Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research. Objective: This study aimed to transform primary care data into the OMOP CDM format. Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard. Results: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data. Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

BACKGROUND: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

BACKGROUND: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Evaluating the safety of high-intensity focused ultrasound treatment for rectal endometriosis: results from a French prospective multicentre study including 60 patients.

STUDY QUESTION: Is increasing the intensity of high-intensity focused ultrasound (HIFU) by 30% in the treatment of rectal endometriosis a safe procedure? SUMMARY ANSWER: This study demonstrates the safety of a 30% increase in the intensity of HIFU in the treatment of rectal endometriosis, with no Clavien-Dindo Grade III complications overall, and namely no rectovaginal fistulae. WHAT IS KNOWN ALREADY: A feasibility study including 20 patients with rectal endometriosis demonstrated, with no severe complications, a significant improvement in digestive disorders, dysmenorrhoea, dyspareunia, and health status, although the volume of the endometriosis nodule did not appear to be reduced. STUDY DESIGN, SIZE, DURATION: A prospective multicentre cohort study was conducted between 2020 and 2022 with 60 patients with symptomatic rectal endometriosis. Following the failure of medical treatment, HIFU treatment was offered as an alternative to surgery. PARTICIPANTS/MATERIALS, SETTING, METHODS: As the main objective of this study was to examine safety, all adverse events observed during the 6 months of follow-up were analysed and graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) and Clavien-Dindo classifications. Secondary objectives included evaluating the evolution of symptoms using validated questionnaires: gynaecological and digestive pain symptoms with a visual analogue scale, health status with the Medical Outcomes Study 36-item Short Form (SF-36) questionnaire, average post-operative daily pain level, and analgesic medication required in the 10 days following treatment. MRI was also performed at Day 1 to detect early complications. Finally, we performed a blinded MRI review of the evolution of the nodule at 6 months post-treatment. MAIN RESULTS AND THE ROLE OF CHANCE: The procedure was performed under spinal anaesthesia for 30% of the patients. The median duration of treatment was 32 min. Fifty-five patients left the hospital on Day 1. MRI scans performed on Day 1 did not highlight any early-onset post-operative complication. Using the Clavien-Dindo classification, we listed 56.7% Grade I events, 3.4% Grade II events, and no events Grade III or higher. At 1, 3, and 6 months, all gynaecologic, digestive and general symptoms, as well as health status, had significantly improved. The evolution of the nodule was also significant (P < 0.001) with a 28% decrease in volume. LIMITATIONS, REASONS FOR CAUTION: The main objective was safety and not effectiveness. The study was not randomized and there was no control group. WIDER IMPLICATIONS OF THE FINDINGS: HIFU treatment for rectal endometriosis results in an improvement of symptoms with low morbidity; as such, for selected patients, it could be a valuable alternative to surgical approaches following the failure of medical treatment. STUDY FUNDING/COMPETING INTEREST(S): The study was funded by the company EDAP TMS. Professors Dubernard and Rousset are consultants for EDAP TMS. Dubernard received travel support from EDAP-TMS. Dr F. Chavrier received industrial grants from EDAP-TMS. He has developed a device for generating focused ultrasonic waves with reduced treatment time. This device has been patented by EDAP-TMS. Dr Lafon received industrial grants from EDAP-TMS; he declares that EDAP-TMS provided funding directly to INSERM to support a young researcher chair in therapeutic ultrasound, which is unrelated to the current study. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov identifier NCT04494568.

Racial and ethnic disparities in clinical presentation, management, and outcomes of patients with inflammatory bowel disease: a narrative review.

Background and Objective: Inflammatory bowel disease (IBD) is a chronic condition that has been increasing in prevalence and incidence worldwide. Although, most cases are described in Caucasian populations, there has been a rise in IBD diagnosis among other populations. In this article, we will discuss the disparities in the presentation, management, medical and surgical outcomes of IBD patients among different racial and ethnic groups. Methods: A literature search was conducted in PubMed, Medline, and Google Scholar. The search strategy included targeted keywords to identify specific studies that provided the current literature on disparities in IBD presentation and management. Articles for presentation were selected by the authors, in accordance with a narrative review format, favoring population-based studies, systematic reviews and meta-analysis over single or multicenter reports. Key Content and Findings: Epidemiological data has shown that there is an increasing incidence in IBD diagnosis among Black, Asian, and Hispanic populations over the past decade. Differences in genetic predispositions have been observed, however it is difficult to ascertain if the minor differences in presentation and medical/surgical management reported are due to innate differences or due to confounding factors such as access to health care. Conclusions: Differences in genetic predisposition, and clinical presentation have been observed to exist among IBD non-Caucasian populations. There were also differences observed in both surgical and medical management, but it is difficult to ascertain if these were innate differences or due to societal factors.

