Acta Psychiatrica Scandinavica最新文献

Objective: While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach.

Method: We combined individual patient data from two randomized controlled trials (total N = 161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD≤7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios.

Results: Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR = 0.75; bootstrapped confidence interval (bCI) = 0.44-1.00) whereas retardation (OR = 1.21; bCI = 1.00-2.02) and hypochondriasis (OR = 1.31; bCI = 1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found.

Conclusion: By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.

Background: Urbanicity is a well-established risk factor for psychosis. Our recent multi-national study found an association between urbanicity and clinical psychosis in Northern Europe but not in Southern Europe. In this study, we hypothesized that the effect of current urbanicity on variation of schizotypy would be greater in North-western Europe countries than in Southern Europe ones.

Methods: We recruited 1080 individuals representative of the populations aged 18-64 of 14 different sites within 5 countries, classified as either North-western Europe (England, France, and The Netherlands) with Southern Europe (Spain and Italy). Our main outcome was schizotypy, assessed through the Structured Interview for Schizotypy-Revised. Our main exposure was current urbanicity, operationalized as local population density. A priori confounders were age, sex, ethnic minority status, childhood maltreatment, and social capital. Schizotypy variation was assessed using multi-level regression analysis. To test the differential effect of urbanicity between North-western and Southern European, we added an interaction term between population density and region of recruitment.

Results: Population density was associated with schizotypy (β = 0.248,95%CI = 0.122-0.375;p < 0.001). The addition of the interaction term improved the model fit (likelihood test ratio:χ² = 6.85; p = 0.009). The effect of urbanicity on schizotypy was substantially stronger in North-western Europe (β = 0.620,95%CI = 0.362-0.877;p < 0.001) compared with Southern Europe (β = 0.190,95%CI = 0.083-0.297;p = 0.001).

Conclusions: The association between urbanicity and both subclinical schizotypy and clinical psychosis, rather than being universal, is context-specific. Considering that urbanization is a rapid and global process, further research is needed to disentangle the specific factors underlying this relationship.

Background: Parents of twins appear to be at increased risk of postpartum depression (PPD), yet little is known about the magnitude and timing of onset in the postpartum period compared to singleton parents.

Methods: We conducted a cohort study using the Danish nationwide health registers. We defined a study population of parents that is, mothers and fathers of all twin and singleton livebirths between 1997 and 2019. Postpartum depression was defined as incident depression diagnosis or a redeemed antidepressant prescription from childbirth through 365 days postpartum. We performed a parametric time-to-event analysis based on Poisson regression. The time scale was time since birth, modeled using restricted cubic splines. From this we estimated the hazard ratio (HR) representing the momentary risk, and the cumulative risk ratio (RR) over the first year postpartum, in twin compared to singleton parents.

Results: The study population was based on 27,095 twin and 1,350,046 singleton births. In adjusted analyses, the HR of twins compared to singletons was highest around 2 months postpartum (HR 1.28, 95% CI 1.10-1.49) for mothers, and around 6 months (1.20, 95% CI 1.02-1.42) for fathers. The 6 months adjusted cumulative RR of PPD in twins compared to singletons was 1.24 (95% CI 1.10-1.40) for mothers and 1.11 (95% CI 0.95-1.30) for fathers.

Conclusions: Twin mothers had increased risk of PPD compared to singleton mothers, which was driven by an immediate increase after childbirth. The risk among twin fathers was not increased immediately after childbirth, but we found slightly elevated risk around 6 months postpartum. This could suggest diverse patterns of PPD symptomatology in twin parents compared to singleton parents and between mothers and fathers. Our findings underline parents of twins as a potentially vulnerable group to PPD and emphasize the need for increased awareness of their mental health.

Background: Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients.

Methods: We analyzed data from 54 adults with BD, who wore Fitbits and completed bi-weekly self-report measures for 9 months. We applied machine learning (ML) models to Fitbit data aggregated over two-week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two-week windows with scores above established clinical cutoffs for the Patient Health Questionnaire-8 (PHQ-8) and Altman Self-Rating Mania Scale (ASRM) respectively.

Results: As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC-AUC) in the validation process. In the testing set, the ROC-AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%).

Conclusion: We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.

Background: Antipsychotics increase the risk of developing diabetes, but clinical trials are not generalizable with short follow-up, while observational studies often lack important information, particularly hemoglobin A1c (HbA1c).

Methods: We followed two Danish cohorts with schizophrenia. First, using Danish nationwide registers, we identified all individuals diagnosed with first-episode schizophrenia (FES) between 1999 and 2019 (n = 31,856). Exposure was a redeemed prescription for an antipsychotic, and the outcome was diabetes, defined via hospital-based diagnosis and redeemed prescriptions for glucose-lowering drugs. Adjusted Cox regression calculated hazard rate ratios (HRR). Second, using data from the Central Denmark Region, we identified all individuals diagnosed with FES from October 2016 to September 2022 (n = 2671). Using a within-subject design, we analyzed the change in HbA1c during the 2 years after initiation of specific antipsychotics compared to the 2 years before.

Results: In the nationwide cohort, 2543 (8.0%) individuals developed diabetes (incidence rate = 9.39 [95% CI = 9.03-9.76] per 1000 person-years). Antipsychotics, compared to periods without, were associated with an increased risk of developing diabetes (HRR = 2.04, 95% CI = 1.75-2.38). We found a dose-response association, particularly for second-generation antipsychotics, and different risk rates for specific antipsychotics. In the Central Denmark Region cohort, a total of 9.2% developed diabetes but mean HbA1c levels remained stable at 37 mmol/mol during the 2 years after initiation of antipsychotic medication.

