Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation

Big data and transformative bioinformatics in genomic diagnostics and beyond

The current era of high-throughput analysis-driven research offers invaluable insights into disease etiologies, accurate diagnostics, pathogenesis, and personalized therapy. In the field of movement disorders, investigators are facing an increasing growth in the volume of produced patient-derived datasets, providing substantial opportunities for precision medicine approaches based on extensive information accessibility and advanced annotation practices. Integrating data from multiple sources, including phenomics, genomics, and multi-omics, is crucial for comprehensively understanding different types of movement disorders. Here, we explore formats and analytics of big data generated for patients with movement disorders, including strategies to meaningfully share the data for optimized patient benefit. We review computational methods that are essential to accelerate the process of evaluating the increasing amounts of specialized data collected. Based on concrete examples, we highlight how bioinformatic approaches facilitate the translation of multidimensional biological information into clinically relevant knowledge. Moreover, we outline the feasibility of computer-aided therapeutic target evaluation, and we discuss the importance of expanding the focus of big data research to understudied phenotypes such as dystonia.

Rapid Whole-Genome Sequencing as a First-Line Test Is Likely to Significantly Reduce the Cost of Acute Care in a Private Payer System

BACKGROUND

Genetic disorders are a leading contributor to morbidity and mortality in neonatal and pediatric intensive care units. Rapid whole-genome sequencing (rWGS) has demonstrated improved clinical outcomes and reduced costs of care. The objective of this study was to predict the effect of rWGS on healthcare spending if implemented as a first-line diagnostic test in the Blue Shield of California (BSC) private payer system.

METHODS

This study applied private payer reimbursement methods and rates to clinical outcomes of rWGS on pediatric inpatient care as determined by a previous study of publicly insured infants in Project Baby Bear. BSC patients who were clinically similar to the Project Baby Bear cohort were identified by matching on diagnosis-related group and severity of illness. Payment data from these BSC patients was used to estimate the financial impact of clinical outcomes resulting from rWGS testing in a commercially insured pediatric population.

RESULTS

The analysis estimated a reduction of $5.8 million to $7.8 million in inpatient payments due to an estimated 457 to 592 avoided inpatient days due to rWGS results. With an estimated cost of sequencing at $2.7 million for the entire cohort (n = 184), the financial impact of rWGS as a first-tier test in the intensive care unit resulted in estimated net savings to BSC of $16 730 to $28 061 per patient sequenced.

CONCLUSIONS

Implementation of rWGS using the protocols established in Project Baby Bear is likely to result in significant reductions in healthcare spending among privately insured patients.

Ethical considerations in AI for child health and recommendations for child-centered medical AI

There does not exist any previous comprehensive review on AI ethics in child health or any guidelines for management, unlike in adult medicine. This review describes ethical principles in AI for child health and provides recommendations for child-centered medical AI. We also introduce the Pediatrics EthicAl Recommendations List for AI (PEARL-AI) framework for clinicians and AI developers to ensure ethical AI enabled systems in healthcare for children.

Application of rapid clinical exome sequencing technology in the diagnosis of critically ill pediatric patients with suspected genetic diseases

Purpose

This study evaluates the efficacy of rapid clinical exome sequencing (CES) and mitochondrial DNA (mtDNA) sequencing for diagnosing genetic disorders in critically ill pediatric patients.

Methods

A multi-centre investigation was conducted, enrolling critically ill pediatric patients suspected of having genetic disorders from March 2019 to December 2020. Peripheral blood samples from patients and their parents were analyzed using CES (proband-parent) and mtDNA sequencing (proband-mother) based on Next-Generation Sequencing (NGS) technology.

Results

The study included 44 pediatric patients (24 males, 20 females) with a median age of 27 days. The median turnaround time for genetic tests was 9.5 days. Genetic disorders were diagnosed in 25 patients (56.8%): 5 with chromosome microduplication/deletion syndromes (11.3%), 1 with UPD-related disease (2.3%), and 19 with monogenic diseases (43.2%). De novo variants were identified in nine patients (36.0%). A neonate was diagnosed with two genetic disorders due to a homozygous SLC25A20 variant and an MT-TL1 gene variation.

Conclusion

Rapid genetic diagnosis is crucial for critically ill pediatric patients with suspected genetic disorders. CES and mtDNA sequencing offer precise and timely results, guiding treatment and reducing mortality and disability, making them suitable primary diagnostic tools.

SeqFirst: Building equity access to a precise genetic diagnosis in critically ill newborns

Access to a precise genetic diagnosis (PrGD) in critically ill newborns is limited and inequitable because the complex inclusion criteria used to prioritize testing eligibility omit many patients at high risk for a genetic condition. SeqFirst-neo is a program to test whether a genotype-driven workflow using simple, broad exclusion criteria to assess eligibility for rapid genome sequencing (rGS) increases access to a PrGD in critically ill newborns. All 408 newborns admitted to a neonatal intensive care unit between January 2021 and February 2022 were assessed, and of 240 eligible infants, 126 were offered rGS (i.e., intervention group [IG]) and compared to 114 infants who received conventional care in parallel (i.e., conventional care group [CCG]). A PrGD was made in 62/126 (49.2%) IG neonates compared to 11/114 (9.7%) CCG infants. The odds of receiving a PrGD were ∼9 times greater in the IG vs. the CCG, and this difference was maintained at 12 months follow-up. Access to a PrGD in the IG vs. CCG differed significantly between infants identified as non-White (34/74, 45.9% vs. 6/29, 20.7%; p = 0.024) and Black (8/10, 80.0% vs. 0/4; p = 0.015). Neonatologists were significantly less successful at predicting a PrGD in non-White than non-Hispanic White infants. The use of a standard workflow in the IG with a PrGD revealed that a PrGD would have been missed in 26/62 (42%) infants. The use of simple, broad exclusion criteria that increase access to genetic testing significantly increases access to a PrGD, improves access equity, and results in fewer missed diagnoses.

The Role of Artificial Intelligence in Pediatric Intensive Care: A Systematic Review

Pediatric intensive care units (PICUs) could transform due to artificial intelligence (AI), which could improve patient outcomes, increase diagnostic accuracy, and streamline repetitive procedures. The goal of this systematic review was to outline how AI can be used to enhance any health outcomes in pediatric intensive care. We searched four databases (PubMed, Scopus, Web of Science, and IEEE Xplore) for relevant studies using a predefined systematic search. We found 267 studies in these four databases. The studies were first screened to remove the duplicates and then screened by titles to remove irrelevant studies. The studies were further screened based on inclusion and exclusion criteria, in which 32 studies were found suitable for inclusion in this study. The studies were assessed for risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST) tool. After AI was implemented, almost 22% (n = 7) of studies showed an immediate effect and enhanced health outcomes. A small number of studies involved AI implementation in actual PICUs, while the majority focused on experimental testing. AI models outperformed conventional clinical modalities among the remaining 78% (n = 25) and might have indirectly impacted patient outcomes. Significant variation in metrics and standardization was found when health outcomes were quantitatively assessed using statistical measures, including specificity (38%; n = 12) and area under the receiver operating characteristic curve (AUROC) (56%; n = 18). There are not sufficient studies showing that AI has significantly enhanced pediatric critical care patients' health outcomes. To evaluate AI's impact, more prospective, experimental research is required, utilizing verified outcome measures, defined metrics, and established application frameworks.

