AMELIE 3: Fully Automated Mendelian Patient Reanalysis at Under 1 Alert per Patient per Year

Abstract

AbstractBackgroundMany thousands of patients with a suspected Mendelian disease have their exomes/genomes sequenced every year, but only about 30% receive a definitive diagnosis. Since a novel Mendelian gene-disease association is published on average every business day, thousands of undiagnosed patient cases could receive a diagnosis each year if their genomes were regularly compared to the latest literature. With millions of genomes expected to be sequenced for rare disease analysis by 2025, and considering the current publication rate of 1.1 million new articles per annum in PubMed, manually reanalyzing the growing cases of undiagnosed patients is not sustainable.MethodsWe describe a fully automated reanalysis framework for patients with suspected, but undiagnosed, Mendelian disorders. The presented framework was tested by automatically parsing all ∼100,000 newly published peer reviewed papers every month and matching them on genotype and phenotype with all stored undiagnosed patients. If a new article contains a possible diagnosis for an undiagnosed patient, the system provides notification. We test the accuracy of the automatic reanalysis system on 110 patients, including 61 with available trio data.ResultsEven when trained only on older data, our system identifies 80% of reanalysis diagnoses, while sending only 0.5-1 alerts per patient per year, a 100-1,000-fold efficiency gain over manual literature surveillance of equivalent yield.ConclusionWe show that automatic reanalysis of patients with suspected Mendelian disease is feasible and has the potential to greatly streamline diagnosis. Our system is not intended to replace clinical judgment. Rather, clinical diagnostic services could greatly benefit from a modest re-allocation of time from manual literature exploration to review of automated reanalysis alerts. Our system additionally supports a new paradigm for medical IT systems: proactive, continuously learning and consequently able to autonomously identify valuable insights as they emerge in digital health records. We have launched automated patient reanalysis, trained on the latest data, with user accounts and daily literature updates at https://AMELIE.stanford.edu.