Estimating the burden and economic impact of pediatric genetic disease

Lipid nanoparticles as the drug carrier for targeted therapy of hepatic disorders

The liver, a complex and vital organ in the human body, is susceptible to various diseases, including metabolic disorders, acute hepatitis, cirrhosis, and hepatocellular carcinoma. In recent decades, these diseases have significantly contributed to global morbidity and mortality. Currently, liver transplantation remains the most effective treatment for hepatic disorders. Nucleic acid therapeutics offer a selective approach to disease treatment through diverse mechanisms, enabling the regulation of relevant genes and providing a novel therapeutic avenue for hepatic disorders. It is expected that nucleic acid drugs will emerge as the third generation of pharmaceuticals, succeeding small molecule drugs and antibody drugs. Lipid nanoparticles (LNPs) represent a crucial technology in the field of drug delivery and constitute a significant advancement in gene therapies. Nucleic acids encapsulated in LNPs are shielded from the degradation of enzymes and effectively delivered to cells, where they are released and regulate specific genes. This paper provides a comprehensive review of the structure, composition, and applications of LNPs in the treatment of hepatic disorders and offers insights into prospects and challenges in the future development of LNPs.

Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence

Importance

The lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches.

Objective

To compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods.

Design, Setting, and Participants

This comparative effectiveness study used generative AI to create images of children with KS and NS. From October 1, 2022, to February 28, 2023, US pediatric residents were provided images through a web-based survey to assess whether these images helped them recognize genetic conditions.

Interventions

Participants categorized 20 images after exposure to 1 of 4 educational interventions (text-only descriptions, real images, and 2 types of images created by generative AI).

Main Outcomes and Measures

Associations between educational interventions with accuracy and self-reported confidence.

Results

Of 2515 contacted pediatric residents, 106 and 102 completed the KS and NS surveys, respectively. For KS, the sensitivity of text description was 48.5% (128 of 264), which was not significantly different from random guessing (odds ratio [OR], 0.94; 95% CI, 0.69-1.29; P = .71). Sensitivity was thus compared for real images vs random guessing (60.3% [188 of 312]; OR, 1.52; 95% CI, 1.15-2.00; P = .003) and 2 types of generative AI images vs random guessing (57.0% [212 of 372]; OR, 1.32; 95% CI, 1.04-1.69; P = .02 and 59.6% [193 of 324]; OR, 1.47; 95% CI, 1.12-1.94; P = .006) (denominators differ according to survey responses). The sensitivity of the NS text-only description was 65.3% (196 of 300). Compared with text-only, the sensitivity of the real images was 74.3% (205 of 276; OR, 1.53; 95% CI, 1.08-2.18; P = .02), and the sensitivity of the 2 types of images created by generative AI was 68.0% (204 of 300; OR, 1.13; 95% CI, 0.77-1.66; P = .54) and 71.0% (247 of 328; OR, 1.30; 95% CI, 0.92-1.83; P = .14). For specificity, no intervention was statistically different from text only. After the interventions, the number of participants who reported being unsure about important diagnostic facial features decreased from 56 (52.8%) to 5 (7.6%) for KS (P < .001) and 25 (24.5%) to 4 (4.7%) for NS (P < .001). There was a significant association between confidence level and sensitivity for real and generated images.

Conclusions and Relevance

In this study, real and generated images helped participants recognize KS and NS; real images appeared most helpful. Generated images were noninferior to real images and could serve an adjunctive role, particularly for rare conditions.

The health-care utilization and economic burden in patients with genetic skeletal disorders

BACKGROUND

Most genetic skeletal disorders (GSD) were complex, disabling and life-threatening without effective diagnostic and treatment methods. However, its impacts on health system have not been well studied. The study aimed to systematically evaluate the health-care utilization and economic burden in GSD patients.

METHODS

The patients were derived from 2018 Nationwide Inpatient Sample and Nationwide Readmissions Database. GSD patients were extracted based on International Classification of Diseases-10th revision codes.

RESULTS

A total of 25,945 (0.12%) records regarding GSD were extracted from all 21,400,282 records in NIS database. GSD patients were likely to have significantly longer length of stay (6.50 ± 0.08 vs. 4.63 ± 0.002, P < 0.001), higher total charges ($85,180.97 ± 1,239.47 vs. $49,884.26 ± 20.99, P < 0.001), suffering more procedure, diagnosis and transferring records in comparison to patients with common conditions. GSD patients had a significantly higher 30-day all-cause readmission rate based on Nationwide Readmissions Database.

CONCLUSIONS

The heavy health-care utilization and economic burden emphasized the urgency for policy leaders, scientific and pharmaceutical researchers, health care providers and employers to identify innovative ways and take effective measurements immediately, and eventually to help improve the care, management, and treatment of these devastating diseases.

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.

Cost-Effectiveness of Whole-Genome vs Whole-Exome Sequencing Among Children With Suspected Genetic Disorders

Importance

The diagnosis of rare diseases and other genetic conditions can be daunting due to vague or poorly defined clinical features that are not recognized even by experienced clinicians. Next-generation sequencing technologies, such as whole-genome sequencing (WGS) and whole-exome sequencing (WES), have greatly enhanced the diagnosis of genetic diseases by expanding the ability to sequence a large part of the genome, rendering a cost-effectiveness comparison between them necessary.

Objective

To assess the cost-effectiveness of WGS compared with WES and conventional testing in children with suspected genetic disorders.

Design, Setting, and Participants

In this economic evaluation, a bayesian Markov model was implemented from January 1 to June 30, 2023. The model was developed using data from a cohort of 870 pediatric patients with suspected genetic disorders who were enrolled and underwent testing in the Ospedale Pediatrico Bambino Gesù, Rome, Italy, from January 1, 2015, to December 31, 2022. The robustness of the model was assessed through probabilistic sensitivity analysis and value of information analysis.

Main Outcomes and Measures

Overall costs, number of definitive diagnoses, and incremental cost-effectiveness ratios per diagnosis were measured. The cost-effectiveness analyses involved 4 comparisons: first-tier WGS with standard of care; first-tier WGS with first-tier WES; first-tier WGS with second-tier WES; and first-tier WGS with second-tier WGS.

Results

The ages of the 870 participants ranged from 0 to 18 years (539 [62%] girls). The results of the analysis suggested that adopting WGS as a first-tier strategy would be cost-effective compared with all other explored options. For all threshold levels above €29 800 (US $32 408) per diagnosis that were tested up to €50 000 (US $54 375) per diagnosis, first-line WGS vs second-line WES strategy (ie, 54.6%) had the highest probability of being cost-effective, followed by first-line vs second-line WGS (ie, 54.3%), first-line WGS vs the standard of care alternative (ie, 53.2%), and first-line WGS vs first-line WES (ie, 51.1%). Based on sensitivity analyses, these estimates remained robust to assumptions and parameter uncertainty.

Conclusions and Relevance

The findings of this economic evaluation encourage the development of policy changes at various levels (ie, macro, meso, and micro) of international health systems to ensure an efficient adoption of WGS in clinical practice and its equitable access.