A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit

The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased.

Breaking barriers: fostering equitable access to pediatric genomics through innovative care models and technologies

The integration of genomic medicine into pediatric clinical practice is a critical need that remains largely unmet, especially in socioeconomically challenged and rural areas where healthcare disparities are most pronounced. This review seeks to summarize the barriers responsible for delayed diagnosis and treatment, and examines diverse care models, technological innovations, and strategies for dissemination and implementation aimed at addressing the evolving genomic needs of pediatric populations. Through a comprehensive review of the literature, we explore proposed methodologies to bridge this gap in pediatric healthcare, with a specific emphasis on understanding and speeding implementation approaches and technologies to mitigate disparities in underserved populations, including rural and marginalized communities. There are both external and internal factors to consider in demographic and social determinants when evaluating patient access. To address these barriers, potential solutions include telegenetic services, alternative care delivery models, pediatric subspecialist expansion, and non-genetic provider education. By improving access to pediatric genomic services, therapeutic interventions will also be more available to all pediatric patients. IMPACT STATEMENT: Genomic testing has clinical utility in pediatric populations but access for people from diverse demographic and social-economic groups is problematic. Understanding barriers responsible for delayed genetic diagnosis and treatment in pediatric populations will improve reach, adoption, implementation, and maintenance of genomic medicine in pediatric healthcare context. Innovative care models, adaptation of appropriate technologies, and strategies aimed at addressing pediatric genomic needs are needed.

Genome sequencing reveals the impact of non-canonical exon inclusions in rare genetic disease

Introduction

Advancements in sequencing technologies have significantly improved clinical genetic testing, yet the diagnostic yield remains around 30-40%. Emerging sequencing technologies are now being deployed in the clinical setting to address the remaining diagnostic gap.

Methods

We tested whether short-read genome sequencing could increase diagnostic yield in individuals enrolled into the UCI-GREGoR research study, who had suspected Mendelian conditions and prior inconclusive clinical genetic testing. Two other collaborative research cohorts, focused on aortopathy and dilated cardiomyopathy, consisted of individuals who were undiagnosed but had not undergone harmonized prior testing.

Results

We sequenced 353 families (754 participants) and found a molecular diagnosis in 54 (15.3%) of them. Of these diagnoses, 55.5% were previously missed because the causative variants were in regions not interrogated by the original testing. In 9 cases, they were deep intronic variants, 5 of which led to abnormal splicing and cryptic exon inclusion, as directly shown by RNA sequencing. All 5 of these variants had inconclusive spliceAI scores. In 26% of newly diagnosed cases, the causal variant could have been detected by exome sequencing reanalysis.

Conclusion

Genome sequencing overcomes multiple limitations of clinical genetic testing, such as inability to call intronic variants and technical limitations. Our findings highlight cryptic exon inclusion as a common mechanism via which deep intronic variants cause Mendelian disease. However, they also reinforce that reanalysis of exome datasets can be a fruitful approach.

Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

Implementation of multi-omics in diagnosis of pediatric rare diseases

The rapid and accurate diagnosis of rare diseases is paramount in directing clinical management. In recent years, the integration of multi-omics approaches has emerged as a potential strategy to overcome diagnostic hurdles. This review examines the application of multi-omics technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, in relation to the diagnostic journey of rare diseases. We explore how these combined approaches enhance the detection of pathogenic genetic variants and decipher molecular mechanisms. This review highlights the groundbreaking potential of multi-omics in advancing the precision medicine paradigm for rare diseases, offering insights into future directions and clinical applications. IMPACT: This review discusses using current tests and emerging technologies to diagnose pediatric rare diseases. We describe the next steps after inconclusive molecular testing and a structure for using multi-omics in further investigations. The use of multi-omics is expanding, and it is essential to incorporate it into clinical practice to enhance individualized patient care.

Promises and challenges of genomic newborn screening (NBS) - lessons from public health NBS programs

Newborn screening (NBS) in the United States began in the 1960s to detect inborn errors of metabolism that benefited from presymptomatic treatment compared with treatment after the development of symptoms and diagnosis. Over time, it expanded to include endocrinological disorders, hematological disorders, immunodeficiencies, and other treatable diseases such as lysosomal storage diseases (LSD), cystic fibrosis, X-linked adrenoleukodystrophy, and spinal muscular dystrophy. This expansion has been driven by new technologies (e.g., tandem mass spectrometry) and novel treatments (e.g., enzyme replacement therapy and stem cell transplant for LSDs). Advances in next-generation gene sequencing (NGS) enable rapid identification of many additional conditions that might benefit from early presymptomatic intervention. We review the NGS technologies that evolved as diagnostic testing and suggest issues to be resolved before their potential application to screening the asymptomatic population. We illustrate the importance of selecting diseases to be screened and propose recommendations to follow when variants of uncertain significance are found. We address ethical issues around achieving equity in the sensitivity of genomic NBS, access to follow-up and management, especially for people from diverse backgrounds, and other considerations. Finally, we discuss the potential benefits and harms of genomic NBS to the overall health of children with monogenic diseases. IMPACT: Genomic newborn screening programs are ongoing worldwide. Public discussion is needed as to whether genomic newborn screening should be offered as a public health program and, if so, what conditions should be screened for. Providers should understand that the sensitivity of genomic newborn screening is especially low for newborns from non-European populations. Methylation, large structural variants and repeat expansion variants are not amenable to next-generation sequencing-based genomic newborn screening. The article serves as a comprehensive guide to understanding issues that need to be solved before genomic newborn screening is implemented as a public health program.

Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review

BACKGROUND

Chromosomal abnormalities are genetic disorders caused by chromosome errors, leading to developmental delays, birth defects, and miscarriages. Currently, invasive procedures such as amniocentesis or chorionic villus sampling are mostly used, which carry a risk of miscarriage. This has led to the need for a noninvasive and innovative approach to detect and prevent chromosomal abnormalities during pregnancy.

OBJECTIVE

This review aims to describe and appraise the potential of internet-based abnormal chromosomal preventive measures as a noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy.

