Massive data screening is a second opportunity to improve the management of patients with familial hypercholesterolemia phenotype.

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.