The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

Videofluoroscopy compared with clinical feeding evaluation in children with suspected aspiration.

AIM: Videofluoroscopy swallow studies (VFSS) are gold standard to diagnose aspiration in children but require resources and radiation compared with clinical feeding evaluation (CFE). We evaluated their added value for diagnosis, feeding management and clinical status. METHODS: A retrospective single-centre cross-sectional study of children aged 0-18 years, with respiratory morbidity, referred for VFSS at a tertiary pediatric hospital. RESULTS: A total of 113 children, median age (range) 2.2 years (0.1-17.9), underwent VFSS. Diagnosis included chronic pulmonary aspiration (CPA), 87 (77%); neurological, 73 (64%); gastrointestinal, 73 (64%) and congenital heart disease, 42 (37%), not mutually exclusive. Forty-six (41%) aspirated, 9 (8%) only overtly and 37 (33%) including silent aspirations. Those with CPA or cerebral palsy were more likely to have VFSS aspiration, OR 3.2 and 9.8 respectively. Feeding recommendations after VFSS differed significantly from those based on prior CFE, p < 0.001: The rate of exclusively orally fed children rose from 65% to 79%, p = 0.006; exclusively enterally fed children from 10% to 14%; p = 0.005. During the year after VFSS, there were significantly less antibiotic courses, total and respiratory admissions. CONCLUSION: In this population with high prevalence of clinically suspected CPA, VFSS altered feeding management compared with CFE and may have contributed to subsequent clinical improvement.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

Efficacy of Problem-Solving Intervention to Improve Adherence in Adolescents and Adults with Cystic Fibrosis.

BACKGROUND: Adherence to treatment by adolescents and adults with cystic fibrosis (CF) is often poor. OBJECTIVES: To assess the impact of a focused clinical intervention on adherence in individual patients, including help in problem-solving key barriers to adherence. To implement a patient-centered problem-solving intervention using CF My Way tools. To identify and overcome a selected barrier to adherence. METHODS: Medication possession ratios (MPRs), number of airway clearance sessions, forced expiratory volume (FEV1), body mass index (BMI), and health-related quality of life (HRQoL) were measured before and after the intervention. RESULTS: Sixteen patients with CF, aged 23.4 ± 6.7 years, participated. MPR increased for colistimethate sodium and tobramycin inhalations from a median of 21 (range 0-100) to 56 (range 0-100), P = 0.04 and 20 (range 0-100) to 33.3 (range 25-100), P = 0.03, respectively. BMI standard deviation score rose from -0.37 to -0.21, P = 0.05. No significant improvements were found in FEV1, airway clearance, or HRQoL scores. CONCLUSIONS: The CF My Way problem-solving intervention increased adherence to medical treatments by removing barriers directly related to the needs and goals of young adults with CF.

GOPHER: Generator Of Probes for capture Hi-C Experiments at high Resolution.

BACKGROUND: Target enrichment combined with chromosome conformation capturing methodologies such as capture Hi-C (CHC) can be used to investigate spatial layouts of genomic regions with high resolution and at scalable costs. A common application of CHC is the investigation of regulatory elements that are in contact with promoters, but CHC can be used for a range of other applications. Therefore, probe design for CHC needs to be adapted to experimental needs, but no flexible tool is currently available for this purpose. RESULTS: We present a Java desktop application called GOPHER (Generator Of Probes for capture Hi-C Experiments at high Resolution) that implements three strategies for CHC probe design. GOPHER's simple approach is similar to the probe design of previous approaches that employ CHC to investigate all promoters, with one probe being placed at each margin of a single digest that overlaps the transcription start site (TSS) of each promoter. GOPHER's simple-patched approach extends this methodology with a heuristic that improves coverage of viewpoints in which the TSS is located near to one of the boundaries of the digest. GOPHER's extended approach is intended mainly for focused investigations of smaller gene sets. GOPHER can also be used to design probes for regions other than TSS such as GWAS hits or large blocks of genomic sequence. GOPHER additionally provides a number of features that allow users to visualize and edit viewpoints, and outputs a range of files useful for documentation, ordering probes, and downstream analysis. CONCLUSION: GOPHER is an easy-to-use and robust desktop application for CHC probe design. Source code and a precompiled executable can be downloaded from the GOPHER GitHub page at https://github.com/TheJacksonLaboratory/Gopher .