METHODS

A thorough review of existing literature and research on chromosomal abnormalities and noninvasive approaches to prenatal diagnosis and therapy was conducted. Electronic databases such as PubMed, Google Scholar, ScienceDirect, CENTRAL, CINAHL, Embase, OVID MEDLINE, OVID PsycINFO, Scopus, ACM, and IEEE Xplore were searched for relevant studies and articles published in the last 5 years. The keywords used included chromosomal abnormalities, prenatal diagnosis, noninvasive, and internet-based, and diagnosis.

RESULTS

The review of literature revealed that internet-based abnormal chromosomal diagnosis is a potential noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. This innovative approach involves the use of advanced technology, including high-resolution ultrasound, cell-free DNA testing, and bioinformatics, to analyze fetal DNA from maternal blood samples. It allows early detection of chromosomal abnormalities, enabling timely interventions and treatment to prevent adverse outcomes. Furthermore, with the advancement of technology, internet-based abnormal chromosomal diagnosis has emerged as a safe alternative with benefits including its cost-effectiveness, increased accessibility and convenience, potential for earlier detection and intervention, and ethical considerations.

CONCLUSIONS

Internet-based abnormal chromosomal diagnosis has the potential to revolutionize prenatal care by offering a safe and noninvasive alternative to invasive procedures. It has the potential to improve the detection of chromosomal abnormalities, leading to better pregnancy outcomes and reduced risk of miscarriage. Further research and development in this field is needed to make this approach more accessible and affordable for pregnant women.

Genetic testing for diffuse lung diseases in children

Newly developing genomic technologies are an increasingly important part of clinical care and thus, it is not only important to understand the technologies and their limitations, but to also interpret the findings in an actionable fashion. Clinical geneticists and genetic counselors are now an integral part of the clinical team and are able to bridge the complexities of this rapidly changing science between the bedside clinicians and patients. This manuscript reviews the terminology, the current technology, some of the known genetic disorders that result in lung disease, and indications for genetic testing with associated caveats. Because this field is evolving quickly, we also provide links to websites that provide continuously updated information important for integrating genomic technology results into clinical decision-making.

Role of next generation sequencing in diagnosis and management of critically ill children with suspected monogenic disorder

Next generation sequencing based diagnosis has emerged as a promising tool for evaluating critically ill neonates and children. However, there is limited data on its utility in developing countries. We assessed its diagnostic rate and clinical impact on management of pediatric patients with a suspected genetic disorder requiring critical care. The study was conducted at a single tertiary hospital in Northern India. We analyzed 70 children with an illness requiring intensive care and obtained a precise molecular diagnosis in 32 of 70 probands (45.3%) using diverse sequencing techniques such as clinical exome, whole exome, and whole genome. A significant change in clinical outcome was observed in 13 of 32 (40.6%) diagnosed probands with a change in medication in 11 subjects and redirection to palliative care in two subjects. Additional benefits included specific dietary management (three cases), avoidance of a major procedure (one case) and better reproductive counseling. Dramatic therapeutic responses were observed in three cases with SCN1A, SCN2A and KCNQ2-related epileptic encephalopathy. A delayed turn-around for sequencing results was perceived as a major limiting factor in the study, as rapid and ultra-rapid sequencing was not available. Achieving a precise molecular diagnosis has great utility in managing critically ill patients with suspected genetic disorders in developing countries.

Natural language processing in the intensive care unit: A scoping review

Objectives

Natural language processing (NLP) is a branch of artificial intelligence focused on enabling computers to interpret and analyse text-based data. The intensive care specialty is known to generate large volumes of data, including free-text, however, NLP applications are not commonly used either in critical care clinical research or quality improvement projects. This review aims to provide an overview of how NLP has been used in the intensive care specialty and promote an understanding of NLP's potential future clinical applications.

Design

Scoping review.

Data sources

A systematic search was developed with an information specialist and deployed on the PubMed electronic journal database. Results were restricted to the last 10 years to ensure currency.

Review methods

Screening and data extraction were undertaken by two independent reviewers, with any disagreements resolved by a third. Given the heterogeneity of the eligible articles, a narrative synthesis was conducted.

Results

Eighty-seven eligible articles were included in the review. The most common type (n = 24) were studies that used NLP-derived features to predict clinical outcomes, most commonly mortality (n = 16). Next were articles that used NLP to identify a specific concept (n = 23), including sepsis, family visitation and mental health disorders. Most studies only described the development and internal validation of their algorithm (n = 79), and only one reported the implementation of an algorithm in a clinical setting.

Conclusions

Natural language processing has been used for a variety of purposes in the ICU context. Increasing awareness of these techniques amongst clinicians may lead to more clinically relevant algorithms being developed and implemented.

A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit

The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the Neonatal Intensive Care Unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased.

Rapid genome sequencing for critically ill infants: an inaugural pilot study from Turkey

Introduction

Rare and ultra-rare genetic conditions significantly contribute to infant morbidity and mortality, often presenting with atypical features and genetic heterogeneity that complicate management. Rapid genome sequencing (RGS) offers a timely and cost-effective approach to diagnosis, aiding in early clinical management and reducing unnecessary interventions. This pilot study represents the inaugural use of next-generation sequencing (NGS) as a diagnostic instrument for critically ill neonatal and pediatric ICU patients in a Turkish hospital setting.

Methods

Ten infants were enrolled based on predefined inclusion criteria, and trio RGS was performed. The mean age of the participants was 124 days, with congenital abnormalities being the most common indication for testing. Three patients had consanguineous parents. The mean turnaround time from enrollment to delivery of results was 169 h, with a diagnostic yield of 50%.

Results

Three patients received a definitive molecular diagnosis, impacting their clinical management. Two patients benefited from the exclusion of Mendelian conditions, leading to alternative diagnoses.

Discussion

This study demonstrates the feasibility and results of RGS in Turkish hospital settings, emphasizing the importance of timely genetic diagnosis in reducing the diagnostic odyssey for families and improving patient care. Further research is needed to evaluate the cost-effectiveness and applicability of RGS in the Turkish healthcare system for children with diseases of uncertain etiology.

Emerging Multi-omic Approaches to the Molecular Diagnosis of Mitochondrial Disease and Available Strategies for Treatment and Prevention

Mitochondria are semi-autonomous organelles present in several copies within most cells in the human body that are controlled by the precise collaboration of mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) encoding mitochondrial proteins. They play important roles in numerous metabolic pathways, such as the synthesis of adenosine triphosphate (ATP), the predominant energy substrate of the cell generated through oxidative phosphorylation (OXPHOS), intracellular calcium homeostasis, metabolite biosynthesis, aging, cell cycles, and so forth. Previous studies revealed that dysfunction of these multi-functional organelles, which may arise due to mutations in either the nuclear or mitochondrial genome, leads to a diverse group of clinically and genetically heterogeneous disorders. These diseases include neurodegenerative and metabolic disorders as well as cardiac and skeletal myopathies in both adults and newborns. The plethora of phenotypes and defects displayed leads to challenges in the diagnosis and treatment of mitochondrial diseases. In this regard, the related literature proposed several diagnostic options, such as high throughput mitochondrial genomics and omics technologies, as well as numerous therapeutic options, such as pharmacological approaches, manipulating the mitochondrial genome, increasing the mitochondria content of the affected cells, and recently mitochondrial diseases transmission prevention. Therefore, the present article attempted to review the latest advances and challenges in diagnostic and therapeutic options for mitochondrial diseases.

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.

Applications of genome sequencing as a single platform for clinical constitutional genetic testing

The number of human disease genes has dramatically increased over the past decade, largely fueled by ongoing advances in sequencing technologies. In parallel, the number of available clinical genetic tests has also increased, including the utilization of exome sequencing for undiagnosed diseases. Although most clinical sequencing tests have been centered on enrichment-based multigene panels and exome sequencing, the continued improvements in performance and throughput of genome sequencing suggest that this technology is emerging as a potential platform for routine clinical genetic testing. A notable advantage is a single workflow with the opportunity to reflexively interrogate content as clinically indicated; however, challenges with implementing routine clinical genome sequencing still remain. This review is centered on evaluating the applications of genome sequencing as a single platform for clinical constitutional genetic testing, including its potential utility for diagnostic testing, carrier screening, cytogenomic molecular karyotyping, prenatal testing, mitochondrial genome interrogation, and pharmacogenomic and polygenic risk score testing.

Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.

3-hour genome sequencing and targeted analysis to rapidly assess genetic risk

Purpose

Rapid genetic testing in the critical care setting may guide diagnostic evaluation, direct therapies, and help families and care providers make informed decisions about goals of care. We tested whether a simplified DNA extraction and library preparation process would enable us to perform ultra-rapid assessment of genetic risk for a Mendelian condition, based on information from an affected sibling, using long-read genome sequencing and targeted analysis.

Methods

Following extraction of DNA from cord blood and rapid library preparation, genome sequencing was performed on an Oxford Nanopore PromethION. FASTQ files were generated from original sequencing data in near real-time and aligned to a reference genome. Variant calling and analysis were performed at timed intervals.

Results

We optimized the DNA extraction and library preparation methods to create sufficient library for sequencing from 500 μL of blood. Real-time, targeted analysis was performed to determine that the newborn was neither affected nor a heterozygote for variants underlying a Mendelian condition. Phasing of the target region and prior knowledge of the affected haplotypes supported our interpretation despite a low level of coverage at 3 hours of life.

Conclusion

This proof-of-concept experiment demonstrates how prior knowledge of haplotype structure or familial variants can be used to rapidly evaluate an individual at risk for a genetic disease. While ultra-rapid sequencing remains both complex and cost prohibitive, our method is more easily automated than prior approaches and uses smaller volumes of blood, thus may be more easily adopted for future studies of ultra-rapid genome sequencing in the clinical setting.

Acute symptomatic seizures in newborns: a narrative review

Acute symptomatic seizures are the main sign of neurological dysfunction in newborns. This is linked to the unique characteristics of the neonatal brain, making it hyperexcitable compared to older ages, and to the common occurrence of some forms of acquired brain injury, namely hypoxic-ischemic encephalopathy. In this narrative review we will provide an overview of neonatal seizures definition, their main underlying etiologies, diagnostic work-up and differential diagnoses, and will discuss about therapeutic options and prognostic outlook. The latest publications from the ILAE Task Force on Neonatal Seizures will be presented and discussed. Of note, they highlight the current lack of robust evidence in this field of clinical neurology. We will also report on specificities pertaining to low-and-middle income countries in terms of incidence, main etiologies and diagnosis. The possibilities offered by telemedicine and automated seizures detection will also be summarized in order to provide a framework for future directions in seizures diagnosis and management with a global perspective. Many challenges and opportunities for improving identification, monitoring and treatment of acute symptomatic seizures in newborns exist. All current caveats potentially represent different lines of research with the aim to provide better care and reach a deeper understanding of this important topic of neonatal neurology.

RUBICON: a framework for designing efficient deep learning-based genomic basecallers

Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.

Ethics of artificial intelligence in prenatal and pediatric genomic medicine

This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.

Lessons learned from rapid exome sequencing for 575 critically ill patients across the broad spectrum of rare disease

Introduction: Rapid exome sequencing (rES) has become the first-choice genetic test for critically ill patients, mostly neonates, young infants, or fetuses in prenatal care, in time-sensitive situations and when it is expected that the genetic test result may guide clinical decision making. The implementation of rES has revolutionized medicine by enabling timely identification of genetic causes for various rare diseases. The utilization of rES has increasingly been recognized as an essential diagnostic tool for the identification of complex and undiagnosed genetic disorders. Methods: We conducted a retrospective evaluation of our experiences with rES performed on 575 critically ill patients from various age groups (prenatal to adulthood), over a four-year period (2016-2019). These patients presented with a wide spectrum of rare diseases, including but not limited to neurological disorders, severe combined immune deficiency, and cancer. Results: During the study period, there was a significant increase in rES referrals, with a rise from a total of two referrals in Q1-2016 to 10 referrals per week in Q4-2019. The median turnaround time (TAT) decreased from 17 to 11 days in the period 2016-2019, with an overall median TAT of 11 days (IQR 8-15 days). The overall diagnostic yield for this cohort was 30.4%, and did not significantly differ between the different age groups (e.g. adults 22.2% vs children 31.0%; p-value 0.35). However, variability in yield was observed between clinical entities: craniofacial anomalies yielded 58.3%, while for three clinical entities (severe combined immune deficiency, aneurysm, and hypogonadotropic hypogonadism) no diagnoses were obtained. Discussion: Importantly, whereas clinical significance is often only attributed to a conclusive diagnosis, we also observed impact on clinical decision-making for individuals in whom no genetic diagnosis was established. Hence, our experience shows that rES has an important role for patients of all ages and across the broad spectrum of rare diseases to impact clinical outcomes.

Filaggrin-Associated Atopic Skin, Eye, Airways, and Gut Disease, Modifying the Presentation of X-Linked Reticular Pigmentary Disorder (XLPDR)

BACKGROUND

X-linked reticular pigmentary disorder (XLPDR) is a rare condition characterized by skin hyperpigmentation, ectodermal features, multiorgan inflammation, and recurrent infections. All probands identified to date share the same intronic hemizygous POLA1 hypomorphic variant (NM_001330360.2(POLA1):c.1393-354A > G) on the X chromosome. Previous studies have supported excessive type 1 interferon (IFN) inflammation and natural killer (NK) cell dysfunction in disease pathogenesis. Common null polymorphisms in filaggrin (FLG) gene underlie ichthyosis vulgaris and atopic predisposition.

