Revising Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for the bipolar disorders: Phase I of the AREDOC project

A naturalistic effectiveness study of maintenance therapies for the bipolar disorders

BACKGROUND

Treatment decision-making for individuals with bipolar disorder can be difficult. Recommendations from clinical practice guidelines can be affected by multiple methodological limitations, while pharmaco-epidemiological data suggest great variety in prescription practices across regions. Given these inconsistencies, this study aimed to provide an alternative perspective on the effectiveness of common bipolar disorder maintenance treatments through considering naturalistic data.

METHODS

A total of 246 individuals with bipolar disorder (84 bipolar I [BP-I], 162 bipolar II [BP-II]) were recruited through clinics and/or websites. All were euthymic and had trialled at least one mood stabiliser. They completed an online survey containing questions on demographics, clinical variables, symptomatology, and the effectiveness/side effect profiles of any mood stabilisers (MSTs) or atypical antipsychotics (AAPs) that they have taken.

RESULTS

Lithium and lamotrigine were the most commonly prescribed MSTs and the most effective at mood stabilisation. Lithium and lamotrigine appeared marginally more effective for BP-I and BP-II respectively, however, only the latter difference was statistically significant. Furthermore, lamotrigine had the more favourable side effect profile. Amongst the AAPs, quetiapine and olanzapine were the most commonly prescribed, but they were negligibly superior to other AAPs.

CONCLUSION

This study clearly established a preference for lamotrigine in the maintenance treatment of BP-II. While the literature consistently emphasises the primacy of lithium in bipolar disorder treatment, its side effect profile as observed in this study remains a concern. Future research considering moderators of treatment response and concomitant medications could help to identify further nuances to consider for treatment decision-making.

Immune function, gastrointestinal hormone levels, and their clinical significance in patients with gastric ulcers complicated with depression

BACKGROUND

Gastric ulcer (GU) is a common digestive tract disease, and medical records of GU combined with depression are increasingly common. Currently, the risk factors and pathogenesis of GU complicated with depression remain unclear. Low immune function and gastrointestinal hormone levels may also be significant risk factors. Therefore, this study explored the immune function and gastrointestinal hormone levels in patients with GU combined with depression.

AIM

To explore the immune function, gastrointestinal hormone level, and clinical significance of patients with GU combined with depression.

METHODS

A retrospective analysis was conducted on 300 patients with GU combined with depression admitted to Guizhou Provincial People's Hospital from January 2021 to June 2022 as the study subjects. According to the Hamilton Depression Scale (HAMD) score, patients were divided into mild-to-moderate (n = 210) and heavy (n = 90) groups. Basic data, immune function indices [immunoglobulin A (IgA), IgM, IgG, serum CD4+ and CD8+ percentage, and CD4+/CD8+ ratio], and gastrointestinal hormone indices [serum gastrin (GAS), cholecystokinin (CCK), and motilin (MTL) levels] were collected. The basic data of the two groups were compared, and the immune function and gastrointestinal hormone indices were analyzed. Multivariate logistic regression was used to analyze the factors influencing the severity of GU complicated with depression. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to analyze the value of the immune function index, gastrointestinal hormone index, and combined index in predicting the severity of GU complicated with depression.

RESULTS

There were no marked differences in sex, age, body mass index, abdominal distension, abdominal pain, belching, nausea, vomiting, or sleep disorders between the heavy and mild-to-moderate groups (P > 0.05). There was a marked difference in the family history of depression between the heavy and mild-to-moderate groups (P < 0.05). There were significant differences in serum IgA and IgM levels and serum CD4+, CD8+, and CD4+/CD8+ ratios between the heavy and mild-to-moderate groups (P < 0.05). Multivariate analysis showed that IgA, IgM, GAS, and CCK serum levels influenced the severity of GU with depression (P < 0.05). The AUC of the ROC curve for serum IgA level predicting GU with depression severity was 0.808 [95% confidence interval (CI): 0.760-0.857], the AUC of the serum IgM level was 0.757 (95%CI: 0.700-0.814), the AUC of the serum GAS level was 0.853 (95%CI: 0.810-0.897), the AUC of the serum CCK level was 0.762 (95%CI: 0.709-0.822), the AUC of immune function (IgA, IgM) and gastrointestinal hormone levels (GAS, CCK) for the prediction of GU with depression severity was 0.958 (95%CI: 0.933-0.976).

CONCLUSION

Important factors influencing GU complicated with depression are serum IgA, IgM, GAS, and CCK indicators. They can be used as indicators to predict the severity of GU complicated with depression.

