Breast and Prostate Cancer Screening by Life Expectancy in Patients with Kidney Failure on Dialysis.

BACKGROUND: The Choosing Wisely campaign suggests an individualized approach to cancer screening among patients receiving dialysis. We aimed to evaluate breast and prostate cancer screening among patients receiving maintenance hemodialysis (HD) by kidney transplant waitlist status and five-year survival probability. METHODS: We conducted a retrospective cohort study using a nationally representative population of HD patients. Patients receiving HD each calendar year from 2003-2018, ≥1 year of Medicare as the Primary Payer, and age 50-69 years were included. The cohort was split into prognosis and cancer screening sets. Models of five-year survival were built in the prognosis set using logistic regression. Five-year survival probabilities were generated in the cancer screening set, excluding patients with prior breast or prostate cancer, and screening over the next year was assessed. RESULTS: 160,537 patients contributed 356,165 person-years to the cancer screening set (59% of the person-years were contributed by males, median age was 60 years). Compared to a benchmark rate of 50% (e.g., mammography every other year), 42% of waitlisted female-years were screened by mammography. Overall, 17% of non-waitlisted female-years were screened (20% among those with >50% probability of five-year survival and 8% among those with <10% probability of five-year survival). Compared to a benchmark rate of 20% [e.g., serum prostate-specific antigen (PSA) screening up to five years apart], 24% of waitlisted male-years were screened with serum PSA. Overall 15% of non-waitlisted male-years were screened (13% among those with >50% probability of five-year survival and 1% among those with <10% probability of five-year survival). Patterns were similar after age-standardization. CONCLUSION: Patients with higher predicted survival have higher rates of cancer screening, suggesting providers consider life expectancy. However, non-waitlisted patients with high probability of five-year survival were less likely to be screened compared to waitlisted patients. Interventions may be needed to close this screening gap.

Factors Associated with Caregivers' Decisions to Pursue a Diagnostic Evaluation After a Positive Autism Screen in Primary Care.

PURPOSE: We sought to understand whether a child's sex, age, race, ethnicity, caregiver education, family income, and/or number of endorsed autism signs are associated with a caregiver's decision to pursue an autism diagnostic evaluation after their child received a positive autism screen. METHODS: 129 children, 17-30 months, received a positive autism screen on the Modified Checklist for Autism in Toddlers-Revised with Follow-Up, and all caregivers were offered ready access to a diagnostic evaluation by a trained professional in English or Spanish at no cost. RESULTS: 88 children received an evaluation and 41 did not. The likelihood of receiving an evaluation was associated with the child's race. Only 58.1% of Black children were evaluated, compared to 80% of Hispanic/Latino and 88.5% of White children. Children of Spanish-speaking caregivers showed high rates of evaluation completion (85.7%). Children who were evaluated versus were not evaluated did not significantly differ in terms of child's sex, number of autism signs endorsed by the caregiver, caregiver's education and preferred language (English versus Spanish), or household income. CONCLUSION: Even though the present study removed many common barriers to receiving a timely diagnostic evaluation, caregivers of Black children were less likely to pursue an autism diagnostic evaluation for their child. Future research is needed to understand the needs and perspectives of Black families to promote engagement in clinical care and reduce disparities in receiving a timely autism diagnosis which is important for accessing supports and services that can improve children's outcomes.

Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.

Objective: Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities. Methods: We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels. Results: The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning. Conclusion: As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Correlates of cannabis use and cannabis use disorder among adolescents with major depressive disorder and bipolar disorder in the National Comorbidity Survey-Adolescent Supplement (NCS-A).

BACKGROUND: Despite evidence regarding prevalence and correlates of cannabis use (CU) and cannabis use disorder (CUD) in major depressive disorder (MDD) and bipolar disorder (BD) in adults, little is known about this topic among adolescents. METHODS: Data is from the 2001-2004 National Comorbidity Survey-Adolescent Supplement, an in-person, cross-sectional epidemiologic survey of mental disorders. Participants included adolescents, ages 13-18 years, with MDD (n = 354 with CU, n = 70 with CUD, n = 688 with no CU), BD (n = 79 with CU, n = 32 with CUD, n = 184 with no CU), or adolescents without mood disorders (n = 1413 with CU, n = 333 with CUD, n = 6970 with no CU). Analyses examined prevalence and correlates of CU and CUD within MDD and BD groups. RESULTS: CU was most prevalent in adolescents with MDD followed by adolescents with BD then controls. CUD was most prevalent in adolescents with BD followed by adolescents with MDD then controls. In covariate-adjusted ordinal logistic regression models, within MDD and BD, CU and CUD groups had significantly higher odds of lifetime suicidal ideation/attempts, as well as other significant indicators of clinical severity. LIMITATIONS: Based on changes in cannabis acceptance, potency, and availability in the two decades since this study was conducted, present findings may underestimate adverse cannabis associations. CONCLUSION: CU and CUD are both associated with adverse clinical characteristics in a community-based sample of adolescents with MDD and BD. Evidence that risks of cannabis use extend across the spectrum of use is important for adolescents with MDD and BD, in whom cannabis-related consequences tend to be more severe.