CASE

A 9-year-old boy born to non-consanguineous parents developed eczema with reticular skin hyperpigmentation in early infancy. He suffered recurrent chest infections with chronic cough, clubbing, and asthma, moderate allergic rhinoconjunctivitis with keratitis, multiple food allergies, and vomiting with growth failure. Imaging demonstrated bronchiectasis, while gastroscopy identified chronic eosinophilic gastroduodenitis. Interestingly, growth failure and bronchiectasis improved over time without specific treatment.

METHODS

Whole-genome sequencing (WGS) using Illumina short-read sequencing was followed by both manual and orthogonal automated bioinformatic analyses for single-nucleotide variants, small insertions/deletions (indels), and larger copy number variations. NK cell cytotoxic function was assessed using 51Cr release and degranulation assays. The presence of an interferon signature was investigated using a panel of six interferon-stimulated genes (ISGs) by QPCR.

RESULTS

WGS identified a de novo hemizygous intronic variant in POLA1 (NM_001330360.2(POLA1):c.1393-354A > G) giving a diagnosis of XLPDR, as well as a heterozygous nonsense FLG variant (NM_002016.2(FLG):c.441del, NP_0020.1:p.(Arg151Glyfs*43)). Compared to healthy controls, the IFN signature was elevated although the degree moderated over time with the improvement in his chest disease. NK cell functional studies showed normal cytotoxicity and degranulation.

CONCLUSION

This patient had multiple atopic manifestations affecting eye, skin, chest, and gut, complicating the presentation of XLPDR. This highlights that common FLG polymorphisms should always be considered when assessing genotype-phenotype correlations of other genetic variation in patients with atopic symptoms. Additionally, while the patient exhibited an enhanced IFN signature, he does not have an NK cell defect, suggesting this may not be a constant feature of XLPDR.

Genetic counselling considerations with genetic/genomic testing in Neonatal and Pediatric Intensive Care Units: A scoping review

Genetic and genomic technologies can effectively diagnose numerous genetic disorders. Patients benefit when genetic counselling accompanies genetic testing and international guidelines recommend pre- and post-test genetic counselling with genome-wide sequencing. However, there is a gap in knowledge regarding the unique genetic counselling considerations with different types of genetic testing in the Neonatal Intensive Care Unit (NICU) and the Pediatric Intensive Care Unit (PICU). This scoping review was conducted to identify the gaps in care with respect to genetic counselling for infants/pediatric patients undergoing genetic and genomic testing in NICUs and PICUs and understand areas in need of improvement in order to optimize clinical care for patients, caregivers, and healthcare providers. Five databases (MEDLINE [Ovid], Embase [Ovid], PsycINFO [Ebsco], CENTRAL [Ovid], and CINHAL [Ebsco]) and grey literature were searched. A total of 170 studies were included and used for data extraction and analysis. This scoping review includes descriptive analysis, followed by a narrative account of the extracted data. Results were divided into three groups: pre-test, post-test, and comprehensive (both pre- and post-test) genetic counselling considerations based on indication for testing. More studies were conducted in the NICU than the PICU. Comprehensive genetic counselling was discussed in only 31% of all the included studies demonstrating the need for both pre-test and post-test genetic counselling for different clinical indications in addition to the need to account for different cultural aspects based on ethnicity and geographic factors.

Phenotype-Driven Molecular Genetic Test Recommendation for Diagnosing Pediatric Rare Disorders

Rare disease patients often endure prolonged diagnostic odysseys and may still remain undiagnosed for years. Selecting the appropriate genetic tests is crucial to lead to timely diagnosis. Phenotypic features offer great potential for aiding genomic diagnosis in rare disease cases. We see great promise in effective integration of phenotypic information into genetic test selection workflow. In this study, we present a phenotype-driven molecular genetic test recommendation (Phen2Test) for pediatric rare disease diagnosis. Phen2Test was constructed using frequency matrix of phecodes and demographic data from the EHR before ordering genetic tests, with the objective to streamline the selection of molecular genetic tests (whole-exome / whole-genome sequencing, or gene panels) for clinicians with minimum genetic training expertise. We developed and evaluated binary classifiers based on 1,005 individuals referred to genetic counselors for potential genetic evaluation. In the evaluation using the gold standard cohort, the model achieved strong performance with an AUROC of 0.82 and an AUPRC of 0.92. Furthermore, we tested the model on another silver standard cohort (n=6,458), achieving an overall AUROC of 0.72 and an AUPRC of 0.671. Phen2Test was adjusted to align with current clinical guidelines, showing superior performance with more recent data, demonstrating its potential for use within a learning healthcare system as a genomic medicine intervention that adapts to guideline updates. This study showcases the practical utility of phenotypic features in recommending molecular genetic tests with performance comparable to clinical geneticists. Phen2Test could assist clinicians with limited genetic training and knowledge to order appropriate genetic tests.

Whole-Genome Sequencing Can Identify Clinically Relevant Variants from a Single Sub-Punch of a Dried Blood Spot Specimen

The collection of dried blood spots (DBS) facilitates newborn screening for a variety of rare, but very serious conditions in healthcare systems around the world. Sub-punches of varying sizes (1.5-6 mm) can be taken from DBS specimens to use as inputs for a range of biochemical assays. Advances in DNA sequencing workflows allow whole-genome sequencing (WGS) libraries to be generated directly from inputs such as peripheral blood, saliva, and DBS. We compared WGS metrics obtained from libraries generated directly from DBS to those generated from DNA extracted from peripheral blood, the standard input for this type of assay. We explored the flexibility of DBS as an input for WGS by altering the punch number and size as inputs to the assay. We showed that WGS libraries can be successfully generated from a variety of DBS inputs, including a single 3 mm or 6 mm diameter punch, with equivalent data quality observed across a number of key metrics of importance in the detection of gene variants. We observed no difference in the performance of DBS and peripheral-blood-extracted DNA in the detection of likely pathogenic gene variants in samples taken from individuals with cystic fibrosis or phenylketonuria. WGS can be performed directly from DBS and is a powerful method for the rapid discovery of clinically relevant, disease-causing gene variants.

Prevalence and Clinical Significance of Commonly Diagnosed Genetic Disorders in Preterm Infants

Background and Objectives

Preterm infants (<34 weeks' gestation) experience high rates of morbidity and mortality before hospital discharge. Genetic disorders substantially contribute to morbidity and mortality in related populations. The prevalence and clinical impact of genetic disorders is unknown in this population. We sought to determine the prevalence of commonly diagnosed genetic disorders in preterm infants, and to determine the association of disorders with morbidity and mortality.

