What question did this study set out to answer?

The research aims to build a high-quality, multimodal European dataset for sickle cell disease to enhance personalized medicine.

December 8, 2025BloodOpen Access

Data validation and quality framework for building a european multimodal real-world dataset for clinical outcome research in sickle cell disease

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Data validation and quality framework improves clinical outcomes in sickle cell disease, suggesting better personalized medicine.

The research aims to build a high-quality, multimodal European dataset for sickle cell disease to enhance personalized medicine.
Developed a data validation and quality framework following FAIR principles and EMA recommendations.
Collected clinical, genomic, and metabolomic data from 1,274 patients across 17 centers in 4 countries.
Applied strict data quality assessments based on integrity, completeness, consistency, and accuracy.
Achieved completeness of ≥80% in 75.7% of variables assessed for AI readiness.
Identified significant associations between clinical outcomes and disease markers, including red blood cell count and serum ferritin.
Validated clinical data joined with rheological, genomic, and metabolomic datasets for comprehensive analysis.

Bartolucci et al. (2025) studied this question.

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December 8, 2025BloodOpen Access

View Full Paper

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Authors

Data validation and quality framework improves clinical outcomes in sickle cell disease, suggesting better personalized medicine.

The research aims to build a high-quality, multimodal European dataset for sickle cell disease to enhance personalized medicine.
Developed a data validation and quality framework following FAIR principles and EMA recommendations.
Collected clinical, genomic, and metabolomic data from 1,274 patients across 17 centers in 4 countries.
Applied strict data quality assessments based on integrity, completeness, consistency, and accuracy.
Achieved completeness of ≥80% in 75.7% of variables assessed for AI readiness.
Identified significant associations between clinical outcomes and disease markers, including red blood cell count and serum ferritin.
Validated clinical data joined with rheological, genomic, and metabolomic datasets for comprehensive analysis.

Bartolucci et al. (2025) studied this question.

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