Person-level contributions of bipolar polygenic risk score to the prediction of new-onset bipolar disorder in at-risk offspring.

BACKGROUND: Previous work indicates that polygenic risk scores (PRS) for bipolar disorder (BD) are elevated in adults and youth with BD, but whether BD-PRS can inform person-level diagnostic prediction is unknown. Here, we test whether BD-PRS improves performance of a previously published risk calculator (RC) for BD. METHODS: 156 parents with BD-I/II and their offspring ages 6-18 were recruited and evaluated with standardized diagnostic assessments every two years for >12 years. DNA was extracted from saliva samples, genotyping performed, and BD-PRS calculated based on a 2021 meta-analysis. Using a bootstrapped and cross-validated penalized Cox regression, we assessed whether BD-PRS (alone and interacting with clinical variables) improved RC performance. RESULTS: Of 227 offspring, 38 developed BD during follow-up. The penalized regression selected BD-PRS and interactions between BD-PRS and parental age at mood disorder onset (AAO), depression, and anxiety. The resulting RC discriminated offspring who developed BD (vs. those that did not) with good accuracy (AUC = 0.81); removing BD-PRS and its interaction terms was associated with a significant decrement to the AUC (decrement = 0.07, p = 0.039). Further exploration of selected interaction terms indicated that all were significant (p-values<0.02), indicating that BD-PRS has a larger effect on the outcome in offspring with depression and anxiety, whose affected parent had a younger AAO. CONCLUSIONS: The addition of BD-PRS to clinical/demographic predictors in the RC significantly improved its accuracy. BD-PRS predicted BD on the person-level, particularly in offspring of parents with earlier AAO who already had symptoms of anxiety and depression at intake.

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.

OBJECTIVE: This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an example of improving early prediction of an autism diagnosis, given its low prevalence, by leveraging correlations between autism and other neurodevelopmental conditions (NDCs). METHODS: To achieve this, we introduced a conditional multi-label model by merging conditional learning and multi-label methodologies. The conditional learning approach breaks a hard task into more manageable pieces in each stage, and the multi-label approach utilizes information from related neurodevelopmental conditions to learn predictive latent features. The study involved forecasting autism diagnosis by age 5.5 years, utilizing data from the first 18 months of life, and the analysis of feature importance correlations to explore the alignment within the feature space across different conditions. RESULTS: Upon analysis of health records from 18,156 children, we are able to generate a model that predicts a future autism diagnosis with moderate performance (AUROC=0.76). The proposed conditional multi-label method significantly improves predictive performance with an AUROC of 0.80 (p < 0.001). Further examination shows that both the conditional and multi-label approach alone provided marginal lift to the model performance compared to a one-stage one-label approach. We also demonstrated the generalizability and applicability of this method using simulated data with high correlation between feature vectors for different labels. CONCLUSION: Our findings underscore the effectiveness of the developed conditional multi-label model for early prediction of an autism diagnosis. The study introduces a versatile strategy applicable to prediction tasks involving limited target populations but sharing underlying features or etiology among related groups.

Addressing cost barriers to healthy eating with Eat Well, a prescription produce subsidy, for patients with diabetes and at risk for food insecurity: Study protocol for a type 1 hybrid effectiveness-implementation pragmatic randomized controlled trial.