Methods

This was a retrospective multicenter cohort study of infants born from 23 to 33 weeks' gestation between 2000 and 2020. Genetic disorders were abstracted from diagnoses present in electronic health records. We excluded infants transferred from or to other health care facilities prior to discharge or death when analyzing clinical outcomes. We determined the adjusted odds of pre-discharge morbidity or mortality after adjusting for known risk factors.

Results

Of 320,582 infants, 4196 (1.3%) had genetic disorders. Infants with trisomy 13, 18, 21, or cystic fibrosis had greater adjusted odds of severe morbidity or mortality. Of the 17,427 infants who died, 566 (3.2%) had genetic disorders. Of the 65,968 infants with a severe morbidity, 1319 (2.0%) had genetic disorders.ConclusionsGenetic disorders are prevalent in preterm infants, especially those with life-threatening morbidities. Clinicians should consider genetic testing for preterm infants with severe morbidity and maintain a higher index of suspicion for life-threatening morbidities in preterm infants with genetic disorders. Prospective genomic research is needed to clarify the prevalence of genetic disorders in this population, and the contribution of genetic disorders to preterm birth and subsequent morbidity and mortality.

Article Summary

Genetic disorders were found in 1.3% of preterm infants and at a higher rate (2.0%) in infants who died or developed severe morbidity.

What’s Known on This Subject

Previous research described the prevalence and associated short-term morbidity and mortality of trisomy 13, 18, and 21 in preterm infants. The prevalence of other commonly diagnosed genetic disorders and associated short-term morbidity and mortality in preterm infants is unknown.

What This Study Adds

In a multicenter, retrospective cohort of 320,582 preterm (<34 weeks' gestation) infants, we found that 1.3% had genetic disorders diagnosed through standard care. Multiple disorders were associated with increased adjusted odds of morbidities or mortality prior to hospital discharge.

Contributors Statement Page

Selin S. Everett conceptualized and designed the study, conducted analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript.Dr. Thomas Hays conceptualized and designed the study, drafted the initial manuscript, and critically reviewed and revised the manuscript.Miles Bomback conceptualized and designed the study and critically reviewed and revised the manuscript.Drs. Veeral N. Tolia and Reese H. Clark coordinated and supervised data collection and critically reviewed and revised the manuscript.Dr. Rakesh Sahni conceptualized and designed the study and critically reviewed and revised the manuscript.Dr. Alex Lyford conducted analyses and critically reviewed and revised the manuscript. Dr. Ronald J. Wapner reviewed and critically revised the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Applications of artificial intelligence in clinical laboratory genomics

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.

Increased diagnostic yield from negative whole genome-slice panels using automated reanalysis

We evaluated the diagnostic yield using genome-slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI-funded GREGoR Consortium site. Genome-wide reanalysis was performed using Moon™, a machine-learning-based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome-wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting.

Implementation and Feasibility of Clinical Genome Sequencing Embedded Into the Outpatient Nephrology Care for Patients With Proteinuric Kidney Disease

Introduction

The diagnosis and management of proteinuric kidney diseases such as focal segmental glomerulosclerosis (FSGS) are challenging. Genetics holds the promise to improve clinical decision making for these diseases; however, it is often performed too late to enable timely clinical action and it is not implemented within routine outpatient nephrology visits.

Methods

We sought to test the implementation and feasibility of clinical rapid genome sequencing (GS) in guiding decision making in patients with proteinuric kidney disease in real-time and embedded in the outpatient nephrology setting.

Results

We enrolled 10 children or young adults with biopsy-proven FSGS (9 cases) or minimal change disease (1 case). The mean age at enrollment was 16.2 years (range 2-30). The workflow did not require referral to external genetics clinics but was conducted entirely during the nephrology standard-of-care appointments. The total turn-around-time from enrollment to return-of-results and clinical decision averaged 21.8 days (12.4 for GS), which is well within a time frame that allows clinically relevant treatment decisions. A monogenic or APOL1-related form of kidney disease was diagnosed in 5 of 10 patients. The genetic findings resulted in a rectified diagnosis in 6 patients. Both positive and negative GS findings determined a change in pharmacological treatment. In 3 patients, the results were instrumental for transplant evaluation, donor selection, and the immunosuppressive treatment. All patients and families received genetic counseling.

Conclusion

Clinical GS is feasible and can be implemented in real-time in the outpatient care to help guiding clinical management. Additional studies are needed to confirm the cost-effectiveness and broader utility of clinical GS across the phenotypic and demographic spectrum of kidney diseases.

At-Risk Genomic Findings for Pediatric-Onset Disorders From Genome Sequencing vs Medically Actionable Gene Panel in Proactive Screening of Newborns and Children

Importance

Although the clinical utility of genome sequencing for critically ill children is well recognized, its utility for proactive pediatric screening is not well explored.

Objective

To evaluate molecular findings from screening ostensibly healthy children with genome sequencing compared with a gene panel for medically actionable pediatric conditions.

Design, Setting, and Participants

This case series study was conducted among consecutive, apparently healthy children undergoing proactive genetic screening for pediatric disorders by genome sequencing (n = 562) or an exome-based panel of 268 genes (n = 606) from March 1, 2018, through July 31, 2022.

Exposures

Genetic screening for pediatric-onset disorders using genome sequencing or an exome-based panel of 268 genes.

Main Outcomes and Measures

Molecular findings indicative of genetic disease risk.

Results

Of 562 apparently healthy children (286 girls [50.9%]; median age, 29 days [IQR, 9-117 days]) undergoing screening by genome sequencing, 46 (8.2%; 95% CI, 5.9%-10.5%) were found to be at risk for pediatric-onset disease, including 22 children (3.9%) at risk for high-penetrance disorders. Sequence analysis uncovered molecular diagnoses among 32 individuals (5.7%), while copy number variant analysis uncovered molecular diagnoses among 14 individuals (2.5%), including 4 individuals (0.7%) with chromosome scale abnormalities. Overall, there were 47 molecular diagnoses, with 1 individual receiving 2 diagnoses; of the 47 potential diagnoses, 22 (46.8%) were associated with high-penetrance conditions. Pathogenic variants in medically actionable pediatric genes were found in 6 individuals (1.1%), constituting 12.8% (6 of 47) of all diagnoses. At least 1 pharmacogenomic variant was reported for 89.0% (500 of 562) of the cohort. In contrast, of 606 children (293 girls [48.3%]; median age, 26 days [IQR, 10-67 days]) undergoing gene panel screening, only 13 (2.1%; 95% CI, 1.0%-3.3%) resulted in potential childhood-onset diagnoses, a significantly lower rate than those screened by genome sequencing (P < .001).

