Identification Of Bipolar Disorder Research Articles

Framework towards critical event classification of bipolar disorder in internet of things ecosystem

<p><span lang="EN-US">Bipolar disorder is quite a challenging mental illness which encounters substantial degree of challenges in confirmed diagnosis irrespective of modernized increasing pace of development in medical science. With the evolving standards of automation in healthcare section integrated with advanced technology, it is imperative to anticipate a reliable on-line diagnosis of mental illness for a given scenario of internet of things (IoT). Review of existing methodologies showcases a wide gap between enormous research work towards identification of bipolar disorder and only few studies towards on-line diagnosis considering patients residing in smart city. Therefore, the proposed scheme introduces a novel computational framework of an underlying architecture of an IoT that not only facilities an effective and simplified transmission of multimodal data autonomously from the patient forwarded to clinical analytical unit but also perform a multitier classification using deep neural network. The study outcome exhibits proposed scheme to offer better data transmission with higher accuracy performance in contrast to existing prevalent schemes.</span></p>

Development, Validation and Application of the Oxidative Stress Injury Biomarkers Model (BIOS Model) for Predicting Bipolar Disorder

Background: Early identification of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools available to estimate the risk of BD. This study aimed to develop and validate a model of oxidative stress injury for predicting BD. Methods: Data of 1,252 BD and 1,359 MDD patients were collected, including 64 MDD patients who were identified as converting to BD from 2009 through 2018. 30 variables from a randomly selected subsample of 1,827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, prealbumin), sex hormones, cytokines, thyroid and liver function, glycolipid metabolism, etc. Univariate analyses and the Least Absolute Shrinkage and Selection Operator (LASSO) were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct BIOS model on nomogram. Internal validation assessed in the remaining 784 patients (30%), and independent external validation done by 3,797 matched patients from five other hospitals in China. Results: 10 predictors, mainly the oxidative stress markers, were shown on nomogram. BIOS model showed good discrimination in the training sample, with AUC of 75.1% (95% CI: 72.9%~77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was well both at internal validation (AUC 72.1%, 68.6~75.6%) and external validation (AUC 65.7%, 63.9~67.5%). Conclusion: A nomogram centered on oxidative stress injury could help individualized prediction of BD. For better real-world practice, a set of measurements especially on oxidative stress markers should be emphasized using big data in psychiatry. Funding: China Key R&D Program (2016YFC1307100), the NSFC (81930033, 81771465, 91232719), SMHC-CRC (CRC2018DSJ01-1), and the Innovative Research Team of High-level Local Universities in Shanghai. Declaration of Interest: None to declare. Ethical Approval: The study was registered at International Clinical Trials Registry Platform with the number of NCT03949218. Ethical issue was approved by the SMHC Institution Review Board (IRB, number of 2019-15R) and the informed consent requirement was omitted according to relevant research.

Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

BackgroundBipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.MethodsIn total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.ResultsAfter using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022).ConclusionsA combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

26.3 CLINICIAN DECISION-MAKING AND EXPERT RATINGS OF PEDIATRIC BIPOLAR DISORDER VIGNETTES

Over recent decades, there has been an increase in the rates of bipolar disorder (BD) in children and adolescents. The increase was likely attributed to both better recognition and identification of BD, as well as overdiagnosis. Diagnostic conceptualization can be affected by heuristics and biases in clinical decision making. Interrater reliability in clinical practice using routine clinical procedures, compared with diagnostic interviews and rating scales, is understudied and often poor. A review of the diagnosis and challenges associated with symptom recognition and conceptualization of BD will be provided, along with the impact of heuristics and biases in clinical decisions.

22.1 Early Identification of Bipolar Disorder: A Meta-Analytic Approach

22.1 Early Identification of Bipolar Disorder: A Meta-Analytic Approach

Bipolar disorder in children

Pediatricians are increasingly confronted with the mental health needs of children. Given the unanticipated role, well-described diagnostic guidelines and treatment protocols are essential: but often lacking. Identification of bipolar disorder in children, a condition which lacks diagnostic criteria consensus, presents a particular challenge. Despite this, it is generally regarded as a condition associated with considerable morbidity and mortality. Extended delays to treatment, typical for the condition, contribute to significantly reduced adult functionality. Most children with bipolar disorder exhibit a subsyndromal course of illness. This has prompted many investigative groups to explore whether such a presentation is developmental or unique. Despite the ongoing debate, there has been a rapid increase in the rate of diagnoses. Concurrently, breakthroughs in neurology, neuroimaging, and genetics have called into question the existing conceptually based psychiatric constructs altogether. New research approaches which reflect these advances are more likely to lead to evidence-based diagnosis and treatment. Such an example is a novel phenotype called Fear of Harm (FOH). A new research perspective resulted in the unification of a broad range of symptoms from bipolar disorder as well as many of the co-occurring disorders. When considered as a whole, the syndrome maps on to a known neural pathway and has led investigators to a putative biomarker. If given the right information and tools, pediatricians are uniquely positioned to interrupt the decline caused by mental illnesses. Importantly, the newly defined FOH syndrome includes clinical symptoms which are frequently first brought to the attention of pediatricians. Although these symptoms are not exclusive to the mood disorder, they could alert pediatricians to the need for further evaluation.