BACKGROUND: Patients with diabetes at risk of food insecurity face cost barriers to healthy eating and, as a result, poor health outcomes. Population health management strategies are needed to improve food security in real-world health system settings. We seek to test the effect of a prescription produce program, 'Eat Well' on cardiometabolic health and healthcare utilization. We will also assess the implementation of an automated, affirmative outreach strategy. METHODS: We will recruit approximately 2400 patients from an integrated academic health system in the southeastern United States as part of a two-arm parallel hybrid type 1 pragmatic randomized controlled trial. Patients with diabetes, at risk for food insecurity, and a recent hemoglobin A1c reading will be eligible to participate. The intervention arm receives, 'Eat Well', which provides a debit card with $80 (added monthly) for 12 months valid for fresh, frozen, or canned fruits and vegetables across grocery retailers. The control arm does not. Both arms receive educational resources with diabetes nutrition and self-management materials, and information on existing care management resources. Using an intent-to-treat analysis, primary outcomes include hemoglobin A1C levels and emergency department visits in the 12 months following enrollment. Reach and fidelity data will be collected to assess implementation. DISCUSSION: Addressing food insecurity, particularly among those at heightened cardiometabolic risk, is critical to equitable and effective population health management. Pragmatic trials provide important insights into the effectiveness and implementation of 'Eat Well' and approaches like it in real-world settings. REGISTRATION: ClinicalTrials.gov Identifier: NCT05896644; Clinical Trial Registration Date: 2023-06-09.

Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.

The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and the growing availability of large EHR datasets. Existing models generally assume access to the same data sources during both training and inference stages. However, this assumption is often challenged by the fact that real-world clinical datasets originate from various data sources (with distinct sets of covariates), which though can be available for training (in a research or retrospective setting), are more realistically only partially available (a subset of such sets) for inference when deployed. So motivated, we introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS), that trains encoders to capture information across different data sources and then leverages them to build classifiers restricting access to a single data source. This approach can be used with existing cross-sectional or longitudinal outcome classification models. We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios.

Controlled Study of Metabolic Syndrome Among Offspring of Parents With Bipolar Disorder.

Objectives: Bipolar disorder (BD) is highly heritable and associated with increased rates of metabolic syndrome (MetS). However, little is known about MetS in offspring of parents with BD. We therefore examined this topic in the Pittsburgh Bipolar Offspring Study cohort.Methods: Participants included 199 parents (n = 116 BD, diagnosed using DSM-IV; n = 83 non-BD) and 330 offspring (mean age 19.9 ± 5.3 years), including 198 high-risk offspring of parents with BD (n = 80 affected with a mood disorder) and 132 control offspring. We defined MetS and its components using International Diabetes Federation (IDF) guidelines (primary) and National Cholesterol Education Program (NCEP) guidelines (secondary). Multivariable analyses controlled for age and socioeconomic status in offspring. Sensitivity analyses controlled for psychotropic medications.Results: There was higher prevalence of MetS in parents with BD as compared to controls. NCEP-defined MetS was significantly more prevalent among affected high-risk offspring (16.3%) and controls (15.2%) vs unaffected high-risk offspring (6.0%; χ2 = 6.54, P = .04). There was greater mean number of MetS components (IDF: 1.7 ± 1.1; NCEP: 1.4 ± 1.0) among affected high-risk offspring vs unaffected high-risk offspring (IDF: 1.2 ± 1.0; NCEP: 1.0 ± 1.0) and controls (IDF: 1.3 ± 1.2; NCEP: 1.1 ± 1.1; IDF: H[2] = 10.26, P = .006; NCEP: H[2] = 9.18, P = .01). Most findings became nonsignificant in multivariable analyses. Some between-group results became nonsignificant after controlling for second-generation antipsychotics.Conclusions: This preliminary study found increased risk of MetS among affected high-risk offspring, which may be attributable to socioeconomic status. Prospective studies may determine timing of MetS onset in relation to mood disorder onset, and the role of socioeconomic status in moderating this association.

Environmental Impact of Outdoor Cannabis Production.

Environmental impacts of cannabis production are of increasing concern because it is a newly legal and growing industry. Although a handful of studies have quantified the impacts of indoor production, very little is known about the impact of outdoor cannabis agriculture. Outdoor production typically uses little direct energy but can require significant fertilizer and other inputs due to dissipative losses via runoff and mineralization. Conversely, fertilizer high in nitrogen can be counterproductive, as it produces flowers with decreased cannabinoid content. This study has two aims: (1) To identify reduced-fertilizer regimes that provide optimal cannabis flower yields with reduced inputs and (2) to quantify how this shifts greenhouse gas emissions, resource depletion (fossil and metal), terrestrial acidification, and the eutrophication potential of outdoor cannabis production. Primary data from a fertilizer response trial are incorporated into a life-cycle assessment model. Results show that outdoor cannabis agriculture can be 50 times less carbon-emitting than indoor production. Dissemination of this knowledge is of utmost importance for producers, consumers, and government officials in nations that have either legalized or will legalize cannabis production.

