The functional impact of 1,570 individual amino acid substitutions in human OTC.

Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Individuals with neonatal onset appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma, and death, outcomes ameliorated by rapid diagnosis and treatment. Here, we develop a high-throughput functional assay for human OTC and individually measure the impact of 1,570 variants, 84% of all SNV-accessible missense mutations. Comparison to existing clinical significance calls, demonstrated that our assay distinguishes known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. This functional stratification allowed us to identify score ranges corresponding to clinically relevant levels of impairment of OTC activity. Examining the results of our assay in the context of protein structure further allowed us to identify a 13 amino acid domain, the SMG loop, whose function appears to be required in human cells but not in yeast. Finally, inclusion of our data as PS3 evidence under the current ACMG guidelines, in a pilot reclassification of 34 variants with complete loss of activity, would change the classification of 22 from variants of unknown significance to clinically actionable likely pathogenic variants. These results illustrate how large-scale functional assays are especially powerful when applied to rare genetic diseases.

High TSLP responses in the human infant airways are associated with pre-activated airway epithelial IFN antiviral immunity.

Thymic stromal lymphopoietin (TSLP) is a primarily epithelial-derived cytokine that drives type 2 allergic immune responses. Early life viral respiratory infections elicit high TSLP production, which leads to the development of type 2 inflammation and airway hyperreactivity. The goal of this study was to examine in vivo and in vitro the human airway epithelial responses leading to high TSLP production during viral respiratory infections in early infancy. A total of 129 infants (<1-24 m, median age 10 m) with severe viral respiratory infections were enrolled for in vivo (n = 113), and in vitro studies (n = 16). Infants were classified as 'high TSLP' or 'low TSLP' for values above or below the 50th percentile. High versus low TSLP groups were compared in terms of type I-III IFN responses and production of chemokines promoting antiviral (CXCL10), neutrophilic (CXCL1, CXCL5, CXCL8), and type 2 responses (CCL11, CCL17, CCL22). Human infant airway epithelial cell (AEC) cultures were used to define the transcriptomic (RNAseq) profile leading to high versus low TSLP responses in vitro in the absence (baseline) or presence (stimulated) of a viral mimic (poly I:C). Infants in the high TSLP group had greater in vivo type III IFN airway production (median type III IFN in high TSLP 183.2 pg/mL vs. 63.4 pg/mL in low TSLP group, p = 0.007) and increased in vitro type I-III IFN AEC responses after stimulation with a viral mimic (poly I:C). At baseline, our RNAseq data showed that infants in the high TSLP group had significant upregulation of IFN signature genes (e.g., IFIT2, IFI6, MX1) and pro-inflammatory chemokine genes before stimulation. Infants in the high TSLP group also showed a baseline AEC pro-inflammatory state characterized by increased production of all the chemokines assayed (e.g., CXCL10, CXCL8). High TSLP responses in the human infant airways are associated with pre-activated airway epithelial IFN antiviral immunity and increased baseline AEC production of pro-inflammatory chemokines. These findings present a new paradigm underlying the production of TSLP in the human infant airway epithelium following early life viral exposure and shed light on the long-term impact of viral respiratory illnesses during early infancy and beyond childhood.

Datamining approaches for examining the low prevalence of N-acetylglutamate synthase deficiency and understanding transcriptional regulation of urea cycle genes.