Bipolar disorder in the International Classification of Diseases-Eleventh version: A review of the changes, their basis, and usefulness

The World Health Organization’s 11^th revision of the International Classification of Diseases (ICD-11) including the chapter on mental disorders has come into effect this year. This review focuses on the “Bipolar or Related Disorders” section of the ICD-11 draft. It describes the benchmarks for the new version, particularly the foremost principle of clinical utility. The alterations made to the diagnosis of bipolar disorder (BD) are evaluated on their scientific basis and clinical utility. The change in the diagnostic requirements for manic and hypomanic episodes has been much debated. Whether the current criteria have achieved an optimum balance between sensitivity and specificity is still not clear. The ICD-11 definition of depressive episodes is substantially different, but the lack of empirical support for the changes has meant that the reliability and utility of bipolar depression are relatively low. Unlike the Diagnostic and Statistical Manual of Mental Disorders, 5^th edition (DSM-5), the ICD-11 has retained the category of mixed episodes. Although the concept of mixed episodes in the ICD-11 is not perfect, it appears to be more inclusive than the DSM-5 approach. Additionally, there are some uncertainties about the guidelines for the subtypes of BD and cyclothymic disorder. The initial results on the reliability and clinical utility of BD are promising, but the newly created diagnostic categories also appear to have some limitations. Although further improvement and research are needed, the focus should now be on facing the challenges of implementation, dissemination, and education and training in the use of these guidelines.

The AREDOC project and its implications for the definition and measurement of the bipolar disorders: A summary report

OBJECTIVES

Judging that the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) criteria for defining mania/hypomania (and thus bipolar I/II disorders, respectively) would benefit from review, we formed an expert taskforce to derive modified criteria for consideration. The aim of this paper is to summarise the component stages and detail the final recommended criteria.

METHODS

We first sought taskforce members' views on the Diagnostic and Statistical Manual of Mental Disorders criteria and how they might be modified. Next, members recruited patients with a bipolar I or II disorder, and who were asked to judge new definitional options and complete a symptom checklist to determine the most differentiating items. The latter task was also completed by a small comparison group of unipolar depressed patients to determine the mood state items that best differentiate unipolar from bipolar subjects. Subsequent reports overviewed analyses arguing for bipolar I and II as being categorically distinct and generated empirically derived diagnostic criteria.

RESULTS

Alternatives to all the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) criteria were generated. Modifications included recognising that impairment is not a necessary criterion, removing hospitalisation as automatically assigning bipolar I status, adding an irritable/angry symptom construct to the symptom list, deleting a mandatory duration period for manic/hypomanic episodes, and requiring a greater number of affirmed symptoms for a bipolar diagnosis to manage the risk of overdiagnosis. Granular symptom criteria were identified by analyses and constructed to assist clinician assessment. A potential bipolar screening measure was developed with analyses showing that it could clearly distinguish bipolar versus unipolar status, whether symptom items were assigned as having equal status or weighted by their quantified diagnostic contribution.

CONCLUSION

While requiring further validation, we suggest that the revised criteria overcome several current Diagnostic and Statistical Manual of Mental Disorders (5th ed.) limitations to defining and differentiating the two bipolar sub-types, while still respecting and preserving the Diagnostic and Statistical Manual of Mental Disorders template. It will be necessary to determine whether the bipolar screening measure has superiority to currently accepted measures.

The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders

BACKGROUND

While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic criterion as having equivalent weighting values; an approach which may compromise diagnostic assignment if symptoms vary considerably in their diagnostic sensitivity. We therefore sought to develop a new measure and examine whether a weighted rating scale was superior to one assigning equivalent weightings to each item.

METHODS

An international sample of 165 bipolar patients and a comparison sample of 29 unipolar patients completed a measure assessing 96 putative manic/hypomanic symptoms. A previous machine learning analysis had identified the twenty most discriminating items. In this study, analysis was undertaken involving only the ten most discriminating items.

RESULTS

Whether items were scored as each having equivalent value or as weighted by their machine learning-generated values, classificatory accuracy was extremely high (in the order of 96%). Analyses also identified optimal cut-off scores. High classificatory accuracy was also obtained when scores for separate bipolar I and bipolar II groups were compared with scores from the unipolar group.

LIMITATIONS

The sample consisted of comparatively few unipolar patients.

CONCLUSIONS

The ten-item set allows a new measure for researchers to evaluate, while the items should assist clinician assessment as to whether a patient has a bipolar or unipolar mood disorder.