Cerebral blood flow in relation to peripheral endothelial function in youth bipolar disorder.

INTRODUCTION: Anomalous cerebral blood flow (CBF) is evident in bipolar disorder (BD), however the extent to which CBF reflects peripheral vascular function in BD is unknown. This study investigated endothelial function, an index of early atherosclerosis and cardiovascular disease risk, in relation to CBF among youth with BD. METHODS: Participants included 113 youth, 13-20 years old (66 BD; 47 healthy controls [HC]). CBF was measured using arterial spin labeling with 3T MRI. Region of interest analyses (ROI; global grey matter, middle frontal gyrus, anterior cingulate cortex, temporal cortex, caudate) were undertaken alongside voxel-wise analyses. Reactive hyperemia index (RHI), a measure of endothelial function, was assessed non-invasively via pulse amplitude tonometry. General linear models were used to examine RHI and RHI-by-diagnosis associations with CBF, controlling for age, sex, and body mass index. Bonferroni correction for multiple comparisons was used for ROI analyses, such that the significance level was divided by the number of ROIs (α = 0.05/5 = 0.01). Cluster-extent thresholding was used to correct for multiple comparisons for voxel-wise analyses. RESULTS: ROI findings were not significant after correction. Voxel-wise analyses found that higher RHI was associated with lower left thalamus CBF in the whole group (p < 0.001). Additionally, significant RHI-by-diagnosis associations with CBF were found in three clusters: left intracalcarine cortex (p < 0.001), left thalamus (p < 0.001), and right frontal pole (p = 0.006). Post-hoc analyses showed that in each cluster, higher RHI was associated with lower CBF in BD, but higher CBF in HC. CONCLUSION: We found that RHI was differentially associated with CBF in youth with BD versus HC. The unanticipated association of higher RHI with lower CBF in BD could potentially reflect a compensatory mechanism. Future research, including prospective studies and experimental designs are warranted to build on the current findings.

Cytochrome P450-soluble epoxide hydrolase oxylipins, depression and cognition in type 2 diabetes.

AIMS: This study examined serum cytochrome P450-soluble epoxide hydrolase (CYP450-sEH) oxylipins and depressive symptoms together in relation to cognitive performance in individuals with type 2 diabetes mellitus (T2DM). METHODS: Clinically cognitively normal T2DM individuals were recruited (NCT04455867). Depressive symptom severity was assessed using the Beck Depression Inventory-II (BDI-II; total scores ≤13 indicated minimal depressive symptoms and ≥ 14 indicated significant depressive symptoms). Executive function and verbal memory were assessed. Fasting serum oxylipins were quantified by ultra-high-performance liquid chromatography tandem mass-spectrometry. RESULTS: The study included 85 participants with minimal depressive symptoms and 27 with significant symptoms (mean age: 63.3 ± 9.8 years, 49 % women). In all participants, higher concentrations of linoleic acid derived sEH (12,13-dihydroxyoctadecamonoenoic acid; DiHOME) and CYP450 (12(13)-epoxyoctadecamonoenoic acid; EpOME) metabolites were associated with poorer executive function (F1,101 = 6.094, p = 0.015 and F1,101 = 5.598, p = 0.020, respectively). Concentrations of multiple sEH substrates interacted with depressive symptoms to predict 1) poorer executive function, including 9(10)-EpOME (F1,100 = 12.137, p < 0.001), 5(6)-epoxyeicosatrienoic acid (5(6)-EpETrE; F1,100 = 6.481, p = 0.012) and 11(12)-EpETrE (F1,100 = 4.409, p = 0.038), and 2) verbal memory, including 9(10)-EpOME (F1,100 = 4.286, p = 0.041), 5(6)-EpETrE (F1,100 = 6.845, p = 0.010), 11(12)-EpETrE (F1,100 = 3.981, p = 0.049) and 14(15)-EpETrE (F1,100 = 5.019, p = 0.027). CONCLUSIONS: Associations of CYP450-sEH metabolites and depressive symptoms with cognition highlight the biomarker and therapeutic potential of the CYP450-sEH pathway in T2DM.