Bayesian cost-effectiveness analysis of Whole genome sequencing versus Whole exome sequencing in a pediatric population with suspected genetic disorders

Genetic diseases are medical conditions caused by sequence or structural changes in an individual's genome. Whole exome sequencing (WES) and whole genome sequencing (WGS) are increasingly used for diagnosing suspected genetic conditions in children to reduce the diagnostic delay and accelerating the implementation of appropriate treatments. While more information is becoming available on clinical efficacy and economic sustainability of WES, the broad implementation of WGS is still hindered by higher complexity and economic issues. The aim of this study is to estimate the cost-effectiveness of WGS versus WES and standard testing for pediatric patients with suspected genetic disorders. A Bayesian decision tree model was set up. Model parameters were retrieved both from hospital administrative datasets and scientific literature. The analysis considered a lifetime time frame and adopted the perspective of the Italian National Health Service (NHS). Bayesian inference was performed using the Markov Chain Monte Carlo simulation method. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI). The present analysis showed that implementing first-line WGS would be a cost-effective strategy, against the majority of the other tested alternatives at a threshold of €30,000-50,000, for diagnosing outpatient pediatric patients with suspected genetic disorders. According to the sensitivity analyses, the findings were robust to most assumption and parameter uncertainty. Lessons learnt from this modeling study reinforces the adoption of first-line WGS, as a cost-effective strategy, depending on actual difficulties for the NHS to properly allocate limited resources.

Physicians' use and perceptions of genetic testing for rare diseases in China: a nationwide cross-sectional study

BACKGROUND

Genetic testing can facilitate the diagnosis and subsequent therapeutic management of rare diseases. However, there is a lack of data on the use of genetic testing for rare diseases. This study aims to describe the utilization rate and troubles encountered by clinicians in treating rare diseases with genetic testing.

METHODS

A cross-sectional electronic questionnaire survey was conducted between June and October 2022 among the medical staff from the hospitals covering all provinces, municipalities, and autonomous regions of China. The survey on genetic testing focused on whether genetic testing was used in the diagnosis and treatment of rare diseases, the specific methods of genetic testing, and the problems encountered when using genetic testing.

RESULTS

A total of 20,132 physicians who had treated rare diseases were included, of whom 35.5% were from the central region, 36.7% were from the eastern region, and 27.8% were from the western region. The total utilization rate of genetic testing for rare diseases was 76.0% (95%CI: 75.4-76.6). The use of genetic testing was highest in the Eastern region (79.2% [95% CI: 78.3-80.1]), followed by the Central (75.9% [95% CI: 74.9-76.9]) and Western regions (71.9% [95% CI: 70.7-73.1]). More than 90% (94.1% [95%CI: 93.4-94.8]) of pediatricians had used genetic testing to treat rare diseases, with surgeons having the lowest use of genetic testing (58.3% [95% CI: 56.6-60.0]). Physicians' departments and education levels affect the use of genetic testing. Most physicians have used a variety of genetic tests in the management of rare diseases, the most popular methods were "Whole-exome sequencing (Proband)" and "Whole-exome sequencing (families of three or more)". Doctors have encountered many problems with the use of genetic testing in the diagnosis and treatment of rare diseases, among which the high price was the main concern of medical workers.

CONCLUSION

Three-quarters of physicians used genetic testing in rare disease practice, and there were regional differences in the use of genetic testing. Recognition of the utilization of genetic testing can help identify patterns of resource utilization in different regions and provide a more comprehensive picture of the epidemiology of rare diseases in jurisdictions.

Generative Methods for Pediatric Genetics Education

Artificial intelligence (AI) is used in an increasing number of areas, with recent interest in generative AI, such as using ChatGPT to generate programming code or DALL-E to make illustrations. We describe the use of generative AI in medical education. Specifically, we sought to determine whether generative AI could help train pediatric residents to better recognize genetic conditions. From publicly available images of individuals with genetic conditions, we used generative AI methods to create new images, which were checked for accuracy with an external classifier. We selected two conditions for study, Kabuki (KS) and Noonan (NS) syndromes, which are clinically important conditions that pediatricians may encounter. In this study, pediatric residents completed 208 surveys, where they each classified 20 images following exposure to one of 4 possible educational interventions, including with and without generative AI methods. Overall, we find that generative images perform similarly but appear to be slightly less helpful than real images. Most participants reported that images were useful, although real images were felt to be more helpful. We conclude that generative AI images may serve as an adjunctive educational tool, particularly for less familiar conditions, such as KS.

An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound

A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model's ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.

A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound

A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic's detection rate.

Rapid exome sequencing as a first-tier test in neonates with suspected genetic disorder: results of a prospective multicenter clinical utility study in the Netherlands

The introduction of rapid exome sequencing (rES) for critically ill neonates admitted to the neonatal intensive care unit has made it possible to impact clinical decision-making. Unbiased prospective studies to quantify the impact of rES over routine genetic testing are, however, scarce. We performed a clinical utility study to compare rES to conventional genetic diagnostic workup for critically ill neonates with suspected genetic disorders. In a multicenter prospective parallel cohort study involving five Dutch NICUs, we performed rES in parallel to routine genetic testing for 60 neonates with a suspected genetic disorder and monitored diagnostic yield and the time to diagnosis. To assess the economic impact of rES, healthcare resource use was collected for all neonates. rES detected more conclusive genetic diagnoses than routine genetic testing (20% vs. 10%, respectively), in a significantly shorter time to diagnosis (15 days (95% CI 10-20) vs. 59 days (95% CI 23-98, p < 0.001)). Moreover, rES reduced genetic diagnostic costs by 1.5% (€85 per neonate).

CONCLUSION

 Our findings demonstrate the clinical utility of rES for critically ill neonates based on increased diagnostic yield, shorter time to diagnosis, and net healthcare savings. Our observations warrant the widespread implementation of rES as first-tier genetic test in critically ill neonates with disorders of suspected genetic origin.

WHAT IS KNOWN

• Rapid exome sequencing (rES) enables diagnosing rare genetic disorders in a fast and reliable manner, but retrospective studies with neonates admitted to the neonatal intensive care unit (NICU) indicated that genetic disorders are likely underdiagnosed as rES is not routinely used. • Scenario modeling for implementation of rES for neonates with presumed genetic disorders indicated an expected increase in costs associated with genetic testing.

WHAT IS NEW

• This unique prospective national clinical utility study of rES in a NICU setting shows that rES obtained more and faster diagnoses than conventional genetic tests. • Implementation of rES as replacement for all other genetic tests does not increase healthcare costs but in fact leads to a reduction in healthcare costs.

Advancing Understanding of Inequities in Rare Disease Genomics

Purpose

Advances in genomic research have led to the diagnosis of rare, early-onset diseases for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other healthcare contexts. Even quantifying and describing inequities in genetic diagnostic yield is challenging due to variation in referrals to clinical genetics practices and other barriers to clinical genetic testing.

Methods

The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access.

Findings

An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, our study participants are overwhelmingly non-disadvantaged, as evidenced by their access to specialist care and genetic testing prior to RGP enrollment, and are also predominantly white.

Implications

We therefore describe our novel initiative to diversify RGP enrollment in order to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of medical equity, this is also critical in order to fully understand the genomic underpinnings of rare disease. We utilize a mixed methods approach to understand the priorities and values of underrepresented communities, existing disparities, and the obstacles to addressing them: all of which is necessary to promote equity in future genomic medicine initiatives.

Hospital-level variation in genetic testing in children's hospitals' neonatal intensive care units from 2016 to 2021

PURPOSE

This study aimed to examine variation in genetic testing between neonatal intensive care units (NICUs) across hospitals over time.

METHODS

We performed a multicenter large-scale retrospective cohort study using NICU discharge data from the Pediatric Hospital Information System database between 2016 and 2021. We analyzed the variation in the percentage of NICU patients who had any genetic testing across hospitals and over time. We used a multivariable multilevel logistic regression model to investigate the potential association between patient characteristics and genetic testing.

RESULTS

The final analysis included 207,228 neonates from 38 hospitals. Overall, 13% of patients had at least 1 genetic test sent, although this varied from 4% to 50% across hospitals. Over the study period, the proportion of patients tested increased, with the increase disproportionately borne by hospitals already testing high proportions of patients. On average, patients who received genetic testing had higher illness severity. Controlling for severity, however, only minimally reduced the degree of hospital-level variation in genetic testing.

CONCLUSION

The percentage of NICU patients who undergo genetic testing varies among hospitals and increasingly so over time. Variation is largely unexplained by differences in severity between hospitals. The degree of variation suggests that clearer guidelines for NICU genetic testing are warranted.

3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema

Rare diseases pose a diagnostic conundrum to even the most experienced clinicians around the world. The technology could play an assistive role in hastening the diagnosis process. Data-driven methodologies can identify distinctive disease features and create a definitive diagnostic spectrum. The healthcare professionals in developed and developing nations would benefit immensely from these approaches resulting in quicker diagnosis and enabling early care for the patients. Hereditary Angioedema is one such rare disease that requires a lengthy diagnostic cascade ensuing massive patient inconvenience and cost burden on the healthcare system. It is hypothesized that facial analysis with advanced imaging and algorithmic association can create an ideal diagnostic peer to the clinician while assimilating signs and symptoms in the hospital. 3D photogrammetry has been applied to diagnose rare diseases in various cohorts. The facial features are captured at a granular level in utmost finer detail. A validated and proven algorithm-powered software provides recommendations in real-time. Thus, paving the way for quick and early diagnosis to well-trained or less trained clinicians in different settings around the globe. The generated evidence indicates the strong applicability of 3 D photogrammetry in association with proprietary Cliniface software to Hereditary Angioedema for aiding in the diagnostic process. The approach, mechanism, and beneficial impact have been sketched out appropriately herein. This blueprint for hereditary angioedema may have far-reaching consequences beyond disease diagnosis to benefit all the stakeholders in the healthcare arena including research and new drug development.

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases

With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers' or adults' face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.

Rare diseases, common barriers: disparities in pediatric clinical genetics outcomes

BACKGROUND

Identifying a precise genetic diagnosis can improve outcomes for individuals with rare disease, though the resources required to do so may impede access and exacerbate healthcare disparities leading to inequitable care. Our objective was therefore to determine the effect of multiple sociodemographic factors on the yield of the diagnostic evaluation for genetics outpatients.

METHODS

This is a retrospective cohort study from 2017 to 2019 of outpatient genetics referrals at a pediatric academic tertiary care center. Exposures included: primary language, insurance type, and neighborhood resources (via the Childhood Opportunity Index, COI). The primary outcome was identification of a genetic diagnosis within 2 years of the initial clinic visit.

RESULTS

COI quintile was not significantly associated with the odds of diagnosis but was significantly associated with clinic attendance, with lower neighborhood resources leading to incomplete referrals. Limited English proficiency was associated with a higher odds of diagnosis, though at an older age. Public insurance was associated with increased access to genetic testing.

CONCLUSIONS

Lower neighborhood resources are negatively associated with clinic attendance. Our findings further suggest delays in care and a referral bias for more severe phenotypes among families with limited English proficiency. Improved access to clinical genetics is needed to improve diagnostic equity.

IMPACT

The resources required to identify a genetic diagnosis may impede access and exacerbate healthcare disparities leading to inequitable care. In an analysis of pediatric outpatient genetics referrals, we observed a significant association between neighborhood resources and clinic attendance but not diagnostic yield for those attending, and a higher diagnostic yield for families with limited English proficiency, suggesting referral bias for more severe phenotypes. Thus, the primary barrier to finding a genetic diagnosis was initiation of care, not the ensuing diagnostic odyssey. Further research efforts should be directed at increasing access to clinical genetics evaluations for children with rare disease.

Noninvasive prenatal diagnosis targeting fetal nucleated red blood cells

Noninvasive prenatal diagnosis (NIPD) aims to detect fetal-related genetic disorders before birth by detecting markers in the peripheral blood of pregnant women, holding the potential in reducing the risk of fetal birth defects. Fetal-nucleated red blood cells (fNRBCs) can be used as biomarkers for NIPD, given their remarkable nature of carrying the entire genetic information of the fetus. Here, we review recent advances in NIPD technologies based on the isolation and analysis of fNRBCs. Conventional cell separation methods rely primarily on physical properties and surface antigens of fNRBCs, such as density gradient centrifugation, fluorescence-activated cell sorting, and magnetic-activated cell sorting. Due to the limitations of sensitivity and purity in Conventional methods, separation techniques based on micro-/nanomaterials have been developed as novel methods for isolating and enriching fNRBCs. We also discuss emerging methods based on microfluidic chips and nanostructured substrates for static and dynamic isolation of fNRBCs. Additionally, we introduce the identification techniques of fNRBCs and address the potential clinical diagnostic values of fNRBCs. Finally, we highlight the challenges and the future directions of fNRBCs as treatment guidelines in NIPD.

Risk Variants in the Exomes of Children With Critical Illness

IMPORTANCE

Diagnostic genetic testing can lead to changes in management in the pediatric intensive care unit. Genetic risk in children with critical illness but nondiagnostic exome sequencing (ES) has not been explored.

OBJECTIVE

To assess the association between loss-of-function (LOF) variants and pediatric critical illness.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study examined ES first screened for causative variants among 267 children at the Morgan Stanley Children's Hospital of NewYork-Presbyterian, of whom 22 were otherwise healthy with viral respiratory failure; 18 deceased children with bronchiolitis from the Office of the Chief Medical Examiner of New York City, of whom 14 were previously healthy; and 9990 controls from the Institute for Genomic Medicine at Columbia University Irving Medical Center. The ES data were generated between January 1, 2015, and December 31, 2020, and analyzed between January 1, 2017, and September 2, 2022.

EXPOSURE

Critical illness.

MAIN OUTCOMES AND MEASURES

Odds ratios and P values for genes and gene-sets enriched for rare LOF variants and the loss-of-function observed/expected upper bound fraction (LOEUF) score at which cases have a significant enrichment.

RESULTS

This study included 285 children with critical illness (median [range] age, 4.1 [0-18.9] years; 148 [52%] male) and 9990 controls. A total of 228 children (80%) did not receive a genetic diagnosis. After quality control (QC), 231 children harbored excess rare LOF variants in genes with a LOEUF score of 0.680 or less (intolerant genes) (P = 1.0 × 10-5). After QC, 176 children without a diagnosis harbored excess ultrarare LOF variants in intolerant genes but only in those without a known disease association (odds ratio, 1.8; 95% CI, 1.3-2.5). After QC, 25 children with viral respiratory failure harbored excess ultrarare LOF variants in intolerant genes but only in those without a known disease association (odds ratio, 2.8; 95% CI, 1.1-6.6). A total of 114 undiagnosed children were enriched for de novo LOF variants in genes without a known disease association (observed, 14; expected, 6.8; enrichment, 2.05).

CONCLUSIONS AND RELEVANCE

In this genetic association study, excess LOF variants were observed among critically ill children despite nondiagnostic ES. Variants lay in genes without a known disease association, suggesting future investigation may connect phenotypes to causative genes.

