Browsing by Author "McCracken, Courtney E."

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    An exploration of concomitant psychiatric disorders in children with autism spectrum disorder
    (Elsevier, 2019-01) Lecavalier, Luc; McCracken, Courtney E.; Aman, Michael G.; McDougle, Christopher J.; McCracken, James T.; Tierney, Elaine; Smith, Tristram; Johnson, Cynthia; King, Bryan; Handen, Benjamin; Swiezy, Naomi B.; Arnold, L. Eugene; Bearss, Karen; Vitiello, Benedetto; Scahill, Lawrence; Psychiatry, School of Medicine
    Objective We explored patterns of concomitant psychiatric disorders in a large sample of treatment-seeking children and adolescents with autism spectrum disorder (ASD). Methods Participants were 658 children with ASD (age 3–17 years; mean = 7.2 years) in one of six federally-funded multisite randomized clinical trials (RCT) between 1999 and 2014. All children were referred for hyperactivity or irritability. Study designs varied, but all used the Child and Adolescent Symptom Inventory or Early Childhood Inventory to assess Attention Deficit Hyperactivity Disorder (ADHD), Oppositional-Defiant Disorder (ODD), Conduct Disorder (CD), Anxiety Disorders, and Mood Disorders. In addition, several measures in common were used to assess demographic and clinical characteristics. Results Of the 658 children, 73% were Caucasian and 59% had an IQ >70. The rates of concomitant disorders across studies were: ADHD 81%, ODD 46%, CD 12%, any anxiety disorder 42%, and any mood disorder 8%. Two or more psychiatric disorders were identified in 66% of the sample. Of those who met criteria for ADHD, 50% also met criteria for ODD and 46% for any anxiety disorder. Associations between types of concomitant disorders and a number of demographic and clinical characteristics are presented. Conclusion In this well-characterized sample of treatment-seeking children with ASD, rates of concomitant psychiatric disorders were high and the presence of two or more co-occurring disorders was common. Findings highlight the importance of improving diagnostic practice in ASD and understanding possible mechanisms of comorbidity.
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    Normalized measures and patient characteristics to identify undernutrition in infants and young children treated for cancer
    (Elsevier, 2020-08) Runco, Daniel V.; Wasilewski-Masker, Karen; McCracken, Courtney E.; Wetzel, Martha; Mazewski, Claire M.; Patterson, Briana C.; Mertens, Ann C.; Pediatrics, School of Medicine
    Background: Various measures and definitions for undernutrition are used in pediatrics. Younger children treated for cancer are at high risk, but lack well-defined risk-based screening and intervention. Methods: A retrospective study collected weight longitudinally for patients less than three years-old over two years after initiating cancer treatment. We included those diagnosed 2007-2015 at a large pediatric cancer center. Exclusion criteria included treatment starting outside our system, secondary or relapsed malignancy, or incomplete information. A decrease ≥1 in weight-for-age or weight-for-height z-score signified clinically significant weight loss. Univariate and multivariate models assessed hazards for developing first episode of clinically significant weight loss. Results: Of 372 patients, only 24.6% of patients lost 10% of weight, but 58.6% lost weight-for-age z-score ≥1 and 64.8% lost ≥1 weight-for-height z-score within two years of treatment initiation. Patients who lost weight were younger (median age 15 vs. 24 months, p < 0.001). Compared to patients diagnosed in the first year of life, those diagnosed 24-35 months were less likely to lose weight (HR 0.62, p < 0.001) and lost weight later (median time to weight loss 144 vs. 35 days). Higher treatment intensity increased weight loss risk (HR 2.30, p < 0.001) and decreased time to weight loss (35 vs. 154 days). No differences were found based on sex, diagnosis, enteral or parenteral nutrition, gastroenterology consults, or intensive care admissions. Conclusions: Using normalized z-scores is more sensitive for identifying weight loss. Younger children are more likely to lose weight with higher intensity cancer therapy. Patient and treatment specific information should be used in risk stratifying weight loss screening and nutritional interventions.