By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children’s deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient’s CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient’s genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments.

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

The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.

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Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

SNIIRAM/SNDS, the French administrative health care database, covers around 99% of the population. Its main limitation is the absence of clinical information and biological results. This report exposes the value of SNIIRAM/SNDS enrichment by external databases, and the linkage issues. It is illustrated by examples: the well-known population-based cohort CONSTANCES created to answer to epidemiological research questions with a specific interest on occupational and social factors, chronic diseases, and aging; the CANARI study, a regional-based study that collected Gleason score in all pathology laboratories in Brittany and then, linked pathology results to an ad hoc extraction from SNIIRAM database; the goal was to investigate the risk of high grade prostate cancer in patients treated by 5-alpha-reductase inhibitors for a symptomatic benign prostatic hyperplasia; the SACHA study, that identified and medically validated major bleeding event referred to emergency wards, then linked those clinical data to SNIIRAM; the goal was to minimize misclassification bias when estimating bleeding risk in patients who were prescribed antithrombotic drugs; the ISO-PSY study linked the SNIIRAM with the national cause of death registry (CepiDc) and the nationwide emergency department surveillance system (OSCOUR® network) to investigate the potential link between isotretinoin and suicidal risk; the EFEMERIS cohort that assesses drugs prescriptions in French pregnant women who delivered in the Haute-Garonne region; the EPI-GETB-AM study that derived a SNIIRAM/SNDS-based algorithm to identify venous thromboembolism and linked SNIIRAM/SNDS to the EPI-GETBO-III survey for validation. Another perspective of SNDS enrichment is clinical trials' data for medico-economic assessment, and extended follow-up without attrition bias. Linkage is not straightforward. Apart from regulatory approbation and authorized data center issues, which could be solved by the Health Data Hub Initiative, a multidisciplinary team with medical, pharmacological and methodological knowledge, as well as with technical skills is essential to handle the whole process.

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01940381/document

French administrative health care database (SNDS): The value of its enrichment.

Omalizumab Therapy for Mast Cell-Mediator Symptoms in Patients with ISM, CM, MMAS, and MCAS

Background and objectives Intensified immunosuppression in steroid-resistant nephrotic syndrome is broadly applied, with disparate outcomes. This review of patients from the United Kingdom National Study of Nephrotic Syndrome cohort aimed to improve disease stratification by determining, in comprehensively genetically screened patients with steroid-resistant nephrotic syndrome, if there is an association between response to initial intensified immunosuppression and disease progression and/or post-transplant recurrence. Design, setting, participants, & measurements Pediatric patients with steroid-resistant nephrotic syndrome were recruited via the UK National Registry of Rare Kidney Diseases. All patients were whole-genome sequenced, whole-exome sequenced, or steroid-resistant nephrotic syndrome gene-panel sequenced. Complete response or partial response within 6 months of starting intensified immunosuppression was ascertained using laboratory data. Response to intensified immunosuppression and outcomes were analyzed according to genetic testing results, pattern of steroid resistance, and first biopsy findings. Results Of 271 patients, 178 (92 males, median onset age 4.7 years) received intensified immunosuppression with response available. A total of 4% of patients with monogenic disease showed complete response, compared with 25% of genetic-testing-negative patients (P=0.02). None of the former recurred post-transplantation. In genetic-testing-negative patients, 97% with complete response to first intensified immunosuppression did not progress, whereas 44% of nonresponders developed kidney failure with 73% recurrence post-transplant. Secondary steroid resistance had a higher complete response rate than primary/presumed resistance (43% versus 23%; P=0.001). The highest complete response rate in secondary steroid resistance was to rituximab (64%). Biopsy results showed no correlation with intensified immunosuppression response or outcome. Conclusions Patients with monogenic steroid-resistant nephrotic syndrome had a poor therapeutic response and no post-transplant recurrence. In genetic-testing-negative patients, there was an association between response to first intensified immunosuppression and long-term outcome. Patients with complete response rarely progressed to kidney failure, whereas nonresponders had poor kidney survival and a high post-transplant recurrence rate. Patients with secondary steroid resistance were more likely to respond, particularly to rituximab.

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Arthur Delapalme

Papers

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse.