The diagnostic yield of genetic and metabolic investigations in syndromic and nonsyndromic patients with autism spectrum disorder, global developmental delay, or intellectual disability from a dedicated neurodevelopmental disorders genetics clinic.

First-tier genetic investigations for patients with neurodevelopmental disorders (NDDs) may include chromosomal microarray, Fragile X testing, and screening for inherited metabolic diseases, but most remain undiagnosed upon completion of testing. Here, we report the diagnostic yields of genetic testing for 537 patients with at least one of autism spectrum disorder, global developmental delay, and/or intellectual disability. Patients were assessed in a single neurodevelopmental genetics clinic, and each underwent a standardized history and physical examination. Each patient was characterized as syndromic or nonsyndromic based on clinical features. Our results demonstrate that multigene sequencing (with an NDD gene panel or exome) had a higher diagnostic yield (8%; 95% confidence interval [CI]: 5%, 13%) than chromosomal microarray and Fragile X testing combined (4%; 95% CI: 3%, 7%). Biochemical screening for inherited metabolic diseases had a diagnostic yield of zero. The diagnostic yield of genetic testing was significantly higher for syndromic patients than for nonsyndromic patients (odds ratio [OR] 3.09; 95% CI: 1.46, 6.83) and higher for female patients than for male (OR 3.21; 95% CI: 1.52, 6.82). These results add to the growing evidence supporting a comprehensive genetic evaluation that includes both copy number analysis and sequencing of known NDD genes for patients with NDDs.

Emulating a Randomised Controlled Trial With Observational Data: An Introduction to the Target Trial Framework.

Randomised controlled trials (RCTs) are often considered to be the highest quality of evidence owing to the absence of baseline confounding, the simplicity of analyses, and direct estimation of causal effects. However, observational studies can be designed to mimic RCTs and estimate causal treatment effects. In this review, we describe the target trial framework to illustrate how observational studies can successfully emulate RCTs. We focus on key design elements of RCTs and how to emulate them with observational data. These elements include 1) eligibility criteria, 2) treatment assignment and randomisation, 3) specification of "time zero", 4) outcomes, 5) follow-up, 6) causal contrasts (intention-to-treat vs per-protocol), and 7) statistical analyses. In addition, we describe the design of an example target trial created to emulate the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel Thrombolysis in Myocardial Infarction (TRITON-TIMI) 38 trial and compare effect estimates. Overall, careful design of a target trial using observational data can produce causal effect estimates that are often comparable to RCTs.