aimed to compare the collection methods on faeces from participants with inflammatory and non-inflammatory gastrointestinal disorders and healthy controls to investigate the most suitable method when considering data yield, human fraction of sequencing reads, and ease of use. We also examined the faecal sample homogeneity. Results Faeces collected in tubes without preservatives resulted in more sequencing reads compared to faeces collected in tubes with 96% ethanol and were also easier to handle. The human fraction of total reads in faeces collected in ethanol from participants with inflammatory bowel disease was higher than all other samples. DNA extraction and sequencing from two different locations in the same faecal sample gave similar results and showed sample homogeneity.

Aim The aim of this study was to explore patients' experiences after gynaecological day surgery one and 30 days postoperatively, as well as potential factors influencing these experiences. Design The study had a multicentre, quantitative, longitudinal design. Methods The study was conducted in three different hospitals' day surgical unit and included patients undergoing gynaecological surgery in general anaesthesia. We used a questionnaire including the European Quality of Life tool (EQ5D3L), the Quality‐of‐Recovery‐15 questionnaire (QoR‐15) and items relating to patient experiences, the first day (T1, n = 444) and 30 days (T2, n = 193) after surgery. Data were collected in the period March 2019 to March 2020. Results Results show that patients mainly had positive experiences and ranged quality of recovery high, even though some areas needed improvement. Patient scores on the QoR‐15 relating to their experiences 24 h postoperative were rated higher at T1 than at T2. Twenty per cent of the respondents experienced complications such as infection, haemorrhage and pain. About 1/5 of these contacted healthcare services, and three per cent was hospitalized. EQ5D score was the only factor that made an statistically significant impact on patients' experiences with quality of recovery (R2 .169, F = 82.87). However, this effect was weak.

Background The aetiology of Crohn’s disease, a chronic inflammatory bowel disease, is multifactorial and not completely understood. However, the association with gut dysbiosis is well-established, and manipulation of the gut microbiota has gained interest as a treatment strategy. This study aimed to investigate the effects of the probiotic strain Bifidobacterium breve , Bif195™ (Bif195) on intestinal inflammation, symptoms, and the gut microbiome composition in patients with small intestinal Crohn’s disease. Methods This was a randomised, double-blind, placebo-controlled trial. Thirty-three patients with small intestinal Crohn’s disease were assigned to eight weeks of treatment with Bif195 or placebo (1:1). The primary outcome was changes in bowel wall thickness measured by intestinal ultrasonography. Other outcomes were changes in symptom severity, quality of life, faecal calprotectin, fatigue, and specific inflammatory parameters on ultrasonography. Changes in the microbiome composition were also examined. Results Bif195 did not affect the bowel wall thickness in the small intestine compared to placebo. Nor did we observe effects on secondary or clinical explorative outcomes. Analysis of the gut microbiome showed that the relative abundance of B. breve rose during the intervention in the Bif195 group, but the result was statistically non-significant. Surprisingly, we observed a clustering of baseline microbiome data into two groups that differed in several aspects including a statistically significant difference in the incidence of previous bowel resections among the participants. Furthermore, changes in symptom scores after eight weeks of intervention were significantly different across the two microbiome groups, with an interaction effect of p  = 0.04. Conclusions Eight weeks of treatment with Bif195 did not affect clinical outcomes for Crohn’s disease. However, variations in baseline microbiome data influenced the results. This underscores the importance of assessing baseline microbiome data in intervention studies in Crohn’s disease. Clinicaltrials.gov NCT04842149

The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer's disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting. The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer's disease. Eighty-one patients clinically suspected of Alzheimer's disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0-100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course. The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer's disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET. The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer's disease compared to 2-[18F]FDG-PET.

