Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data.

Background: Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Background: Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

The risk of diabetes and HbA1c deterioration during antipsychotic drug treatment: A Danish two-cohort study among patients with first-episode schizophrenia.

Background: Antipsychotics increase the risk of developing diabetes, but clinical trials are not generalizable with short follow-up, while observational studies often lack important information, particularly hemoglobin A1c (HbA1c).

Methods: We followed two Danish cohorts with schizophrenia. First, using Danish nationwide registers, we identified all individuals diagnosed with first-episode schizophrenia (FES) between 1999 and 2019 (n = 31,856).

Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness.

Background: Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.

Lexical stability of psychiatric clinical notes from electronic health records over a decade.

Objective: Natural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice - as well as the systems and databases in which clinical notes are recorded and stored - change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models.

Effect of the GLP-1 receptor agonist semaglutide on metabolic disturbances in clozapine-treated or olanzapine-treated patients with a schizophrenia spectrum disorder: study protocol of a placebo-controlled, randomised clinical trial (SemaPsychiatry).

Introduction: Clozapine and olanzapine are some of the most effective antipsychotics, but both are associated with weight gain and relevant metabolic disturbances, including pre-diabetes and diabetes. Non-pharmacological/behavioural interventions have had limited effects counteracting these adverse effects. Semaglutide, a glucagon-like peptide 1 receptor agonist, is approved for the treatment of type 2 diabetes and obesity.

Validation of ratings on the six-item Positive and Negative Syndrome Scale obtained via the Simplified Negative and Positive Symptoms Interview among outpatients with schizophrenia.

Background: The six-item Positive and Negative Syndrome Scale (PANSS-6) is a measure of the severity of core symptoms of schizophrenia, which can be administered via the brief Simplified Negative and Positive Symptoms Interview (SNAPSI). A recent study has confirmed the validity of PANSS-6 ratings as derived by SNAPSI (PANSS-6) among inpatients with schizophrenia.

Aims: We aimed to test the validity of PANSS-6 among outpatients with schizophrenia using PANSS-6 ratings extracted from the 30-item PANSS-30 as derived by the Structured Clinical Interview for the Positive and Negative Syndrome Scale (PANSS-6) as a gold standard reference.

Stability of diagnostic coding of psychiatric outpatient visits across the transition from the second to the third version of the Danish National Patient Registry.

Objective: In Denmark, data on hospital contacts are reported to the Danish National Patient Registry (DNPR). The ICD-10 main diagnoses from the DNPR are often used as proxies for mental disorders in psychiatric research. With the transition from the second version of the DNPR (DNPR2) to the third (DNPR3) in February-March 2019, the way main diagnoses are coded in relation to outpatient treatment changed substantially.

Monitoring of COVID-19 pandemic-related psychopathology using machine learning.

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright 'COVID-19-related psychopathology'. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region.

The PSYchiatric clinical outcome prediction (PSYCOP) cohort: leveraging the potential of electronic health records in the treatment of mental disorders.

Background: The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalised predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes.

Splice cassette II of Na+,HCO3(-) cotransporter NBCn1 (slc4a7) interacts with calcineurin A: implications for transporter activity and intracellular pH control during rat artery contractions.

Activation of Na(+),HCO3(-) cotransport in vascular smooth muscle cells (VSMCs) contributes to intracellular pH (pH(i)) control during artery contraction, but the signaling pathways involved have been unknown. We investigated whether physical and functional interactions between the Na(+),HCO3(-) cotransporter NBCn1 (slc4a7) and the Ca(2+)/calmodulin-activated serine/threonine phosphatase calcineurin exist and play a role for pHi control in VSMCs. Using a yeast two-hybrid screen, we found that splice cassette II from the N terminus of NBCn1 interacts with calcineurin Aβ.