- Email: [email protected]
893. Adverse Life Events, Psychiatric Comorbidity, and Biological Predictors of Postpartum Depression in an Ethnically Diverse Sample of Postpartum Women
Biological Psychiatry
Saturday Abstracts
their immigration travel than Latina immigrant women with no current psychiatric diagnosis. Methods: 14 volunteers were assessed for trauma exposure, difficult life events, and depressive symptoms. 7 in experimental group had established depression diagnosis from FQHC. 7 recruited from community family centers as controls had no psychiatric diagnosis. Structured Clinician Interview for DSM III, Adverse Childhood Events Survey, and modified Life Events Difficulties Schedule were implemented. The Wilcoxon RankSum and Fisher’s Exact were used for comparison. Results: Control group reported increased challenge severity (p5.032). No significant difference found in trauma incidence (p5.26), childhood adversity (p5.5) or depressive symptoms in the first year following arrival to the US (p51). 71% of controls and 42% of experimental group traveled using human smuggler, aka “coyote”. Comparing modes of travel among groups, trauma exposure was increased in coyote travel (p5.031). Sleep deprivation (p5.011), intrusiveness (p5.008), and goal frustration (p5 .048) were increased among coyote travelers. Coyote travel trended towards an increase in short term threat (p5.061). Conclusions: With exception of perceived challenges, diagnosis of clinical depressive disorder did not significantly correlate with reported trauma or hardship. Of note, “coyote” travel among both groups significantly correlated with trauma exposure. No difference in depressive symptomatology one year after arrival suggest a high-risk adjustment period warranting a high-degree of clinical suspicion and screening in all newly immigrated patients. Supported By: University of Arizona Tucson Department of Psychiatry Keywords: Immigration, Adverse Childhood Experiences, Depression, Trauma, Latina
892. Evolution of Depression before, during and after a Major Social Movement Michael Ni1, Tom Li1, Herbert Pang1, Brandford Chan1, Ichiro Kawachi2, Vish Viswanath3, C Mary Schooling4, and Gabriel Leung1 University of Hong Kong, 2Department of Social & Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, 3 Department of Social & Behavioral Sciences, Harvard T. H. Chan School of Public Health Center for CommunityBased Research, Dana-Farber Cancer Institute, 4University of Hong Kong; School of Public Health, Hunter College and CUNY 1
Background: Social movements could have a profound impact on population mental health - yet their mental health consequences remain sparsely documented. We sought to examine the longitudinal patterns and predictors of depression trajectories before, during, and after the 2014 “Occupy Central/Umbrella Movement” (OCUM) in Hong Kong. Methods: Prospective study of 1170 adults randomly sampled from the population-representative FAMILY Cohort. We administered interviews at six time points from March 2009 to November 2015: twice each before, during, and after
S360
OCUM. The Patient Health Questionnaire-9 (PHQ-9) was used to assess depressive symptoms and probable major depression (PHQ $ 10). We investigated pre-event and timevarying predictors of depressive symptoms, including sociodemographics, general health status, resilience, family support, family harmony, and neighbourhood cohesion. Results: Four trajectories were identified: “resistant” (22 6% of sample), “resilient” (37 0%), “mild depressive symptoms” (32 5%), and “persistent moderate depression” (8 0%). Baseline predictors that appeared to protect against “persistent moderate depression” included higher household income (OR 0 18, 95% CI 0 06-0 56), greater psychological resilience (OR 0 62, 95% CI 0 48-0 80), more family harmony (OR 0 68, 95% CI 0 54-0 86), higher family support (OR 0 80, 95% CI 0 69-0 92), better self-rated health (OR 0 30, 95% CI 0 17-0 55), and fewer depressive symptoms (OR 0 59, 95% CI 0 44-0 79). Conclusions: Depression trajectories following a major protest were comparable to those in the wake of major population events. Health care professionals should be vigilant of the mental health consequences during and after social movements, particularly among individuals lacking social support. Supported By: Jockey Club Charities Trust Keywords: Depression, Longitudinal, Epidemiology, Trajectory, Social movement
893. Adverse Life Events, Psychiatric Comorbidity, and Biological Predictors of Postpartum Depression in an Ethnically Diverse Sample of Postpartum Women Jerry Guintivano1, Patrick Sullivan2, Alison Stuebe2, Thomas Penders3, John Thorp2, Karen Putnam2, David Rubinow2, and Samantha Meltzer-Brody4 1