Ammonia, which is toxic to the brain, is converted into non-toxic urea, through a pathway of six enzymatically catalyzed steps known as the urea cycle. In this pathway, N-acetylglutamate synthase (NAGS, EC 2.3.1.1) catalyzes the formation of N-acetylglutamate (NAG) from glutamate and acetyl coenzyme A. NAGS deficiency (NAGSD) is the rarest of the urea cycle disorders, yet is unique in that ureagenesis can be restored with the drug N-carbamylglutamate (NCG). We investigated whether the rarity of NAGSD could be due to low sequence variation in the NAGS genomic region, high NAGS tolerance for amino acid replacements, and alternative sources of NAG and NCG in the body. We also evaluated whether the small genomic footprint of the NAGS catalytic domain might play a role. The small number of patients diagnosed with NAGSD could result from the absence of specific disease biomarkers and/or short NAGS catalytic domain. We screened for sequence variants in NAGS regulatory regions in patients suspected of having NAGSD and found a novel NAGS regulatory element in the first intron of the NAGS gene. We applied the same datamining approach to identify regulatory elements in the remaining urea cycle genes. In addition to the known promoters and enhancers of each gene, we identified several novel regulatory elements in their upstream regions and first introns. The identification of cis-regulatory elements of urea cycle genes and their associated transcription factors holds promise for uncovering shared mechanisms governing urea cycle gene expression and potentially leading to new treatments for urea cycle disorders.

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study.

OBJECTIVES: Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness. DESIGN: Analysis of electronic health data from January 1, 2018, to February 29, 2020. SETTING: PICU and cardiac ICU at an academic children's hospital. PATIENTS: All PICU patients. Data included descriptive information, outcome, and independent variables used in the Criticality Index-Mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 8,399 admissions with 312 deaths (3.7%). Mortality risk determined every three hours using the Criticality Index-Mortality, a machine learning algorithm calibrated to this hospital. Since the sample sizes were sufficiently large to expect statical differences, we also used two measures of effect size, the proportion of time deaths had greater instability than survivors, and the rank-biserial correlation, to assess the magnitude of the effect and complement our hypothesis tests. Within patient changes were compared for survivors and deaths. All comparisons of survivors versus deaths were less than 0.001. For all time intervals, two measures of effect size indicated that the differences between deaths and survivors were not clinically important. However, the within-patient maximum risk increase (clinical deterioration) and maximum risk decrease (clinical improvement) were both substantially greater in deaths than survivors for all time intervals. For deaths, the maximum risk increase ranged from 11.1% to 16.1% and the maximum decrease ranged from -7.3% to -10.0%, while the median maximum increases and decreases for survivors were all less than ± 0.1%. Both measures of effect size indicated moderate to high clinical importance. The within-patient volatility was greater than 4.5-fold greater in deaths than survivors during the first ICU day, plateauing at ICU days 4-5 at 2.5 greater volatility. CONCLUSIONS: Episodic clinical instability measured with mortality risk is a reliable sign of increasing severity of illness. Mortality risk changes during four time intervals demonstrated deaths have greater maximum and within-patient clinical instability than survivors. This observation confirms the clinical teaching that clinical instability is a sign of severity of illness.

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

OBJECTIVES: Assess a machine learning method of serially updated mortality risk. DESIGN: Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO). SETTING: Hospitals caring for children in ICUs. PATIENTS: A total of 27,354 admissions cared for in ICUs from 2009 to 2018. INTERVENTIONS: None. MAIN OUTCOME: Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths. MEASUREMENTS AND MAIN RESULTS: The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p < 0.001). CONCLUSIONS: Machine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M's framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time.

Enabling precision medicine via standard communication of HTS provenance, analysis, and results.

A personalized approach based on a patient's or pathogen's unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the "Open-Stand.org principles for collaborative open standards development." With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

Severity Trajectories of Pediatric Inpatients Using the Criticality Index.

