Comparative effectiveness of valproic acid in different serum concentrations for maintenance treatment of bipolar disorder: A retrospective cohort study using target trial emulation framework

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Valproic acid monitoring: Serum prediction using a machine learning framework from multicenter real-world data

BACKGROUND

Our study employs machine learning to predict serum valproic acid (VPA) concentrations, aiming to contribute to the development of non-invasive assays for therapeutic drug monitoring.

METHODS

Medical records from 2002 to 2019 were obtained from the Taiwan Chang Gung Research Database. Using various machine learning algorithms, we developed predictive models to classify serum VPA concentrations into two categories (1-50 μg/ml or 51-100 μg/ml) and predicted the exact concentration value. The models were trained on 5142 samples and tested on 644 independent samples. Accuracy was the main metric used to evaluate model performance, with a tolerance of 20 μg/ml for continuous variables. Furthermore, we identified important features and developed simplified models with fewer features.

RESULTS

The models achieved an average accuracy of 0.80-0.86 for binary outcomes and 0.72-0.88 for continuous outcome. Ten top features associated with higher serum VPA levels included higher VPA last and daily doses, bipolar disorder or schizophrenia spectrum disorder diagnoses, elevated levels of serum albumin, calcium, and creatinine, low platelet count, low percentage of segmented white blood cells, and low red cell distribution width-coefficient of variation. The simplified models had an average accuracy of 0.82-0.86 for binary outcome and 0.70-0.86 for continuous outcome.

LIMITATIONS

The study's predictive model lacked external test data from outside the hospital for validation.

CONCLUSIONS

Machine learning models have the potential to integrate real-world data and predict VPA concentrations, providing a promising tool for reducing the need for frequent monitoring of serum levels in clinical practice.

Mortality and Lithium-Protective Effects after First-Episode Mania Diagnosis in Bipolar Disorder: A Nationwide Retrospective Cohort Study in Taiwan

INTRODUCTION

This study aimed to estimate all-cause mortality in patients after a first-episode mania (FEM) and examine whether six guideline-recommended medications can reduce mortality.

METHODS

The cohort included population-based FEM samples and matched controls from Taiwan, spanning 2007 to 2018. The primary outcomes assessed were all-cause/suicide-related mortality, while the secondary outcome focused on mortality associated with pharmacological treatments. We compared mortality in post-FEM patients and age-/sex-matched controls without any diagnosed bipolar disorders and patients with and without psychopharmacological treatment using Cox regression analysis, respectively. Statistics were presented with time-to-event adjusted hazard ratios (AHRs) and 95% confidence intervals (CIs).

RESULTS

The study included 54,092 post-FEM patients and 270,460 controls, totaling 2,467,417 person-years of follow-up. Post-FEM patients had higher risks of all-cause mortality (AHR 2.38, 95% CI: 2.31-2.45) and suicide death (10.80, 5.88-19.84) than controls. Lithium (0.62, 0.55-0.70), divalproex (0.89, 0.83-0.95), and aripiprazole (0.81, 0.66-1.00) were associated with reduced all-cause mortality compared to non-users. There were no significant all-cause mortality differences for quetiapine (0.95, 0.89-1.01), risperidone (0.92, 0.82-1.02), and paliperidone (1.24, 0.88-1.76) users. When accounting for drug action onset times in sensitivity analyses, only lithium significantly reduced all-cause mortality (AHR range 0.65-0.72). There were 35 and 16 suicide deaths in post-FEM patients and controls, respectively. No drug had a significant effect on suicide deaths (lithium: 6; divalproex: 7; aripiprazole: 0; quetiapine: 10; risperidone: 4; paliperidone: 1).

CONCLUSION

Post-FEM patients had a higher risk of all-cause/suicide-related mortality, and lithium treatment might reduce all-cause mortality.

Effects of Sex Differences and Combined Use of Clozapine on Initial Dosage Optimization of Valproic Acid in Patients with Bipolar Disorder

BACKGROUND

Due to the narrow therapeutic window and large pharmacokinetic variation of valproic acid (VPA), it is difficult to make an optimal dosage regimen. The present study aims to optimize the initial dosage of VPA in patients with bipolar disorder.

METHODS

A total of 126 patients with bipolar disorder treated by VPA were included to construct the VPA population pharmacokinetic model retrospectively. Sex differences and combined use of clozapine were found to significantly affect VPA clearance in patients with bipolar disorder. The initial dosage of VPA was further optimized in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.

RESULTS

The CL/F and V/F of VPA in patients with bipolar disorder were 11.3 L/h and 36.4 L, respectively. It was found that sex differences and combined use of clozapine significantly affected VPA clearance in patients with bipolar disorder. At the same weight, the VPA clearance rates were 1.134, 1, 1.276884, and 1.126 in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. This study further optimized the initial dosage of VPA in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively.

CONCLUSION

This study is the first to investigate the initial dosage optimization of VPA in patients with bipolar disorder based on sex differences and the combined use of clozapine. Male patients had higher clearance, and the recommended initial dose decreased with increasing weight, providing a reference for the precision drug use of VPA in clinical patients with bipolar disorder.

Alterations in salivary biochemical composition and redox state disruption induced by the anticonvulsant valproic acid in male rat salivary glands

OBJECTIVE

To investigate the effects of the anticonvulsant valproic acid (VPA) on salivary glands in male rat using biochemical, functional, histomorphometric, and redox state parameters.

MATERIALS AND METHODS

Twenty-four male Wistar rats were randomly distributed into three groups (n = 8 per group): Control (0.9% saline solution), VPA100 (100 mg/kg), and VPA400 (400 mg/kg). After 21 consecutive days of treatment with by intragastric gavage. Pilocarpine-induced saliva was collected to determine salivary flow rate, pH, buffering capacity, and biochemical composition. Analyses of histomorphometric parameters and redox balance markers were performed on the parotid and submandibular glands.

RESULTS

Salivary flow rate, pH, buffering capacity, total protein, potassium, sodium, and chloride were similar between groups. However, phosphate and calcium were reduced in VPA400, while amylase was increased in both VPA100 and VPA400. We did not detect significant differences in the areas of acini, ducts, and connective tissue in the salivary glands between the groups. There were no significant changes in the redox status of the submandibular glands. In turn, in the parotid glands we detected reduced total oxidizing capacity and lipid peroxidation, measured as thiobarbituric acid reactive substances (TBARs) and higher uric acid concentration in both the VPA100 and VPA400 groups, and increased superoxide dismutase (SOD) in the VPA400 group.

CONCLUSION

Chronic treatment with VPA modified the salivary biochemical composition and caused disruption in the redox state of the parotid gland in rats.

Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review

Importance

Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice.

Objective

To assess the reporting of observational studies that explicitly aimed to emulate a target trial.

Evidence Review

We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation.

Findings

A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation.

Conclusion and Relevance

In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

Introduction

Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data.

Methods

We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models.

Results

In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke.

Discussion

Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

Therapeutic and Toxic Effects of Valproic Acid Metabolites

Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington's disease, Parkinson's disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains the diversity of its active and inactive metabolites, which have therapeutic, neutral, or toxic effects. It is known that some active metabolites of VPA may have a stronger clinical effect than VPA itself. These reasons explain the relevance of this narrative review, which summarizes the results of studies of blood (serum, plasma) and urinary metabolites of VPA from the standpoint of the pharmacogenomics and pharmacometabolomics. In addition, a new personalized approach to assessing the cumulative risk of developing VPA-induced adverse reactions is presented and ways for their correction are proposed depending on the patient's pharmacogenetic profile and the level of therapeutic and toxic VPA metabolites in the human body fluids (blood, urine).