Background In Norway, the anaesthesia team normally consists of a nurse anaesthetist and an anaesthetist. Digital anesthesia information management systems (AIMS) that collect patient information directly from the anaesthesia workstation, and transmit the data into documentation systems have recently been implemented in Norway. Earlier studies have indicated that implementation of digital AIMS impacts the clinical workflow patterns and distracts the anaesthesia providers. These studies have mainly had a quantitative design and focused on functionality, installation designs, benefits and challenges associated with implementing and using AIMS. Hence, the aim of this study was to qualitatively explore anaesthesia personnel’s perspectives on implementing and using digital AIMS. Methods The study had an exploratory and descriptive design. The study was conducted within three non-university hospitals in Southern Norway. Qualitative, individual interviews with nurse anaesthetists ( n  = 9) and anaesthetists ( n  = 9) were conducted in the period September to December 2020. Data were analysed using qualitative content analysis according to the recommendations of Graneheim and Lundman. Results Four categories were identified: 1) Balance between clinical assessment and monitoring, 2) Vigilance in relation to the patient, 3) The nurse-physician collaboration, and 4) Software issues. Participants described that anaesthesia included a continuous balance between clinical assessment and monitoring. They experienced that the digital AIMS had an impact on their vigilance in relation to the patient during anaesthesia. The digital AIMS affected the nurse-physician collaboration. Moreover, participants emphasised a lack of user participation and aspects of user-friendliness regarding the implementation of digital AIMS. Conclusion Digital AIMS impacts vigilance in relation to the patient. Hence, collaboration and acceptance of the mutual responsibility between nurse anaesthetists and anaesthetists for both clinical observation and digital AIMS administration is essential. Anaesthesia personnel should be included in development and implementation processes to facilitate implementation.

INTRODUCTION Differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) is challenging. Seed amplification assay (SAA) is sensitive for the detection of misfolded α‐synuclein. METHODS Patients with DLB (N = 31) and AD (N = 25) were recruited and evaluated. Misfolded α‐synuclein was assessed in cerebrospinal fluid (CSF), skin, urine, and olfactory mucosa using SAA. RESULTS The accuracy of α‐synuclein‐SAA for DLB was 87% (95% confidence interval [CI]: 77% to 98%) in CSF, 85% (95% CI: 75% to 98%) in skin, 58% (95% CI: 47% to 69%) in olfactory mucosa, and 59% (95% CI: 51% to 66%) in urine. The core symptoms – fluctuations, REM sleep behavior disorder, and parkinsonism – had accuracies for SAA positivity of ≥79%. Notably, 95% of SAA‐positive patients also had hyposmia. DISCUSSION These findings support the use of CSF and skin α‐synuclein‐SAAs as diagnostic tools for DLB, with strong associations between SAA and clinical phenotype. In particular, intact olfactory function is associated with a lower risk of SAA positivity. Highlights CSF and skin biopsies show high diagnostic accuracy for α‐synuclein, demonstrating good concordance. Strong correlations exist between core symptoms of DLB and pathological α‐synuclein. A very high sensitivity of hyposmia for pathological α‐synuclein is observed. A novel proof‐of‐concept is offered for the potential detection of pathological α‐synuclein in urine, marking the first such comparative analysis between patients with DLB and AD.

Background In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods In this prospective multicenter study, we included 429 patients with SCD ( n  = 230) and MCI ( n  = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p  = 0.79). However, restricting the analysis to patients with more certain classifications ( n  = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p  = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p  = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p  < .0001). Conclusions Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

Introduction Web‐based cognitive tests have potential for standardized screening in neurodegenerative disorders. We examined accuracy and consistency of cCOG, a computerized cognitive tool, in detecting mild cognitive impairment (MCI) and dementia. Methods Clinical data of 306 cognitively normal, 120 mild cognitive impairment (MCI), and 69 dementia subjects from three European cohorts were analyzed. Global cognitive score was defined from standard neuropsychological tests and compared to the corresponding estimated score from the cCOG tool containing seven subtasks. The consistency of cCOG was assessed comparing measurements administered in clinical settings and in the home environment. Results cCOG produced accuracies (receiver operating characteristic‐area under the curve [ROC‐AUC]) between 0.71 and 0.84 in detecting MCI and 0.86 and 0.94 in detecting dementia when administered at the clinic and at home. The accuracy was comparable to the results of standard neuropsychological tests (AUC 0.69–0.77 MCI/0.91–0.92 dementia). Discussion cCOG provides a promising tool for detecting MCI and dementia with potential for a cost‐effective approach including home‐based cognitive assessments.

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further. •Performance of diagnostic tests in pairwise diagnostics differs by diagnostic groups.•Accuracy seems to increase when combining diagnostic tests.•Diagnostic groups might be better separated by different combinations of biomarkers.