While shared clinical decision-making (SDM) is the preferred approach to decision-making in mental health care, its implementation in everyday clinical practice is still insufficient. The European Psychiatric Association undertook a study aiming to gather data on the clinical decision-making style preferences of psychiatrists working in Europe.

We conducted a cross-sectional online survey involving a sample of 751 psychiatrists and psychiatry specialist trainees from 38 European countries in 2021, using the Clinical Decision-Making Style – Staff questionnaire and a set of questions regarding clinicians’ expertise, training, and practice.

SDM was the preferred decision-making style across all European regions ([central and eastern Europe, CEE], northern and western Europe [NWE], and southern Europe [SE]), with an average of 73% of clinical decisions being rated as SDM. However, we found significant differences in non-SDM decision-making styles: participants working in NWE countries more often prefer shared and active decision-making styles rather than passive styles when compared to other European regions, especially to the CEE. Additionally, psychiatry specialist trainees (compared to psychiatrists), those working mainly with outpatients (compared to those working mainly with inpatients) and those working in community mental health services/public services (compared to mixed and private settings) have a significantly lower preference for passive decision-making style.

The preferences for SDM styles among European psychiatrists are generally similar. However, the identified differences in the preferences for non-SDM styles across the regions call for more dialogue and educational efforts to harmonize practice across Europe.

Triage is a tool used to determine patients’ severity of illness or injury within minutes of arrival. This study aims to assess the reliability and validity of a new computer-based triage decision support tool, ANKUTRIAGE, prospectively.

ANKUTRIAGE, a 5-level triage tool was established considering 2 major factors, patient’s vital signs and characteristics of the admission complaint. Adult patients admitted to the ED between July and October, 2019 were consecutively and independently double triaged by 2 assessors using ANKUTRIAGE system. To measure inter-rater reliability, quadratic-weighted kappa coefficients (Kw) were calculated. For the validity, associations among urgency levels, resource use, and clinical outcomes were evaluated.

The inter-rater reliability between users of ANKUTRIAGE was excellent with an agreement coefficient (Kw) greater than 0.8 in all compared groups. In the validity phase, hospitalization rate, intensive care unit admission and mortality rate decreased from level 1 to 5. Likewise, according to the urgency levels, resource use decreased significantly as the triage level decreased (P < 0.05).

ANKUTRIAGE proved to be a valid and reliable tool in the emergency department. The results showed that displaying the key discriminator for each complaint to assist decision leads to a high inter-rater agreement with good correlation between urgency levels and clinical outcomes, as well as between urgency levels and resource consumptions.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

Cognitive impairment is a key element in most mental disorders. Its objective assessment at initial patient contact in primary care can lead to better adjusted and timely care with personalised treatment and recovery. To enable this, we designed the Mindmore self-administrative cognitive screening battery. What is presented here is normative data for the Mindmore battery for the Swedish population.

A total of 720 healthy adults (17 to 93 years) completed the Mindmore screening battery, which consists of 14 individual tests across five cognitive domains: attention and processing speed, memory, language, visuospatial functions and executive functions. Regression-based normative data were established for 42 test result measures, investigating linear, non-linear and interaction effects between age, education and sex.

The test results were most affected by age and to a lesser extent by education and sex. All but one test displayed either linear or accelerated age-related decline, or a U-shaped association with age. All but two tests showed beneficial effects of education, either linear or subsiding after 12 years of educational attainment. Sex affected tests in the memory and executive domains. In three tests, an interaction between age and education revealed an increased benefit of education later in life.

This study provides normative models for 14 traditional cognitive tests adapted for self-administration through a digital platform. The models will enable more accurate interpretation of test results, hopefully leading to improved clinical decision making and better care for patients with cognitive impairment.

The National Neuropsychology Network (NNN) is a multicenter clinical research initiative funded by the National Institute of Mental Health (NIMH; R01 MH118514) to facilitate neuropsychology’s transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency.

The NNN includes four clinical research sites (Emory University; Medical College of Wisconsin; University of California, Los Angeles (UCLA); University of Florida) and Pearson Clinical Assessment. Pearson Q-interactive (Q-i) is used for data capture for Pearson published tests; web-based data capture tools programmed by UCLA, which serves as the Coordinating Center, are employed for remaining measures.

NNN is acquiring item-level data from 500–10,000 patients across 47 widely used Neuropsychology (NP) tests and sharing these data via the NIMH Data Archive. Modern psychometric methods (e.g., item response theory) will specify the constructs measured by different tests and determine their positive/negative predictive power regarding diagnostic outcomes and relationships to other clinical, historical, and demographic factors. The Structured History Protocol for NP (SHiP-NP) helps standardize acquisition of relevant history and self-report data.

NNN is a proof-of-principle collaboration: by addressing logistical challenges, NNN aims to engage other clinics to create a national and ultimately an international network. The mature NNN will provide mechanisms for data aggregation enabling shared analysis and collaborative research. NNN promises ultimately to enable robust diagnostic inferences about neuropsychological test patterns and to promote the validation of novel adaptive assessment strategies that will be more efficient, more precise, and more sensitive to clinical contexts and individual/cultural differences.