Catastrophic complications following microvascular free tissue transfer: A 10-year review of NSQIP data.

INTRODUCTION: There is an absence of literature regarding the risks of catastrophic medical outcomes (CMOs) such as stroke, cardiac arrest, and pulmonary embolism in microvascular free tissue transfer. This study aims to determine the CMO and mortality rates, as well as risk factors, associated with microvascular reconstruction of the head and neck, extremity, and breast. METHODS: This study uses data from the American College of Surgeons National Surgical Quality Improvement Program. Cases of microvascular free tissue transfer from 2012 to 2021 were analyzed to assess the 30-day rates of CMOs, including death, as well as associated risk factors. RESULTS: Of the 22,839 included patients, 785 (3.44%) experienced 1043 CMOs, including 99 (0.43%) deaths. Pulmonary complications of prolonged respiratory failure and pulmonary embolism were the most common. Independent risk factors included age, male sex, underweight status, longer operation times, American Society of Anesthesiologists (ASA) class of III or above, wound classification other than clean, and underlying conditions such as diabetes, hypertension, chronic obstructive pulmonary disorder, dyspnea, metastatic cancer, and ventilator dependence. CMOs were associated with an average 10-day delay in hospital discharge. Multivariate regression analysis revealed that head and neck reconstructions were associated with increased risk of CMO (OR 4.96; p < 0.0001). CONCLUSION: This is the largest study to examine CMOs following microvascular free tissue transfer. Compared to previous literature spanning the period between 2006 and 2011, we observed a decreased rate of CMOs but a slight increase in 30-day mortality. Our data provide updated and comprehensive criteria for risk stratification and patient counseling. The modifiable risk factors reported in our study should be considered in elective, non-urgent cases of microvascular reconstruction.

Evaluating MedDRA-to-ICD terminology mappings.

BACKGROUND: In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. RESULTS: The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. CONCLUSION: Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.

Associations between the stanford integrated psychosocial assessment for transplant and one-year lung transplant medical and psychosocial outcomes.

The Stanford Integrated Psychosocial Assessment for Transplant (SIPAT) is a standardized measure of the psychosocial risk profile of solid organ transplant candidates. While studies have found associations between this measure and transplant outcomes, to date this has not been examined in lung transplant recipients. We examined relations between pre-transplant SIPAT scores and 1-year lung transplant medical and psychosocial outcomes in a sample of 45 lung transplant recipients. The SIPAT was significantly associated with 6-minute walk test (χ2(1) = 6.47, p = .010), number of readmissions (χ2(1) = 6.47, p = .011), and mental health services utilization (χ2(1) = 18.15, p < .001). It was not a significantly associated with the presence of organ rejection or mortality (ps > 0.10). Results suggest that the SIPAT can help identify patients who are at an elevated risk for transplant complications and thus would benefit from services to mitigate risk factors and improve outcomes.

Poor subjective sleep reported by people living with HIV is associated with impaired working memory.

Poor sleep can undermine health and may be especially disruptive to those with chronic conditions including HIV infection. Here, clinically well-described people living with HIV [PLWH] (74 men, 35 women) and healthy control (38 men, 35 women) participants were administered the Pittsburgh Sleep Quality Index (PSQI), a validated measure of subjective sleep with a global score ≥5 able to distinguish good from poor sleepers. In addition, participants completed a battery of neuropsychological tests. PLWH (6.8 ± 3.7) had higher global PSQI scores than healthy controls (4.1 ± 2.8): 39.7 % of uninfected controls and 68.8 % of PLWH had a PSQI≥5 indicative of poor sleep. There were no relations between the global PSQI score and any evaluated variables among uninfected individuals or with demographic or HIV-related variables in PLWH. Instead, a higher global PSQI score among PLWH was associated with worse "Quality of Life" scores [Global Assessment of Functioning (GAF, p=0.0007), Medical Outcomes Study survey (21-item short form, SF-21, p<0.0001), and Activities of Daily Living-Instrumental (ADL-I, p=0.0041)] and higher Beck Depression Index (BDI, p<0.0001) depressive symptoms. Further, in PLWH, higher global PSQI scores were associated with poor performance on a working memory task, the digit backward span (p=0.0036). In PLWH, the 5 variables together explained 32.3 % of the global PSQI score variance; only 3 variables - the SF-21, BDI, and digit backward scores - explained 30.6 % of the variance. To the extent that poor subjective sleep contributes to impaired working memory in HIV, we speculate that this impairment may be ameliorated by improved sleep health.

Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study.

Background: National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP CDM. Objective: This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based research. Methods: To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health Data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP CDM. Results: Our vocabulary preparation approach was able to map 3 missing German vocabularies to standard concepts and prepare 8 vocabularies as new 2-billion concepts. The completeness evaluation showed that the prepared vocabularies cover 44.3% (3288/7417) of the source codes contained in German claims data. The correctness evaluation revealed that the specified validity periods in the OMOP CDM are compliant for the majority (705,531/706,032, 99.9%) of source codes and associated dates in German claims data. The calculation of the vocabulary coverage showed a noticeable decrease of missing vocabularies from 55% (11/20) to 10% (2/20) due to our preparation approach. Conclusions: By preparing 10 vocabularies, we showed that our approach is applicable to any type of vocabulary used in a source data set. The prepared vocabularies are currently limited to German vocabularies, which can only be used in national OMOP CDM research projects, because the mapping of new 2-billion concepts to standard concepts is missing. To participate in international OHDSI network studies with German claims data, future work is required to map the prepared 2-billion concepts to standard concepts.

An Extract-Transform-Load Process Design for the Incremental Loading of German Real-World Data Based on FHIR and OMOP CDM: Algorithm Development and Validation.

Background: In the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium, an IT-based clinical trial recruitment support system was developed based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Currently, OMOP CDM is populated with German Fast Healthcare Interoperability Resources (FHIR) using an Extract-Transform-Load (ETL) process, which was designed as a bulk load. However, the computational effort that comes with an everyday full load is not efficient for daily recruitment. Objective: The aim of this study is to extend our existing ETL process with the option of incremental loading to efficiently support daily updated data. Methods: Based on our existing bulk ETL process, we performed an analysis to determine the requirements of incremental loading. Furthermore, a literature review was conducted to identify adaptable approaches. Based on this, we implemented three methods to integrate incremental loading into our ETL process. Lastly, a test suite was defined to evaluate the incremental loading for data correctness and performance compared to bulk loading. Results: The resulting ETL process supports bulk and incremental loading. Performance tests show that the incremental load took 87.5% less execution time than the bulk load (2.12 min compared to 17.07 min) related to changes of 1 day, while no data differences occurred in OMOP CDM. Conclusions: Since incremental loading is more efficient than a daily bulk load and both loading options result in the same amount of data, we recommend using bulk load for an initial load and switching to incremental load for daily updates. The resulting incremental ETL logic can be applied internationally since it is not restricted to German FHIR profiles.

Methods Used in the Development of Common Data Models for Health Data: Scoping Review.

BACKGROUND: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems. OBJECTIVE: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. RESULTS: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. CONCLUSIONS: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

Caregiver Quality of Life During Pediatric Influenza-Like Illness: A Cross-Sectional Study During the COVID-19 Pandemic.

The objective of this study was to quantify the quality of life (QoL) of caregivers with children with influenza-like illnesses (ILI) and to identify factors associated with worse QoL. This was a cross-sectional cohort study of caregivers in a pediatric emergency department with previously healthy young children with ILI. The primary outcome was caregiver QoL. Additional measures included health literacy, social support, and caregiver health status. Two hundred and eighty-one caregivers completed the study. And 41% reported overall QoL was worse during their child's illness. The median QoL score was 3.8 [3.1, 4.6] in a 7-point scale. Illness duration was associated with worse overall QoL score (0.128 worse for each additional day of illness). The median emotions domain score was 2.5 [1.5, 4.0], the worst of any domain. Caregivers who perceived worse illness severity had lower emotions domain scores (2.61 vs 6.00, P = .0269). Caregivers with adequate literacy had lower mean QoL scores (3.08 vs 4.44, P < .0001). Childhood illnesses worsen caregiver QoL. Factors associated with worse QoL were perception of illness severity and duration. Addressing caregiver QoL could mitigate the impact of childhood acute illnesses on caregiver wellbeing.

Building the observational medical outcomes partnership's T-MSIS Analytic File common data model.

Objectives: This effort used Databricks to create an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methods: Our process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. Results: The final CDM contained 119,048,562 individuals and 24,806,828,121 clinical observations from 2014 through 2018. Discussion: The transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. Conclusion: Our effort successfully used Databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies.

