- Email: [email protected]
Abstract Thinking: E Pluribus Unum
HERE IN THIS ISSUE
T
he quest for risk factors associated with mental disorders has been one of the major endeavors of current research in psychiatry. During recent decades, longitudinal designs have allowed for the recognition of a multitude of developmentally plausible connections between early characteristics and later effects. This issue of the Journal presents 3 studies that tried to identify predictors of subsequent psychiatric outcomes. Højgaard and colleagues (p. 940) investigated the longterm effects of individually delivered, exposure-based cognitive-behavioral therapy for children and adolescents with obsessive-compulsive disorder (OCD). Of the initial 269 participants in the Nordic Long-term OCD Treatment Study, 177 responders to acute treatment (14 weekly 75-minute sessions) were eligible for the 12-month follow-up. Previous data indicated that from a group of 8 possible predictors of immediate response, only younger age (7–11 years) was associated with a better response to cognitive-behavioral therapy. Results for the 1-year follow-up assessment were available for 155 youth: of these, 78.1% were in remission, and the average on the Children’s Yale-Brown ObsessiveCompulsive Scale (CY-BOCS) decreased 1.72 points during this period, suggesting that treatment gains among responders were only slightly improved. There was no evidence that age predicted outcomes in the follow-up assessments, with no differences in CY-BOCS severity scores between children and adolescents. Overall, findings corroborate the idea that a manual-based cognitivebehavioral therapy intervention for pediatric OCD delivered in a community setting can provide durable effects for responders to an initial course of treatment, but specific factors associated with maintenance of treatment gains among youth are still to be elucidated. Two other articles in this issue based their findings on the Great Smoky Mountain Study, a communityrepresentative sample of more than 1,000 youth prospectively followed in an accelerated cohort design. Hill and colleagues (p. 966) investigated childhood and late adolescence risk factors associated with specific patterns of cannabis use in early adulthood. The 4 patterns they identified were non-problematic use in late adolescence and early adulthood (76.3% of participants); limited problematic use in late adolescence (13.3%); persistent problematic use in late adolescence and early adulthood (6.7%); and delayed problematic use in early adulthood
JOURNAL OF THE AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 56 NUMBER 11 NOVEMBER 2017
(3.7%). Individuals with limited, persistent, and delayed problematic use—irrespective of the developmental period of onset or offset—exhibited higher levels of childhood and late-adolescent risk factors (psychiatric diagnoses, use of other substances, and economic/social disadvantage) than those with non-problematic patterns. Childhood predictors of a persistent pattern included higher rates of depression and anxiety, early use of tobacco and illicit drugs, and school expulsion. Several childhood risk factors were differentially associated patterns of limited, persistent, or delayed use: for instance, family dysfunction and instability were more common among those with limited patterns, whereas persistent use was more strongly associated with anxiety disorders in childhood; those with a delayed use pattern showed lower rates of alcohol use. For risk factors in late adolescence, several other associations emerged, such as between limited and persistent patterns and higher rates of depressive disorders, all types of other substance use, lower educational attainment, drug-using peers, and moderate/violent criminal offenses. Further refinement of patterns and risk profiles might be helpful in the development of tailored interventions in the future. The other study based on data from the Great Smoky Mountain Study focused on adult outcomes associated with childhood suicidal thoughts and behaviors (STBs). Copeland and colleagues (p. 958) report data on suicidality (including passive and active ideation, plans, and attempts) collected at ages 9 to 16 years and young adulthood outcomes at ages 19 and 30 (for psychiatric diagnoses, STBs, and functional performance). Although STBs represent extremely relevant events per se, youth presenting with these symptoms also might be at increased risk for subsequent difficulties in adulthood. Results indicated that childhood STBs were relatively common (7%) and were associated with increased levels of anxiety and STBs in adulthood. Suicidality early in life also predated poor adulthood functioning in the financial, health, risky/illegal behaviors, and social domains. Adjusted models indicated that predicted effects of STBs in childhood were, to a large extent, attributable to other risk factors such as maltreatment, depression, and disruptive behavior disorders. Regardless of causality effects, STBs in childhood constitute an important marker of higher risk for mental health problems and an increased risk for a disrupted transition to adulthood.