University of North Carolina at Chapel Hill/School of Medicine, 2University of North Carolina, 3East Carolina University, 4University of North Carolina at Chapel Hill Background: Race, psychiatric history, and adverse life events have all been independently associated with postpartum depression (PPD). However, the role these play together in black and Latina women remains inadequately studied. Therefore, we performed a case-control study of PPD including comprehensive assessments of symptoms and biomarkers, while examining the effects of ancestry. Methods: We recruited our sample (549 cases, 968 controls) at six weeks postpartum from clinics in North Carolina. PPD status was determined using the MINI-plus. Psychiatric history was extracted from medical records. Participants were administered self-report instruments to assess depression (Edinburgh Postnatal Depression Scale) and adverse life events. Levels of estradiol, progesterone, brain-derived neurotrophic factor (BDNF), oxytocin, and allopregnanalone were assayed. Principal components from genotype data were used to estimate genetic ancestry and logistic regression was used to identify predictors of case status. Results: Results: This population was racially diverse (68% black, 13% Latina, and 18% European). PPD status was predicted by a history of major depression (p , 0.0001), history of anxiety
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
Biological Psychiatry
Saturday Abstracts
disorders (p , 0.0001), and adverse life events (p , 0.0001); genetic ancestry provided negligible predictive power (, 1% variance explained) albeit statistically significant. There were no significant differences between groups in any hormones or neurosteroids. Conclusions: Conclusions: Psychiatric comorbidity and multiple exposures to adverse life events were significant predictors of PPD in a population of minority, low-income women. Ancestry and hormone levels were not predictive of case status. Increased genetic vulnerability in conjunction with risk factors may predict the onset of PPD, whereas ancestry does not appear predictive. Supported By: R01, T32 Keywords: Postpartum Depression, psychiatric comorbidities, Ancestry, Sex-steroid hormones, Trauma Exposure
894. Suicide Prediction Using Machine Learning Techniques in Screening and Clinician-Derived Data Laura Hack1, Tanja Jovanovic1, Sierra Carter1, Kerry Ressler2, and Alicia Smith1 1
Emory University School of Medicine, 2Harvard - McLean
Background: Machine learning (ML) techniques are promising tools for prediction in psychiatry, as they are well suited to handle complex data sets with many correlated predictors. We sought to compare the performance of commonly used ML algorithms to predict suicide attempts using screening vs. clinician-derived data. Methods: We utilized data from the Grady Trauma Project, which assesses trauma exposure in subjects seeking primary care from a large urban hospital. Subjects undergo a screening interview based on self-reported scales (sociodemographic data, PSS, CTQ, TEI, BDI) and selected subjects are invited to participate in a clinician-administered interview for DSM-IV diagnoses (SCID/MINI). Subjects were split into balanced training (N5814, 80%) and testing (N5203, 20%) sets, each of which had 16% suicide attempters. Suicide risk factors were entered into support vector machines (SVM) and least absolute shrinkage and selection operator (LASSO) models with 100 iterations in which each iteration included all attempters and an equal number of non-attempters. Results: Self-reported screening data (PSS, CTQ, TEI, employment, psychiatric hospitalization) provided reasonable sensitivity (64%) and specificity (76%; AUC 70%). Areas Under the Curve (AUC) did not differ when clinician-derived data (major depressive disorder and any psychotic disorder) was available for selection. The LASSO (AUC 70%) and SVM (AUC 71%) models did not differ substantially in accuracy. Conclusions: We describe a ML-derived algorithm for suicide prediction and suggest risk factors that are most relevant for prediction. In this cohort, the addition of clinician-obtained data did not improve prediction accuracy over self-reported screening data. Supported By: R25 Keywords: Suicide risk factors, Machine learning, Precision psychiatry, Screening vs. clinician-administered instruments, Urban population sample