A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools.

COVID-19 highlighted the need for use of real-world data (RWD) in critical care as a near real-time resource for clinical, research, and policy efforts. Analysis of RWD is gaining momentum and can generate important evidence for policy makers and regulators. Extracting high quality RWD from electronic health records (EHRs) requires sophisticated infrastructure and dedicated resources. We sought to customize freely available public tools, supporting all phases of data harmonization, from data quality assessments to de-identification procedures, and generation of robust, data science ready RWD from EHRs. These data are made available to clinicians and researchers through CURE ID, a free platform which facilitates access to case reports of challenging clinical cases and repurposed treatments hosted by the National Center for Advancing Translational Sciences/National Institutes of Health in partnership with the Food and Drug Administration. This commentary describes the partnership, rationale, process, use case, impact in critical care, and future directions for this collaborative effort.

An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology.

BACKGROUND: To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. OBJECTIVE: The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. METHODS: First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. RESULTS: Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. CONCLUSIONS: This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.

Novel Biomarkers, Including tcdB PCR Cycle Threshold, for Predicting Recurrent Clostridioides difficile Infection.

Traditional clinical models for predicting recurrent Clostridioides difficile infection do not perform well, likely owing to the complex host-pathogen interactions involved. Accurate risk stratification using novel biomarkers could help prevent recurrence by improving underutilization of effective therapies (i.e., fecal transplant, fidaxomicin, bezlotoxumab). We used a biorepository of 257 hospitalized patients with 24 features collected at diagnosis, including 17 plasma cytokines, total/neutralizing anti-toxin B IgG, stool toxins, and PCR cycle threshold (CT) (a proxy for stool organism burden). The best set of predictors for recurrent infection was selected by Bayesian model averaging for inclusion in a final Bayesian logistic regression model. We then used a large PCR-only data set to confirm the finding that PCR CT predicts recurrence-free survival using Cox proportional hazards regression. The top model-averaged features were (probabilities of >0.05, greatest to least): interleukin 6 (IL-6), PCR CT, endothelial growth factor, IL-8, eotaxin, IL-10, hepatocyte growth factor, and IL-4. The accuracy of the final model was 0.88. Among 1,660 cases with PCR-only data, cycle threshold was significantly associated with recurrence-free survival (hazard ratio, 0.95; P < 0.005). Certain biomarkers associated with C. difficile infection severity were especially important for predicting recurrence; PCR CT and markers of type 2 immunity (endothelial growth factor [EGF], eotaxin) emerged as positive predictors of recurrence, while type 17 immune markers (IL-6, IL-8) were negative predictors. In addition to novel serum biomarkers (particularly, IL-6, EGF, and IL-8), the readily available PCR CT may be critical to augment underperforming clinical models for C. difficile recurrence.

Influenza in Liver and Kidney Transplant Recipients: Incidence and Outcomes.

Influenza is a common respiratory tract infection in solid organ transplant (SOT) recipients. We aimed to investigate the incidence, risk factors, and complications of influenza in a large cohort of kidney and liver transplant recipients over 10 consecutive seasons. We conducted a retrospective study, including 378 liver and 683 kidney transplant recipients who were transplanted from January 1, 2010, to October 1, 2019. The data on influenza were retrieved from MiBa, which is a nationwide database that contains all of the microbiology results in Denmark. Clinical data were retrieved from patient records. Incidence rates and cumulative incidences were calculated, and risk factors were investigated using time-updated Cox proportional hazards models. The cumulative incidence of influenza in the first 5 years posttransplantation was 6.3% (95% CI: 4.7 to 7.9%). Of the 84 influenza positive recipients, 63.1% had influenza A, 65.5% were treated with oseltamivir, 65.5% were hospitalized, and 16.7% developed pneumonia. There were no significant differences in outcomes when comparing patients with influenza A and B. We found no significant effect of same-season influenza vaccination, sex, age, or comorbidities on the risk of acquiring influenza. The incidence of influenza in kidney and liver recipients is high, and 65.5% of infected transplant recipients required hospitalization. We were not able to confirm a reduction in influenza incidence or in the risk of complications associated with vaccination. IMPORTANCE Influenza is a common respiratory virus in solid organ transplant recipients that may have severe complications, including pneumonia and hospitalization. This study investigates the incidence, risk factors, and complications of influenza in a Danish cohort of kidney and liver transplant recipients over 10 consecutive influenza seasons. The study shows a high incidence of influenza and a high frequency of both pneumonia and hospitalization. This emphasizes the importance of continuous focus on influenza in this vulnerable group. During the COVID-19 pandemic, the incidence of influenza has been low due to COVID-related restrictions, and immunity may have waned. However, as most countries have now opened up, the incidence of influenza is expected to be high this season.

