Early childhood exposures to phthalates in association with attention-deficit/hyperactivity disorder behaviors in middle childhood and adolescence in the ReCHARGE study.

BACKGROUND: Early-life exposure to phthalates alters behaviors in animals. However, epidemiological evidence on childhood phthalate exposure and attention-deficit/hyperactivity disorder (ADHD) behaviors is limited. METHODS: This study included 243 children from the ReCHARGE (Revisiting Childhood Autism Risks from Genetics and Environment) study, who were previously classified as having autism spectrum disorder (ASD), developmental delay, other early concerns, and typical development in the CHARGE case-control study. Twenty phthalate metabolites were measured in spot urine samples collected from children aged 2-5 years. Parents reported on children's ADHD symptoms at ages 8-18 years using Conners-3 Parent Rating Scale. Covariate-adjusted negative binomial generalized linear models were used to investigate associations between individual phthalate metabolite concentrations and raw scores. Weighted quantile sum (WQS) regression with repeated holdout validation was used to examine mixture effects of phthalate metabolites on behavioral scores. Effect modification by child sex was evaluated. RESULTS: Among 12 phthalate metabolites detected in >75% of the samples, higher mono-2-heptyl phthalate (MHPP) was associated with higher scores on Inattentive (ß per doubling = 0.05, 95% confidence interval [CI]: 0.02, 0.08) and Hyperactive/Impulsive scales (ß = 0.04, 95% CI: 0.00, 0.07), especially among children with ASD. Higher mono-carboxy isooctyl phthalate (MCiOP) was associated with higher Hyperactivity/Impulsivity scores (ß = 0.07, 95% CI: -0.01, 0.15), especially among typically developing children. The associations of the molar sum of high molecular weight (HMW) phthalate metabolites and a phthalate metabolite mixture with Hyperactivity/Impulsivity scores were modified by sex, showing more pronounced adverse associations among females. CONCLUSION: Exposure to phthalates during early childhood may impact ADHD behaviors in middle childhood and adolescence, particularly among females. Although our findings may not be broadly generalizable due to the diverse diagnostic profiles within our study population, our robust findings on sex-specific associations warrant further investigations.

Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility.

OBJECTIVE: To develop and validate a phenotyping algorithm for the identification of patients with type 1 and type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic health records. PATIENTS AND METHODS: We used first-order logic rules (if-then-else rules) to imply the presence or absence of DM types 1 and 2. The "if" clause of each rule is a conjunction of logical and, or predicates that provides evidence toward or against the presence of DM. The rule includes International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes, outpatient prescription information, laboratory values, and positive annotation of DM in patients' clinical notes. This study was conducted from March 2, 2015, through February 10, 2016. The performance of our rule-based approach and similar approaches proposed by other institutions was evaluated with a reference standard created by an expert reviewer and implemented for routine clinical care at an academic medical center. RESULTS: A total of 4208 surgical patients (mean age, 52 years; males, 48%) were analyzed to develop the phenotyping algorithm. Expert review identified 685 patients (16.28% of the full cohort) as having DM. Our proposed method identified 684 patients (16.25%) as having DM. The algorithm performed well-99.70% sensitivity, 99.97% specificity-and compared favorably with previous approaches. CONCLUSION: Among patients undergoing surgery, determination of DM can be made with high accuracy using simple, computationally efficient rules. Knowledge of patients' DM status before surgery may alter physicians' care plan and reduce postsurgical complications. Nevertheless, future efforts are necessary to determine the effect of first-order logic rules on clinical processes and patient outcomes.

Predicting Prolonged Stay in the ICU Attributable to Bleeding in Patients Offered Plasma Transfusion.

In blood transfusion studies, plasma transfusion (PPT) and bleeding are known to be associated with risk of prolonged ICU length of stay (ICU-LOS). However, as patients can show significant heterogeneity in response to a treatment, there might exists subgroups with differential effects. The existence and characteristics of these subpopulations in blood transfusion has not been well-studied. Further, the impact of bleeding in patients offered PPT on prolonged ICU-LOS is not known. This study presents a causal and predictive framework to examine these problems. The two-step approach first estimates the effect of bleeding in PPT patients on prolonged ICU-LOS and then estimates risks of bleeding and prolonged ICU-LOS. The framework integrates a classification model for risks prediction and a regression model to predict actual LOS. Results showed that the effect of bleeding in PPT patients significantly increases risk of prolonged ICU-LOS (55%, p=0.00) while no bleeding significantly reduces ICU-LOS (4%, p=0.046).