Scoping review and classification of deep learning in medical genetics

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

Medical student acceptance on gene therapy to increase children's well-being with genetic diseases: a study in Indonesia

Aim: Gene therapy is expected to improve patients' quality of life. Medical students need to be aware about this technology as its application is becoming wider. Materials & methods: A web-based survey was conducted to measure the acceptance of Indonesian medical students regarding gene therapy. Results: Data from 621 valid responses showed that Indonesian medical students have little knowledge of this technology, with 34.4% of them ever heard of gene therapy. However, most of them support the approved gene therapy for health-related matters, but not on the non-health related matters. Their acceptance was determined by the sex, domicile and studentship status. Conclusion: Increasing medical students' knowledge of gene therapy is important to minimize the future conflict of gene therapy application.

Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes

Background: In medical genetics, one application of neural networks is the diagnosis of genetic diseases based on images of patient faces. While these applications have been validated in the literature with primarily pediatric subjects, it is not known whether these applications can accurately diagnose patients across a lifespan. We aimed to extend previous works to determine whether age plays a factor in facial diagnosis as well as to explore other factors that may contribute to the overall diagnostic accuracy.

Methods: To investigate this, we chose two relatively common conditions, Williams syndrome and 22q11.2 deletion syndrome. We built a neural network classifier trained on images of affected and unaffected individuals of different ages and compared classifier accuracy to clinical geneticists. We analyzed the results of saliency maps and the use of generative adversarial networks to boost accuracy.

Results: Our classifier outperformed clinical geneticists at recognizing face images of these two conditions within each of the age groups (the performance varied between the age groups): 1) under 2 years old, 2) 2–9 years old, 3) 10–19 years old, 4) 20–34 years old, and 5) ≥35 years old. The overall accuracy improvement by our classifier over the clinical geneticists was 15.5 and 22.7% for Williams syndrome and 22q11.2 deletion syndrome, respectively. Additionally, comparison of saliency maps revealed that key facial features learned by the neural network differed with respect to age. Finally, joint training real images with multiple different types of fake images created by a generative adversarial network showed up to 3.25% accuracy gain in classification accuracy.

Conclusion: The ability of clinical geneticists to diagnose these conditions is influenced by the age of the patient. Deep learning technologies such as our classifier can more accurately identify patients across the lifespan based on facial features. Saliency maps of computer vision reveal that the syndromic facial feature attributes change with the age of the patient. Modest improvements in the classifier accuracy were observed when joint training was carried out with both real and fake images. Our findings highlight the need for a greater focus on age as a confounder in facial diagnosis.

Testing and extending strategies for identifying genetic disease-related encounters in pediatric patients

PURPOSE

To better understand health care utilization and develop decision support tools, methods for identifying patients with suspected genetic diseases (GDs) are needed. Previous studies had identified inpatient-relevant International Classification of Diseases (ICD) codes that were possibly, probably, or definitely indicative of GDs. We assessed whether these codes identified GD-related inpatient, outpatient, and emergency department encounters among pediatric patients with suspected GDs from a previous study (the North Carolina Clinical Genomic Evaluation by Next-Generation Exome Sequencing [NCGENES] study).

METHODS

Using the electronic medical records of 140 pediatric patients from the NCGENES study, we characterized the presence of ICD codes representing possible, probable, or definite GD-related diagnoses across encounter types. In addition, we examined codes from encounters for which initially no GD-related codes had been found and determined whether these codes were indicative of a GD.

RESULTS

Among NCGENES patients with visits between 2014 and 2017, 92% of inpatient, 75% of emergency department, and 63% of outpatient encounters included ≥1 GD-related code. Encounters with highly specific (ie, definite) GD codes had fewer low-specificity GD codes than encounters with only low-specificity GD codes. We identified an additional 32 ICD-9 and 56 ICD-10 codes possibly indicative of a GD.

CONCLUSION

Code-based strategies can be refined to assess health care utilization among pediatric patients and may contribute to a systematic approach to identify patients with suspected GDs.

Addressing Reproductive Healthcare Disparities through Equitable Carrier Screening: Medical Racism and Genetic Discrimination in United States’ History Highlights the Needs for Change in Obstetrical Genetics Care

Carrier screening, a nearly half-century old practice, aims to provide individuals and couples with information about their risk of having children with serious genetic conditions. Traditionally, the conditions for which individuals were offered screening depended on their self-reported race or ethnicity and which conditions were seen commonly in that population. This process has led to disparities and inequities in care as the multi-racial population in the U.S. has grown exponentially, yet databases used to determine clinical practice guidelines are made up of primarily White cohorts. Technological advancements now allow for pan-ethnic expanded carrier screening (ECS), which screens for many conditions regardless of self-reported race or ethnicity. ECS presents a unique opportunity to promote equitable genetic testing practices in reproductive medicine. However, this goal can only be achieved if we acknowledge and appreciate the innumerable inequities evidenced in reproductive medicine and other socio-legal practices in the United States, and if we intentionally work in concert with healthcare providers, policy makers, advocates, and community health champions to reduce current and future reproductive health disparities. Herein, we provide a brief review of the way that US medical racism and genetic discrimination has shaped the current landscape of carrier screening.

Correlation Between National Institutes of Health Funding for Pediatric Research and Pediatric Disease Burden in the US

IMPORTANCE

The US National Institutes of Health (NIH) is the largest government funding source for biomedical research globally. Burden of disease is one of the factors considered by the NIH in making funding allocations, though it is not known how funding patterns are associated with disease burden for pediatric conditions.

OBJECTIVE

To determine the correlation between NIH funding and disease burden across pediatric conditions.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study evaluates NIH grants funding pediatric research from 2015 to 2018 in the US. Pediatric grants were classified according to disease categories studied. Disease burden for each category was determined using measures from the Institute of Health Metrics and Evaluation and hospitalization data from the 2016 Kids' Inpatient Database.

MAIN OUTCOME AND MEASURE

Correlation between NIH funding and pediatric disease burden using Spearman rank order coefficients and predicted amounts of disease-specific funding based on disease burden estimated from linear regression models.

RESULTS

This study analyzed 14 060 disease-specific pediatric grants awarded by the NIH from 2015 to 2018 in the US. Annual funding for disease categories ranged from $0 to $382 849 631. Funding for pediatric research was correlated with pediatric disability-adjusted life-years (DALYs), deaths, years lived with disability, and years of life lost (r, 0.56-0.63; P < 0.001 for all measures). There was also a correlation between funding and hospital-based metrics, including hospital days, number of hospital admissions, and hospital charges (r, 0.67-0.69; P < .001 for all measures). Eight disease categories received greater than $500 million more than predicted levels relative to DALYs, while 5 disease categories were funded more than $50 million less than predicted levels. Based on predicted levels of funding, congenital birth defects; endocrine, metabolic, blood, and immune disorders; and HIV/AIDS were the most overfunded categories relative to DALYs and hospital days. Conditions identified as most underfunded differed depending on use of DALYs or hospital days in estimating predicted funding levels.

CONCLUSIONS AND RELEVANCE

NIH funding for pediatric research was correlated with pediatric disease burden in the US with variable correlation based on the disease metric applied. There was substantial overfunding and underfunding of certain conditions. Ongoing evaluation of pediatric funding patterns using a complementary set of disease measures may help inform and prioritize pediatric research funding.

Current Trends in Genetics and Neonatal Care

BACKGROUND

Genetic and genomic health applications are rapidly changing. A clear and updated description of these applications for the neonatal population is needed to guide current nursing practice.

PURPOSE

To provide scientific evidence and guidance on the current genetic and genomic applications pertinent to neonatal care.

METHODS

A search of CINAHL and PubMed was conducted using the search terms "newborn/neonatal" and "genetics," "genomics," "newborn screening," "pharmacogenomics," "ethical," and "legal." Google searches were also conducted to synthesize professional guidelines, position statements, and current genetic practices.