895. Neuroleptic Malignant Syndrome Diagnosis Risk is Greatest in Young Adult Men Ronald Gurrera VA Boston Healthcare System Background: Sex and age are suspected but unproven risk factors for neuroleptic malignant syndrome (NMS), a potentially fatal adverse drug reaction associated with antipsychotic medications. This study determined the sex ratio of NMS cases identified by a systematic review of the world literature in order to estimate the relative risk for men and women of being diagnosed with NMS. The sex-specific age distribution of NMS was also examined. Methods: EMBASE and PubMed databases were searched using unrestricted criteria to identify all published observations of NMS. Any accessible and interpretable report published between 1998 and 2014 was eligible for inclusion. Primary sources were given preference over secondary sources (e.g., reviews). Redundant reports and cases in which an NMS diagnosis was less likely were excluded. The sex distributions observed in studies and large (n.10) case series were treated as independent estimates; single case reports and small case series (i.e., n,11) were combined into a single series for the purpose of computing a single sex ratio for the aggregate sample. Standard graphical analysis and measures of association were used to examine sex ratio and age distributions. Results: Most (75%) of the 28 sex ratio estimates showed male preponderance (median sex ratio 1.47, 95% CI 1.20-1.80). NMS frequency was highest in the 20-25 years age group, and declined progressively with advancing age; males outnumbered females at all ages. These results are limited by heterogeneity of case ascertainment procedures and potential publication bias. Conclusions: Young adult men appear to be at greatest risk for NMS. Keywords: neuroleptic malignant syndrome, Gender differences, Risk factor, Age
896. Genetic and Phenotypic Overlap of Specific Obsessive-Compulsive Subtypes with Tourette Syndrome Matthew Hirschtritt1, Sabrina M. Darrow2, Cornelia Illmann3, Lisa Osiecki3, Marco Grados4, Paul Sandor5, Yves Dion6, Robert A. King7, David Pauls3, Cathy L. Budman8, Danielle C. Cath9, Erica Greenberg3, Gholson J. Lyon10, Dongmei Yu3, Lauren M. McGrath11, William M. McMahon12, Paul C. Lee13, Kevin L. Delucchi2, Jeremiah M. Scharf3, and Carol A. Mathews14 UCSF Department of Psychiatry, San Francisco, 2UCSF Department of Psychiatry, 3Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetics Research, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 4Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5Department of Psychiatry, University of Toronto and University Health Network, Youthdale Treatment Centers, 6Department of Psychiatry, University of Montreal, 7Yale Child Study Center, 1
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
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Saturday Abstracts
their immigration travel than Latina immigrant women with no current psychiatric diagnosis. Methods: 14 volunteers were assessed for trauma exposure, difficult life events, and depressive symptoms. 7 in experimental group had established depression diagnosis from FQHC. 7 recruited from community family centers as controls had no psychiatric diagnosis. Structured Clinician Interview for DSM III, Adverse Childhood Events Survey, and modified Life Events Difficulties Schedule were implemented. The Wilcoxon RankSum and Fisher’s Exact were used for comparison. Results: Control group reported increased challenge severity (p5.032). No significant difference found in trauma incidence (p5.26), childhood adversity (p5.5) or depressive symptoms in the first year following arrival to the US (p51). 71% of controls and 42% of experimental group traveled using human smuggler, aka “coyote”. Comparing modes of travel among groups, trauma exposure was increased in coyote travel (p5.031). Sleep deprivation (p5.011), intrusiveness (p5.008), and goal frustration (p5 .048) were increased among coyote travelers. Coyote travel trended towards an increase in short term threat (p5.061). Conclusions: With exception of perceived challenges, diagnosis of clinical depressive disorder did not significantly correlate with reported trauma or hardship. Of note, “coyote” travel among both groups significantly correlated with trauma exposure. No difference in depressive symptomatology one year after arrival suggest a high-risk adjustment period warranting a high-degree of clinical suspicion and screening in all newly immigrated patients. Supported By: University of Arizona Tucson Department of Psychiatry Keywords: Immigration, Adverse Childhood Experiences, Depression, Trauma, Latina
892. Evolution of Depression before, during and after a Major Social Movement Michael Ni1, Tom Li1, Herbert Pang1, Brandford Chan1, Ichiro Kawachi2, Vish Viswanath3, C Mary Schooling4, and Gabriel Leung1 University of Hong Kong, 2Department of Social & Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, 3 Department of Social & Behavioral Sciences, Harvard T. H. Chan School of Public Health Center for CommunityBased Research, Dana-Farber Cancer Institute, 4University of Hong Kong; School of Public Health, Hunter College and CUNY 1
