Alberto Zamora1, Guillem Paluzie2, Joan García-Vilches3, Oriol Alonso Gisbert4, Ana Inés Méndez Martínez4, Núria Plana5, Cèlia Rodríguez-Borjabad5, Daiana Ibarretxe5, Anabel Martín-Urda6, Luis Masana5. 1. Unidad de Lípidos y Riesgo Vascular, Servicio de Medicina Interna, Hospital de Blanes, Corporació de Salut del Maresme i la Selva, Blanes, Girona, España; Grupo de Medicina Traslacional y Ciencias de la Decisión, Departamento de Ciencias Médicas, Facultad de Medicina, Universidad de Girona, Girona, España; Grupo Epidemiología Cardiovascular y Genética. CIBER, Enfermedades Cardiovasculares (CIBERCV), Barcelona, España. Electronic address: azamora@salutms.cat. 2. Unidad de Información y Documentación Asistencial, Corporació de Salut del Maresme I la Selva, Barcelona, España. 3. Departamento de Informática, Corporació de Salut del Maresme i la Selva, Barcelona, España. 4. Servicio de Medicina Interna, Hospital Sant Jaume de Calella, Corporació de Salut del Maresme i la Selva, Barcelona, España. 5. Unitat de Medicina Vascular i Metabolisme, Hospital Universitari Sant Joan de Reus, IISPV, Universitat Rovira i Virgili, CIBERDEM, Reus, España. 6. Servicio de Medicina Interna, Hospital de Palamòs, Serveis de Salut Integrats Baix Empordà, Girona, España.
Abstract
INTRODUCTION: Familial Hypercholesterolemia (FH) is an autosomal dominant disease with an estimated prevalence between 1/200-250. It is under-treated and underdiagnosed. Massive data screening can increase the detection of patients with FH. METHODS: Study population: Residents in the health coverage area (N: 195.000 inhabitants) and with at least one determination of cholesterol linked to low-density lipoproteins (LDL-C) carried out between January 1, 2010 and December 30, 2019. The highest LDL-C values were selected. EXCLUSION CRITERIA: nephrotic syndrome, hypothyroidism, Hypothyroid treatment or triglycerides> 400 mg / dL. Seven algorithms suggestive of Familial Hypercholesterolemia Phenotype (HF-P) were analyzed, selecting the most efficient algorithm that could easily be translated into clinical practice. RESULTS: Based on 6.264.877 assistances and 288.475 patients, after applying the inclusion-exclusion criteria, 504.316 tests were included, corresponding to 106.382 adults and 10.509 <18 years. The selected algorithm presented a prevalence of 0.62%. 840 patients with HF-P were detected, 55.8% being women and 178 <18 years old, 9.3% had a history of cardiovascular disease (CVD) and 16.4% had died. 65% of the patients in primary prevention had LDL-C values> 130 mg / dL and 83% in secondary prevention values> 70mg / dL. A ratio of 7.64 (1-18) patients with HF-P per analytical requesting physician was obtained. CONCLUSIONS: Massive data screening and patient profiling are effective tools and easily applicable in clinical practice for the detection of patients with FH.
INTRODUCTION: Familial Hypercholesterolemia (FH) is an autosomal dominant disease with an estimated prevalence between 1/200-250. It is under-treated and underdiagnosed. Massive data screening can increase the detection of patients with FH. METHODS: Study population: Residents in the health coverage area (N: 195.000 inhabitants) and with at least one determination of cholesterol linked to low-density lipoproteins (LDL-C) carried out between January 1, 2010 and December 30, 2019. The highest LDL-C values were selected. EXCLUSION CRITERIA: nephrotic syndrome, hypothyroidism, Hypothyroid treatment or triglycerides> 400 mg / dL. Seven algorithms suggestive of Familial Hypercholesterolemia Phenotype (HF-P) were analyzed, selecting the most efficient algorithm that could easily be translated into clinical practice. RESULTS: Based on 6.264.877 assistances and 288.475 patients, after applying the inclusion-exclusion criteria, 504.316 tests were included, corresponding to 106.382 adults and 10.509 <18 years. The selected algorithm presented a prevalence of 0.62%. 840 patients with HF-P were detected, 55.8% being women and 178 <18 years old, 9.3% had a history of cardiovascular disease (CVD) and 16.4% had died. 65% of the patients in primary prevention had LDL-C values> 130 mg / dL and 83% in secondary prevention values> 70mg / dL. A ratio of 7.64 (1-18) patients with HF-P per analytical requesting physician was obtained. CONCLUSIONS: Massive data screening and patient profiling are effective tools and easily applicable in clinical practice for the detection of patients with FH.
Keywords:
Familial Hypercholesterolemia; Hipercolesterolemia Familiar; Massive data screening; Perfilado de pacientes; Rastreo masivo de datos; profiling of patients