Familial Hypercholesterolemia Identification Algorithm in Patients with Acute Cardiovascular Events in A Large Hospital Electronic Database in Bulgaria: A Call for Implementation

Abstract

Background: Familial hypercholesterolemia (FH) is a genetic disorder characterized by a high level of low-density lipoprotein cholesterol (LDL-C) and is an important cause for premature cardiovascular disease. Because of underdiagnoses, an acute event is often the first clinical manifestation of FH. There are limited data on the prevalence and treatment of FH among adults admitted for treatment of acute cardiovascular events in Bulgaria. Our objective was to assess the proportion and management of FH patients from those admitted to hospital for treatment of acute symptomatic acute atherosclerotic cardiovascular events (ASCVD), the achievement of LDL-C targets of European Society of Cardiology/European Atherosclerosis Society guidelines and related public healthcare resources. Objective: Digitalized healthcare records for patients admitted for treatment of symptomatic ASCVD acute events between August 2018 and August 2019 were used for the analysis. Five cardiology hospitals provided data for hospitalizations, laboratory tests, and ambulatory follow-ups up to February 2020. Patients'

Acceptability, Appropriateness, and Feasibility of Automated Screening Approaches and Family Communication Methods for Identification of Familial Hypercholesterolemia: Stakeholder Engagement Results from the IMPACT-FH Study

“…Healthcare systems have pivoted to investing in automated methods to assist with many healthcare tasks, including identifying undiagnosed individuals for a variety of health conditions [ 9 , 10 , 17 , 18 ]. In particular, automated screening approaches to flag individuals who require a diagnostic evaluation for FH using machine learning, or other data mining approaches, have already been developed and tested worldwide [ 9 , 10 , 17 , 18 ]. Some believe that machine learning approaches may soon become diagnostic for FH [ 20 ] or will be able to predict the presence of a pathogenic genetic variant associated with FH [ 35 ].…”

“…Automated screening approaches have been used to predict and identify individuals who require screening for various health conditions [ 3 , 15 , 16 , 17 ]. Phenotype-based approaches use natural language processing and machine learning algorithms that utilize data from electronic health records (EHR), and health insurance claims.…”

Acceptability, Appropriateness, and Feasibility of Automated Screening Approaches and Family Communication Methods for Identification of Familial Hypercholesterolemia: Stakeholder Engagement Results from the IMPACT-FH study

“…Healthcare systems have pivoted to investing in automated methods to assist with many healthcare tasks, including identifying undiagnosed individuals for a variety of health conditions [9,10,17,18]. In particular, automated screening approaches to flag individuals who require a diagnostic evaluation for FH using machine learning, or other data mining approaches, have already been developed and tested worldwide [9,10,17,18]. Some believe that machine learning approaches may soon become diagnostic for FH [20], or will be able to predict the presence of a pathogenic genetic variant associated with FH [35].…”

“…Automated screening approaches have been used to predict and identify individuals that require screening for various health conditions [3,[15][16][17]. Phenotype-based approaches use natural language processing and machine learning algorithms that utilize data from electronic health records (EHR), and health insurance claims.…”