Conclusion: This comprehensive real-world two-cohort study emphasizes that diabetes affects almost 10% of patients with FES. Antipsychotics increase this risk, while HbA1c deterioration requires longer treatment. These findings are important for clinicians and young patients with FES.

Introduction: There is a "traditional belief" that antidepressant side effect complaints improve with medication persistence; however, support for this theory has remained inconclusive. We aimed to examine if side effect complaints improved over time by modeling the relationship between side effect complaints and time at dropout for patients receiving citalopram during the first level of acute treatment in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial.

Methods: We categorized the 2833 patients into five patterns by week of dropout. We used pattern-mixture modeling to model change in side effect complaints (frequency, intensity, and burden) over the 12-week course of treatment, while accounting for attrition and depressive severity. Using post-hoc linear contrasts, we compared the attrition patterns with the completers' pattern for severity of side effect complaints at each respective last visit prior to dropout as well as averaged side effect complaints across the duration of treatment. We also reported frequencies and tolerability of side effects for nine organ/function systems over the course of treatment.

Results: Patients who dropped out early exhibited worsening side effect burden and patients who dropped out later showed improvements in side effect frequency and intensity. Treatment completers improved in all side effect complaints over the course of treatment. Early attrition patterns had more severe side effect complaints for both tests of post-hoc linear contrasts than later attrition patterns and completers.

Conclusions: Side effect complaints from antidepressant treatment improve over time, but only for some types of patients. As a precaution for early dropout, clinicians should monitor patients who exhibit worsening and more severe side effect complaints-especially in the first 6 weeks of antidepressant treatment. In addition, clinicians may want to consider changing the type of treatment early on for these patients, rather than encouraging them to persist with their current medication.

Background: Finding effective treatment regimens for bipolar disorder is challenging, as many patients suffer from significant symptoms despite treatment. This study investigated the risk of relapse (psychiatric hospitalization) and treatment safety (non-psychiatric hospitalization) associated with different doses of antipsychotics and mood stabilizers in persons with bipolar disorder.

Methods: Individuals aged 15-65 with bipolar disorder were identified from Finnish national health registers in 1996-2018. Studied antipsychotics included olanzapine, risperidone, quetiapine, aripiprazole; mood stabilizers lithium, valproic acid, lamotrigine, and carbamazepine. Medication use was divided into three time-varying dose categories: low, standard, and high. The studied outcomes were risk of psychiatric hospitalization (relapse) and the risk of non-psychiatric hospitalization (treatment safety). Stratified Cox regression in within-individual design was used.

Results: The cohort included 60,045 individuals (mean age 41.7 years, SD 15.8; 56.4% female). Mean follow-up was 8.3 years (SD 5.8). Of antipsychotics, olanzapine and aripiprazole were associated with a decreased risk of relapse in low and standard doses, and risperidone in low dose. The lowest adjusted hazard ratio (aHR) was observed for standard dose aripiprazole (aHR 0.68, 95% CI 0.57-0.82). Quetiapine was not associated with a decreased risk of relapse at any dose. Mood stabilizers were associated with a decreased risk of relapse in low and standard doses; lowest aHR was observed for standard dose lithium (aHR 0.61, 95% CI 0.56-0.65). Apart from lithium, high doses of antipsychotics and mood stabilizers were associated with an increased risk of non-psychiatric hospitalization. Lithium was associated with a decreased risk of non-psychiatric hospitalization in low (aHR 0.88, 95% CI 0.84-0.93) and standard doses (aHR 0.81, 95% CI 0.74-0.88).

Conclusions: Standard doses of lithium and aripiprazole were associated with the lowest risk of relapse, and standard dose of lithium with the lowest risk of non-psychiatric hospitalization. Quetiapine was not associated with decreased risk of relapse at any dose.

Introduction: People with severe mental illness (SMI) have a higher risk of suicide compared with the general population. However, variations in suicide methods between people with different SMIs have not been examined. The aim of this pre-registered (PROSPERO CRD42022351748) systematic review was to pool the odds of people with SMI who die by suicide versus those with no SMI, stratified by suicide method.

Methods: Searches were conducted on December 11, 2023 across PubMed, PsycInfo, CINAHL, and Embase. Eligible studies were those that reported suicide deaths stratified by SMI and suicide methods. Studies were pooled in a random-effects meta-analysis, and risk of bias was measured by the Joanna Briggs Institute checklist.

Results: After screening, 12 studies were eligible (n = 380,523). Compared with those with no SMI, people with schizophrenia had 3.38× higher odds of jumping from heights (95% CI: 2.08-5.50), 1.93× higher odds of drowning (95% CI: 1.50-2.48). People with bipolar disorder also had 3.2× higher odds of jumping from heights (95% CI: 2.70-3.78). Finally, people with major depression had 3.11× higher odds of drug overdose (95% CI: 1.53-6.31), 2.11× higher odds of jumping from heights (95% CI: 1.93-2.31), and 2.33× lower odds of dying by firearms (OR = 0.43, 95% CI: 0.33-0.56). No studies were classified as high risk of bias, and no outcomes had high levels of imprecision or indirectness.

Conclusion: These findings could inform lethal means counselling practices in this population. Additionally individual, clinical, community and public health interventions for people with SMI should prioritise, where feasible, means restriction including access to heights or drugs to overdose.