Lack of association between erythropoietin treatment and risk of depression in patients with end-stage kidney disease on maintenance dialysis: a nationwide database study in Taiwan

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.

Month of birth and the risk of narcolepsy: a systematic review and meta-analysis

STUDY OBJECTIVES

The aim of this study is to evaluate the relationship between the month of birth (MOB) and the risk of narcolepsy.

METHODS

We conducted a systematic review of electronic databases, namely PubMed, Embase, and Cochrane CENTRAL, from their inception to September 30, 2021. We also added data on narcolepsy from the National Database in Taiwan. Then we extracted the relative risk ratios (RR) of narcolepsy in each month of birth to that of the general population and transformed them from month of birth to season. A random-effects model was used to calculate pooled RRs from the meta-analysis and 95% confidence interval (CI).

RESULTS

The current meta-analysis analyzed seven studies and included 3776 patients from eight areas. The RR was highest in March (RR 1.11 [95% CI 0.99-1.26]) or August (1.11 [0.98-1.26]) and lowest in April (0.90 [0.78-1.03]). However, none of the MOBs reached statistical significance. Moreover, the patterns of the three continents were different. In North America, the highest and lowest significant risks were found in March (1.47 [1.20-1.79]) and September (0.75 [95% CI 0.56-0.99]). In Asia, the notable lowest risk was in April (0.80 [0.66-0.97]). In Europe, the risk of narcolepsy is not significantly related to any MOB. In terms of seasons, only spring births in North America had a significantly higher risk (1.21 [1.06-1.38]).

CONCLUSIONS

The findings indicated that the risk of narcolepsy and MOB differed across the three continents. This study indicates the important role of environmental factors in narcolepsy.

SYSTEMATIC REVIEW REGISTRATION

Registry: PROSPERO; Identifier: CRD42020186660.