Identification of inherited metabolic diseases patients at Unidade Local de Saúde São João: improving the description of institutional casuistry

Abstract

Background: Inherited metabolic diseases (IMD) are a group of disorders with complex and heterogeneous clinical manifestations. The identification of cases is hindered by coding limitations, patient scattering across specialities, and incomplete information. To address this, we assessed the total number of patients assessed and/or treated within our institution for suspected IMD and developed a systematic methodology to streamline the identification process, aiming to enhance diagnostic efficiency and patient care. Methods: A retrospective cohort study was conducted using data from electronic health records (SONHO, SClinico, jOne) and two quality management tools of the reference center in IMD: an Excel file filled in manually and a Power BI dashboard. Patients were identified based on predefined criteria, including ICD-9, ICD-10, and ORPHAcode diagnosis codes. Data were compiled into a central database, considering 15 diagnoses groups. Descriptive data analysis was used to characterize patient distribution, assess the contribution of each criterion to the final cohort, and evaluate concordance between criteria. Results: The final cohort included 2151 patients, with the reference center's dashboard contributing the most (n=1795) and ORPHAcode diagnoses the least (n=284). Most patients in the dashboard and Excel subpopulations lacked a registered coded diagnosis (n=1198 and n=164, respectively), and ICD codes were associated with having been hospitalized. Overlap analysis showed that only 179 met all four criteria, whereas 1047 patients were identified exclusively by the dashboard. When comparing subpopulations identified by each specific criterion, sex and place of residence distribution was balanced, and birth year patterns reflected coding and information systems transitions. Specialized IMD appointments were recorded for 76.9% of patients, mostly pediatric (56.2%). Conclusions: This study, which identified 2151 patients at ULSSJ with IMD or assessed for that possible diagnosis, proposes a novel, integrative methodology that enhances case identification, and provides actionable insights for optimizing healthcare strategies, digital health infrastructure, clinical records and rare disease surveillance.