Conclusions and Relevance

In this case series study, genome sequencing as a proactive screening approach for children, due to its unrestrictive gene content and technical advantages in comparison with an exome-based gene panel for medically actionable childhood conditions, uncovered a wide range of heterogeneous high-penetrance pediatric conditions that could guide early interventions and medical management.

Early onset critically ill infants with Schaaf-Yang syndrome: a retrospective study from the China neonatal genomes project and literature review

Background

Schaaf-Yang syndrome (SYS) is a recently identified rare neurodevelopmental disorder characterized by neonatal hypotonia, feeding difficulty, joint contractures, autism spectrum disorder and development delay/intellectual disability. It is mainly caused by truncating variants in maternally imprinted gene MAGEL2 within the Prader-Willi syndrome critical region 15q11-q13. Clinical diagnosis of SYS is difficult for clinicians due to its rarity and highly variable phenotypes, while unique inheritance patterns also complicate genetic diagnosis. To date, no published papers have analyzed the clinical consequences and molecular changes in Chinese patients.

Methods

In this study, we retrospectively investigated the mutation spectrums and phenotypic features of 12 SYS infants. The data were from a cohort of critically ill infants from the China neonatal genomes project (CNGP), sponsored by Children's Hospital of Fudan University. We also reviewed relevant literature.

Results

Six previously reported mutations and six novel pathogenic variations of MAGEL2 were identified in 12 unrelated infants. Neonatal respiratory problems were the major complaint for hospitalization, which occurred in 91.7% (11/12) cases. All babies displayed feeding difficulties and a poor suck postnatally, and neonatal dystonia was present in 11 of the cases; joint contractures and multiple congenital defects were also observed. Interestingly, we found that 42.5% (57/134) of the reported SYS patients, including ours carried variants in the c.1996 site, particularly the c.1996dupC variant. The mortality rate was 17.2% (23/134), with the median age of death between 24 gestational weeks in fetuses and 1-month-old in infants. Respiratory failure was the leading cause of death in live-born patients (58.8%, 10/17), especially during the neonatal period.

Conclusions

Our findings expanded the genotype and phenotype spectrum of neonatal SYS patients. The results demonstrated that respiratory dysfunction was a typical characteristic among Chinese SYS neonates that should attract physicians' attention. The early identification of such disorders allows early intervention and can further provide genetic counseling as well as reproductive options for the affected families.

Diagnostic genomic sequencing in critically ill children

Rare genetic diseases are a major cause of severe illnesses and deaths in new-borns and infants. Disease manifestation in critically ill children may be atypical or incomplete, making a monogenetic disease difficult to diagnose clinically. Rapid exome or genome ("genomic") sequencing in critically ill children demonstrated profound diagnostic and clinical value, and there is growing evidence that the faster a molecular diagnosis is established in such children, the more likely clinical management is influenced positively. An early molecular diagnosis enables treatment of critically ill children with precision medicine, has the potential to improve patient outcome and leads to healthcare cost savings. In this review, we outline the status quo of rapid genomic sequencing and possible future implications.

Open-Source Artificial Intelligence System Supports Diagnosis of Mendelian Diseases in Acutely Ill Infants

Mendelian disorders are prevalent in neonatal and pediatric intensive care units and are a leading cause of morbidity and mortality in these settings. Current diagnostic pipelines that integrate phenotypic and genotypic data are expert-dependent and time-intensive. Artificial intelligence (AI) tools may help address these challenges. Dx29 is an open-source AI tool designed for use by clinicians. It analyzes the patient's phenotype and genotype to generate a ranked differential diagnosis. We used Dx29 to retrospectively analyze 25 acutely ill infants who had been diagnosed with a Mendelian disorder, using a targeted panel of ~5000 genes. For each case, a trio (proband and both parents) file containing gene variant information was analyzed, alongside patient phenotype, which was provided to Dx29 by three approaches: (1) AI extraction from medical records, (2) AI extraction with manual review/editing, and (3) manual entry. We then identified the rank of the correct diagnosis in Dx29's differential diagnosis. With these three approaches, Dx29 ranked the correct diagnosis in the top 10 in 92-96% of cases. These results suggest that non-expert use of Dx29's automated phenotyping and subsequent data analysis may compare favorably to standard workflows utilized by bioinformatics experts to analyze genomic data and diagnose Mendelian diseases.

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning

BACKGROUND

Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS.

METHODS

Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search Engine (MPSE).

RESULTS

MPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children's Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients.

CONCLUSIONS

Our results indicate that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.

Scaling genetic resources: New paradigms for diagnosis and treatment of rare genetic disease

Development of genetic tests for rare genetic diseases has traditionally focused on individual diseases. Similarly, development of new therapies occurred one disease at a time. With >10,000 rare genetic diseases, this approach is not feasible. Diagnosis of genetic disorders has already transcended old paradigms as whole exome and genome sequencing have allowed expedient interrogation of all relevant genes in a single test. The growth of newborn screening has allowed identification of diseases in presymptomatic babies. Similarly, the ability to develop therapies is rapidly expanding due to technologies that leverage platform technology that address multiple diseases. However, movement from the basic science laboratory to clinical trials is still hampered by a regulatory system rooted in traditional trial design, requiring a fresh assessment of safe ways to obtain approval for new drugs. Ultimately, the number of nucleic acid-based therapies will challenge the ability of clinics focused on rare diseases to deliver them safely with appropriate evaluation and long-term follow-up. This manuscript summarizes discussions arising from a recent National Institutes of Health conference on nucleic acid therapy, with a focus on scaling technologies for diagnosis of rare disorders and provision of therapies across the age and disease spectrum.

Whole-genome sequencing holds the key to the success of gene-targeted therapies

Rare genetic disorders affect as many as 3%-5% of all babies born. Approximately 10,000 such disorders have been identified or hypothesized to exist. Treatment is supportive except in a limited number of instances where specific therapies exist. Development of new therapies has been hampered by at least two major factors: difficulty in diagnosing diseases early enough to enable treatment before irreversible damage occurs, and the high cost of developing new drugs and getting them approved by regulatory agencies. Whole-genome sequencing (WGS) techniques have become exponentially less expensive and more rapid since the beginning of the human genome project, such that return of clinical data can now be achieved in days rather than years and at a cost that is comparable to other less expansive genetic testing. Thus, it is likely that WGS will ultimately become a mainstream, first-tier NBS technique at least for those disorders without appropriate high-throughput functional tests. However, there are likely to be several steps in the evolution to this end. The clinical implications of these advances are profound but highlight the bottlenecks in drug development that still limit transition to treatments. This article summarizes discussions arising from a recent National Institute of Health conference on nucleic acid therapy, with a focus on the impact of WGS in the identification of diagnosis and treatment of rare genetic disorders.