OBJECTIVES: To assess severity of illness trajectories described by the Criticality Index for survivors and deaths in five patient groups defined by the sequence of patient care in ICU and routine patient care locations. DESIGN: The Criticality Index developed using a calibrated, deep neural network, measures severity of illness using physiology, therapies, and therapeutic intensity. Criticality Index values in sequential 6-hour time periods described severity trajectories. SETTING: Hospitals with pediatric inpatient and ICU care. PATIENTS: Pediatric patients never cared for in an ICU (n = 20,091), patients only cared for in the ICU (n = 2,096) and patients cared for in both ICU and non-ICU care locations (n = 17,023) from 2009 to 2016 Health Facts database (Cerner Corporation, Kansas City, MO). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Criticality Index values were consistent with clinical experience. The median (25-75th percentile) ICU Criticality Index values (0.878 [0.696-0.966]) were more than 80-fold higher than the non-ICU values (0.010 [0.002-0.099]). Non-ICU Criticality Index values for patients transferred to the ICU were 40-fold higher than those never transferred to the ICU (0.164 vs 0.004). The median for ICU deaths was higher than ICU survivors (0.983 vs 0.875) (p < 0.001). The severity trajectories for the five groups met expectations based on clinical experience. Survivors had increasing Criticality Index values in non-ICU locations prior to ICU admission, decreasing Criticality Index values in the ICU, and decreasing Criticality Index values until hospital discharge. Deaths had higher Criticality Index values than survivors, steeper increases prior to the ICU, and worsening values in the ICU. Deaths had a variable course, especially those who died in non-ICU care locations, consistent with deaths associated with both active therapies and withdrawals/limitations of care. CONCLUSIONS: Severity trajectories measured by the Criticality Index showed strong validity, reflecting the expected clinical course for five diverse patient groups.

Criticality: A New Concept of Severity of Illness for Hospitalized Children.

OBJECTIVES: To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care. DESIGN: Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database. SETTING: Hospitals with pediatric routine inpatient and ICU care. PATIENTS: Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations. CONCLUSIONS: The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

Medications for Children Receiving Intensive Care: A National Sample.

OBJECTIVE: To examine medication administration records through electronic health record data to provide a broad description of the pharmaceutical exposure of critically ill children. DESIGN: Retrospective cohort study using the Cerner Health Facts database. SETTING: United States. PATIENTS: A total of 43,374 children 7 days old to less than 22 years old receiving intensive care with available pharmacy data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 907,440 courses of 1,080 unique medications were prescribed with a median of nine medications (range, 1-99; 25-75th percentile, 5-16) per patient. The most common medications were acetaminophen, ondansetron, and morphine. Only 45 medications (4.2%) were prescribed to more than 5% of patients, and these accounted for 442,067 (48.7%) of the total courses of medications. Each additional medication was associated with increased univariate risk of mortality (odds ratio, 1.05; 95% CI, 1.05-1.06; p < 0.001). CONCLUSIONS: Children receiving intensive care receive a median of nine medications per patient and one quarter are prescribed at least than 16 medications. Only 45 medications were prescribed to more than 5% of patients, but these accounted for almost half of all medication courses.

Sedation, Analgesia, and Neuromuscular Blockade: An Assessment of Practices From 2009 to 2016 in a National Sample of 66,443 Pediatric Patients Cared for in the ICU.

OBJECTIVES: To describe the pharmaceutical management of sedation, analgesia, and neuromuscular blockade medications administered to children in ICUs. DESIGN: A retrospective analysis using data extracted from the national database Health Facts. SETTING: One hundred sixty-one ICUs in the United States with pediatric admissions. PATIENTS: Children in ICUs receiving medications from 2009 to 2016. EXPOSURE/INTERVENTION: Frequency and duration of administration of sedation, analgesia, and neuromuscular blockade medications. MEASUREMENTS AND MAIN RESULTS: Of 66,443 patients with a median age of 1.3 years (interquartile range, 0-14.5), 63.3% (n = 42,070) received nonopioid analgesic, opioid analgesic, sedative, and/or neuromuscular blockade medications consisting of 83 different agents. Opioid and nonopioid analgesics were dispensed to 58.4% (n = 38,776), of which nonopioid analgesics were prescribed to 67.4% (n = 26,149). Median duration of opioid analgesic administration was 32 hours (interquartile range, 7-92). Sedatives were dispensed to 39.8% (n = 26,441) for a median duration of 23 hours (interquartile range, 3-84), of which benzodiazepines were most common (73.4%; n = 19,426). Neuromuscular-blocking agents were dispensed to 17.3% (n = 11,517) for a median duration of 2 hours (interquartile range, 1-15). Younger age was associated with longer durations in all medication classes. A greater proportion of operative patients received these medication classes for a longer duration than nonoperative patients. A greater proportion of patients with musculoskeletal and hematologic/oncologic diseases received these medication classes. CONCLUSIONS: Analgesic, sedative, and neuromuscular-blocking medications were prescribed to 63.3% of children in ICUs. The durations of opioid analgesic and sedative medication administration found in this study can be associated with known complications, including tolerance and withdrawal. Several medications dispensed to pediatric patients in this analysis are in conflict with Food and Drug Administration warnings, suggesting that there is potential risk in current sedation and analgesia practice that could be reduced with practice changes to improve efficacy and minimize risks.