Diagnosis of Cystic Fibrosis in Screened Populations.

OBJECTIVE: Cystic fibrosis (CF) can be difficult to diagnose, even when newborn screening (NBS) tests yield positive results. This challenge is exacerbated by the multitude of NBS protocols, misunderstandings about screening vs diagnostic tests, and the lack of guidelines for presumptive diagnoses. There is also confusion regarding the designation of age at diagnosis. STUDY DESIGN: To improve diagnosis and achieve standardization in definitions worldwide, the CF Foundation convened a committee of 32 experts with a mission to develop clear and actionable consensus guidelines on diagnosis of CF with an emphasis on screened populations, especially the newborn population. A comprehensive literature review was performed with emphasis on relevant articles published during the past decade. RESULTS: After reviewing the common screening protocols and outcome scenarios, 14 of 27 consensus statements were drafted that apply to screened populations. These were approved by 80% or more of the participants. CONCLUSIONS: It is recommended that all diagnoses be established by demonstrating dysfunction of the CF transmembrane conductance regulator (CFTR) channel, initially with a sweat chloride test and, when needed, potentially with newer methods assessing membrane transport directly, such as intestinal current measurements. Even in babies with 2 CF-causing mutations detected via NBS, diagnosis must be confirmed by demonstrating CFTR dysfunction. The committee also recommends that the latest classifications identified in the Clinical and Functional Translation of CFTR project [http://www.cftr2.org/index.php] should be used to aid with CF diagnosis. Finally, to avoid delays in treatment, we provide guidelines for presumptive diagnoses and recommend how to determine the age of diagnosis.

Lung transplantation in cystic fibrosis patients in Israel: The importance of ethnicity and nutritional status.

OBJECTIVES: To assess the characteristics that correlate with better outcomes after lung transplantation for patients with cystic fibrosis (CF). METHODS: We retrospectively reviewed the charts of all patients with CF who underwent lung transplantation between 1996 and 2014 at Rabin Medical Center, Israel. RESULTS: Fifty patients with CF underwent 55 lung transplantations. Eighteen patients (36%) died during the study period. Actuarial survival was 83%, 68%, 62%, and 39% at 1, 3, 5, and 10 years, respectively. Better survival correlated with: BMI at 6 months and 1 year after transplantation (P = .002 and P = .003, respectively), ischemic time of less than 300 minutes (P = .023), absence of liver disease (P = .012), and Jewish compared to Arab ethnicity (P = .007). Freedom from bronchiolitis obliterans syndrome (BOS) was 87%, 75%, and 72% at 1, 3, and 5 years, respectively. BOS was more common and appeared earlier in the Arab than in the Jewish population (P = .012, P = .007). Additionally, prolonged time free of BOS correlated with male gender (P = .039), older age (P < .001), absence of liver disease (P = .012), and higher BMI 1 year after transplantation (P < .001). CONCLUSIONS: Clinically important determinants for survival include BMI pre- and 1-year post-transplantation and improved freedom from BOS. Arab ethnicity correlated with higher incidence and earlier onset of BOS compared to Jewish ethnicity in Israel.

Replacing non-biomedical concepts improves embedding of biomedical concepts.

Objectives: Concept embeddings are low-dimensional vector representations of concepts such as MeSH:D009203 (Myocardial Infarction), whose similarity in the embedded vector space reflects their semantic similarity. Here, we test the hypothesis that non-biomedical concept synonym replacement can improve the quality of biomedical concepts embeddings. Materials and methods: We developed an approach that leverages WordNet to replace sets of synonyms with the most common representative of the synonym set. Results: We tested our approach on 1055 concept sets and found that, on average, the mean intracluster distance was reduced by 8% in the vector-space. Assuming that homophily of related concepts in the vector space is desirable, our approach tends to improve the quality of embeddings. Discussion and Conclusion: This pilot study shows that non-biomedical synonym replacement tends to improve the quality of embeddings of biomedical concepts using the Word2Vec algorithm. We have implemented our approach in a freely available Python package available at https://github.com/TheJacksonLaboratory/wn2vec .

Cardiopulmonary exercise testing in adolescence following extremely premature birth.