Background: Social movements could have a profound impact on population mental health - yet their mental health consequences remain sparsely documented. We sought to examine the longitudinal patterns and predictors of depression trajectories before, during, and after the 2014 “Occupy Central/Umbrella Movement” (OCUM) in Hong Kong. Methods: Prospective study of 1170 adults randomly sampled from the population-representative FAMILY Cohort. We administered interviews at six time points from March 2009 to November 2015: twice each before, during, and after
S360
OCUM. The Patient Health Questionnaire-9 (PHQ-9) was used to assess depressive symptoms and probable major depression (PHQ $ 10). We investigated pre-event and timevarying predictors of depressive symptoms, including sociodemographics, general health status, resilience, family support, family harmony, and neighbourhood cohesion. Results: Four trajectories were identified: “resistant” (22 6% of sample), “resilient” (37 0%), “mild depressive symptoms” (32 5%), and “persistent moderate depression” (8 0%). Baseline predictors that appeared to protect against “persistent moderate depression” included higher household income (OR 0 18, 95% CI 0 06-0 56), greater psychological resilience (OR 0 62, 95% CI 0 48-0 80), more family harmony (OR 0 68, 95% CI 0 54-0 86), higher family support (OR 0 80, 95% CI 0 69-0 92), better self-rated health (OR 0 30, 95% CI 0 17-0 55), and fewer depressive symptoms (OR 0 59, 95% CI 0 44-0 79). Conclusions: Depression trajectories following a major protest were comparable to those in the wake of major population events. Health care professionals should be vigilant of the mental health consequences during and after social movements, particularly among individuals lacking social support. Supported By: Jockey Club Charities Trust Keywords: Depression, Longitudinal, Epidemiology, Trajectory, Social movement
893. Adverse Life Events, Psychiatric Comorbidity, and Biological Predictors of Postpartum Depression in an Ethnically Diverse Sample of Postpartum Women Jerry Guintivano1, Patrick Sullivan2, Alison Stuebe2, Thomas Penders3, John Thorp2, Karen Putnam2, David Rubinow2, and Samantha Meltzer-Brody4 1
University of North Carolina at Chapel Hill/School of Medicine, 2University of North Carolina, 3East Carolina University, 4University of North Carolina at Chapel Hill Background: Race, psychiatric history, and adverse life events have all been independently associated with postpartum depression (PPD). However, the role these play together in black and Latina women remains inadequately studied. Therefore, we performed a case-control study of PPD including comprehensive assessments of symptoms and biomarkers, while examining the effects of ancestry. Methods: We recruited our sample (549 cases, 968 controls) at six weeks postpartum from clinics in North Carolina. PPD status was determined using the MINI-plus. Psychiatric history was extracted from medical records. Participants were administered self-report instruments to assess depression (Edinburgh Postnatal Depression Scale) and adverse life events. Levels of estradiol, progesterone, brain-derived neurotrophic factor (BDNF), oxytocin, and allopregnanalone were assayed. Principal components from genotype data were used to estimate genetic ancestry and logistic regression was used to identify predictors of case status. Results: Results: This population was racially diverse (68% black, 13% Latina, and 18% European). PPD status was predicted by a history of major depression (p , 0.0001), history of anxiety
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
Biological Psychiatry
Saturday Abstracts
disorders (p , 0.0001), and adverse life events (p , 0.0001); genetic ancestry provided negligible predictive power (, 1% variance explained) albeit statistically significant. There were no significant differences between groups in any hormones or neurosteroids. Conclusions: Conclusions: Psychiatric comorbidity and multiple exposures to adverse life events were significant predictors of PPD in a population of minority, low-income women. Ancestry and hormone levels were not predictive of case status. Increased genetic vulnerability in conjunction with risk factors may predict the onset of PPD, whereas ancestry does not appear predictive. Supported By: R01, T32 Keywords: Postpartum Depression, psychiatric comorbidities, Ancestry, Sex-steroid hormones, Trauma Exposure
894. Suicide Prediction Using Machine Learning Techniques in Screening and Clinician-Derived Data Laura Hack1, Tanja Jovanovic1, Sierra Carter1, Kerry Ressler2, and Alicia Smith1 1
Emory University School of Medicine, 2Harvard - McLean