FINDINGS/RESULTS

Components of the newborn genetic assessment, including details on the newborn physical examination, family history, and laboratory tests pertinent to the newborn, are reported. The history and process of newborn screening are described, in addition to the impact of advancements, such as whole exome and genome sequencing, on newborn screening. Pharmacogenomics, a genomic application that is currently utilized primarily in the research context for neonates, is described and future implications stated. Finally, the specific ethical and legal implications for these genetic and genomic applications are detailed, along with genetic/genomic resources for nurses.

IMPLICATIONS FOR PRACTICE

Providing nurses with the most up-to-date evidence on genetic and genomic applications ensures their involvement and contributions to quality neonatal care.

IMPLICATIONS FOR RESEARCH

Ongoing genetic/genomic research is needed to understand the implications of genetic/genomic applications on the neonatal population and how these new applications will change neonatal care.

Effect of Whole-Genome Sequencing on the Clinical Management of Acutely Ill Infants With Suspected Genetic Disease: A Randomized Clinical Trial

IMPORTANCE

Whole-genome sequencing (WGS) shows promise as a first-line genetic test for acutely ill infants, but widespread adoption and implementation requires evidence of an effect on clinical management.

OBJECTIVE

To determine the effect of WGS on clinical management in a racially and ethnically diverse and geographically distributed population of acutely ill infants in the US.

DESIGN, SETTING, AND PARTICIPANTS

This randomized, time-delayed clinical trial enrolled participants from September 11, 2017, to April 30, 2019, with an observation period extending to July 2, 2019. The study was conducted at 5 US academic medical centers and affiliated children's hospitals. Participants included infants aged between 0 and 120 days who were admitted to an intensive care unit with a suspected genetic disease. Data were analyzed from January 14 to August 20, 2020.

INTERVENTIONS

Patients were randomized to receive clinical WGS results 15 days (early) or 60 days (delayed) after enrollment, with the observation period extending to 90 days. Usual care was continued throughout the study.

MAIN OUTCOMES AND MEASURES

The main outcome was the difference in the proportion of infants in the early and delayed groups who received a change of management (COM) 60 days after enrollment. Additional outcome measures included WGS diagnostic efficacy, within-group COM at 90 days, length of hospital stay, and mortality.

RESULTS

A total of 354 infants were randomized to the early (n = 176) or delayed (n = 178) arms. The mean participant age was 15 days (IQR, 7-32 days); 201 participants (56.8%) were boys; 19 (5.4%) were Asian; 47 (13.3%) were Black; 250 (70.6%) were White; and 38 (10.7%) were of other race. At 60 days, twice as many infants in the early group vs the delayed group received a COM (34 of 161 [21.1%; 95% CI, 15.1%-28.2%] vs 17 of 165 [10.3%; 95% CI, 6.1%-16.0%]; P = .009; odds ratio, 2.3; 95% CI, 1.22-4.32) and a molecular diagnosis (55 of 176 [31.0%; 95% CI, 24.5%-38.7%] vs 27 of 178 [15.0%; 95% CI, 10.2%-21.3%]; P < .001). At 90 days, the delayed group showed a doubling of COM (to 45 of 161 [28.0%; 95% CI, 21.2%-35.6%]) and diagnostic efficacy (to 56 of 178 [31.0%; 95% CI, 24.7%-38.8%]). The most frequent COMs across the observation window were subspecialty referrals (39 of 354; 11%), surgery or other invasive procedures (17 of 354; 4%), condition-specific medications (9 of 354; 2%), or other supportive alterations in medication (12 of 354; 3%). No differences in length of stay or survival were observed.

CONCLUSIONS AND RELEVANCE

In this randomized clinical trial, for acutely ill infants in an intensive care unit, introduction of WGS was associated with a significant increase in focused clinical management compared with usual care. Access to first-line WGS may reduce health care disparities by enabling diagnostic equity. These data support WGS adoption and implementation in this population.

TRAIL REGISTRATION

ClinicalTrials.gov Identifier: NCT03290469.

The diagnostic trajectory of infants and children with clinical features of genetic disease

We characterized US pediatric patients with clinical indicators of genetic diseases, focusing on the burden of disease, utilization of genetic testing, and cost of care. Curated lists of diagnosis, procedure, and billing codes were used to identify patients with clinical indicators of genetic disease in healthcare claims from Optum's de-identified Clinformatics® Database (13,076,038 unique patients). Distinct cohorts were defined to represent permissive and conservative estimates of the number of patients. Clinical phenotypes suggestive of genetic diseases were observed in up to 9.4% of pediatric patients and up to 44.7% of critically-ill infants. Compared with controls, patients with indicators of genetic diseases had higher utilization of services (e.g., mean NICU length of stay of 31.6d in a cohort defined by multiple congenital anomalies or neurological presentations compared with 10.1d for patients in the control population (P < 0.001)) and higher overall costs. Very few patients received any genetic testing (4.2-8.4% depending on cohort criteria). These results highlight the substantial proportion of the population with clinical features associated with genetic disorders and underutilization of genetic testing in these populations.

Cost-effectiveness of genome sequencing for diagnosing patients with undiagnosed rare genetic diseases

PURPOSE

To estimate the cost-effectiveness of genome sequencing (GS) for diagnosing critically ill infants and noncritically ill pediatric patients (children) with suspected rare genetic diseases from a United States health sector perspective.

METHODS

A decision-analytic model was developed to simulate the diagnostic trajectory of patients. Parameter estimates were derived from a targeted literature review and meta-analysis. The model simulated clinical and economic outcomes associated with 3 diagnostic pathways: (1) standard diagnostic care, (2) GS, and (3) standard diagnostic care followed by GS.

RESULTS

For children, costs of GS ($7284) were similar to that of standard care ($7355) and lower than that of standard care followed by GS pathways ($12,030). In critically ill infants, when cost estimates were based on the length of stay in the neonatal intensive care unit, the lowest cost pathway was GS ($209,472). When only diagnostic test costs were included, the cost per diagnosis was $17,940 for standard, $17,019 for GS, and $20,255 for standard care followed by GS.

CONCLUSION

The results of this economic model suggest that GS may be cost neutral or possibly cost saving as a first line diagnostic tool for children and critically ill infants.

Children with genetic conditions in the United States: Prevalence estimates from the 2016-2017 National Survey of Children's Health

PURPOSE

Estimating the overall prevalence of genetic conditions among children in the United States and the burden of these conditions on children and their families has been challenging. The redesigned National Survey of Children's Health provides an opportunity to examine the prevalence and burden.

METHODS

We used the combined 2016-2017 National Survey of Children's Health to estimate the prevalence of genetic conditions among children aged 0 to 17 years (N = 71,522). Bivariate analyses were used to assess differences in sociodemographic characteristics, health-related characteristics, and health care utilization between children with and without genetic conditions.

RESULTS

In 2016-2017, the prevalence of children aged 0 to 17 years with a reported genetic condition was approximately 0.039, roughly equating to 2.8 million children. A greater percentage of children with genetic conditions had a physical (50.9% vs 24.8%), mental (27.9% vs 5.8%), or behavioral/developmental/intellectual condition (55.6% vs 14.4%) than children without a genetic condition. Furthermore, they used more care and had more unmet health needs (7.6% vs 2.9%).

CONCLUSION

This study provides an estimate of the overall prevalence of children living with genetic conditions in the United States based on a nationally representative sample. It also highlights the physical, mental, and behavioral health needs among children with genetic conditions and their unmet health care needs.