Disease-causing mutations in the promoter and enhancer of the ornithine transcarbamylase gene.

The ornithine transcarbamylase (OTC) gene is on the X chromosome and its product catalyzes the formation of citrulline from ornithine and carbamylphosphate in the urea cycle. About 10%-15% of patients, clinically diagnosed with OTC deficiency (OTCD), lack identifiable mutations in the coding region or splice junctions of the OTC gene on routine molecular testing. We collected DNA from such patients via retrospective review and by prospective enrollment. In nine of 38 subjects (24%), we identified a sequence variant in the OTC regulatory regions. Eight subjects had unique sequence variants in the OTC promoter and one subject had a novel sequence variant in the OTC enhancer. All sequence variants affect positions that are highly conserved in mammalian OTC genes. Functional studies revealed reduced reporter gene expression with all sequence variants. Two sequence variants caused decreased binding of the HNF4 transcription factor to its mutated binding site. Bioinformatic analyses combined with functional assays can be used to identify and authenticate pathogenic sequence variants in regulatory regions of the OTC gene, in other urea cycle disorders or other inborn errors of metabolism.

AAV gene therapy corrects OTC deficiency and prevents liver fibrosis in aged OTC-knock out heterozygous mice.

Ornithine transcarbamylase (OTC) deficiency is an X-linked disorder of the urea cycle. Hemizygous males and heterozygous females may experience life-threatening elevations of ammonia in blood and brain, leading to irreversible cognitive impairment, coma, and death. Recent evidence of acute liver failure and fibrosis/cirrhosis is also emerging in OTC-deficient patients. Here, we investigated the long-term consequences of abnormal ureagenesis in female mice heterozygous (Het) for a null mutation in the OTC gene. Two-month-old Het OTC knockout (KO) mice received a single dose of self-complementary adeno-associated virus (AAV) encoding a codon-optimized human OTC gene at 1×1010, 3×1010, or 1×1011 vector genome copies per mouse. We compared liver pathology from 18-month-old treated Het OTC-KO mice, age-matched untreated Het OTC-KO mice, and WT littermates, and assessed urinary orotic acid levels and vector genome copies in liver at 4, 10, and 16months following vector administration. Het OTC-KO female mice showed evidence of liver inflammation and the eventual development of significant fibrosis. Treatment with AAV gene therapy not only corrected the underlying metabolic abnormalities, but also prevented the development of liver fibrosis. Our study demonstrates that early treatment of OTC deficiency with gene therapy may prevent clinically relevant consequences of chronic liver damage from developing.

External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.

OBJECTIVE: To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future. DESIGN: Prognostic study comparing multi-institutional CI-D models' performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset. PARTICIPANTS: All pediatric inpatients admitted from January 1st 2018 -February 29th 2020 through the emergency department. MAIN OUTCOME(S) AND MEASURE(S): The main outcome was inpatient care in routine or ICU care locations. RESULTS: A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15-157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493-0.545 and area under the precision-recall curve (AUPRC) was 0.262-0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906-0.944 and AUPRC range from 0.754-0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care. CONCLUSION AND RELEVANCE: Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.

EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort.

