Dataset Information

Clinical genome sequencing in an unbiased pediatric cohort.

ABSTRACT: PURPOSE:We report for the first time, the use of clinical genome sequencing (GS) in an unbiased pediatric cohort. We describe the clinical validation, patient metrics, ordering patterns, results, reimbursement, and physician retrieval of results for the first consecutive 80 cases. METHODS:Clinical GS was performed for both inpatients and outpatients undergoing etiologic evaluations. Results were reported in the electronic medical record. Evidence of report retrieval by clinicians and whether interpretation was concordant with laboratory report was obtained through retrospective chart review. RESULTS:Twenty definitive diagnoses were made in 19 patients (24%; n?=?80). Except for two partial gene deletions, all diagnostic variants would have been detectable by our exome methods. Surprisingly, there was no documentation of communication of results to the family in the medical record for 17.5% of patients, and in 7.5%, physician and laboratory interpretations were discordant. Average insurance reimbursement was 30.2%, with yield for commercial payers significantly higher, at 54.1%. CONCLUSIONS:The detection rate of GS is equivalent and potentially superior to exome sequencing (ES). Reimbursement rates were variable but overall satisfactory for commercial insurers, and poor for government entities. In addition, we identify opportunities for improvement in the communication of results to families, likely translatable to other tests and other institutions.

SUBMITTER: Thiffault I

PROVIDER: S-EPMC6752301 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical genome sequencing in an unbiased pediatric cohort.

Thiffault Isabelle I Farrow Emily E Zellmer Lee L Berrios Courtney C Miller Neil N Gibson Margaret M Caylor Raymond R Jenkins Janda J Faller Deb D Soden Sarah S Saunders Carol C

Genetics in medicine : official journal of the American College of Medical Genetics 20180716 2

<h4>Purpose</h4>We report for the first time, the use of clinical genome sequencing (GS) in an unbiased pediatric cohort. We describe the clinical validation, patient metrics, ordering patterns, results, reimbursement, and physician retrieval of results for the first consecutive 80 cases.<h4>Methods</h4>Clinical GS was performed for both inpatients and outpatients undergoing etiologic evaluations. Results were reported in the electronic medical record. Evidence of report retrieval by clinicians ...[more]

PMID: 30008475

Similar Datasets

Project description:The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions.IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children's hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions.

Dataset Information

Clinical genome sequencing in an unbiased pediatric cohort.

Publications

Clinical genome sequencing in an unbiased pediatric cohort.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets