How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

BACKGROUND: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. METHODS: In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. RESULTS: We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. CONCLUSION: The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Cutaneous Basal Cell Carcinoma: Does Noninvasive Ventilation Accelerate Tumor Progression?

A 56-year-old woman presented to the emergency to be department with diarrhea, asthenia, cough, and dysgeusia. The patient had chronic obstructive pulmonary disease (COPD) and was found infected with coronavirus disease 2019 (COVID-19). On physical examination, a small basal cell carcinoma (BCC) lesion was identified on her scalp; however, following the administration of noninvasive ventilation, the appearance of both macroscopic and microscopic BCC worsened dramatically. Our findings point to positive pressure noninvasive ventilation used to treat COVID-19 associated with COPD as a possible causative agent for the progression of cutaneous BCC. (SKINmed. 2022;20:463-465).

A faecal microbiota signature with high specificity for pancreatic cancer.

BACKGROUND: Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. OBJECTIVE: To explore the faecal and salivary microbiota as potential diagnostic biomarkers. METHODS: We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase. RESULTS: Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. CONCLUSION: Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

High-Intensity Focused Ultrasound for Pain Management in Patients with Cancer.

Cancer-related pain affects up to 80% of patients with malignancies. Pain is an important distressing symptom that diminishes the quality of life and negatively affects the survival of patients. Opioid analgesics are generally the primary therapy for cancer-related pain, with surgery, radiation therapy, chemotherapy, and other interventions used in cases of treatment-resistant pain. These treatments, which can be associated with substantial side effects and systemic toxicity, may not be effective. High-intensity focused ultrasound is an entirely noninvasive technique that is approved for treatment of uterine fibroids, bone metastases, and essential tremors. With magnetic resonance imaging or ultrasonographic guidance, high-intensity ultrasound waves are focused on a small well-demarcated region to result in precise localized ablation. This treatment may represent a multimodality approach to treating patients with malignant diseases-facilitating pain palliation, enhanced local drug delivery and radiation therapy effects, and stimulation of anticancer specific immune responses, and potentially facilitating local tumor control. Focused ultrasound can be used to achieve pain palliation by producing several effects, including tissue denervation, tumor mass reduction, and neuromodulation, that can influence different pathways at the origin of the pain. This technology has several key advantages compared with other analgesic therapies: It is completely noninvasive, might be used to achieve rapid pain control, can be safely repeated, and can be used in combination with chemotherapy and radiation therapy to enhance their effects. Online supplemental material is available for this article. ©RSNA, 2018.