The purpose of this paper was to examine national differences in the desire to participate in decision-making of people with severe mental illness in six European countries.

The data was taken from a European longitudinal observational study (CEDAR; ISRCTN75841675). A sample of 514 patients with severe mental illness from the study centers in Ulm, Germany, London, England, Naples, Italy, Debrecen, Hungary, Aalborg, Denmark and Zurich, Switzerland were assessed as to desire to participate in medical decision-making. Associations between desire for participation in decision-making and center location were analyzed with generalized estimating equations.

We found large cross-national differences in patients’ desire to participate in decision-making, with the center explaining 47.2% of total variance in the desire for participation (P < 0.001). Averaged over time and independent of patient characteristics, London (mean = 2.27), Ulm (mean = 2.13) and Zurich (mean = 2.14) showed significantly higher scores in desire for participation, followed by Aalborg (mean = 1.97), where scores were in turn significantly higher than in Debrecen (mean = 1.56). The lowest scores were reported in Naples (mean = 1.14). Over time, the desire for participation in decision-making increased significantly in Zurich (b = 0.23) and decreased in Naples (b = −0.14). In all other centers, values remained stable.

This study demonstrates that patients’ desire for participation in decision-making varies by location. We suggest that more research attention be focused on identifying specific cultural and social factors in each country to further explain observed differences across Europe.

Relapse prevention strategies based on monitoring of early warning signs (EWS) are advocated for the management of psychosis. However, there has been a lack of research exploring how staff, carers and patients make sense of the utility of EWS, or how these are implemented in context.

To develop a multiperspective theory of how EWS are understood and used, which is grounded in the experiences of mental health staff, carers and patients.

Twenty-five focus groups were held across Glasgow and Melbourne (EMPOWER Trial, ISRCTN: 99559262). Participants comprised 88 mental health staff, 21 patients and 40 carers from UK and Australia (total n = 149). Data were analysed using constructivist grounded theory.

All participants appeared to recognise EWS and acknowledged the importance of responding to EWS to support relapse prevention. However, recognition of and acting on EWS were constructed in a context of uncertainty, which appeared linked to risk appraisals that were dependent on distinct stakeholder roles and experiences. Within current relapse management, a process of weighted decision-making (where one factor was seen as more important than others) described how stakeholders weighed up the risks and consequences of relapse alongside the risks and consequences of intervention and help-seeking.

Mental health staff, carers and patients speak about using EWS within a weighted decision-making process, which is acted out in the context of relationships that exist in current relapse management, rather than an objective response to specific signs and symptoms.

To propose a new classification of inner-ear anomalies that is more clinically oriented and surgically relevant: the SMS (Sawai Man Singh) classification of cochleovestibular malformations.

A retrospective multicentric study was conducted of 436 cochlear implantations carried out in 3 Indian tertiary care institutes. Patients with anomalous anatomy were included and classified, as per the new SMS classification, into cochleovestibular malformation types I, II, III and IV, based on cochlear morphology, modiolus and lamina cribrosa.

There were 19, 23, 8 and 4 patients with cochleovestibular malformation types I, II, III and IV, respectively. Two-year post-operative Meaningful Auditory Integration Scale scores were statistically analysed.

This new classification for inner-ear anomalies is a simpler, more practical, outcome-oriented classification that can be used to better plan the surgery. These merits make it a more uniform classification for recording results.

The aim of the study is to improve patient safety by identifying factors influencing gatekeeping decisions by crisis resolution and home treatment teams. A theoretical sampling method was used to recruit clinicians. Semi-structured interviews to elicit various aspects of clinical decision-making were carried out. The transcripts were thematically analysed using a grounded theory approach.

Patient needs (safety and treatment) was the primary driver behind decisions. The research also revealed that information gathered was processed using heuristics. We identified five key themes (anxiety, weighting, agenda, resource and experience), which were constructed into an acronym ‘AWARE’.

AWARE provides a framework to make explicit drivers for decision-making that are often implicit. Incorporating these drivers into reflective practice will help staff be more mindful of undue influences and result in improved clinical decisions.

None.

There is currently no general consensus on patulous Eustachian tube management. Injection of autologous fat, cartilage or hydroxylapatite has been described for Eustachian tube occlusion, with promising results. However, complete resolution of symptoms is not achieved in all cases. This could be connected to the amount of material injected into the surroundings of the Eustachian tube, as this greatly differs among existing studies. Identifying the appropriate volume of injected material could be challenging because anatomical conditions vary among patients, and there is always a risk of chronic Eustachian tube obstruction and its related complications when too much long-standing material is injected.

A case is presented wherein saline was injected under local anaesthesia to determine the volume required and to predict the success of patulous Eustachian tube augmentation with long-standing material.

This approach could allow more personalised treatment and help identify patients likely to benefit from the procedure.