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Abstract Purpose Adult Growth Hormone Deficiency (AGHD) is a complex and under-recognized condition, mainly managed in outpatient settings and characterized by fragmented and heterogeneous clinical data. Population registries provide high-quality evidence but require long timeframes and substantial resources. The aim of this study was to design and validate an artificial intelligence (AI)-driven methodology for the construction of a disease-specific AGHD Data Mart from routine clinical data within a single high-volume center, to support real-world evidence generation and future advanced analytics. Methods A standardized Data Science framework, based on automated extraction from hospital data warehouses and electronic medical records, integrating structured data and unstructured clinical narratives through natural language processing, was implemented. AGHD patients were identified using a combination of ICD-9 codes and text-mining applied to outpatient reports. A multidisciplinary workflow ensured clinical validation of extracted data. The Data Mart described patient identification from first hospital access (T0) and included diagnostic modality, etiology, biochemical data, comorbidities, and growth hormone replacement therapy. Results Among 210 identified patients, 188 were validated as AGHD after expert review. Diagnoses were based on dynamic testing (28.2%), panhypopituitarism with low IGF-1 (54.3%), or AI-assisted identification (17.6%). Etiology was retrieved in 87.8% of cases, with post-surgical causes being the most frequent. 37.8% of patients were receiving rhGH therapy. Specific trends for IGF-1 values for single patients were described. Conclusion This study represents the first AI-driven AGHD Data Mart and demonstrates the feasibility of constructing the Data Mart from routine clinical data. This approach offers a complementary framework for structured RWD extraction and may support future longitudinal analyses and AI-based clinical support in AGHD.
Vergani et al. (Tue,) studied this question.