Histological characterization of liver involvement in systemic mastocytosis.

BACKGROUND AND AIMS: Systemic mastocytosis (SM) is characterized by the accumulation of atypical mast cells (MCs) in organs. Liver histology of SM has been marginally described and accurate histological classification is critical, given the consequences of aggressive SM diagnosis. We aimed to describe the histological features associated with liver SM using updated tools. METHODS: Using the database of the French Reference Centre for Mastocytosis, we retrospectively identified patients with a liver biopsy (LB) and a diagnosis of SM. All LB procedures were performed according to the local physician in charge and centrally reviewed by an expert pathologist. RESULTS: A total of 28 patients were included: 6 had indolent SM, 9 had aggressive SM, and 13 had SM with an associated hematologic neoplasm. Twenty-five (89%) patients presented hepatomegaly, and 19 (68%) had portal hypertension. The LB frequently showed slight sinusoid dilatation (82%). Fibrosis was observed in 3/6 indolent SM and in almost all advanced SM cases (21/22), but none of them showed cirrhosis. A high MC burden (>50 MCs/high-power field) was correlated with elevated blood alkaline phosphatase levels (p = .030). The presence of portal hypertension was associated with a higher mean fibrosis grade (1.6 vs. 0.8 in its absence; p = .026). In advanced SM, the presence of nodular regenerative hyperplasia (NRH) was associated with decreased overall survival (9.5 vs. 46.3 months, p = .002). CONCLUSIONS: MC infiltration induced polymorphic hepatic lesions and the degree of fibrosis is associated with portal hypertension. NRH identifies a poor prognosis subgroup of patients with advanced SM. Assessing liver histology can aid in SM prognostic evaluation.

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.

OBJECTIVE: Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. METHOD: We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. RESULTS: The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. CONCLUSION: We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

Vesico-ureteral reflux diagnosis after initial kidney abscess: Results from a Paediatric Tertiary Hospital.

AIMS: Guidelines regarding voiding cystourethrogram (VCUG) indications following a paediatric kidney abscess are lacking. This study evaluates vesicoureteral reflux (VUR) prevalence and outcome after a first kidney abscess. METHODS: This retrospective study included all children presenting to a tertiary paediatric reference centre with de-novo kidney abscesses from 2011 to 2022, diagnosed through imaging (ultrasonography or computed tomography). VCUG's clinical utility was assessed by exploring outcomes related to interventions. RESULTS: Among the 17 patients (median age 9 months, IQR; 6 months-6 years), VCUG identified VUR in 7 (41%; 95% CI: 18-65%), including two with grade IV-V. Median abscess size was 19 mm (IQR; 14-27). 7/8 (88%) children with DMSA scan presented scars, including 4 with hypofunctioning (20%-44%), and one with a non-functioning kidney. Scarring on the DMSA scan was similar regardless of identified VUR. Six children had subsequent pyelonephritis. Three of the remaining 11 had grade I-III and two IV-V VUR. Surgery was required in four children overall: three for recurrent pyelonephritis and one for high-grade VUR and scars. CONCLUSION: Among initial kidney abscess cases, 41% had VUR, similar to children experiencing their first uncomplicated pyelonephritis. VCUG results guided antibiotic prophylaxis but not surgical decisions. We suggest considering VCUG following recurrent pyelonephritis/kidney abscess and/or kidney scarring.

Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.

BACKGROUND: There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients' care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. METHODS: Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. RESULTS: A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as "expert-level". Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. CONCLUSION: Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

Humanitarian Facial Recognition for Rare Craniofacial Malformations.

Children with congenital disorders are unfortunate collateral victims of wars and natural disasters. Improved diagnosis could help organize targeted medical support campaigns. Patient identification is a key issue in the management of life-threatening conditions in extreme situations, such as in oncology or for diabetes, and can be challenging when diagnosis requires biological or radiological investigations. Dysmorphology is a central element of diagnosis for craniofacial malformations, with high sensibility and specificity. Massive amounts of public data, including facial pictures circulate daily on news channels and social media, offering unique possibilities for automatic diagnosis based on facial recognition. Furthermore, AI-based algorithms assessing facial features are currently being developed to decrease diagnostic delays. Here, as a case study, we used a facial recognition algorithm trained on a large photographic database to assess an online picture of a family of refugees. Our aim was to evaluate the relevance of using an academic tool on a journalistic picture and discuss its potential application to large-scale screening in humanitarian perspectives. This group picture featured one child with signs of Apert syndrome, a rare condition with risks of severe complications in cases of delayed management. We report the successful automatic screening of Apert syndrome on this low-resolution picture, suggesting that AI-based facial recognition could be used on public data in crisis conditions to localize at-risk patients.