Incorporating informatively collected laboratory data from EHR in clinical prediction models.

BACKGROUND: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance. METHODS: We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals. RESULTS: We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results. CONCLUSION: Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models.

Enhancing Clinical Decision Support in Nephrology: Addressing Algorithmic Bias Through Artificial Intelligence Governance.

There has been a steady rise in the use of clinical decision support (CDS) tools to guide nephrology as well as general clinical care. Through guidance set by federal agencies and concerns raised by clinical investigators, there has been an equal rise in understanding whether such tools exhibit algorithmic bias leading to unfairness. This has spurred the more fundamental question of whether sensitive variables such as race should be included in CDS tools. In order to properly answer this question, it is necessary to understand how algorithmic bias arises. We break down 3 sources of bias encountered when using electronic health record data to develop CDS tools: (1) use of proxy variables, (2) observability concerns and (3) underlying heterogeneity. We discuss how answering the question of whether to include sensitive variables like race often hinges more on qualitative considerations than on quantitative analysis, dependent on the function that the sensitive variable serves. Based on our experience with our own institution's CDS governance group, we show how health system-based governance committees play a central role in guiding these difficult and important considerations. Ultimately, our goal is to foster a community practice of model development and governance teams that emphasizes consciousness about sensitive variables and prioritizes equity.

Repurposing General Anesthetic Drugs to Treat Depression: A New Frontier for Anesthesiologists in Neuropsychiatric Care.

During the last 100 years, the role of anesthesiologists in psychiatry has focused primarily on facilitating electroconvulsive therapy and mitigating postoperative delirium and other perioperative neurocognitive disorders. The discovery of the rapid and sustained antidepressant properties of ketamine, and early results suggesting that other general anesthetic drugs (including nitrous oxide, propofol, and isoflurane) have antidepressant properties, has positioned anesthesiologists at a new frontier in the treatment of neuropsychiatric disorders. Moreover, shared interest in understanding the biologic underpinnings of anesthetic drugs as psychotropic agents is eroding traditional academic boundaries between anesthesiology and psychiatry. This article presents a brief overview of anesthetic drugs as novel antidepressants and identifies promising future candidates for the treatment of depression. The authors issue a call to action and outline strategies to foster collaborations between anesthesiologists and psychiatrists as they work toward the common goals of repurposing anesthetic drugs as antidepressants and addressing mood disorders in surgical patients.

Pilot study of circulating cell-free mitochondrial DNA in relation to brain structure in youth bipolar disorder.

BACKGROUND: Mitochondrial dysfunction is implicated in the neuropathology of bipolar disorder (BD). Higher circulating cell-free mitochondrial DNA (ccf-mtDNA), generally reflecting poorer mitochondrial health, has been associated with greater symptoms severity in BD. The current study examines the association of serum ccf-mtDNA and brain structure in relation to youth BD. We hypothesized that higher ccf-mtDNA will be associated with measures of lower brain structure, particularly in the BD group. METHODS: Participants included 40 youth (BD, n = 19; Control group [CG], n = 21; aged 13-20 years). Serum ccf-mtDNA levels were assayed. T1-weighted brain images were acquired using 3T-MRI. Region of interest (ROI) analyses examined prefrontal cortex (PFC) and whole brain gray matter, alongside exploratory vertex-wise analyses. Analyses examined ccf-mtDNA main-effects and ccf-mtDNA-by-diagnosis interaction effects controlling for age, sex, and intracranial volume. RESULTS: There was no significant difference in ccf-mtDNA levels between BD and CG. In ROI analyses, higher ccf-mtDNA was associated with higher PFC surface area (SA) (ß = 0.32 p < 0.001) and PFC volume (ß = 0.32 p = 0.002) in the overall sample. In stratified analyses, higher ccf-mtDNA was associated with higher PFC SA within both subgroups (BD: ß = 0.39 p = 0.02; CG: ß = 0.24 p = 0.045). Higher ccf-mtDNA was associated with higher PFC volume within the BD group (ß = 0.39 p = 0.046). In vertex-wise analyses, higher ccf-mtDNA was associated with higher SA and volume in frontal clusters within the overall sample and within the BD group. There were significant ccf-mtDNA-by-diagnosis interactions in three frontal and parietal clusters, whereby higher ccf-mtDNA was associated with higher neurostructural metrics in the BD group but lower neurostructural metrics in CG. CONCLUSIONS: Contrasting our hypothesis, higher ccf-mtDNA was consistently associated with higher, rather than lower, regional neuralstructural metrics among youth with BD. While this finding may reflect a compensatory mechanism, future repeated-measures prospective studies evaluating the inter-relationship among ccf-mtDNA, mood, and brain structure across developmental epochs and illness stages are warranted.