Electro-Acupuncture Combined Methadone for Withdrawal Symptoms of Opioid Addiction: A Protocol for Systematic Review and Meta-Analysis

Background<br/> Opioid addiction is a chronic brain disorder characterized by a series of withdrawal symptoms in behavioral, psychological, and neurobiological manifestations. Withdrawal symptoms are the main cause of relapse after periods of abstinence; thus, the treatment is focused on abstinence symptoms. Due to most of all types of opioid agonist drugs carry a potential for addiction and exacerbation of withdrawal symptoms, nondrug methods have great potentials in clinical applications. Electro-acupuncture (EA), as a novel nonpharmacological approach, combined with methadone has a long-term positive efficacy on treating addiction. Therefore, we designed a protocol to evaluate the adjuvant effect of EA for treating withdrawal symptoms of opioid addiction.<br/> Method<br/> To review reports of relevant clinical trials, we will search English language databases (EMBASE, PubMed, and the Cochrane Central Register of Controlled Trials) and Chinese databases (Chinese Biomedical Literatures, China National Knowledge Infrastructure, Wanfang, and VIP). We will collect documents from the earliest possible date up to May 2020. We will also search online trial registries such as ClinicalTrials. gov (ClinicalTrials.gov/), the European Medicine Agency (www.ema.europa.eu/ema/), and WHO International Clinical Trials Registry Platform (www.who.int/ictrp). We will select randomized controlled trials (RCT) for withdrawal from opioid addiction involving EA-methadone and methadone alone treatment. We will use psychological assessment scales to evaluate treatment major outcomes which include numerous components such as OWS, VAS, HAMD, HAMA; then urinalysis and methadone dosage also will be measure as the additional outcomes. Finally, RevMan5 software will be used for literature quality evaluation and data analysis.<br/> Result:<br/> To evaluate the efficacy of EA in combination therapy by observing the outcomes of corresponding scale, urinalysis and decreasing methadone.<br/> Conclusion:<br/> This protocol will be used to evaluate the efficacy and safety of EA in combination with methadone in treatment of opioid addiction withdrawal symptoms.<br/> Abbreviations: Opioid dependence, OWS=Opiate Withdrawal Scale, VAS=Craving Visual Analog Scale, PWSS=Post-withdrawal symptoms Scale, HAMD=Hamilton Depression Scale, HAMA=Hamilton Anxiety Scale, RCTs=Randomized Controlled Trials, EA=Electrical Acupuncture, PRISMA=Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Categorical differentiation of the unipolar and bipolar disorders

There has been a longstanding debate as to whether the bipolar disorders differ categorically or dimensionally, with some dimensional or spectrum models including unipolar depressive disorders within a bipolar spectrum model. We analysed manic/hypomanic symptom data in samples of clinically diagnosed bipolar I, bipolar II and unipolar patients, employing latent class analyses to determine if separate classes could be identified. Mixture analyses were also undertaken to determine if a unimodal, bimodal or a trimodal pattern was present. For both a refined 15-item set and an extended 30-item set of manic/hypomanic symptoms, our latent class analyses favoured three-class solutions, while mixture analyses identified trimodal distributions of scores. Findings argue for a categorical distinction between unipolar and bipolar disorders, as well as between bipolar I and bipolar II disorders. Future research should aim to consolidate these results in larger samples, particularly given that the size of the unipolar group in this study was a salient limitation.

Differentiating mania/hypomania from happiness using a machine learning analytic approach

BACKGROUND

This study aimed to improve the accuracy of bipolar disorder diagnoses by identifying symptoms that help to distinguish mania/hypomania in bipolar disorders from general 'happiness' in those with unipolar depression.

METHODS

An international sample of 165 bipolar and 29 unipolar depression patients (as diagnosed by their clinician) were recruited. All participants were required to rate a set of 96 symptoms with regards to whether they typified their experiences of manic/hypomanic states (for bipolar patients) or when they were 'happy' (unipolar patients). A machine learning paradigm (prediction rule ensembles; PREs) was used to derive rule ensembles that identified which of the 94 non-psychotic symptoms and their combinations best predicted clinically-allocated diagnoses.

RESULTS

The PREs were highly accurate at predicting clinician bipolar and unipolar diagnoses (92% and 91% respectively). A total of 20 items were identified from the analyses, which were all highly discriminating across the two conditions. When compared to a classificatory approach insensitive to the weightings of the items, the ensembles were of comparable accuracy in their discriminatory capacity despite the unbalanced sample. This illustrates the potential for PREs to supersede traditional classificatory approaches.

LIMITATIONS

There were considerably less unipolar than bipolar patients in the sample, which limited the overall accuracy of the PREs.

CONCLUSIONS

The consideration of symptoms outlined in this study should assist clinicians in distinguishing between bipolar and unipolar disorders. Future research will seek to further refine and validate these symptoms in a larger and more balanced sample.

How Does Adding the DSM-5 Criterion Increased Energy/Activity for Mania Change the Bipolar Landscape?

According to DSM-IV, the criterion (A) for diagnosing hypomanic/manic episodes is mood change (i.e., elevated, expansive or irritable mood). Criterion (A) was redefined in DSM-5 in 2013, adding increased energy/activity in addition to mood change. This paper examines a potential change of prevalence data for bipolar I or II when adding increased energy/activity to the criterion (A) for the diagnosis of hypomania/mania. Own research suggests that the prevalence of manic/hypomanic episodes drops by at least one third when using DSM-5 criteria. Whether this has positive or negative impact on clinical practice and research still needs further evaluation.