Inflammatory rebound and postinfectious inflammatory response in children with pleural infection: A single-center retrospective study.

INTRODUCTION: As pleural inflammation plays a central role in pleural infection (PI), corticosteroids are increasingly being considered as a potential therapy. However, the timing of treatment and the identification of patients who might benefit most remain unresolved. The aim of this study was therefore to investigate the inflammatory trajectories of children with PI. METHODS: This retrospective single-center study included children aged 3 months to 17 years and 11 months hospitalized for PI due to Streptococcus pyogenes, Streptococcus pneumonia, and Staphylococcus aureus over 10 years. An inflammatory rebound was defined biologically as a reincrease in C-reactive protein (CRP) of at least 50 mg/L after an initial decrease in CRP of at least 50 mg/L. RESULTS: We included 53 cases of PI, including 16 due to S. pyogenes, 27 due to S. pneumonia, and 10 due to S. aureus. An inflammatory rebound occurred in 20 patients (38%) after a median of 4.5 (3-6) days. This inflammatory rebound occurred in 9 (56%) children with S. pyogenes, 8 (30%) children with S. pneumonia, and 3 (30%) children with S. aureus. Children with an inflammatory rebound also had a higher rate of persistent fever after Day 7 and a longer length of stay (p = .01 for both). CONCLUSION: We postulate that the inflammatory rebound identified in nearly 40% of our patients corresponds to an early postinfectious inflammatory response, and thus that corticosteroids may be most beneficial for children with PI if administered early (between Days 2 and 5).

An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse.

Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification. To enhance interpretability, discriminant phenotypes characterizing each cluster are provided. Users can visually test hypotheses by marking patients exhibiting specific keywords in the EHR like genes, drugs and procedures. Implemented through a web interface, the pipeline enables clinicians to navigate through different modules, discover intricate patterns and generate interpretable insights that may advance rare diseases understanding, guide decision-making, and ultimately improve patient outcomes.

Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.

BACKGROUND: Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). METHODS AND RESULTS: We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. CONCLUSIONS: Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.

Charting a course for global progress in PIDs by 2030 - proceedings from the IPOPI global multi-stakeholders' summit (September 2023).

The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its second Global Multi-Stakeholders' Summit, an annual stimulating and forward-thinking meeting uniting experts to anticipate pivotal upcoming challenges and opportunities in the field of primary immunodeficiency (PID). The 2023 summit focused on three key identified discussion points: (i) How can immunoglobulin (Ig) therapy meet future personalized patient needs? (ii) Pandemic preparedness: what's next for public health and potential challenges for the PID community? (iii) Diagnosing PIDs in 2030: what needs to happen to diagnose better and to diagnose more? Clinician-Scientists, patient representatives and other stakeholders explored avenues to improve Ig therapy through mechanistic insights and tailored Ig preparations/products according to patient-specific needs and local exposure to infectious agents, amongst others. Urgency for pandemic preparedness was discussed, as was the threat of shortage of antibiotics and increasing antimicrobial resistance, emphasizing the need for representation of PID patients and other vulnerable populations throughout crisis and care management. Discussion also covered the complexities of PID diagnosis, addressing issues such as global diagnostic disparities, the integration of patient-reported outcome measures, and the potential of artificial intelligence to increase PID diagnosis rates and to enhance diagnostic precision. These proceedings outline the outcomes and recommendations arising from the 2023 IPOPI Global Multi-Stakeholders' Summit, offering valuable insights to inform future strategies in PID management and care. Integral to this initiative is its role in fostering collaborative efforts among stakeholders to prepare for the multiple challenges facing the global PID community.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.