Critical Predictors for the Early Detection of Conversion From Unipolar Major Depressive Disorder to Bipolar Disorder: Nationwide Population-Based Retrospective Cohort Study

Factors associated with self-perceived treatment-resistance in bipolar disorder

Patients with bipolar disorder often report self-perceived treatment resistance. However, it is not known to what extent it is due to actual treatment resistance. The Juntendo University provides "Bipolar Disorder Treatment Rebuilding Program," in which patients with self-reported treatment resistant bipolar disorder are hospitalized for 2 weeks and undergo detailed examinations. In this study, we report our experience with the initial 43 patients hospitalized during the one and half years after the launch of the program. Among the patients who underwent full assessment, only one was regarded as having genuine treatment-resistant bipolar disorder without comorbidity. In other cases, ten were not diagnosed with bipolar disorder, 3 had organic brain diseases, 12 had comorbid mental disorders and its symptoms were regarded as treatment-resistant bipolar symptoms by the patients, and 18 did not receive adequate treatment because attendant physicians did not adhere to the treatment guidelines or patients did not adhere to the treatment because of lack of insight. The number of participants was not large, and selection bias hampered the generalization of the findings. Insight and adherence were assessed without the use of validated tools. We could not verify recovery after adequate treatment because of the limited hospitalization period. The findings suggest that most patients with self-perceived treatment-resistant bipolar disorder may not have genuine treatment-resistant bipolar disorder. These results shed light on the difficulties of public education of bipolar disorder and importance of providing appropriate services for diagnosis and treatment of bipolar disorder in the community.

An analysis of Chinese nursing electronic medical records to predict violence in psychiatric inpatients using text mining and machine learning techniques

BACKGROUND

The prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance.

OBJECTIVE

The present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients.

METHODS

We collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data.

RESULTS

The results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence.

CONCLUSIONS

Our findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.

A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

BACKGROUND

Bipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion.

METHOD

By following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods.

RESULTS

It was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability.

CONCLUSION

The risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study's advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients' survival risk before a CABG operation, early prevention and disease management would be possible.

Association between Depression, Antidepression Medications, and the Risk of Developing Type 2 Diabetes Mellitus: A Nationwide Population-Based Retrospective Cohort Study in Taiwan

The relationship between depression, antidepressant medications (ADMs), and the risk of subsequent type 2 diabetes mellitus (T2DM) development remains controversial. Thus, we investigated this aspect by a population-based retrospective cohort study using the Longitudinal Health Insurance Database 2000 available in Taiwan. This large, observational study included 46,201 patients with depression and a 1 : 1 age- and sex-matched nondepression cohort enrolled between January 1, 2000, and December 31, 2013, and the newly diagnosed T2DM incidence rates were determined. We estimated the effects of depression on T2DM and the cumulative incidence curves by Cox proportional regression hazard models and Kaplan-Meier methods, respectively. We found that 47.97% of the patients with depression did not receive ADM. Among patients with depression who received ADM, 29.71%, 6.29%, 0.05%, 9.65%, and 6.32% received selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), monoamine oxidase inhibitors (MAOIs), heterocyclic antidepressants, and other medications, respectively. Patients without ADM treatment had a 39% higher risk of developing T2DM. However, those who received ADM treatment had a significantly lower risk of T2DM development in every treatment category. Depressive disorder treated with ADMs, especially with long-term use, was associated with an 11–48% decrease in the risk of T2DM in all ADM groups; however, heterocyclic antidepressant treatment for shorter periods (<80 days) was not significantly associated with a decreased risk of T2DM. The incidence of T2DM in Taiwan was found to be associated with an a priori history of depression and was inversely correlated with ADM treatment.