This prospective 12-week open-label trial evaluates the tolerability and efficacy of memantine hydrochloride for the treatment of core social and cognitive deficits in adults with high-functioning autism spectrum disorder (ASD). Measures for assessment of therapeutic response included the Social Responsiveness Scale-Adult Research Version (SRS-A), disorder-specific Clinical Global Impression scales, Behavior Rating Inventory of Executive Functioning-Adult Self-Report, Diagnostic Analysis of Nonverbal Accuracy Scale, and Cambridge Neuropsychological Test Automated Battery. Eighteen adults (mean age, 28 ± 9.5 years) with high-functioning ASD (SRS-A raw score, 99 ± 17) were treated with memantine (mean dose, 19.7 ± 1.2 mg/d; range, 15-20 mg), and 17 (94%) completed the trial. Treatment with memantine was associated with significant reduction on informant-rated (SRS-A, -28 ± 25; P < 0.001) and clinician-rated (Clinical Global Impression-Improvement subscale ≤2, 83%) measures of autism severity. In addition, memantine treatment was associated with significant improvement in ADHD and anxiety symptom severity. Significant improvement was noted in nonverbal communication on the Diagnostic Analysis of Nonverbal Accuracy Scale test and in executive function per self-report (Behavior Rating Inventory of Executive Functioning-Adult Self-Report Global Executive Composite, -6 ± 8.8; P < 0.015) and neuropsychological assessments (Cambridge Neuropsychological Test Automated Battery). Memantine treatment was generally well tolerated and was not associated with any serious adverse events. Treatment with memantine appears to be beneficial for the treatment of ASD and associated psychopathology and cognitive dysfunction in intellectually capable adults. Future placebo-controlled trials are warranted.
BACKGROUND: The American College of Surgeons (ACS) NSQIP risk calculator helps guide operative decision making. In patients with significant surgical risk, it may be unclear whether to proceed with “Hail Mary”–type interventions. To refine predictions, a local interpretable model-agnostic explanations machine (LIME) learning algorithm was explored to determine weighted patient-specific factors’ contribution to mortality. STUDY DESIGN: The ACS-NSQIP database was queried for all surgical patients with mortality probability greater than 50% between 2012 and 2019. Preoperative factors (n = 38) were evaluated using stepwise logistic regression; 26 significant factors were used in gradient boosted machine (GBM) modeling. Data were divided into training and testing sets, and model performance was substantiated with 10-fold cross validation. LIME provided individual subject mortality. The GBM-trained model was interpolated to LIME, and predictions were made using the test dataset. RESULTS: There were 6,483 deaths (53%) among 12,248 admissions. GBM modeling displayed good performance (area under the curve = 0.65, 95% CI 0.636–0.671). The top 5 factors (% contribution) to mortality included: septic shock (27%), elevated International Normalized Ratio (22%), ventilator-dependence (14%), thrombocytopenia (14%), and elevated serum creatinine (5%). LIME modeling subset personalized patients by factors and weights on survival. In the entire cohort, mortality positive predictive value with 2 factor combinations was 53.5% (specificity 0.713), 3 combinations 64.2% (specificity 0.835), 4 combinations 72.1% (specificity 0.943), and all 5 combinations 77.9% (specificity 0.993). Conversely, mortality positive predictive value fell to 34% in the absence of 4 factors. CONCLUSIONS: Through the application of machine learning algorithms (GBM and LIME), our model individualized predicted mortality and contributing factors with substantial ACS-NSQIP predicted mortality. USE of machine learning techniques may better inform operative decisions and family conversations in cases of significant surgical risk.
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