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1.
J Psychiatr Res ; 103: 237-243, 2018 08.
Article in English | MEDLINE | ID: mdl-29894922

ABSTRACT

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment.


Subject(s)
Bipolar Disorder/diagnostic imaging , Brain Mapping , Brain/diagnostic imaging , Machine Learning , Magnetic Resonance Imaging/methods , Adult , Correlation of Data , Female , Humans , Image Processing, Computer-Assisted , Male , Middle Aged , Psychiatric Status Rating Scales , Statistics, Nonparametric
2.
Braz. J. Psychiatry (São Paulo, 1999, Impr.) ; 37(2): 121-125, 12/05/2015. tab, graf
Article in English | LILACS | ID: lil-748986

ABSTRACT

Objectives: Staging models for medical diseases are widely used to guide treatment and prognosis. Bipolar disorder (BD) is a chronic condition and it is among the most disabling disorders in medicine. The staging model proposed by Kapczinski in 2009 presents four progressive clinical stages of BD. Our aim was to evaluate pharmacological maintenance treatment across these stages in patients with BD. Methods: One hundred and twenty-nine subjects who met DSM-IV criteria for BD were recruited from the Bipolar Disorders Program at Hospital de Clínicas de Porto Alegre, Brazil. All patients were in remission. The subjects were classified according to the staging model: 31 subjects were classified as stage I, 44 as stage II, 31 as stage III, and 23 as stage IV. Results: Patterns of pharmacological treatment differed among the four stages (p = 0.001). Monotherapy was more frequent in stage I, and two-drug combinations in stage II. Patients at stages III and IV needed three or more medications or clozapine. Impairment in functional status (Functioning Assessment Short Test [FAST] scale scores) correlated positively with the number of medications prescribed. Conclusions: This study demonstrated differences in pharmacological treatment in patients with stable BD depending on disease stage. Treatment response can change with progression of BD. Clinical guidelines could consider the staging model to guide treatment effectiveness. .


Subject(s)
Adult , Female , Humans , Male , Middle Aged , Anticonvulsants/administration & dosage , Antidepressive Agents/administration & dosage , Antipsychotic Agents/administration & dosage , Bipolar Disorder/drug therapy , Clozapine/administration & dosage , Bipolar Disorder/classification , Brazil , Clinical Protocols , Disease Progression , Evidence-Based Practice , Neuropsychological Tests , Practice Patterns, Physicians' , Psychiatric Status Rating Scales , Severity of Illness Index , Socioeconomic Factors
3.
Braz J Psychiatry ; 37(2): 121-5, 2015.
Article in English | MEDLINE | ID: mdl-26018648

ABSTRACT

OBJECTIVES: Staging models for medical diseases are widely used to guide treatment and prognosis. Bipolar disorder (BD) is a chronic condition and it is among the most disabling disorders in medicine. The staging model proposed by Kapczinski in 2009 presents four progressive clinical stages of BD. Our aim was to evaluate pharmacological maintenance treatment across these stages in patients with BD. METHODS: One hundred and twenty-nine subjects who met DSM-IV criteria for BD were recruited from the Bipolar Disorders Program at Hospital de Clínicas de Porto Alegre, Brazil. All patients were in remission. The subjects were classified according to the staging model: 31 subjects were classified as stage I, 44 as stage II, 31 as stage III, and 23 as stage IV. RESULTS: Patterns of pharmacological treatment differed among the four stages (p = 0.001). Monotherapy was more frequent in stage I, and two-drug combinations in stage II. Patients at stages III and IV needed three or more medications or clozapine. Impairment in functional status (Functioning Assessment Short Test [FAST] scale scores) correlated positively with the number of medications prescribed. CONCLUSIONS: This study demonstrated differences in pharmacological treatment in patients with stable BD depending on disease stage. Treatment response can change with progression of BD. Clinical guidelines could consider the staging model to guide treatment effectiveness.


Subject(s)
Anticonvulsants/administration & dosage , Antidepressive Agents/administration & dosage , Antipsychotic Agents/administration & dosage , Bipolar Disorder/drug therapy , Clozapine/administration & dosage , Adult , Bipolar Disorder/classification , Brazil , Clinical Protocols , Disease Progression , Evidence-Based Practice , Female , Humans , Male , Middle Aged , Neuropsychological Tests , Practice Patterns, Physicians' , Psychiatric Status Rating Scales , Severity of Illness Index , Socioeconomic Factors
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