www.jaacap.org
905
THERE ABSTRACT THINKING E Pluribus Unum Spins round the stirring hand; lose by degrees Their separate powers the parts, and comes at last From many several colors one that rules —Virgil, circa 55
A
BC
n important task in the prevention of mental disorders is the identification of individuals at increased risk. However, the complexity of psychiatric disorders and risk factors makes it unlikely that the search for individual risk factors will allow us to achieve an adequate prognostic performance in identifying who is and who is not going to develop full-blown diagnoses in the future. Another shortcoming frequently observed in risk research conducted up to now is the assumption that risk operates as a discrete factor, although evidence points in the opposite direction. Therefore, combining risk factors into composite multivariate algorithms as has been done in other areas of medicine (the Framingham Risk Score to estimate cardiovascular risk or the Model for End-Stage Liver Disease score for prioritizing the allocation of liver transplants) is a promising approach to advance our ability to parse individuals at high and low risk. Two recent initiatives have built on these principles to devise composite risk scores for psychosis and bipolar disorder. Fusar-Poli et al.1 studied a clinical cohort of 91,199 patients in secondary mental health care to identify a set of routinely collected features for the prediction of psychosis. The final model comprised variables such as index diagnosis, age, sex, and race/ethnicity. Importantly, they divided the sample into 2 subgroups, one for generating and the other for validating the model. The final performance of the composite score (as measured by the C-statistic, an index of goodness of fit that ranges from 0.5 to 1) was 0.80 and 0.79 for the generation and validation samples, respectively. Interestingly, a diagnosis of bipolar disorder or transient psychosis exhibited a similar and higher risk of psychosis than the at-risk mental state construct, suggesting that a trans-diagnostic approach might be more adequate for the prediction of psychosis in secondary mental health care. REFERENCES
1. Fusar-Poli P, Rutigliano G, Stahl D, et al. Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry. 2017;74:493-500.
906
www.jaacap.org
Hafeman et al.2 adopted a similar approach to develop a composite risk score for the prediction of bipolar spectrum disorder in 412 offspring of parents with the same diagnosis. Individuals free of bipolar disorder diagnosis at baseline (one group with and another without a family history of bipolar disorder) were followed up to predict 5-year risks. The final model included features such as mood and anxiety symptoms, general psychosocial functioning, and parental age at mood disorder onset. Combined, these variables exhibited a discriminative performance of 0.76. Although achieving discriminative performance similar to other composite scores in general medicine, the science of combining risk factors to predict mental disorders is still in its infancy. Methodologic issues—most relevantly those related to the external replication models in diverse settings—still represent important challenges for the field. Notwithstanding these concerns, risk calculators could be of extreme usefulness to researchers and clinicians. In routine clinical practice, electronic calculators might be valuable and easy-to-use tools for the stratification of individuals for whom closer monitoring might be indicated. On the research front, the identification of high-risk groups (especially if informed by nontraditional factors such as subsyndromal features or family history) might allow for in-depth neurobiological and phenotypic characterization that could lead to innovative preventive strategies. Christian Kieling,
MD, PhD
Dr. Kieling is with the Hospital de Clınicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. Disclosure: Dr. Kieling has received support from Brazilian governmental research funding agencies (Conselho Nacional de Desenvolvimento Cientıfico ~o de Aperfeic¸oamento de Pessoal de e Tecnol ogico [CNPq], Coordenac¸a ~o de Amparo a Pesquisa do Estado do Nıvel Superior [CAPES], and Fundac¸a Rio Grande do Sul [Fapergs]). He has served on the editorial boards of Revista de Psiquiatria Clınica, Jornal Brasileiro de Psiquiatria, Indian Journal of Social Psychiatry, and Global Mental Health. He also has received authorship royalties from Brazilian publishers Artmed and Manole. Correspondence to Christian Kieling, MD, PhD: [email protected] https://doi.org/10.1016/j.jaac.2017.09.419
2. Hafeman DM, Merranko J, Goldstein TR, et al. Assessment of a personlevel risk calculator to predict new-onset bipolar spectrum disorder in youth at familial risk. JAMA Psychiatry. 2017;74:841-847.
JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 56 NUMBER 11 NOVEMBER 2017
T
he quest for risk factors associated with mental disorders has been one of the major endeavors of current research in psychiatry. During recent decades, longitudinal designs have allowed for the recognition of a multitude of developmentally plausible connections between early characteristics and later effects. This issue of the Journal presents 3 studies that tried to identify predictors of subsequent psychiatric outcomes. Højgaard and colleagues (p. 940) investigated the longterm effects of individually delivered, exposure-based cognitive-behavioral therapy for children and adolescents with obsessive-compulsive disorder (OCD). Of the initial 269 participants in the Nordic Long-term OCD Treatment Study, 177 responders to acute treatment (14 weekly 75-minute sessions) were eligible for the 12-month follow-up. Previous data indicated that from a group of 8 possible predictors of immediate response, only younger age (7–11 years) was associated with a better response to cognitive-behavioral therapy. Results for the 1-year follow-up assessment were available for 155 youth: of these, 78.1% were in remission, and the average on the Children’s Yale-Brown ObsessiveCompulsive Scale (CY-BOCS) decreased 1.72 points during this period, suggesting that treatment gains among responders were only slightly improved. There was no evidence that age predicted outcomes in the follow-up assessments, with no differences in CY-BOCS severity scores between children and adolescents. Overall, findings corroborate the idea that a manual-based cognitivebehavioral therapy intervention for pediatric OCD delivered in a community setting can provide durable effects for responders to an initial course of treatment, but specific factors associated with maintenance of treatment gains among youth are still to be elucidated. Two other articles in this issue based their findings on the Great Smoky Mountain Study, a communityrepresentative sample of more than 1,000 youth prospectively followed in an accelerated cohort design. Hill and colleagues (p. 966) investigated childhood and late adolescence risk factors associated with specific patterns of cannabis use in early adulthood. The 4 patterns they identified were non-problematic use in late adolescence and early adulthood (76.3% of participants); limited problematic use in late adolescence (13.3%); persistent problematic use in late adolescence and early adulthood (6.7%); and delayed problematic use in early adulthood
JOURNAL OF THE AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 56 NUMBER 11 NOVEMBER 2017
(3.7%). Individuals with limited, persistent, and delayed problematic use—irrespective of the developmental period of onset or offset—exhibited higher levels of childhood and late-adolescent risk factors (psychiatric diagnoses, use of other substances, and economic/social disadvantage) than those with non-problematic patterns. Childhood predictors of a persistent pattern included higher rates of depression and anxiety, early use of tobacco and illicit drugs, and school expulsion. Several childhood risk factors were differentially associated patterns of limited, persistent, or delayed use: for instance, family dysfunction and instability were more common among those with limited patterns, whereas persistent use was more strongly associated with anxiety disorders in childhood; those with a delayed use pattern showed lower rates of alcohol use. For risk factors in late adolescence, several other associations emerged, such as between limited and persistent patterns and higher rates of depressive disorders, all types of other substance use, lower educational attainment, drug-using peers, and moderate/violent criminal offenses. Further refinement of patterns and risk profiles might be helpful in the development of tailored interventions in the future. The other study based on data from the Great Smoky Mountain Study focused on adult outcomes associated with childhood suicidal thoughts and behaviors (STBs). Copeland and colleagues (p. 958) report data on suicidality (including passive and active ideation, plans, and attempts) collected at ages 9 to 16 years and young adulthood outcomes at ages 19 and 30 (for psychiatric diagnoses, STBs, and functional performance). Although STBs represent extremely relevant events per se, youth presenting with these symptoms also might be at increased risk for subsequent difficulties in adulthood. Results indicated that childhood STBs were relatively common (7%) and were associated with increased levels of anxiety and STBs in adulthood. Suicidality early in life also predated poor adulthood functioning in the financial, health, risky/illegal behaviors, and social domains. Adjusted models indicated that predicted effects of STBs in childhood were, to a large extent, attributable to other risk factors such as maltreatment, depression, and disruptive behavior disorders. Regardless of causality effects, STBs in childhood constitute an important marker of higher risk for mental health problems and an increased risk for a disrupted transition to adulthood.
