Assessment of Associations Between Serum Lipoprotein (a) Levels and Atherosclerotic Vascular Diseases in Hungarian Patients With Familial Hypercholesterolemia Using Data Mining and Machine Learning.

Background and aims: Premature mortality due to atherosclerotic vascular disease is very high in Hungary in comparison with international prevalence rates, though the estimated prevalence of familial hypercholesterolemia (FH) is in line with the data of other European countries. Previous studies have shown that high lipoprotein(a)- Lp(a) levels are associated with an increased risk of atherosclerotic vascular diseases in patients with FH. We aimed to assess the associations of serum Lp(a) levels and such vascular diseases in FH using data mining methods and machine learning techniques in the Northern Great Plain region of Hungary. Methods: Medical records of 590,500 patients were included in our study. Based on the data from previously diagnosed FH patients using the Dutch Lipid Clinic Network scores (≥7 was evaluated as probable or definite FH), we trained machine learning models to identify FH patients. Results: We identified 459 patients with FH and 221 of them had data available on Lp(a). Patients with FH had significantly higher Lp(a) levels compared to non-FH subjects [236 (92.5; 698.5) vs. 167 (80.2; 431.5) mg/L, p < .01]. Also 35.3% of FH patients had Lp(a) levels >500 mg/L. Atherosclerotic complications were significantly more frequent in FH patients compared to patients without FH (46.6 vs. 13.9%). However, contrary to several other previous studies, we could not find significant associations between serum Lp(a) levels and atherosclerotic vascular diseases in the studied Hungarian FH patient group. Conclusion: The extremely high burden of vascular disease is mainly explained by the unhealthy lifestyle of our patients (i.e., high prevalence of smoking, unhealthy diet and physical inactivity resulting in obesity and hypertension). The lack of associations between serum Lp(a) levels and atherosclerotic vascular diseases in Hungarian FH patients may be due to the high prevalence of these risk factors, that mask the deleterious effect of Lp(a).

Identifying patients with familial hypercholesterolemia using data mining methods in the Northern Great Plain region of Hungary.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is one of the most frequent diseases with monogenic inheritance. Previous data indicated that the heterozygous form occurred in 1:250 people. Based on these reports, around 36,000-40,000 people are estimated to have FH in Hungary, however, there are no exact data about the frequency of the disease in our country. Therefore, we initiated a cooperation with a clinical site partner company that provides modern data mining methods, on the basis of medical and statistical records, and we applied them to two major hospitals in the Northern Great Plain region of Hungary to find patients with a possible diagnosis of FH. METHODS: Medical records of 1,342,124 patients were included in our study. From the mined data, we calculated Dutch Lipid Clinic Network (DLCN) scores for each patient and grouped them according to the criteria to assess the likelihood of the diagnosis of FH. We also calculated the mean lipid levels before the diagnosis and treatment. RESULTS: We identified 225 patients with a DLCN score of 6-8 (mean total cholesterol: 9.38 ±â€¯3.0 mmol/L, mean LDL-C: 7.61 ±â€¯2.4 mmol/L), and 11,706 patients with a DLCN score of 3-5 (mean total cholesterol: 7.34 ±â€¯1.2 mmol/L, mean LDL-C: 5.26 ±â€¯0.8 mmol/L). CONCLUSIONS: The analysis of more regional and country-wide data and more frequent measurements of total cholesterol and LDL-C levels would increase the number of FH cases discovered. Data mining seems to be ideal for filtering and screening of FH in Hungary.