Background: Machine learning (ML) techniques are promising tools for prediction in psychiatry, as they are well suited to handle complex data sets with many correlated predictors. We sought to compare the performance of commonly used ML algorithms to predict suicide attempts using screening vs. clinician-derived data. Methods: We utilized data from the Grady Trauma Project, which assesses trauma exposure in subjects seeking primary care from a large urban hospital. Subjects undergo a screening interview based on self-reported scales (sociodemographic data, PSS, CTQ, TEI, BDI) and selected subjects are invited to participate in a clinician-administered interview for DSM-IV diagnoses (SCID/MINI). Subjects were split into balanced training (N5814, 80%) and testing (N5203, 20%) sets, each of which had 16% suicide attempters. Suicide risk factors were entered into support vector machines (SVM) and least absolute shrinkage and selection operator (LASSO) models with 100 iterations in which each iteration included all attempters and an equal number of non-attempters. Results: Self-reported screening data (PSS, CTQ, TEI, employment, psychiatric hospitalization) provided reasonable sensitivity (64%) and specificity (76%; AUC 70%). Areas Under the Curve (AUC) did not differ when clinician-derived data (major depressive disorder and any psychotic disorder) was available for selection. The LASSO (AUC 70%) and SVM (AUC 71%) models did not differ substantially in accuracy. Conclusions: We describe a ML-derived algorithm for suicide prediction and suggest risk factors that are most relevant for prediction. In this cohort, the addition of clinician-obtained data did not improve prediction accuracy over self-reported screening data. Supported By: R25 Keywords: Suicide risk factors, Machine learning, Precision psychiatry, Screening vs. clinician-administered instruments, Urban population sample
895. Neuroleptic Malignant Syndrome Diagnosis Risk is Greatest in Young Adult Men Ronald Gurrera VA Boston Healthcare System Background: Sex and age are suspected but unproven risk factors for neuroleptic malignant syndrome (NMS), a potentially fatal adverse drug reaction associated with antipsychotic medications. This study determined the sex ratio of NMS cases identified by a systematic review of the world literature in order to estimate the relative risk for men and women of being diagnosed with NMS. The sex-specific age distribution of NMS was also examined. Methods: EMBASE and PubMed databases were searched using unrestricted criteria to identify all published observations of NMS. Any accessible and interpretable report published between 1998 and 2014 was eligible for inclusion. Primary sources were given preference over secondary sources (e.g., reviews). Redundant reports and cases in which an NMS diagnosis was less likely were excluded. The sex distributions observed in studies and large (n.10) case series were treated as independent estimates; single case reports and small case series (i.e., n,11) were combined into a single series for the purpose of computing a single sex ratio for the aggregate sample. Standard graphical analysis and measures of association were used to examine sex ratio and age distributions. Results: Most (75%) of the 28 sex ratio estimates showed male preponderance (median sex ratio 1.47, 95% CI 1.20-1.80). NMS frequency was highest in the 20-25 years age group, and declined progressively with advancing age; males outnumbered females at all ages. These results are limited by heterogeneity of case ascertainment procedures and potential publication bias. Conclusions: Young adult men appear to be at greatest risk for NMS. Keywords: neuroleptic malignant syndrome, Gender differences, Risk factor, Age
896. Genetic and Phenotypic Overlap of Specific Obsessive-Compulsive Subtypes with Tourette Syndrome Matthew Hirschtritt1, Sabrina M. Darrow2, Cornelia Illmann3, Lisa Osiecki3, Marco Grados4, Paul Sandor5, Yves Dion6, Robert A. King7, David Pauls3, Cathy L. Budman8, Danielle C. Cath9, Erica Greenberg3, Gholson J. Lyon10, Dongmei Yu3, Lauren M. McGrath11, William M. McMahon12, Paul C. Lee13, Kevin L. Delucchi2, Jeremiah M. Scharf3, and Carol A. Mathews14 UCSF Department of Psychiatry, San Francisco, 2UCSF Department of Psychiatry, 3Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetics Research, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 4Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5Department of Psychiatry, University of Toronto and University Health Network, Youthdale Treatment Centers, 6Department of Psychiatry, University of Montreal, 7Yale Child Study Center, 1
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
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