www.jaacap.org
905
THERE ABSTRACT THINKING E Pluribus Unum Spins round the stirring hand; lose by degrees Their separate powers the parts, and comes at last From many several colors one that rules —Virgil, circa 55
A
BC
n important task in the prevention of mental disorders is the identification of individuals at increased risk. However, the complexity of psychiatric disorders and risk factors makes it unlikely that the search for individual risk factors will allow us to achieve an adequate prognostic performance in identifying who is and who is not going to develop full-blown diagnoses in the future. Another shortcoming frequently observed in risk research conducted up to now is the assumption that risk operates as a discrete factor, although evidence points in the opposite direction. Therefore, combining risk factors into composite multivariate algorithms as has been done in other areas of medicine (the Framingham Risk Score to estimate cardiovascular risk or the Model for End-Stage Liver Disease score for prioritizing the allocation of liver transplants) is a promising approach to advance our ability to parse individuals at high and low risk. Two recent initiatives have built on these principles to devise composite risk scores for psychosis and bipolar disorder. Fusar-Poli et al.1 studied a clinical cohort of 91,199 patients in secondary mental health care to identify a set of routinely collected features for the prediction of psychosis. The final model comprised variables such as index diagnosis, age, sex, and race/ethnicity. Importantly, they divided the sample into 2 subgroups, one for generating and the other for validating the model. The final performance of the composite score (as measured by the C-statistic, an index of goodness of fit that ranges from 0.5 to 1) was 0.80 and 0.79 for the generation and validation samples, respectively. Interestingly, a diagnosis of bipolar disorder or transient psychosis exhibited a similar and higher risk of psychosis than the at-risk mental state construct, suggesting that a trans-diagnostic approach might be more adequate for the prediction of psychosis in secondary mental health care. REFERENCES
1. Fusar-Poli P, Rutigliano G, Stahl D, et al. Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry. 2017;74:493-500.
906
www.jaacap.org
Hafeman et al.2 adopted a similar approach to develop a composite risk score for the prediction of bipolar spectrum disorder in 412 offspring of parents with the same diagnosis. Individuals free of bipolar disorder diagnosis at baseline (one group with and another without a family history of bipolar disorder) were followed up to predict 5-year risks. The final model included features such as mood and anxiety symptoms, general psychosocial functioning, and parental age at mood disorder onset. Combined, these variables exhibited a discriminative performance of 0.76. Although achieving discriminative performance similar to other composite scores in general medicine, the science of combining risk factors to predict mental disorders is still in its infancy. Methodologic issues—most relevantly those related to the external replication models in diverse settings—still represent important challenges for the field. Notwithstanding these concerns, risk calculators could be of extreme usefulness to researchers and clinicians. In routine clinical practice, electronic calculators might be valuable and easy-to-use tools for the stratification of individuals for whom closer monitoring might be indicated. On the research front, the identification of high-risk groups (especially if informed by nontraditional factors such as subsyndromal features or family history) might allow for in-depth neurobiological and phenotypic characterization that could lead to innovative preventive strategies. Christian Kieling,
MD, PhD
Dr. Kieling is with the Hospital de Clınicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. Disclosure: Dr. Kieling has received support from Brazilian governmental research funding agencies (Conselho Nacional de Desenvolvimento Cientıfico ~o de Aperfeic¸oamento de Pessoal de e Tecnol ogico [CNPq], Coordenac¸a ~o de Amparo a Pesquisa do Estado do Nıvel Superior [CAPES], and Fundac¸a Rio Grande do Sul [Fapergs]). He has served on the editorial boards of Revista de Psiquiatria Clınica, Jornal Brasileiro de Psiquiatria, Indian Journal of Social Psychiatry, and Global Mental Health. He also has received authorship royalties from Brazilian publishers Artmed and Manole. Correspondence to Christian Kieling, MD, PhD: [email protected] https://doi.org/10.1016/j.jaac.2017.09.419
2. Hafeman DM, Merranko J, Goldstein TR, et al. Assessment of a personlevel risk calculator to predict new-onset bipolar spectrum disorder in youth at familial risk. JAMA Psychiatry. 2017;74:841-847.
JOURNAL
OF THE
AMERICAN ACADEMY OF C HILD & ADOLESCENT PSYCHIATRY VOLUME 56 NUMBER 11 NOVEMBER 2017