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Many studies have reported an increased risk of autism spectrum disorder (ASD) associated with some maternal diagnoses in pregnancy. However, such associations have not been studied systematically, accounting for comorbidity between maternal disorders. Therefore our aim was to comprehensively test the associations between maternal diagnoses around pregnancy and ASD risk in offspring.
This exploratory case–cohort study included children born in Israel from 1997 to 2008, and followed up until 2015. We used information on all ICD-9 codes received by their mothers during pregnancy and the preceding year. ASD risk associated with each of those conditions was calculated using Cox proportional hazards regression, adjusted for the confounders (birth year, maternal age, socioeconomic status and number of ICD-9 diagnoses during the exposure period).
The analytic sample consisted of 80 187 individuals (1132 cases, 79 055 controls), with 822 unique ICD-9 codes recorded in their mothers. After extensive quality control, 22 maternal diagnoses were nominally significantly associated with offspring ASD, with 16 of those surviving subsequent filtering steps (permutation testing, multiple testing correction, multiple regression). Among those, we recorded an increased risk of ASD associated with metabolic [e.g. hypertension; HR = 2.74 (1.92–3.90), p = 2.43 × 10−8], genitourinary [e.g. non-inflammatory disorders of cervix; HR = 1.88 (1.38–2.57), p = 7.06 × 10−5] and psychiatric [depressive disorder; HR = 2.11 (1.32–3.35), p = 1.70 × 10−3] diagnoses. Meanwhile, mothers of children with ASD were less likely to attend prenatal care appointment [HR = 0.62 (0.54–0.71), p = 1.80 × 10−11].
Sixteen maternal diagnoses were associated with ASD in the offspring, after rigorous filtering of potential false-positive associations. Replication in other cohorts and further research to understand the mechanisms underlying the observed associations with ASD are warranted.
The extent and profiles of heterogeneity in cognitive functioning among participants in clinical trials of antidementia medication are unknown. We aimed to quantify and identify profiles of heterogeneity of cognition in Alzheimer’s disease.
Individual-level participant data were analyzed from five pivotal clinical trials of donepezil for Alzheimer’s disease (N = 2,919). Based on Alzheimer’s Disease Assessment Scale–Cognitive Subscale total scores from baseline up to week 12, a latent class model was used to identify heterogeneous groups. A logistic regression model was used to examine factors associated with group membership. Sensitivity analysis was conducted, restricted to the donepezil, and then the placebo arm.
The latent class model identified three classes labeled as low scorers (i.e., least cognitive impairment; N = 1,666, 76.04%), improvers (N = 27, 1.23%), and high scorers (N = 498, 22.73%). Logistic modeling showed that donepezil compared to placebo was significantly (p < 0.05) positively associated with membership in the improvers class (OR = 6.88, 95% CI = 2.03, 42.95), and negatively with high scorers (OR = 0.79, 95% CI = 0.64, 0.98). Sensitivity analysis restricted to the placebo, then donepezil arms replicated similar heterogeneity patterns.
Our results inform clinicians regarding the extent of heterogeneity in cognitive functioning during treatment and contribute to trial design considerations.
Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample.
We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]).
All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset.
We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.
Age of onset is considered central to understanding the course of schizophrenia, yet little is known regarding its association with quality of life in general, and specifically among males and females.
To examine the association between the age of schizophrenia onset and quality of life, in general, and among males and females, using data from a national sample and competing statistical models.
Participants with a diagnosis of schizophrenia (N = 1624) completed the Manchester Short Assessment of Quality of Life (MSA-QoL) and were rated on a parallel measure by their professional caregivers (N = 578). Multiple regression analysis models were computed for self-appraised quality of life, and mixed models with random intercepts were used for caregivers. Six competing models were tested for parsimony for each rating source. Three models without adjustment and three models adjusted for confounding variables. Sensitivity analyses were conducted for males and females separately.
Age of onset was statistically significantly (P <.05) negatively associated with self-appraised and caregiver-appraised quality of life on aggregate and among females. Among males, a significant (P <.01) quadratic effect of onset age on self-appraised quality of life demonstrated a negative association up to onset age of 36.67 years, after which the association was positive.
An earlier age of onset is associated with a better quality of life in schizophrenia which is tentatively explained by social decline. Specific trends in psychiatric symptom severity may account for this association among females while social advantages may account for the particular results found among males.