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Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation.

ABSTRACT: We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications. The main outcome was at least one diagnostic code reflective of a bipolar disorder diagnosis within 3 months of index antidepressant prescription. Logistic regression and random forests using diagnostic and procedure codes as well as sociodemographic features were used to predict this outcome, with discrimination and calibration assessed in a held-out test set and then a second academic medical center. Among 67,807 individuals who received at least one antidepressant medication, 925 (1.36%) subsequently received a diagnosis of bipolar disorder within 3 months. Models incorporating coded diagnoses and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80). Standard supervised machine learning methods enabled development of discriminative and transferable models to predict transition to bipolar disorder. With further validation, these scores may enable physicians to more precisely calibrate follow-up intensity for high-risk patients after antidepressant initiation.

SUBMITTER: Pradier MF

PROVIDER: S-EPMC7852537 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder.

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| S-EPMC10864308 | biostudies-literature

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| S-EPMC7300293 | biostudies-literature

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| S-EPMC4047134 | biostudies-other

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| S-EPMC3140026 | biostudies-literature

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| S-EPMC7006450 | biostudies-literature

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| S-EPMC6294408 | biostudies-literature

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Project description:Antidepressants exhibit similar efficacy, but varying tolerability, in randomized controlled trials. Predicting tolerability in real-world clinical populations may facilitate personalization of treatment and maximize adherence. This retrospective longitudinal cohort study aimed to determine the extent to which incorporating patient history from electronic health records improved prediction of unplanned treatment discontinuation at index antidepressant prescription. Clinical data were analyzed from individuals from health networks affiliated with two large academic medical centers between March 1, 2008 and December 31, 2014. In total, the study cohorts included 51,683 patients with at least one International Classification of Diseases diagnostic code for major depressive disorder or depressive disorder not otherwise specified who initiated antidepressant treatment. Among 70,121 total medication changes, 16,665 (23.77%) of them were followed by failure to return; maximum risk was observed with paroxetine (27.71% discontinuation), and minimum with venlafaxine (20.78% discontinuation); Mantel-Haenzel ?2 (8?df)?=?126.44, p?=?1.54e-23 <1e-6. Models incorporating diagnostic and procedure codes and medication prescriptions improved per-medication Areas Under the Curve (AUCs) to a mean of 0.69 [0.64-0.73] (ranging from 0.62 for paroxetine to 0.80 for escitalopram), with similar performance in the second, replication health system. Machine learning applied to coded electronic health records facilitates identification of individuals at high-risk for treatment dropout following change in antidepressant medication. Such methods may assist primary care physicians and psychiatrists in the clinic to personalize antidepressant treatment on the basis not solely of efficacy, but of tolerability.

| S-EPMC7026064 | biostudies-literature

Subthreshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder.

Project description:The authors assessed whether subthreshold hypomanic symptoms in patients with major depression predicted new-onset mania or hypomania.The authors identified 550 individuals followed for at least 1 year in the National Institute of Mental Health Collaborative Depression Study with a diagnosis of major depression at intake. All participants were screened at baseline for five manic symptoms: elevated mood, decreased need for sleep, unusually high energy, increased goal-directed activity, and grandiosity. Participants were followed prospectively for a mean of 17.5 years and up to 31 years. The Longitudinal Interval Follow-up Examination was used to monitor course of illness and to identify any hypomania or mania. The association of subthreshold hypomanic symptoms at baseline with subsequent hypomania or mania was determined in survival analyses using Cox proportional hazards regression.With a cumulative probability of one in four on survival analysis, 19.6% (N=108) of the sample experienced hypomania or mania, resulting in revision of diagnoses for 12.2% to bipolar II disorder and 7.5% to bipolar I disorder. Number of subthreshold hypomanic symptoms, presence of psychosis, and age at illness onset predicted progression to bipolar disorder. Decreased need for sleep, unusually high energy, and increased goal-directed activity were specifically implicated.Symptoms of hypomania, even when of low intensity, were frequently associated with subsequent progression to bipolar disorder, although the majority of patients who converted did not have any symptoms of hypomania at baseline. These results suggest that continued monitoring for the possibility of progression to bipolar disorder is necessary over the long-term course of major depressive disorder.

| S-EPMC3042431 | biostudies-literature

Affect Regulation-Related Emergent Brain Network Properties Differentiate Depressed Bipolar Disorder From Major Depression and Track Risk for Bipolar Disorder.