BACKGROUND: Although extremely premature birth disrupts lung development, adolescent survivors of extreme prematurity show good clinical and physiologic outcomes. Cardiopulmonary limitations may not be clinically evident at rest. Data regarding exercise limitation in adolescents following preterm birth in the postsurfactant era are limited. RESEARCH QUESTION: What are the long-term effects of bronchopulmonary dysplasia (BPD) and extreme prematurity (<29 weeks) on ventilatory response during exercise in adolescents in the postsurfactant era? STUDY DESIGN AND METHODS: We followed a longitudinally recruited cohort of children aged 13-19 years who were born at a gestational age of <29 weeks (study group - SG). We compared the cardiopulmonary exercise testing (CPET) results of those with and without BPD, to their own CPET results from elementary school age (mean 9.09 ± 1.05 years). RESULTS: Thirty-seven children aged 15.73 ± 1.31 years, mean gestational age 26 weeks ( ± 1.19), completed the study. CPET parameters in adolescence were within the normal range for age, including mean V̇O2 peak of 91% predicted. The BPD and non-BPD subgroups had similar results. In the longitudinal analysis of the SG, improvement was observed in adolescence, compared with elementary school age, in breathing reserve (36.37 ± 18.99 vs. 26.58 ± 17.92, p = 0.044), tidal volume as a fraction of vital capacity achieved at maximal load (0.51 ± 0.13 vs. 0.37 ± 0.08, p < 0.001), and respiratory exchange ratio at maximal load (1.18 ± 0.13 vs. 1.11 ± 0.10, p = 0.021). INTERPRETATION: In the current cohort, adolescents born extremely premature have essentially normal ventilatory response during exercise, unrelated to BPD diagnosis. CPET results in this population improve over time.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

Dynamic Hyperinflation While Exercising-A Potential Predictor of Pulmonary Deterioration in Cystic Fibrosis.

BACKGROUND: Lung function deterioration in cystic fibrosis (CF) is typically measured by a decline in the forced expiratory volume in one second (FEV1%), which is thought to be a late marker of lung disease. Dynamic hyperinflation (DH) is seen in obstructive lung diseases while exercising. Our aim was to assess whether DH could predict pulmonary deterioration in CF; a secondary measure was the peak VO2. METHODS: A retrospective study was conducted of people with CF who performed cardiopulmonary exercise tests (CPETs) during 2012-2018. The tests were classified as those demonstrating DH non-DH. Demographic, genetic, and clinical data until 12.2022 were extracted from patient charts. RESULTS: A total of 33 patients aged 10-61 years performed 41 valid CPETs with valid DH measurements; sixteen (39%) demonstrated DH. At the time of the CPETs, there was no difference in the FEV1% measurements between the DH and non-DH groups (median 83.5% vs. 87.6%, respectively; p = 0.174). The FEV1% trend over 4 years showed a decline in the DH group compared to the non-DH group (p = 0.009). A correlation was found between DH and the lung clearance index (LCI), as well as the FEV1% (r = 0.36 and p = 0.019 and r = -0.55 and p = 0.004, respectively). Intravenous (IV) antibiotic courses during the 4 years after the CPETs were significantly more frequent in the DH group (p = 0.046). The peak VO2 also correlated with the FEV1% and LCI (r = 0.36 and p = 0.02 and r = -0.46 and p = 0.014, respectively) as well as with the IV antibiotic courses (r = -0.46 and p = 0.014). CONCLUSIONS: In our cohort, the DH and peak VO2 were both associated with lung function deterioration and more frequent pulmonary exacerbations. DH may serve as a marker to predict pulmonary deterioration in people with CF.

Predictive models of long COVID.

BACKGROUND: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future. METHODS: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models - logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the 'long COVID' label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741). FINDINGS: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75. INTERPRETATION: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology. FUNDING: NCATS U24 TR002306, NCATS UL1 TR003015, Axle Informatics Subcontract: NCATS-P00438-B, NIH/NIDDK/OD, PSR2015-1720GVALE_01, G43C22001320007, and Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231.

The association between Attention-Deficit-Hyperactivity-Disorder (ADHD) symptoms and disease severity in people with Cystic Fibrosis (pwCF).