The bipolar disorders: A case for their categorically distinct status based on symptom profiles

BACKGROUND

It is unclear whether the bipolar disorders (i.e. BP-I/BP-II) differ dimensionally or categorically. This study sought to clarify this issue.

METHODS

We recruited 165 patients, of which 69 and 96 had clinician-assigned diagnoses of BP-I and BP-II respectively. Their psychiatrists completed a data sheet seeking information on clinical variables about each patient, while the patients completed a different data sheet and scored a questionnaire assessing the prevalence and severity of 96 candidate manic/hypomanic symptoms.

RESULTS

We conducted a series of analyses examining a set (and two sub-sets) of fifteen symptoms that were significantly more likely to be reported by the clinically diagnosed BP-I patients. Latent class analyses favoured two-class solutions, while mixture analyses demonstrated bimodality, thus arguing for a BP-I/BP-II categorical distinction. Statistically defined BP-I class members were more likely when manic to have experienced psychotic features and over-valued ideas. They were also more likely to have been hospitalised, and to have been younger when they received their bipolar diagnosis and first experienced a depressive or manic episode.

LIMITATIONS

The lack of agreement between some patients and managing clinicians in judging the presence of psychotic features could have compromised some analyses. It is also unclear whether some symptoms (e.g. grandiosity, noting mystical events) were capturing formal psychotic features or not.

CONCLUSIONS

Findings replicate our earlier study in providing evidence to support the modelling of BP-I and BP-II as categorically discrete conditions. This should advance research into aetiological factors and determining optimal (presumably differing) treatments for the two conditions.

Refined diagnostic criteria for the bipolar disorders: phase two of the AREDOC project

OBJECTIVE

As limitations exist across DSM criteria sets for defining and differentiating the bipolar disorders generally and their component bipolar I (BP-1) and bipolar II (BP-II) sub-types, we sought to generate empirically based criteria.

METHOD

We formed an international Task Force (TF) comprising members with bipolar disorder expertise, and who recruited 74 patients with a TF-diagnosed bipolar I and 104 with a bipolar II condition (with patients responding to definitional queries and symptom questionnaires), while 33 unipolar depressed patients recruited by the first author also completed the symptom questionnaire. A factor analysis sought to determine granular hypo/manic constructs.

RESULTS

The bipolar disorder subjects strongly affirmed a new general definition of a bipolar disorder (capturing both manic and hypomanic episodes). While DSM-5 requires impaired functioning, we established that a high percentage of individuals with a BP-I or a BP-II disorder reported improved functioning and therefore modified this criterion. Analyses identified syptoms with differential high rates in individuals with bipolar disorder and its sub-types (and thus not simply capturing happiness), while a factor analysis generated seven symptom constructs both linked with and differing from DSM-5 bipolar symptom criteria.

CONCLUSION

This second-stage report details a new set of criteria for differentiating the bipolar disorders from unipolar depressive conditions, while arguing for BP-I and BP-II disorders being differentiated principally by the respective presence or absence of psychotic features. Future studies will evaluate whether further modifications are required and examine for differential treatment benefits for those with a BP-I versus a BP-II condition.

A prospective longitudinal study searching for predictors of response to group psychoeducation in bipolar disorder

BACKGROUND

The efficacy of adjunctive group psychoeducation in bipolar disorder has been proven although treatment response differ among individuals. The aim of this study was to characterize responders and non-responders to group psychoeducation in order to identify baseline variables that could predict treatment response.

METHODS

The sample was composed of 103 medicated euthymic patients with bipolar disorder referred to 21 sessions of group psychoeducation (6 months). Sociodemographic and clinical variables, temperament, circadian rhythms, BDNF, cognitive and psychosocial functioning were collected. At the 18-month endpoint, the patients were split in two groups on the basis of having suffered any recurrence. Significant group differences were included in a logistic regression analysis.

RESULTS

Ninety patients out of 103 engaged in group psychoeducation, 47 of whom (52.2%) responded to psychoeducation and 43 (47.8%) did not. Recurrences occurred more often in the follow-up, the most common being depression. Responders and non-responders differed in gender, age at diagnosis, latency of diagnosis, temperament, attention composite score and BDNF. Lower age at diagnosis of bipolar disorder, lower cyclothimic temperament scores and being male -which was associated with bipolar type I and a trend to more previous manic episodes- were significantly related to a better response to psychoeducation in the regression analysis.

LIMITATIONS

No control group.

CONCLUSIONS

This study identifies age at diagnosis as a significant modifiable risk factor of treatment response, highlighting the need for early identification of bipolar disorder. Existing programs should be adjusted to the characteristics of specific subpopulations in the framework of a personalized approach.