Exome first approach to reduce diagnostic costs and time - retrospective analysis of 111 individuals with rare neurodevelopmental disorders

This single-center study aims to determine the time, diagnostic procedure, and cost saving potential of early exome sequencing in a cohort of 111 individuals with genetically confirmed neurodevelopmental disorders. We retrospectively collected data regarding diagnostic time points and procedures from the individuals' medical histories and developed criteria for classifying diagnostic procedures in terms of requirement, followed by a cost allocation. All genetic variants were re-evaluated according to ACMG recommendations and considering the individuals' phenotype. Individuals who developed first symptoms of their underlying genetic disorder when Next Generation Sequencing (NGS) diagnostics were already available received a diagnosis significantly faster than individuals with first symptoms before this cutoff. The largest amount of potentially dispensable diagnostics was found in genetic, metabolic, and cranial magnetic resonance imaging examinations. Out of 407 performed genetic examinations, 296 (72.7%) were classified as potentially dispensable. The same applied to 36 (27.9%) of 129 cranial magnetic resonance imaging and 111 (31.8%) of 349 metabolic examinations. Dispensable genetic examinations accounted 302,947.07€ (90.2%) of the total 335,837.49€ in potentially savable costs in this cohort. The remaining 32,890.42€ (9.8%) are related to non-required metabolic and cranial magnetic resonance imaging diagnostics. On average, the total potentially savable costs in our study amount to €3,025.56 per individual. Cost savings by first tier exome sequencing lie primarily in genetic, metabolic, and cMRI testing in this German cohort, underscoring the utility of performing exome sequencing at the beginning of the diagnostic pathway and the potential for saving diagnostic costs and time.

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study

BACKGROUND

Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child's risk of presenting with a genetic syndrome for use at the point of care.

METHODS

In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes. We trained the machine learning models on facial photographs from children (aged <21 years) with a clinical or molecular diagnosis of a genetic syndrome and controls without a genetic syndrome matched for age, sex, and race or ethnicity. Images were obtained from three publicly available databases (the Atlas of Human Malformations in Diverse Populations of the National Human Genome Research Institute, Face2Gene, and the dataset available from Ferry and colleagues) and the archives of the Children's National Hospital (Washington, DC, USA), in addition to photographs taken on a standard smartphone at the Children's National Hospital. We designed a deep learning architecture structured into three neural networks, which performed image standardisation (Network A), facial morphology detection (Network B), and genetic syndrome risk estimation, accounting for phenotypic variations due to age, sex, and race or ethnicity (Network C). Data were divided randomly into 40 groups for cross validation, and the performance of the model was evaluated in terms of accuracy, sensitivity, and specificity in both the total population and stratified by race or ethnicity, age, and sex.

FINDINGS

Our dataset included 2800 facial photographs of children (1318 [47%] female and 1482 [53%] male; 1576 [56%] White, 432 [15%] African, 430 [15%] Hispanic, and 362 [13%] Asian). 1400 children with 128 genetic conditions were included (the most prevalent being Williams-Beuren syndrome [19%], Cornelia de Lange syndrome [17%], Down syndrome [16%], 22q11.2 deletion [13%], and Noonan syndrome [12%] syndrome) in addition to 1400 photographs of matched controls. In the total population, our deep learning-based model had an accuracy of 88% (95% CI 87-89) for the detection of a genetic syndrome, with 90% sensitivity (95% CI 88-92) and 86% specificity (95% CI 84-88). Accuracy was greater in White (90%, 89-91) and Hispanic populations (91%, 88-94) than in African (84%, 81-87) and Asian populations (82%, 78-86). Accuracy was also similar in male (89%, 87-91) and female children (87%, 85-89), and similar in children younger than 2 years (86%, 84-88) and children aged 2 years or older (eg, 89% [87-91] for those aged 2 years to <5 years).

INTERPRETATION

This genetic screening technology could support early risk stratification at the point of care in global populations, which has the potential accelerate diagnosis and reduce mortality and morbidity through preventive care.

FUNDING

Children's National Hospital and Government of Abu Dhabi.

From late fatherhood to prenatal screening of monogenic disorders: evidence and ethical concerns

BACKGROUND

With the help of ART, an advanced parental age is not considered to be a serious obstacle for reproduction anymore. However, significant health risks for future offspring hide behind the success of reproductive medicine for the treatment of reduced fertility associated with late parenthood. Although an advanced maternal age is a well-known risk factor for poor reproductive outcomes, understanding the impact of an advanced paternal age on offspring is yet to be elucidated. De novo monogenic disorders (MDs) are highly associated with late fatherhood. MDs are one of the major sources of paediatric morbidity and mortality, causing significant socioeconomic and psychological burdens to society. Although individually rare, the combined prevalence of these disorders is as high as that of chromosomal aneuploidies, indicating the increasing need for prenatal screening. With the help of advanced reproductive technologies, families with late paternity have the option of non-invasive prenatal testing (NIPT) for multiple MDs (MD-NIPT), which has a sensitivity and specificity of almost 100%.

OBJECTIVE AND RATIONALE

The main aims of the current review were to examine the effect of late paternity on the origin and nature of MDs, to highlight the role of NIPT for the detection of a variety of paternal age-associated MDs, to describe clinical experiences and to reflect on the ethical concerns surrounding the topic of late paternity and MD-NIPT.

SEARCH METHODS

An extensive search of peer-reviewed publications (1980-2021) in English from the PubMed and Google Scholar databases was based on key words in different combinations: late paternity, paternal age, spermatogenesis, selfish spermatogonial selection, paternal age effect, de novo mutations (DNMs), MDs, NIPT, ethics of late fatherhood, prenatal testing and paternal rights.

OUTCOMES

An advanced paternal age provokes the accumulation of DNMs, which arise in continuously dividing germline cells. A subset of DNMs, owing to their effect on the rat sarcoma virus protein-mitogen-activated protein kinase signalling pathway, becomes beneficial for spermatogonia, causing selfish spermatogonial selection and outgrowth, and in some rare cases may lead to spermatocytic seminoma later in life. In the offspring, these selfish DNMs cause paternal age effect (PAE) disorders with a severe and even life-threatening phenotype. The increasing tendency for late paternity and the subsequent high risk of PAE disorders indicate an increased need for a safe and reliable detection procedure, such as MD-NIPT. The MD-NIPT approach has the capacity to provide safe screening for pregnancies at risk of PAE disorders and MDs, which constitute up to 20% of all pregnancies. The primary risks include pregnancies with a paternal age over 40 years, a previous history of an affected pregnancy/child, and/or congenital anomalies detected by routine ultrasonography. The implementation of NIPT-based screening would support the early diagnosis and management needed in cases of affected pregnancy. However, the benefits of MD-NIPT need to be balanced with the ethical challenges associated with the introduction of such an approach into routine clinical practice, namely concerns regarding reproductive autonomy, informed consent, potential disability discrimination, paternal rights and PAE-associated issues, equity and justice in accessing services, and counselling.

WIDER IMPLICATIONS

Considering the increasing parental age and risks of MDs, combined NIPT for chromosomal aneuploidies and microdeletion syndromes as well as tests for MDs might become a part of routine pregnancy management in the near future. Moreover, the ethical challenges associated with the introduction of MD-NIPT into routine clinical practice need to be carefully evaluated. Furthermore, more focus and attention should be directed towards the ethics of late paternity, paternal rights and paternal genetic guilt associated with pregnancies affected with PAE MDs.

Principles of Genomic Newborn Screening Programs: A Systematic Review

IMPORTANCE

Genomic newborn screening (gNBS) may optimize the health and well-being of children and families. Screening programs are required to be evidence based, acceptable, and beneficial.