Project description:BackgroundIndividuals with or at risk for bipolar disorder (BD) often present initially for the treatment of depressive symptoms. Unfortunately, pharmacological treatments for major depressive disorder (MDD) can be iatrogenic, precipitating mania that may not have otherwise occurred. Current diagnostic procedures rely solely on self-reported/observable symptoms, and thus alternative data sources, such as brain network properties, are needed to supplement current self-report/observation-based indices of risk for mania.MethodsBrain connectivity during affect maintenance/regulation was examined in a large (N = 249), medication-free sample of currently depressed patients with BD (n = 50) and MDD (n = 116) and healthy control subjects (n = 83). BD risk was categorized in a subset of patients with MDD. We used graph theory to identify emergent network properties that differentiated between patients with BD and MDD and between patients with MDD at high and low risk for BD.ResultsBD and MDD differed in the extent to which the rostral anterior cingulate cortex was embedded in the local network, amount of influence the hippocampus exerted over global network communication, and clarity of orbitofrontal cortex communication. Patients with MDD at high risk for BD showed a pattern of local network clustering around the right amygdala that was similar to the pattern observed in healthy control subjects, whereas patients with MDD at low risk for BD deviated from this pattern.ConclusionsBD and MDD differed in emergent network mechanisms subserving affect regulation, and amygdala properties tracked BD risk in patients with MDD. If replicated, our findings may be combined with other markers to assess the presence of BD and/or BD risk in individuals presenting with depressive symptoms to prevent the use of iatrogenic treatments.

| S-EPMC8993939 | biostudies-literature

Adolescent residential mobility, genetic liability and risk of schizophrenia, bipolar disorder and major depression.

Project description:BackgroundResidential mobility during upbringing, and especially adolescence, is associated with multiple negative mental health outcomes. However, whether associations are confounded by unmeasured familial factors, including genetic liability, is unclear.AimsWe used a population-based case-cohort study to assess whether polygenic risk scores (PRSs) for schizophrenia, bipolar disorder and major depression were associated with mobility from ages 10-14 years, and whether PRS and parental history of mental disorder together explained associations between mobility and each disorder.MethodInformation on cases (n = 4207 schizophrenia, n = 1402 bipolar disorder, n = 18 215 major depression) and a random population sample (n = 17 582), born 1981-1997, was linked between Danish civil and psychiatric registries. Genome-wide data were obtained from the Danish Neonatal Screening Biobank and PRSs were calculated based on results of separate, large meta-analyses.ResultsPRSs for schizophrenia and major depression were weakly associated with moving once (odds ratio 1.07, 95% CI 1.00-1.16; and odds ratio 1.10, 95% CI 1.04-1.17, respectively), but not twice or three or more times. Mobility was positively associated with each disorder, with more moves associated with greater risk. Adjustment for PRS produced slight reductions in the magnitude of associations. Adjustment for PRS and parental history of mental disorder together reduced estimates by 5-11%. In fully adjusted models mobility was associated with all three disorders; hazard ratios ranged from 1.33 (95% CI 1.08-1.62; one move and bipolar disorder) to 3.05 (95% CI 1.92-4.86; three or more moves and bipolar disorder).ConclusionsAssociations of mobility with schizophrenia, bipolar disorder and depression do not appear to be attributable to genetic liability as measured here. Potential familial confounding of mobility associations may be predominantly environmental in nature.

| S-EPMC8130005 | biostudies-literature

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