Rapid Whole Genome Sequencing Diagnoses and Guides Treatment in Critically Ill Children in Belgium in Less than 40 Hours

Rapid Whole Genome Sequencing (rWGS) represents a valuable exploration in critically ill pediatric patients. Early diagnosis allows care to be adjusted. We evaluated the feasibility, turnaround time (TAT), yield, and utility of rWGS in Belgium. Twenty-one unrelated critically ill patients were recruited from the neonatal intensive care units, the pediatric intensive care unit, and the neuropediatric unit, and offered rWGS as a first tier test. Libraries were prepared in the laboratory of human genetics of the University of Liège using Illumina DNA PCR-free protocol. Sequencing was performed on a NovaSeq 6000 in trio for 19 and in duo for two probands. The TAT was calculated from the sample reception to the validation of results. Clinical utility data were provided by treating physicians. A definite diagnosis was reached in twelve (57.5%) patients in 39.80 h on average (range: 37.05-43.7). An unsuspected diagnosis was identified in seven patients. rWGS guided care adjustments in diagnosed patients, including a gene therapy, an off-label drug trial and two condition-specific treatments. We successfully implemented the fastest rWGS platform in Europe and obtained one of the highest rWGS yields. This study establishes the path for a nationwide semi-centered rWGS network in Belgium.

Scalable, high quality, whole genome sequencing from archived, newborn, dried blood spots

Universal newborn screening (NBS) is a highly successful public health intervention. Archived dried bloodspots (DBS) collected for NBS represent a rich resource for population genomic studies. To fully harness this resource in such studies, DBS must yield high-quality genomic DNA (gDNA) for whole genome sequencing (WGS). In this pilot study, we hypothesized that gDNA of sufficient quality and quantity for WGS could be extracted from archived DBS up to 20 years old without PCR (Polymerase Chain Reaction) amplification. We describe simple methods for gDNA extraction and WGS library preparation from several types of DBS. We tested these methods in DBS from 25 individuals who had previously undergone diagnostic, clinical WGS and 29 randomly selected DBS cards collected for NBS from the California State Biobank. While gDNA from DBS had significantly less yield than from EDTA blood from the same individuals, it was of sufficient quality and quantity for WGS without PCR. All samples DBS yielded WGS that met quality control metrics for high-confidence variant calling. Twenty-eight variants of various types that had been reported clinically in 19 samples were recapitulated in WGS from DBS. There were no significant effects of age or paper type on WGS quality. Archived DBS appear to be a suitable sample type for WGS in population genomic studies.

A Retrospective Analysis of Clinically Focused Exome Sequencing Results of 372 Infants with Suspected Monogenic Disorders in China

Objective

The context was designed to optimize the diagnostic utility of clinically focused exome sequencing (CFES) and shorten the diagnostic odyssey among pediatric patients suspected of monogenic disorders (MDs).

Methods

Here, we retrospectively analyzed the clinical notes of 372 patients from different areas in the Jiangxi province that were referred for a diagnostic CFES and analysis from June 2018 to March 2022 with symptoms suggestive of MDs. In our study, preliminary tests using the proband-only clinical exome sequencing as a cost-effective first-tier diagnostic test for pediatric patients with unidentified MDs, supplemented by family segregation studies for targeted variants when indicated.

Results

Probands with confirmed diagnostic (CD) or likely diagnostic (LD) genetic influences accounted for 12% of all cases, whereas those with an uncertain diagnosis accounted for 48%. We also found that systemic primary carnitine deficiency (CDSP) (SLC22A5 gene) and phenylketonuria (PAH gene) were relatively more prevalent, and these patients with CDSP had the most frequent c.1400C > G variant (p.S467C) and c.51C > G variant (p. F17L) in this study. In addition, statistical analysis revealed that the estimates of diagnostic yields varied across certain phenotypic features of patients, and patients with specific phenotypic traits tended to benefit more from CFES.

Conclusion

The CFES may be a first-line genetic test for diagnosing young children with suspected genetic conditions, as it validates the identification of molecular genetics alterations and facilitates comprehensive medical management. Moreover, we found that infants exhibiting metabolism/homeostasis abnormalities, craniofacial /otolaryngology/ ophthalmologic abnormalities, and/or the integument were significantly more likely to receive a genetic diagnosis via CFES than infants without such features. However, due to the current study's low diagnostic yield and inherent limitations, high-quality clinical studies with larger sample sizes are still needed to provide more likely results and confirm our findings.

Reclassification of the Etiology of Infant Mortality With Whole-Genome Sequencing

Importance

Understanding the causes of infant mortality shapes public health, surveillance, and research investments. However, the association of single-locus (mendelian) genetic diseases with infant mortality is poorly understood.

Objective

To determine the association of genetic diseases with infant mortality.

Design, Setting, and Participants

This cohort study was conducted at a large pediatric hospital system in San Diego County (California) and included 546 infants (112 infant deaths [20.5%] and 434 infants [79.5%] with acute illness who survived; age, 0 to 1 year) who underwent diagnostic whole-genome sequencing (WGS) between January 2015 and December 2020. Data analysis was conducted between 2015 and 2022.

Exposure

Infants underwent WGS either premortem or postmortem with semiautomated phenotyping and diagnostic interpretation.

Main Outcomes and Measures

Proportion of infant deaths associated with single-locus genetic diseases.

Results

Among 112 infant deaths (54 girls [48.2%]; 8 [7.1%] African American or Black, 1 [0.9%] American Indian or Alaska Native, 8 [7.1%] Asian, 48 [42.9%] Hispanic, 1 [0.9%] Native Hawaiian or Pacific Islander, and 34 [30.4%] White infants) in San Diego County between 2015 and 2020, single-locus genetic diseases were the most common identifiable cause of infant mortality, with 47 genetic diseases identified in 46 infants (41%). Thirty-nine (83%) of these diseases had been previously reported to be associated with childhood mortality. Twenty-eight death certificates (62%) for 45 of the 46 infants did not mention a genetic etiology. Treatments that can improve outcomes were available for 14 (30%) of the genetic diseases. In 5 of 7 infants in whom genetic diseases were identified postmortem, death might have been avoided had rapid, diagnostic WGS been performed at time of symptom onset or regional intensive care unit admission.

Conclusions and Relevance

In this cohort study of 112 infant deaths, the association of genetic diseases with infant mortality was higher than previously recognized. Strategies to increase neonatal diagnosis of genetic diseases and immediately implement treatment may decrease infant mortality. Additional study is required to explore the generalizability of these findings and measure reduction in infant mortality.