OBJECTIVES

To identify what has been discovered following the reporting of the first gNBS pilot projects and to provide a summary of key points for the design of gNBS.

EVIDENCE REVIEW

A systematic literature review was performed on April 14, 2021, identifying 36 articles that addressed the following questions: (1) what is the interest in and what would be the uptake of gNBS? (2) what diseases and genes should be included? (3) what is the validity and utility of gNBS? and (4) what are the ethical, legal, and social implications? Articles were only included if they generated new evidence; all opinion pieces were excluded.

FINDINGS

In the 36 articles included, there was high concordance, except for gene disease inclusion, which was highly variable. Key findings were the need for equitable access, appropriate educational materials, and informed and flexible consent. The process for selecting genes for testing should be transparent and reflect that parents value the certainty of prediction over actionability. Data should be analyzed in a way that minimizes uncertainty and incidental findings. The expansion of traditional newborn screening (tNBS) to identify more life-threatening and treatable diseases needs to be balanced against the complexity of consenting parents of newborns for genomic testing as well as the risk that overall uptake of tNBS may decline. The literature reflected that the right of a child to self-determination should be valued more than the possibility of the whole family benefiting from a newborn genomic test.

CONCLUSIONS AND RELEVANCE

The findings of this systematic review suggest that implementing gNBS will require a nuanced approach. There are gaps in our knowledge, such as the views of diverse populations, the capabilities of health systems, and health economic implications. It will be essential to rigorously evaluate outcomes and ensure programs can evolve to maximize benefit.

Therapeutic Gene Editing with CRISPR: A Laboratory Medicine Perspective

Therapeutic gene editing with the clustered regularly interspaced short palindromic repeat (CRISPR)-Cas system offers significant improvements in specificity and programmability compared with previous methods. CRISPR editing strategies can be used ex vivo and in vivo with many theoretic disease applications. Off-target effects of CRISPR-mediated gene editing are an important outcome to be aware of, minimize, and detect. The current methods of regulatory approval for personalized therapies are complex and may be proved inefficient as these therapies are implemented more widely. The role of pathologists and laboratory medicine practitioners is vital to the clinical implementation of therapeutic gene editing.

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Paediatric genomic testing: Navigating medicare rebatable genomic testing

Genomic testing for a genetic diagnosis is becoming standard of care for many children, especially those with a syndromal intellectual disability. While previously this type of specialised testing was performed mainly by clinical genetics teams, it is increasingly being 'mainstreamed' into standard paediatric care. With the introduction of a new Medicare rebate for genomic testing in May 2020, this type of testing is now available for paediatricians to order, in consultation with clinical genetics. Children must be aged less than 10 years with facial dysmorphism and multiple congenital abnormalities or have global developmental delay or moderate to severe intellectual disability. This rebate should increase the likelihood of a genetic diagnosis, with accompanying benefits for patient management, reproductive planning and diagnostic certainty. Similar to the introduction of chromosomal microarray into mainstream paediatrics, this genomic testing will increase the number of genetic diagnoses, however, will also yield more variants of uncertain significance, incidental findings, and negative results. This paper aims to guide paediatricians through the process of genomic testing, and represents the combined expertise of educators, clinical geneticists, paediatricians and genomic pathologists around Australia. Its purpose is to help paediatricians navigate choosing the right genomic test, consenting patients and understanding the possible outcomes of testing.

A retrospective review of the contribution of rare diseases to paediatric mortality in Ireland

Aims

To ascertain the number of paediatric deaths (0–14 years) with an underlying rare disease in the Republic of Ireland between the years 2006–2016, and to analyse bed usage by a paediatric cohort of rare disease inpatients prior to in-hospital death.

Background

Rare diseases are often chronically debilitating and sometimes life-threatening diseases, with the majority (69.9%) of rare diseases being of paediatric onset. The Orphanet database contains information on 6172 unique rare diseases. Under-representation of rare diseases in hospital healthcare coding systems leads to a paucity of rare disease epidemiological data required for healthcare planning. Studies have cited variable incidence rates for rare disease, however the burden of rare diseases to healthcare services still remains unclear. This study represents a thorough effort to identify the percentage of child mortality and paediatric bed usage attributable to rare diseases in the Republic of Ireland, thus addressing a major gap in the rare disease field.

Methods

Retrospective analysis of paediatric death registration details for the Republic of Ireland in the 11-year period 2006–2016 from the National Paediatric Mortality Register. Data was subcategorised as Neonatal (0–28 days), Post Neonatal (29 days < 1 year) and older (1–14 years). Bed usage data (ICD-10 code, narrative and usage) of paediatric inpatients who died during hospitalisation from January 2015 to December 2016 was extracted from the National Quality Assurance Improvement System of in-patient data. Orphacodes were assigned to rare disease cases from ICD-10 codes or diagnostic narrative of both datasets.

Results

There were 4044 deaths registered from 2006–2016, aged < 15 years, of these 2368 (58.6%) had an underlying rare disease. Stratifying by age group; 55.6% (1140/2050) of neonatal deaths had a rare disease, 57.8% (450/778) post-neonatal, and 64% (778/1216) of children aged 1–14 years. Mortality coding using ICD-10 codes identified 42% of rare disease cases with the remainder identified using death certificate narrative records. Rare disease patients occupied 87% of bed days used by children < 15 years who died during hospitalisation from January 2015 to December 2016.

Conclusion

Additional routine rare disease coding is necessary to identify rare diseases within Irish healthcare systems to enable better healthcare planning. Rare disease patients are overrepresented in paediatric mortality statistics and in-patient length of stay during hospital admission prior to death.

Asylum-Seeking Children with Medical Complexity and Rare Diseases in a Tertiary Hospital in Switzerland

The aim of this study was to assess the characteristics of asylum-seeking children with medical complexity visiting a tertiary care hospital in Switzerland, detailing their underlying medical conditions and management. Asylum-seeking patients with frequent visits between January 2016 and December 2017 were identified using administrative and electronic health records. Of 462 patients, 19 (4%) fulfilled the inclusion criteria with 811 (45%) visits. The age of the 19 patients ranged from 0 to 16.7 years (median of 7 years) with two main age groups identified: < 2 years and > 12 years. Nine (47%) patients originated from Syria. A total of 34/811(4%) visits were hospital admissions, 66/811 (8%) emergency department visits and 320/811(39%) outpatient department visits. In children < 2 years genetic diseases (5/8; 63%) and nutritional problems (6/8; 75%) were most common; in adolescents, orthopedic diseases (4/8; 50%) and mental health problems (4/8; 50%). Asylum-seeking children with medical complexity represent a small but important group of patients requiring frequent medical consultations. The high proportion of young patients with genetic diseases and severe nutritional problems suggests that new strategies are required in the management of this specific group of asylum-seeking children. This could be achieved by improved co-ordination between hospital and non-hospital care exploring options for integrated care.

Genome Sequencing as a Diagnostic Test in Children With Unexplained Medical Complexity

Importance

Children with medical complexity (CMC) represent a growing population in the pediatric health care system, with high resource use and associated health care costs. A genetic diagnosis can inform prognosis, anticipatory care, management, and reproductive planning. Conventional genetic testing strategies for CMC are often costly, time consuming, and ultimately unsuccessful.

Objective

To evaluate the analytical and clinical validity of genome sequencing as a comprehensive diagnostic genetic test for CMC.