Comparing Symptoms of Major Depression in Youth with Confirmed Versus Suspected Bipolar Disorder.

Background: While numerous studies have compared symptoms of major depressive episodes (MDEs) associated with bipolar disorder (BD; i.e., bipolar depression) versus major depressive disorder (MDD; i.e., unipolar depression), little is known about this topic in youth. We compared MDE symptoms in youth with BD with youth with suspected BD who have similar clinical and familial characteristics aside from having BD. Methods: MDE symptoms based on Kiddie Schedule for Affective Disorders and Schizophrenia for School Age Children (K-SADS) Depression Rating Scale items for the most severe past episode were compared in youth, ages 13-21 years, with BD (n = 208) versus suspected BD (n = 165). Diagnoses were confirmed via semistructured interviews. Symptoms with between-group differences (p < 0.05) in univariate analyses were evaluated in a multivariate forward stepwise regression. All analyses controlled for age and sex. Results: Youth with BD had significantly higher (more severe) ratings on depressed mood (p = 0.001, η2 = 0.05), irritability (p = 0.037, η2 = 0.02), anhedonia (p = 0.004, η2 = 0.04), negative self-image (p < 0.001, η2 = 0.07), hopelessness (p = 0.04, η2 = 0.02), fatigue (p = 0.001, η2 = 0.05), hypersomnia (p = 0.001, η2 = 0.05), suicidal ideation (p = 0.04, η2 = 0.02), and recurrent thoughts of death (p < 0.001, η2 = 0.05). In regression analyses, the only symptom that remained significant in the BD group was depressed mood (p = 0.002). Conclusions: These findings demonstrate greater severity of depressive symptoms in youth with BD versus MDD across mood, and cognitive and neurovegetative symptom domains. These differences are especially noteworthy given that the MDD group was highly similar to the BD group, aside from BD diagnosis. Present findings emphasize the need for novel treatment approaches to bipolar depression in youth, and for studies examining potential mechanisms underlying the increased severity of bipolar depression.

Self-harm presentations to hospital trauma centre emergency departments during the first year of the COVID-19 pandemic.

The COVID-19 pandemic raised concerns regarding increased suicide-related behaviours. We compared characteristics and counts of Emergency Department (ED) presentations for self-harm, an important suicide-related outcome, during versus prior to the pandemic's first year. We included patients presenting with self-harm to the ED of two trauma centres in Toronto, Canada. Time series models compared intra-pandemic (March 2020-February 2021) presentation counts to predictions from pre-pandemic data. The self-harm proportion of ED presentations was compared between the intra-pandemic period and preceding three years. A retrospective chart review of eligible patients seen from March 2019-February 2021 compared pre- vs. intra-pandemic patient and injury characteristics. While monthly intra-pandemic self-harm counts were largely within expected ranges, the self-harm proportion of total presentations increased. Being widowed (OR=9.46; 95 %CI=1.10-81.08), employment/financial stressors (OR=1.65, 95 %CI=1.06-2.58), job loss (OR=3.83; 95 %CI=1.36-10.76), and chest-stabbing self-harm (OR=2.50; 95 %CI=1.16-5.39) were associated with intra-pandemic presentations. Intra-pandemic self-harm was also associated with Intensive Care Unit (ICU) admission (OR=2.18, 95 %CI=1.41-3.38). In summary, while the number of self-harm presentations to these trauma centres did not increase during the early pandemic, their proportion was increased. The association of intra-pandemic self-harm with variables indicating medically severe injury, economic stressors, and being widowed may inform future suicide and self-harm prevention strategies.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.

RATIONALE & OBJECTIVE: The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD. STUDY DESIGN: Predictive modeling study. SETTING & PARTICIPANTS: 42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers. NEW PREDICTORS & ESTABLISHED PREDICTORS: Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR. OUTCOME: For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival). ANALYTICAL APPROACH: We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points. RESULTS: All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%. LIMITATIONS: The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids. CONCLUSIONS: A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality. PLAIN-LANGUAGE SUMMARY: Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.