BACKGROUND: The hallmarks of Cystic fibrosis (CF), chronic infection and inflammation, require intensive daily treatment to maintain and improve quality of life and outcome. The incidence of Attention Deficit/Hyperactivity Disorder (ADHD) is increased in chronic inflammatory diseases. Previous studies suggested that the prevalence of ADHD in people with CF (pwCF) is higher than in the general population. The objective of this study was to evaluate the association between ADHD symptoms and parameters of CF disease severity, measured by demographic and clinical data. METHODS: Based on our previous study, the results of ADHD questionnaires and the MOXOCPT (continuous performance task) from 143 pwCF (7-68 years old) were analyzed and linked to patient data such as forced expiratory volume in 1 second (FEV1)%predicted, body mass index (BMI), number of pulmonary exacerbations, days of antibiotic (Abx) treatment and serum inflammatory markers. RESULTS: A positive correlation between FEV1 and ADHD questionnaire's score (p = 0.046) was observed in the children's group. Furthermore, BMI, white blood cells (WBC) count, and days of Abx treatment showed a positive correlation with some of the MOXOCPT parameters. CONCLUSION: There is an association between ADHD symptoms and some parameters of CF disease severity. These results highlight the need for an early diagnosis of ADHD in pwCF, which have the potential to improve their ability to deal with the burden of their disease and consequently their quality of life. Additional research is needed to understand the full spectrum of ADHD pathophysiology and the relationship with chronic inflammatory diseases such as CF.

De novo TRPM3 missense variant associated with neurodevelopmental delay and manifestations of cerebral palsy.

We identified a de novo heterozygous transient receptor potential cation channel subfamily M (melastatin) member 3 (TRPM3) missense variant, p.(Asn1126Asp), in a patient with developmental delay and manifestations of cerebral palsy (CP) using phenotype-driven prioritization analysis of whole-genome sequencing data with Exomiser. The variant is localized in the functionally important ion transport domain of the TRPM3 protein and predicted to impact the protein structure. Our report adds TRPM3 to the list of Mendelian disease-associated genes that can be associated with CP and provides further evidence for the pathogenicity of the variant p.(Asn1126Asp).

The new face of cystic fibrosis in the era of population genetic carrier screening.

BACKGROUND: Population genetic carrier screening (PGCS) for cystic fibrosis (CF) has been offered to couples in Israel since 1999 and was included in a fully subsidized national program in 2008. We evaluated the impact of PGCS on CF incidence, genetic and clinical features. METHODS: This was a retrospective national study. Demographic and clinical characteristics of children with CF born in Israel between 2008 and 2018 were obtained from the national CF registry and from patients' medical records. Data on CF births, preimplantation genetic testing (PGT), pregnancy termination and de-identified data from the PGCS program were collected. RESULTS: CF births per 100,000 live births decreased from 8.29 in 2008 to 0.54 in 2018 (IRR = 0.84, p < 0.001). The CF pregnancy termination rate did not change (IRR = 1, p=  0.9) while the CF-related PGT rate increased markedly (IRR = 1.33, p < 0.001). One hundred and two children were born with CF between 2008 and 2018 with a median age at diagnosis of 4.8 months, range 0-111 months. Unlike the generally high uptake nationally, 65/102 had not performed PGCS. Even if all had utilized PGCS, only 51 would have been detected by the existing genetic screening panel. Clinically, 34 % of children were pancreatic sufficient compared to 23 % before 2008 (p = 0.04). CONCLUSIONS: Since institution of a nationwide PGCS program, the birth of children with CF decreased markedly. Residual function variants and pancreatic sufficiency were more common. A broader genetic screening panel and increased PGCS utilization may further decrease the birth of children with CF.

The Medical Action Ontology: A Tool for Annotating and Analyzing Treatments and Clinical Management of Human Disease.

Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes. MAxO was developed with logical structures that make it compatible with several other ontologies within the Open Biological and Biomedical Ontologies (OBO) Foundry. These cover a wide range of biomedical domains, from human anatomy and investigations to the chemical and protein entities involved in biological processes. We have created a database of over 16000 annotations that describe diagnostic modalities for specific phenotypic abnormalities as defined by the Human Phenotype Ontology (HPO). Additionally, 413 annotations are provided for medical actions for 189 rare diseases. We have developed a web application called POET (https://poet.jax.org/) for the community to use to contribute MAxO annotations. MAxO provides a computational representation of treatments and other actions taken for the clinical management of patients. The development of MAxO is closely coupled to the Mondo Disease Ontology (Mondo) and the Human Phenotype Ontology (HPO) and expands the scope of our computational modeling of diseases and phenotypic features to include diagnostics and therapeutic actions. MAxO is available under the open-source CC-BY 4.0 license (https://github.com/monarch-initiative/MAxO).

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.