Design, Setting, and Participants

In this cohort study of the prospective use of genome sequencing and comparison with standard-of-care genetic testing, CMC were recruited from May 1, 2017, to November 30, 2018, from a structured complex care program based at a tertiary care pediatric hospital in Toronto, Canada. Recruited CMC had at least 1 chronic condition, technology dependence (child is dependent at least part of each day on mechanical ventilators, and/or child requires prolonged intravenous administration of nutritional substances or drugs, and/or child is expected to have prolonged dependence on other device-based support), multiple subspecialist involvement, and substantial health care use. Review of the care plans for 545 CMC identified 143 suspected of having an undiagnosed genetic condition. Fifty-four families met inclusion criteria and were interested in participating, and 49 completed the study. Probands, similarly affected siblings, and biological parents were eligible for genome sequencing.

Exposures

Genome sequencing was performed using blood-derived DNA from probands and family members using established methods and a bioinformatics pipeline for clinical genome annotation.

Main Outcomes and Measures

The primary study outcome was the diagnostic yield of genome sequencing (proportion of CMC for whom the test result yielded a new diagnosis).

Results

Genome sequencing was performed for 138 individuals from 49 families of CMC (29 male and 20 female probands; mean [SD] age, 7.0 [4.5] years). Genome sequencing detected all genomic variation previously identified by conventional genetic testing. A total of 15 probands (30.6%; 95% CI 19.5%-44.6%) received a new primary molecular genetic diagnosis after genome sequencing. Three individuals had novel diseases and an additional 9 had either ultrarare genetic conditions or rare genetic conditions with atypical features. At least 11 families received diagnostic information that had clinical management implications beyond genetic and reproductive counseling.

Conclusions and Relevance

This study suggests that genome sequencing has high analytical and clinical validity and can result in new diagnoses in CMC even in the setting of extensive prior investigations. This clinical population may be enriched for ultrarare and novel genetic disorders. Genome sequencing is a potentially first-tier genetic test for CMC.

Precision Child Health: an Emerging Paradigm for Paediatric Quality and Safety

Purpose of Review

Precision child health (PCH) is an emerging branch of precision medicine that focuses on the unique needs of the paediatric population. A PCH approach has the potential to enhance both quality of care and patient safety. Genome-wide sequencing can be used as a specific exemplar to showcase current opportunities and forecast future developments.

Recent Findings

Information gained from genome-wide sequencing can increase awareness of common and rare medical complications. Care provided to children and their families may then shift from reactive to proactive. Pertinent categories of results from genetic testing include primary diagnostic findings, genetic modifiers of disease expression, and secondary findings. In addition, an individual's unifying genetic diagnosis, disease subtype, and pharmacogenomic profile can all inform drug selection and treatment outcome. Recent lessons learned from the integration of genome-wide sequencing into the clinic may be generalizable to other "big data"-driven interventions.

Summary

Quality of care and patient safety are key targets of a PCH approach. The genomic revolution offers insights into this proposed new paradigm for healthcare delivery by showcasing the value of accurate diagnosis, disease subtyping with molecular markers, and awareness of individual- or family-specific risk factors for adverse outcomes.

Socioeconomic status and uptake of reproductive carrier screening in Australia

Reproductive carrier screening enables the early identification of genetic conditions that may impact the long-term health of a child, including cystic fibrosis, fragile X syndrome, and spinal muscular atrophy. We used unique data from the major providers of pathology services in Australia to profile women who intend on becoming, or who are, pregnant and access basic to advanced testing for genetic conditions. We found a strong socioeconomic gradient in the uptake of reproductive carrier screening, with women living in the most advantaged postcodes across Australia significantly being more likely to have reproductive carrier screening than those living in the most disadvantaged areas. These results highlight the need to minimise social and financial barriers that are currently limiting access.

Exome sequencing compared with standard genetic tests for critically ill infants with suspected genetic conditions

PURPOSE

As exome sequencing (ES) is increasingly used as a diagnostic tool, we aimed to compare ES with status quo genetic diagnostic workup for infants with suspected genetic disorders in terms of identifying diagnoses, survival, and cost of care.

METHODS

We studied newborns and infants admitted to intensive care with a suspected genetic etiology within the first year of life at a US quaternary-referral children's hospital over 5 years. In this propensity-matched cohort study using electronic medical record data, we compared patients who received ES as part of a diagnostic workup (ES cohort, n = 368) with clinically similar patients who did not receive ES (No-ES cohort, n = 368).

RESULTS

Diagnostic yield (27.4% ES, 25.8% No-ES; p = 0.62) and 1-year survival (80.2% ES, 84.8% No-ES; p = 0.10) were no different between cohorts. ES cohort patients had higher cost of admission, diagnostic investigation, and genetic testing (all p < 0.01).

CONCLUSION

ES did not differ from status quo genetic testing collectively in terms of diagnostic yield or patient survival; however, it had high yield as a single test, led to complementary classes of diagnoses, and was associated with higher costs. Further work is needed to define the most efficient use of diagnostic ES for critically ill newborns and infants.

Review of 10 years of preimplantation genetic diagnosis in South Africa: implications for a low-to-middle-income country

PURPOSE

To evaluate the preimplantation genetic diagnosis (PGD) service, for the period of January 2006 to December 2016, through a South African academic and diagnostic Human Genetics Unit, and to assess the outcomes and cost of PGD.

METHODS

A retrospective review of PGD files available at the Human Genetics Unit was performed. Data was collected from genetic counseling, fertility, and PGD-specific records.

RESULTS

Amongst the 22 couples who had PGD, 42 in vitro fertilisation cycles were completed with 228 embryos biopsied and included in the analysis. Most (59%) of the conditions for which PGD was requested were autosomal recessive. Of the biopsied embryos, 71/228 (31.1%) were suitable for transfer and 41/71 (57.7%) were transferred. Of these, 14/41 (34.0%) successfully implanted and 11/14 (78.6%) resulted in a liveborn infant. The clinical pregnancy rate per embryo transfer was 29.3%. Overall, 10/22 (45.5%) couples had a successful cycle resulting in a liveborn infant. On average, one cycle of PGD costs USD 9525.

CONCLUSIONS

This is the first study to assess the success rates and the cost of PGD in South Africa and provides evidence for the feasibility in a low-to-middle-income country. The success rates in this sample are comparable to those achieved globally. South Africa has the infrastructure and expertise to provide PGD; the limiting factor is the lack of funding initiatives for PGD. Although the sample size was small, the findings from this study will enable genetic counselors to offer couples in South Africa evidence-based and locally accurate information regarding outcomes, success rates, and costs.

Case for genome sequencing in infants and children with rare, undiagnosed or genetic diseases

Up to 350 million people worldwide suffer from a rare disease, and while the individual diseases are rare, in aggregate they represent a substantial challenge to global health systems. The majority of rare disorders are genetic in origin, with children under the age of five disproportionately affected. As these conditions are difficult to identify clinically, genetic and genomic testing have become the backbone of diagnostic testing in this population. In the last 10 years, next-generation sequencing technologies have enabled testing of multiple disease genes simultaneously, ranging from targeted gene panels to exome sequencing (ES) and genome sequencing (GS). GS is quickly becoming a practical first-tier test, as cost decreases and performance improves. A growing number of studies demonstrate that GS can detect an unparalleled range of pathogenic abnormalities in a single laboratory workflow. GS has the potential to deliver unbiased, rapid and accurate molecular diagnoses to patients across diverse clinical indications and complex presentations. In this paper, we discuss clinical indications for testing and historical testing paradigms. Evidence supporting GS as a diagnostic tool is supported by superior genomic coverage, types of pathogenic variants detected, simpler laboratory workflow enabling shorter turnaround times, diagnostic and reanalysis yield, and impact on healthcare.