Sex-Specific Associations Between Bipolar Disorder Pharmacological Maintenance Therapies and Inpatient Rehospitalizations: A 9-Year Swedish National Registry Study

Ragazan, Dragos C.; Eberhard, Jonas; Berge, Jonas

doi:10.3389/fpsyt.2020.598946

Cited by 7 publications

(5 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a post hoc analysis (n = 929) female patients appeared to be at a greater risk for relapse and recurrence with valproate but not with lithium maintenance treatment [ 66 ], while in a nationwide cohort study (n = 15,988) lithium and valproate are comparable between males and females patients for reducing rehospitalization rate [ 95 ].…”

Section: Resultsmentioning

confidence: 99%

Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors

Crapanzano¹,

Casolaro

Amendola

et al. 2022

Clin Psychopharmacol Neurosci

View full text Add to dashboard Cite

Since decades, lithium and valproate remain the pharmacological cornerstone to treat bipolar disorder. Different response patterns occur according to the phases of illness. At same time, individual pretreatment variables may concur to determine a specific drug-response. Our narrative review focuses on these two key clinical aspects to summarize the state of art. Information from i) clinical trials and ii) the most relevant international guidelines is collected to assess the clinical and preclinical factors that may guide the use of lithium rather than valproate. Lithium may be effective in treating acute mania, and lithium efficacy is maximized when used to prevent both manic and depressive episodes. Lithium may be a better treatment choice in patients with: positive family history for bipolar disorder, mania-depression-interval pattern, few previous affective episodes/hospitalizations, high risk for suicide, no comorbidities. Valproate may be more effective as antimanic rather than prophylactic agent. Valproate might be a better choice in patients with many previous affective episodes/hospitalizations and psychiatric comorbidities. Finally, neither lithium nor valproate are suggested for the treatment of acute mixed states or bipolar depression. To consider clinical and preclinical factors may thus be useful to select the best treatment strategy.

show abstract

Section: Resultsmentioning

confidence: 99%

Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors

Crapanzano¹,

Casolaro

Amendola

et al. 2022

Clin Psychopharmacol Neurosci

View full text Add to dashboard Cite

show abstract

“…To our best knowledge, this is one of the first studies assessing response to mood stabilizer treatment in subjects suffering from BD by using a validated tool. Previous studies investigated differences in treatment response as evaluated by indirect indexes, e.g., rehospitalizations (27). The lack of significant sex differences in response to mood stabilizers already emerged from prior research (27).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies investigated differences in treatment response as evaluated by indirect indexes, e.g., rehospitalizations (27). The lack of significant sex differences in response to mood stabilizers already emerged from prior research (27). When stratifying subjects according to the prescribed drug, better treatment response to anticonvulsants was demonstrated among men, despite the Alda scale score were classified in an intermediate response range for both sexes.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Sex Differences in Bipolar Disorders: Impact on Psychopathological Features and Treatment Response

et al. 2022

View full text Add to dashboard Cite

IntroductionSex differences were demonstrated in bipolar disorders (BD) concerning epidemiological, clinical, and psychopathological characteristics, but consensus is lacking. Moreover, data concerning the influence of sex on treatment response in BD is contrasting. The present cross-sectional study aimed to analyze sex differences in a population of BD subjects, with specific focus on psychopathological features and treatment response.Materials and MethodsSubjects diagnosed with BD according to the Diagnostic and Statistical Manual of Mental Disorders, 5th version (DSM-5) were recruited. Socio-demographic and clinical characteristics were collected. The Hamilton Rating Scale for Depression, the Mania Rating Scale (MRS), the brief version of the Temperament Evaluation of Memphis, Pisa and San Diego—Münster version (briefTEMPS-M), and the Barratt Impulsiveness Scale−11 items (BIS-11) were used for psychopathological assessment. Treatment response was appraised with the Alda Scale. We performed bivariate analyses to compare socio-demographic, clinical, and psychopathological characteristics between men and women (p < 0.05). A logistic regression was run to analyze features that were significantly associated with female sex.ResultsAmong the recruited 219 BD subjects, 119 (54.3%) were females. Women had a lower scholarity (p = 0.015) and were less frequently employed (p = 0.001). As for psychopathological features, a higher MRS total score (p < 0.001) was detected among women, as well as higher BIS-11 total score (p = 0.040), and briefTEMPS-M score for anxious temperament (p = 0.006). Men showed higher prevalence of DSM-5 mixed features (p = 0.025), particularly during a depressive episode (p = 0.014). Women reported longer duration of untreated illness (DUI) (p < 0.001). There were no sex differences in the Alda Scale total score when considering the whole sample, but this was significantly higher among men (p = 0.030) when evaluating subjects treated with anticonvulsants. At the logistic regression, female sex was positively associated with longer DUI (p < 0.001; OR 1.106, 95% CI 1.050–1.165) and higher MRS total score (p < 0.001; OR 1.085, 95% CI 1.044–1.128) and negatively associated with employment (p = 0.003; OR 0.359, 95% CI 0.185–0.698) and DSM-5 mixed features (p = 0.006; OR 0.391, 95% CI 0.200–0.762).ConclusionsThe clinical presentation of BD may differ depending on sex. The severity of BD should not be neglected among women, who may also display worse treatment response to anticonvulsants.

show abstract

“…For example, men with schizophrenia spectrum disorders rated higher on average on the BPRS and SANS than men with bipolar disorder, while women with schizophrenia spectrum disorders on average scored lower than women with bipolar disorder on all rating scales. Alternatively, notable sex-specific variations in the prevalence, onset, symptom profiles, and outcome have been identified in the literature and have been attributed to differences in premorbid functioning, psychosocial response to symptoms, and differing levels of circulating hormones and receptors [67][68][69][70]. Audiovisual data may therefore detect subtle physiological differences unique to each sex and present in the expression of psychiatric disorders.…”

Section: Discussionmentioning

confidence: 99%

Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development

Birnbaum¹,

Abrami²,

Heisig³

et al. 2022

JMIR Ment Health

View full text Add to dashboard Cite

Background In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. Objective We aimed to investigate whether reliable inferences—psychiatric signs, symptoms, and diagnoses—can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. Methods We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. Results The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner–pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). Conclusions This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

show abstract

Sex-Specific Associations Between Bipolar Disorder Pharmacological Maintenance Therapies and Inpatient Rehospitalizations: A 9-Year Swedish National Registry Study

Cited by 7 publications

References 57 publications

Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors

Lithium and Valproate in Bipolar Disorder: From International Evidence-based Guidelines to Clinical Predictors

Sex Differences in Bipolar Disorders: Impact on Psychopathological Features and Treatment Response

Acoustic and Facial Features From Clinical Interviews for Machine Learning–Based Psychiatric Diagnosis: Algorithm Development

Contact Info

Product

Resources

About