Scientist Michelle Clark, from Rady Children’s Institute for Genomic Medicine, and her colleagues, have built an automated pipeline to analyse EHR data and genome sequencing data from dried blood spots to deliver a potential diagnosis for hospitalised, often critically ill, children with suspected genetic diseases.

Their pipeline required minimal user intervention, increasing usability and shortening time to diagnosis, delivering a provisional finding in a median time of less than 24 hours. Although this pipeline would need to be adapted for use at different hospital systems, such an automated tool could help clinicians diagnose genetic diseases more quickly and accurately, potentially hastening lifesaving changes to patient care.

Genetic diseases are the leading cause of infant mortality in the US, particularly among the around 15 percent of infants admitted to neonatal, pediatric, and cardiovascular intensive care units (ICUs). Rapid disease progression demands an equally fast diagnosis to help inform interventions that lessen suffering and mortality, yet routinely employed genome sequencing takes weeks to return results, which is too slow to guide patient management.

To combat this, Clark and her colleagues analysed the electronic health record (EHR) and genomic sequencing data from both fresh and dried blood samples using a sequencing platform that offered a faster and less labour-intensive approach, as samples could be prepared in batches by an automated robot.

The platform also included a written pipeline of computational scripts that automatically processed the childrens’ EHR data and ranked the likelihood of specific disease-causing variants for each child.

The researchers found that diagnoses matched expert interpretation in 95 children with 97 genetic diseases with 97 percent sensitivity and 99 percent precision. The platform also correctly diagnosed three of seven seriously ill ICU infants with 100 percent sensitivity and precision. 

For further information on the automated pipeline, read the full research article as published in the Science Translational Medicine.