Bipolar Disorder is Common in Depressed Primary Care Patients

Objective: To examine the prevalence and treatment of typical and subsyndromal bipolar disorder (BD) in depressed primary care patients. Methods: Patients with a diagnosis of a mood disorder being treated with antidepressants in 2 academic family practice clinics underwent a structured diagnostic interview (Mini International Neuropsychiatric Inventory) for manic and hypomanic symptoms. Results: Of 58 patients evaluated, 19% met full criteria for bipolar I disorder and 8.6% for bipolar II disorder; 31% had subsyndromal BD (ie, persistent elation or irritability accompanied by additional symptoms of hypomania but not enough symptoms to meet full criteria for bipolar I or II disorder). Only 5 patients with BD were receiving mood stabilizers, which had not been instituted in any patient by the primary care physician. Conclusions: The high prevalence of patients meeting full criteria for BD and the low rate of identification of BD in primary care patients are consistent with estimates using self-administered questionnaires, but the interview revealed a substantial additional population that could be considered to have subsyndromal BD. Because subsyndromal forms of BD are associated with significant impairment and comorbidity as well as progression to frank BD, recognition of both full and subthreshold BD in primary care practice should be improved.

Depression diagnoses following the identification of bipolar disorder: costly incongruent diagnoses

BackgroundPrevious research has documented that the symptoms of bipolar disorder are often mistaken for unipolar depression prior to a patient's first bipolar diagnosis. The assumption has been that once a patient receives a bipolar diagnosis they will no longer be given a misdiagnosis of depression. The objectives of this study were 1) to assess the rate of subsequent unipolar depression diagnosis in individuals with a history of bipolar disorder and 2) to assess the increased cost associated with this potential misdiagnosis.MethodsThis study utilized a retrospective cohort design using administrative claims data from 2002 and 2003. Patient inclusion criteria for the study were 1) at least 2 bipolar diagnoses in 2002, 2) continuous enrollment during 2002 and 2003, 3) a pharmacy benefit, and 4) age 18 to 64. Patients with at least 2 unipolar depression diagnoses in 2003 were categorized as having an incongruent diagnosis of unipolar depression. We used propensity scoring to control for selection bias. Utilization was evaluated using negative binomial models. We evaluated cost differences between patient cohorts using generalized linear models.ResultsOf the 7981 patients who met all inclusion criteria for the analysis, 17.5% (1400) had an incongruent depression diagnosis (IDD). After controlling for background differences, individuals who received an IDD had higher rates of inpatient and outpatient psychiatric utilization and cost, on average, an additional $1641 per year compared to individuals without an IDD.ConclusionsA strikingly high proportion of bipolar patients are given the differential diagnosis of unipolar depression after being identified as having bipolar disorder. Individuals with an IDD had increased acute psychiatric care services, suggesting higher levels of relapses, and were at risk for inappropriate treatment, as antidepressant therapy without a concomitant mood-stabilizing medication is contraindicated in bipolar disorder. Further prospective research is needed to validate the findings from this retrospective administrative claims-based analysis.

Identification of bipolar disorder in women with postpartum depression

No studies to date have assessed the pharmacological management of treatment-resistant postpartum depression. We reviewed the pharmacological treatment of postpartum depression in patients diagnosed with treatment-resistant 'unipolar' depression. We conducted a chart review of patients treated consecutively at a perinatal clinic. Treatment-resistant postpartum depression was defined as a failure to respond to at least one adequate antidepressant trial. Patients were diagnosed using the DSM-IV criteria, and the Clinical Global Impression-Improvement (CGI-I) rating scale was used to assess response to various pharmacological interventions. The majority of patients (57%, 34/60) referred for postpartum depression actually suffered from bipolar disorder. All patients were on antidepressants at the time of referral, but by the end of the study 37% (22/60) continued on antidepressants alone or in combination with other medications. CGI-I ratings showed appreciable improvement in depression at the end of six months following the initial consultation. Very much improvement was noted in 65% (39/60) of patients, and 22% (13/60) were considered much improved. The most common change in medication was a switch to or addition of an atypical neuroleptic. Retrospective design, small sample size, and lack of a control group. Management of treatment resistance in women with postpartum depression should be considered within the context of types of mood disorders. Atypical neuroleptics and mood stabilizers used alone or as adjuncts should be considered in the treatment of resistant postpartum depression in patients with a bipolar diathesis.

A population-based analysis of distinguishers of bipolar disorder from major depressive disorder

BackgroundMany people with bipolar disorder (BD) in the community are misdiagnosed with major depressive disorder (MDD). A probabilistic model has been proposed to assist in the identification of BD among patients with depressive symptoms, however there are limited population-based data on the key distinguishers of BD from MDD. The objective of this study was to identify distinguishers of BD from MDD in a population-based sample. MethodsPopulation-based data were extracted from the Canadian Community Health Survey: Mental Health and Well-Being. Sociodemographic variables, clinical variables, and depressive symptomatology were compared between subjects with BD (N=467) and MDD (N=4145). Logistic regression analysis was used to identify significant correlates of BD, and areas under the receiver operating characteristic curves (AUCs) were determined for each model. ResultsBD and MDD subjects differed across a number of characteristics. Clinical variables significantly associated with BD included greater number of lifetime depressive episodes, earlier age of first depressive episode, lifetime anxiety disorder, problematic substance use, and lifetime suicide attempt. Symptoms significantly more common during a major depressive episode among BD subjects included agitation, suicidal ideation, anxious symptoms, and irritability. AUCs for these models ranged from 0.72 to 0.81. LimitationsData were not available for all potential distinguishers; subgroups of BD could not be determined; cross-sectional data. ConclusionsThese population-based results reinforce the effort to establish a generalizable probabilistic model that incorporates clinical and symptom variables in order to assist clinicians in the diagnostic assessment of BD.