Fracture liaison services combined with online home nursing care in fragility hip fracture: a comparative effectiveness pilot study.

Fracture liaison service (FLS) is a management system for osteoporotic fractures, its difficulty lies in long-term management. Through this pilot single-center study, we found that FLS combined with an internet-based follow-up service (online home nursing care) can economically and conveniently monitor patients, reduce falls and refractures, and improve care and medication adherence. INTRODUCTION: Among potential e-health platforms, mobile internet encompasses the largest user group of mobile instant messaging software in Asia and offers strong interaction, low cost, and fast speed. The online home nursing care model prevents unnecessary hospital admissions and readmissions. This study aims to explore the effects of a fracture liaison service (FLS) model combined with online home nursing care on patients with fragility hip fracture. METHODS: Patients discharged after November 2020 received FLS care combined with online home nursing care. Patients discharged from May 2020 to November 2020 received only routine discharge guidance and were classified as the control group. The Parker Mobility Score (PMS), Medical Outcomes Study 36-item short-form health survey (MOS SF-36), general medication adherence scale (GMAS), complication rate, and fall/refracture rates were used to evaluate the efficacy of the FLS combined with online home nursing care during the 52-week follow-up period. RESULTS: Eighty-nine patients with complete follow-up information were included in the analysis at the 52-week follow-up. The FLS combined with online home nursing care was associated with improved osteoporosis patient care, including increased medication adherence (64.58% in the control group and 90.24% in the observation group), improved mental quality of life, reduced fall/refracture rate (12.5% and 4.88%, respectively), and reduced rates of bedsores and joint stiffness; however, there was no effect on functional recovery within 1 year. CONCLUSIONS: We recommend the combination of FLS with online home nursing care, considering the local environment, to economically and conveniently monitor patients, reduce falls and refractures, and improve care and medication adherence.

Patient-reported outcomes with direct-acting antiviral treatment for hepatitis C in West and Central Africa (TAC ANRS 12311 trial).

Background & Aims: Patient-reported outcomes (PROs) are poorly documented for patients with chronic hepatitis C on direct-acting antiviral (DAA) treatment in low-to-middle-income countries. We documented PROs during and after DAA treatment in participants of the TAC ANRS 12311 trial (West and Central Africa). Methods: Trial participants received a 12-week regimen containing either sofosbuvir plus ribavirin (HCV genotype 2, n = 40), or sofosbuvir plus ledipasvir (HCV genotypes 1 and 4, n = 80). Health-related quality of life (SF-12), fatigue (Piper Fatigue scale), and self-reported symptoms (35-symptom list) were assessed at enrolment (Week (W) 0), during treatment (W2, W4, W8 and W12) and after treatment (W24 and W36). These PROs were compared between W0 and W36 (Wilcoxon signed-rank or McNemar tests). Mixed-effects linear regression models helped identify correlates of physical and mental quality of life component summaries (PCS and MCS) in a longitudinal analysis. Results: Most PROs were significantly improved 24 weeks after treatment end (W36), without significant differences between treatment groups. For the post-treatment period, multivariable analysis showed significant increases in PCS for patients with cirrhosis and in MCS for patients in the sofosbuvir plus ribavirin group. A higher number of self-reported symptoms at W0 was associated with lower PCS and MCS, older age and cirrhosis with lower PCS, and male sex and HCV cure with higher PCS. Conclusions: Sofosbuvir-based DAA therapy was associated with a significant improvement in PROs 6 months after treatment end in patients with chronic HCV infection from Central and West Africa. These findings may guide HCV treatment providers in low-to-middle-income countries to deliver pre-treatment information concerning the benefits of DAAs beyond viral eradication. ClinicalTrialsgov Identifier: NCT02405013. Impact and implications: Perceptions and experiences (i.e. "patient-reported outcomes") of patients with chronic hepatitis C receiving direct-acting antivirals (DAAs) are poorly documented in the African setting. This study shows significant improvements in health-related quality of life, fatigue, and self-reported symptoms 24 weeks after the end of a 12-week sofosbuvir-based DAA regimen in 120 patients from Central and West Africa. These findings substantially add to the body of knowledge about DAA therapy in the African setting. Treatment providers should be encouraged to inform patients of the benefits of DAAs beyond viral eradication, to increase treatment adherence and retention in care.