BMC Medical Informatics and Decision Making最新文献

The integration of clinical research data across various institutions faces hurdles due to differing definitions, inconsistent terminologies, and inadequate support for interoperable metadata. While biomedical ontologies offer valuable tools for structuring clinical data, they have not yet been fully utilized for creating comprehensive metadata descriptors, such as variable semantics, statistical summaries, and governance elements essential for data discovery and alignment. We present the Clinical Metadata Exploration Ontology (CMEO) that builds upon well-established ontologies to provide a cohesive representation of study designs, data elements, exploratory statistics, and data reuse permissions. CMEO facilitates semantic querying for study exploration and comparison of data elements across studies, particularly when individual-level data cannot be shared. We demonstrate its utility using metadata from five studies: four heart-failure studies and one wearable-based type 1 diabetes study. After serializing, we executed SPARQL queries that operationalized study-level discovery, variable alignment across studies, and governance-constrained reuse. This FAIR-compliant, metadata-driven integration across heterogeneous sources enables scalable, privacy-conscious research and underpins federated clinical data exploration.

Background: Chronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may offer new opportunities for early CKD detection.

Methods: We enrolled 66,587 outpatients with estimated glomerular filtration rate (eGFR) data from January 2010 to October 2020. A total of 72,618 ECGs from 49,632 patients were used to develop DLMs. Internal validation was performed on 16,955 nonoverlapping patients, and external validation involved 10,476 patients from a community hospital. The primary outcome was the detection of CKD, defined as eGFR < 60 mL/min/1.73 m². Secondary outcomes included all-cause mortality and major cardiovascular events.

Results: The DLM achieved an AUC of 0.885 and 0.861 in the internal and external validation sets, respectively. Patients flagged by the DLM as having CKD showed more clinical risk factors for CKD progression and cardiovascular disease. Among patients without baseline CKD, those with a positive DLM screen had a significantly higher risk of incident CKD (hazard ratios 2.14 and 1.38; 95% CIs: 1.76-2.60 and 1.09-1.74). DLM stratification also predicted adverse outcomes such as stroke, heart failure, and atrial fibrillation more effectively than eGFR classification alone.

Conclusion: An ECG-based deep learning model can help identify individuals at risk for CKD and its complications, even before laboratory abnormalities emerge. This approach may support early detection and risk stratification in clinical practice.

Clinical trial number: Not applicable.

Introduction: Epidemiological data on rare diseases (RDs) affect the accurate scientific assessment of these diseases and lead to many issues in policy-making, healthcare systems, and legislation. The coding system is crucial for accurately identifying and calculating the incidence rates of each RD. This study focuses on the effectiveness of collecting RD data via the ICD-11 and examines whether the ICD-11 can fully support RD statistics. The findings of this study should provide a foundation for replacing the ICD-10 with the ICD-11.

Methods: This study included 121 RDs from the first "Rare Disease Catalogue"in China. The diseases were recoded independently by two experts in the ICD-11 MMS. A comparative analysis was conducted on the distributions of chapters, code types, and index terms in the ICD-10 and ICD-11 MMS.

Results: This study analysed 121 rare diseases (RDs) from China's first Rare Disease Catalogue. These RDs mapped to 204 ICD-10 codes (1.4% of all codes), including 76 (37.3%) non-index terms, and to 171 ICD-11 MMS codes (0.96% of all codes). The proportion of RD codes was significantly lower in ICD-11 than in ICD-10 (0.96% vs. 1.4%, P < 0.001), indicating greater dilution of RDs in ICD-11. All ICD-11 MMS codes were indexed (100% vs. 62.7% in ICD-10, P < 0.001), and 51 ICD-11 MMS codes (29.8%, P < 0.001) provided more detailed classifications. When using the ICD-11 to code RDs for subsequent statistical analyses, it is recommended that a network system of RD index terms be established in advance.

Conclusion: The ICD-11 can replace the ICD-10 for coding RDs. However, many RD terms do not have accurate codes and must be uniquely identified with URIs in the ICD-11. To ensure the reliability of RD-related data, establishing a local RD database for reporting data via the ICD-11 in China is essential.