Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care.

To extract information about suicidality status from the admission notes in electronic health records (EHRs) using privacy-sensitive, locally hosted LLMs, specifically evaluating the efficacy of Llama-2 models.

We compared the performance of several variants of the open source LLM Llama-2 in extracting suicidality status from 100 psychiatric reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies.

A German fine-tuned Llama-2 model showed the highest accuracy (87.5%), sensitivity (83.0%) and specificity (91.8%) in identifying suicidality, with significant improvements in sensitivity and specificity across various prompt designs.

The study demonstrates the capability of LLMs, particularly Llama-2, in accurately extracting information on suicidality from psychiatric records while preserving data privacy. This suggests their application in surveillance systems for psychiatric emergencies and improving the clinical management of suicidality by improving systematic quality control and research.

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Previous studies in individual countries have identified inconsistent predictors of length of stay (LoS) in psychiatric inpatient units. This may reflect methodological inconsistencies across studies or true differences of predictors. In this study we assessed predictors of LoS in five European countries and explored whether their effect varies across countries.

Prospective cohort study. All patients admitted over 14 months to 57 psychiatric inpatient units in Belgium, Germany, Italy, Poland and United Kingdom were screened. Putative predictors were collected from medical records and in face-to-face interviews and tested for their association with LoS.

Average LoS varied from 17.9 days in Italy to 55.1 days in Belgium. In the overall sample being homeless, receiving benefits, social isolation, diagnosis of psychosis, greater symptom severity, substance use, history of previous admission and being involuntarily admitted predicted longer LoS. Several predictors showed significant interaction effects with countries in predicting LoS. One variable, homelessness, predicted a different LoS even in opposite directions, whilst for other predictors the direction of the association was the same, but the strength of the association with LoS varied across countries.

The same patient characteristics have a different impact on LoS in different contexts. Thus, although some predictor variables related to clinical severity and social dysfunction appear of generalisable relevance, national studies on LoS are required to understand the complex influence of different patient characteristics on clinical practice in the given contexts.

Patient satisfaction is a key indicator of inpatient care quality and is associated with clinical outcomes following admission. Different patient characteristics have been inconsistently linked with satisfaction. This study aims to overcome previous limitations by assessing which patient characteristics are associated with satisfaction within a large study of psychiatric inpatients conducted across five European countries.

All patients with a diagnosis of psychotic (F2), affective (F3) or anxiety/somataform (F4) disorder admitted to 57 psychiatric inpatient units in Belgium, Germany, Italy, Poland and the UK were included. Data were collected from medical records and face-to-face interviews, with patients approached within 2 days of admission. Satisfaction with inpatient care was measured on the Client Assessment of Treatment Scale.

Higher satisfaction scores were associated with being older, employed, living with others, having a close friend, less severe illness and a first admission. In contrast, higher education levels, comorbid personality disorder and involuntary admission were associated with lower levels of satisfaction. Although the same patient characteristics predicted satisfaction within the five countries, there were significant differences in overall satisfaction scores across countries. Compared to other countries, patients in the UK were significantly less satisfied with their inpatient care.

Having a better understanding of patient satisfaction may enable services to improve the quality of care provided as well as clinical outcomes for all patients. Across countries, the same patient characteristics predict satisfaction, suggesting that similar analytical frameworks can and should be used when assessing satisfaction both nationally and internationally.

In Europe, at discharge from a psychiatric hospital, patients with severe mental illness may be exposed to one of two main care approaches: personal continuity, where one clinician is responsible for in- and outpatient care, and specialisation, where various clinicians are. Such exposure is decided through patient-clinician agreement or at the organisational level, depending on the country’s health system. Since personal continuity would be more suitable for patients with complex psychosocial needs, the aim of this study was to identify predictors of patients’ exposure to care approaches in different European countries.

Data were collected on 7302 psychiatric hospitalised patients in 2015 in Germany, Poland, and Belgium (patient-level exposure); and in the UK and Italy (organisational-level exposure). At discharge, patients were exposed to one of the care approaches according to usual practice. Putative predictors of exposure at patients’ discharge were assessed in both groups of countries.

Socially disadvantaged patients were significantly more exposed to personal continuity. In all countries, the main predictor of exposure was the admission hospital, except in Germany, where having a diagnosis of psychosis and a higher education status were predictors of exposure to personal continuity. In the UK, hospitals practising personal continuity had a more socially disadvantaged patient population.

Even in countries where exposure is decided through patient-clinician agreement, it was the admission hospital, not patient characteristics, that predicted exposure to care approaches. Nevertheless, organisational decisions in hospitals tend to expose socially disadvantaged patients to personal continuity.

Evaluate antimicrobial stewardship interventions targeted to reduce highly active antiretroviral therapy (HAART)– or opportunistic infection (Ol)–related medication errors and increase error resolution.

Retrospective before-after study.

Academic medical center.

Inpatients who were prescribed antiretroviral therapy before the intervention (January 1, 2011, to October 31, 2011) and after the intervention (July 1, 2012, to December 31, 2012). Patients treated with lamivudine or tenofovir monotherapy for hepatitis B were excluded.

Antimicrobial stewardship interventions included education, modification of electronic medication records, collaboration with the infectious diseases (ID) department, and prospective audit and review of HAART and OI regimens by an ID clinical pharmacist.

Data for 162 admissions from the preintervention period and 110 admissions from the postintervention period were included. The number of admissions with a medication error was significantly reduced after the intervention (81 [50%] of 162 admissions vs 37 (34%) of 110 admissions; P < .00)1. A total of 124 errors occurred in the preintervention group (mean no. of errors, 1.5 per admission), and 43 errors occurred in the postintervention group (mean no. of errors, 1.2 per admission). The most common error types were major drug interactions and dosing in the preintervention group and renal adjustment and OI-related errors in the postintervention group. A significantly higher error resolution rate was observed in the postintervention group (36% vs 74%; P < .001). After adjustment for potential confounders with logistic regression, admission in the postintervention group was independently associated with fewer medication errors (odds ratio, 0.4 [95% confidence interval, 0.24-0.77]; P = .005). Overall, presence of an ID consultant demonstrated a higher error resolution rate (32% without a consultation vs 68% with a consultation; P = .002).

Multifaceted, multidisciplinary stewardship efforts reduced the rate and increased the overall resolution of HAART-related medication errors.