Journal of Clinical Psychiatry最新文献

Persons with mental health disorders are at increased risk of dental disease, including lost teeth. Dental implants are the preferred option for most persons who have lost teeth. Recent studies suggest that antidepressant drugs, especially the selective serotonin reuptake inhibitors, are associated with an increased risk of dental implant failure. This article provides a background about the epidemiology of loss of teeth, the causes of tooth loss, the need to replace lost teeth, and the use of dental implants to replace lost teeth. Two meta-analyses of retrospective cohort studies of the association between antidepressant use and implant failure are examined in detail. One meta-analysis included 6 studies and the other, 10 studies. An additional retrospective cohort study, published after the meta-analyses, is also examined. In summary, there is consistent evidence for a higher risk of implant failure in patients taking antidepressants, and for a higher number of implants failing in patients taking antidepressants, relative to patients not taking antidepressants. Broad findings were that, at the patient level, implant failure occurred in 6%-23% of antidepressant users vs 2%-8% of nonusers, and at the implant level, implant failure occurred in 6%-22% of antidepressant users vs 2%-9% of nonusers. Because unadjusted risks were more than doubled in antidepressant users, it implies that, in the real world, antidepressant use is a clinically important marker for risk of implant failure; it is hard to draw cause and effect inferences from the studies reviewed because of inadequacies in study designs and statistical methods. Action points are that antidepressant users should be educated about the risk of implant failure, and vigorous efforts should be made to identify and negate, to the extent possible, other risk factors for implant failure in these patients. Suggestions are offered for future research in the field.

Objective: Neuropsychiatric symptoms (NPS) constitute a major challenge in Alzheimer disease (AD). We applied a component-based symptom paradigm by deriving Neuropsychiatric Inventory Questionnaire (NPI-Q) clusters and evaluating their longitudinal associations with regional brain volumes and functional outcomes (instrumental activities of daily living, activities of daily living [ADLs]).

Methods: Participants with AD (N=111) were from the Ontario Neurodegenerative Disease Research Initiative. NPS were assessed using the NPI-Q. Symptom clusters were identified via principal components analysis at baseline. Magnetic resonance imaging-derived volumes for 34 cortical and 9 subcortical regions were obtained annually over 3 years. Longitudinal associations between NPS clusters and functional outcomes were examined using linear mixed-effects models adjusting for age, sex, Montreal Cognitive Assessment (MoCA), education, visit number, and cholinesterase inhibitor use.

Results: Four clusters explained 62% of variance: hyperactivity (disinhibition, irritability, motor disturbance, agitation), psychosis (hallucinations, delusions, euphoria), neurovegetative (apathy, appetite), and affective (depression, anxiety, nighttime behavior). The hyperactivity cluster was associated with the left middle temporal (β=-0.24, P=.025) and right nucleus accumbens (β=-0.28, P=.007). The neurovegetative cluster was associated with the left middle temporal (β=-0.50, P<.001) and right nucleus accumbens (β=-0.55, P<.001). The affective cluster showed the strongest associations with the left rostral anterior cingulate (β=-0.42, P=.002) and right medial orbitofrontal cortex (β=-0.47, P=.001). All clusters predicted iADL outcomes; clusters 1, 3, and 4 also predicted ADL outcomes. Greater NPS burden, male sex, age, lower MoCA, and later visits predicted worse function.

Conclusion: NPS in AD separate into hyperactivity, psychosis, neurovegetative, and affective clusters, supporting a cluster-based paradigm linking co-occurring behavioral symptoms with brain structure and functional decline.

Objective: Major depressive disorder with peripartum onset (also known as postpartum depression [PPD]) (DSM-5) is a debilitating condition that can be characterized by difficulty transitioning out of a negative affective state. Given the rapid clinical response typically observed with brexanolone, the treatment period provides a rare opportunity to study the dynamics of this transition. The present study examined the timing and pattern of symptom changes, including leading and residual symptoms and associated changes in self-reported maternal functioning.

Methods: Using a single-arm, open-label, descriptive pilot study, 10 women with moderate-to-severe PPD received a 60-hour intravenous infusion of brexanolone following the FDA-approved regimen. Symptoms comprising the affective state were assessed at high frequency before, during, and after the infusion to track the timing and sequence of symptom changes. Exploratory analyses used repeated-measures analysis of variance with Greenhouse-Geisser correction and post hoc comparisons with adjustment for multiple measures. Data were collected from July 2022 to November 2023.

Results: All participants showed clinical response, and most reached remission within 44 hours. Symptom improvement was broad, though no single symptom consistently led the affective shift. The timing of maximal change in subjective satisfaction with mood often differed from changes in standard symptom scales. Significant (P<.001) improvements were observed in anhedonia (↓89%), rumination (↓29%), and (↑56%) maternal functioning, all of which persisted at 30-day follow-up.

Conclusions: This study provides novel insights into the rapid and individualized symptom trajectories during recovery from PPD following brexanolone infusion. These findings could have broader implications for understanding the pathophysiology of mood disorders and for developing targeted interventions that modulate brain dynamics to promote recovery from pathological affective states.

Trial Registration: ClinicalTrials.gov identifier: NCT05543746.

Objective: To identify and compare predictors of nonfatal and fatal suicidal events within 180 days of emergency department (ED) visits for mental health disorders.

Methods: This longitudinal cohort analysis assessed risk of nonfatal and fatal suicide events among a national sample of 511,559 patients presenting to an ED between 2015 and 2022 with 1 or more mental health disorder. Machine learning models were used to predict events incorporating demographic and clinical characteristics, incremental value of clinical feature sets, feature importance rankings across models, and accuracy with which a model trained to predict nonfatal outcomes predicted suicide deaths. Nonfatal attempts within 6 months of an ED visit were identified using electronic health records (n=4,525), while fatal events were identified from the National Death Index (n=434).

Results: The final sample of 872,627 ED episodes represented 511,559 individuals. The full model with 143 features achieved stronger performance for nonfatal events (area under the curve [AUC]=0.874) than for suicide deaths (AUC=0.787). The relative importance of features was highly correlated across models (r=0.82), with 73% of overlap between the top 15 predictors for nonfatal and fatal events. When the nonfatal model was applied to fatal outcomes on the same sample, performance declined (AUC=0.724), indicating limited generalizability despite similar predictors (sensitivity=78.1%, specificity=67.3%).

Conclusions: Although nonfatal and fatal suicidal events share predictors, their predictive strength and function differ. These differences underscore challenges in using models trained on nonfatal outcomes to identify risk of suicide death and contribute to the ongoing debate over whether suicidal thoughts and behaviors reflect a continuum of severity or distinct clinical pathways.