Objective: Long COVID, marked by persistent, recurring, or new symptoms post-COVID-19 infection, impacts children's well-being yet lacks a unified clinical definition. This study evaluates the performance of an empirically derived Long COVID case identification algorithm, or computable phenotype, with manual chart review in a pediatric sample. This approach aims to facilitate large-scale research efforts to understand this condition better. Methods: The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences. Results: The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74). Conclusions: Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.

Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19.

Background: The impact of COVID-19 on gastrointestinal (GI) outcomes in children during the post-acute and chronic phases of the disease is not well understood. Methods: We conducted a retrospective cohort study across twenty-nine healthcare institutions from March 2020 to September 2023, including 413,455 pediatric patients with confirmed SARS-CoV-2 infection and 1,163,478 controls without infection. Infection was confirmed via polymerase chain reaction (PCR), serology, antigen tests, or clinical diagnosis of COVID-19 and related conditions. We examined the incidence of predefined GI symptoms and disorders during the post-acute (28 to 179 days post-infection) and chronic (180 to 729 days post-infection) phases. The adjusted risk ratios (aRRs) were calculated using stratified Poisson regression, with stratification based on propensity scores. Results: Our cohort comprised 1,576,933 patients, with females representing 48.0% of the sample. The analysis revealed that children with SARS-CoV-2 infection had an increased risk of developing at least one GI symptom or disorder in both the post-acute (8.64% vs. 6.85%; aRR 1.25, 95% CI 1.24-1.27) and chronic phases (12.60% vs. 9.47%; aRR 1.28, 95% CI 1.26-1.30) compared to uninfected peers. Specifically, the risk of abdominal pain was higher in COVID-19 positive patients during the post-acute phase (2.54% vs. 2.06%; aRR 1.14, 95% CI 1.11-1.17) and chronic phase (4.57% vs. 3.40%; aRR 1.24, 95% CI 1.22-1.27). Interpretation: Children with a history of SARS-CoV-2 infection are at an increased risk of GI symptoms and disorders during the post-acute and chronic phases of COVID-19. This highlights the need for ongoing monitoring and management of GI outcomes in this population.

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

A novel biochemically salvageable animal model of hyperammonemia devoid of N-acetylglutamate synthase.

All knockout mouse models of urea cycle disorders die in the neonatal period or shortly thereafter. Since N-acetylglutamate synthase (NAGS) deficiency in humans can be effectively treated with N-carbamyl-l-glutamate (NCG), we sought to develop a mouse model of this disorder that could be rescued by biochemical intervention, reared to adulthood, reproduce, and become a novel animal model for hyperammonemia. Founder NAGS knockout heterozygous mice were obtained from the trans-NIH Knock-Out Mouse Project. Genotyping of the mice was performed by PCR and confirmed by Western blotting of liver and intestine. NCG and L-citrulline (Cit) were used to rescue the NAGS knockout homozygous (Nags(-/-)) pups and the rescued animals were characterized. We observed an 85% survival rate of Nags(-/-) mice when they were given intraperitoneal injections with NCG and Cit during the newborn period until weaning and supplemented subsequently with both compounds in their drinking water. This regimen has allowed for normal development, apparent health, and reproduction. Interruption of this rescue intervention resulted in the development of severe hyperammonemia and death within 48 h. In addition to hyperammonemia, interruption of rescue supplementation was associated with elevated plasma glutamine, glutamate, and lysine, and reduced citrulline, arginine, ornithine and proline levels. We conclude that NAGS deprived mouse model has been developed which can be rescued by NCG and Cit and reared to reproduction and beyond. This biochemically salvageable mouse model recapitulates the clinical phenotype of proximal urea cycle disorders and can be used as a reliable model of induced hyperammonemia by manipulating the administration of the rescue compounds.

Preclinical evaluation of a clinical candidate AAV8 vector for ornithine transcarbamylase (OTC) deficiency reveals functional enzyme from each persisting vector genome.

Ornithine transcarbamylase deficiency (OTCD), the most common and severe urea cycle disorder, is an excellent model for developing liver-directed gene therapy. No curative therapy exists except for liver transplantation which is limited by available donors and carries significant risk of mortality and morbidity. Adeno-associated virus 8 (AAV8) has been shown to be the most efficient vector for liver-directed gene transfer and is currently being evaluated in a clinical trial for treating hemophilia B. In this study, we generated a clinical candidate vector for a proposed OTC gene therapy trial in humans based on a self-complementary AAV8 vector expressing codon-optimized human OTC (hOTCco) under the control of a liver-specific promoter. Codon-optimization dramatically improved the efficacy of OTC gene therapy. Supraphysiological expression levels and activity of hOTC were achieved in adult spf(ash) mice following a single intravenous injection of hOTCco vector. Vector doses as low as 1×10(10) genome copies (GC) achieved robust and sustained correction of the OTCD biomarker orotic aciduria and clinical protection against an ammonia challenge. Functional expression of hOTC in 40% of liver areas was found in mice treated with a low vector dose of 1×10(9) GC. We suggest that the clinical candidate vector we have developed has the potential to achieve therapeutic effects in OTCD patients.

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.

Background: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. Objectives: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. Population: There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. Model: A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. Results: Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors. Conclusions: The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US.

Importance: Identifying the associations between severe COVID-19 and individual cardiovascular conditions in pediatric patients may inform treatment. Objective: To assess the association between previous or preexisting cardiovascular conditions and severity of COVID-19 in pediatric patients. Design, Setting, and Participants: This retrospective cohort study used data from a large, multicenter, electronic health records database in the US. The cohort included patients aged 2 months to 17 years with a laboratory-confirmed diagnosis of COVID-19 or a diagnosis code indicating infection or exposure to SARS-CoV-2 at 85 health systems between March 1, 2020, and January 31, 2021. Exposures: Diagnoses for 26 cardiovascular conditions between January 1, 2015, and December 31, 2019 (before infection with SARS-CoV-2). Main Outcomes and Measures: The main outcome was severe COVID-19, defined as need for supplemental oxygen or in-hospital death. Mixed-effects, random intercept logistic regression modeling assessed the significance and magnitude of associations between 26 cardiovascular conditions and COVID-19 severity. Multiple comparison adjustment was performed using the Benjamini-Hochberg false discovery rate procedure. Results: The study comprised 171 416 pediatric patients; the median age was 8 years (IQR, 2-14 years), and 50.28% were male. Of these patients, 17 065 (9.96%) had severe COVID-19. The random intercept model showed that the following cardiovascular conditions were associated with severe COVID-19: cardiac arrest (odds ratio [OR], 9.92; 95% CI, 6.93-14.20), cardiogenic shock (OR, 3.07; 95% CI, 1.90-4.96), heart surgery (OR, 3.04; 95% CI, 2.26-4.08), cardiopulmonary disease (OR, 1.91; 95% CI, 1.56-2.34), heart failure (OR, 1.82; 95% CI, 1.46-2.26), hypotension (OR, 1.57; 95% CI, 1.38-1.79), nontraumatic cerebral hemorrhage (OR, 1.54; 95% CI, 1.24-1.91), pericarditis (OR, 1.50; 95% CI, 1.17-1.94), simple biventricular defects (OR, 1.45; 95% CI, 1.29-1.62), venous embolism and thrombosis (OR, 1.39; 95% CI, 1.11-1.73), other hypertensive disorders (OR, 1.34; 95% CI, 1.09-1.63), complex biventricular defects (OR, 1.33; 95% CI, 1.14-1.54), and essential primary hypertension (OR, 1.22; 95% CI, 1.08-1.38). Furthermore, 194 of 258 patients (75.19%) with a history of cardiac arrest were younger than 12 years. Conclusions and Relevance: The findings suggest that some previous or preexisting cardiovascular conditions are associated with increased severity of COVID-19 among pediatric patients in the US and that morbidity may be increased among individuals children younger than 12 years with previous cardiac arrest.