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001-es BibID:	BIBFORM121962
035-os BibID:	(Scopus)85197174794 (WoS)001256023400001
Első szerző:	Márk László (belgyógyász, kardiológus)
Cím:	The Evaluation of Lipid-Lowering Treatment in Patients with Acute Coronary Syndrome in a Hungarian Invasive Centre in 2015, 2017, and during the COVID-19 Pandemic - The Comparison of the Achieved LDL-Cholesterol Values Calculated with Friedewald and Martin-Hopkins Methods / Laszlo Mark, Péter Fülöp, Hajnalka Lőrincz, Győző Dani, Krisztina Fazekas Tajtiné, Attila Thury, György Paragh
Dátum:	2024
ISSN:	2077-0383
Megjegyzések:	Abstract: Background/Objectives: Patients with acute coronary syndrome (ACS) represent a vulnerable population. We aimed to investigate serum lipid levels of patients with ACS upon admission and during one year of the COVID-19 pandemic in a rural county hospital, and compared these findings with the data of patients with ACS in 2015 and 2017. The secondary aim of this paper was the comparison of the LDL-C values calculated with the Friedewald and Martin?Hopkins methods. Methods: A retrospective analysis of lipid-lowering data of patients treated with ACS in 2015, 2017 and in a COVID-19 year (1 April 2020?31 March 2021) was performed; the patient's numbers were 454, 513 and 531, respectively. Results: In the COVID-19 period one year after the index event, only 42% of the patients had lipid values available, while these ratios were 54% and 73% in 2017 and in 2015, respectively. Using the Friedewald formula, in the COVID-19 era the median of LDL cholesterol (LDL-F) was 1.64 (1.09?2.30) mmol/L at six months and 1.60 (1.19?2.27) mmol/L at one year, respectively. These values were 1.92 (1.33?2.27) mmol/L and 1.73 (1.36?2.43) mmol/L using the Martin?Hopkins method (LDL-MH). The LDL-F yielded significantly lower values (15% lower at six months, p = 0.044; and 8% lower at one year, p = 0.014). The LDL-F reached the previous target of 1.8 mmol/L during the COVID-19 pandemic 36% at one year vs. 48% in 2017, and 37% in 2015. The recent target LDL-C level of 1.4 mmol/L was achieved in 22% of cases in the COVID-19 pandemic, 16% in 2015 and 19% in 2017. Conclusions: A significantly lower proportion of patients with ACS had available lipid tests during the COVID-19 pandemic. Besides the lower number of available samples, the proportion of achieved 1.4 mmol/L LDL-C target lipids was stable. More rigorous outpatient care in the follow-up period may help to improve the quality of lipid lowering treatments and subsequent secondary cardiovascular prevention. If direct LDL-C determination is not available, we prefer the LDL calculation with the Martin?Hopkins method.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk acute coronary syndrome COVID-19 pandemic LDL-cholesterol Friedewald formula Martin-Hopkins method
Megjelenés:	Journal of Clinical Medicine. - 13 : 12 (2024), p. 1-12. -
További szerzők:	Fülöp Péter (1974-) (belgyógyász, endokrinológus, lipidológus) Lőrincz Hajnalka (1986-) (biológus) Dani Győző Tajtiné Fazekas Krisztina Thury Attila Paragh György (1953-) (belgyógyász)
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001-es BibID:	BIBFORM102878
035-os BibID:	(Wos)000839128300001 (Scopus)85136936484
Első szerző:	Németh Ákos (gyógyszer-vegyészmérnök, közgazdász)
Cím:	Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods / Németh Ákos, Harangi Mariann, Daróczy Bálint, Juhász Lilla, Paragh György, Fülöp Péter
Dátum:	2022
ISSN:	2077-0383
Megjegyzések:	Background: There are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of the FCS score. Methods: Analyzing medical records of 1,342,124 patients, the FCS score of each patient was calculated. Based on the data of previously diagnosed FCS patients, we trained machine learning models to identify other features that may improve FCS score calculation. Results: We identified 26 patients with an FCS score of ?10. From the trained models, boosting tree models and support vector machines performed the best for patient recognition with overall AUC above 0.95, while artificial neural networks accomplished above 0.8, indicating less efficacy. We identified laboratory features that can be considered as additions to the FCS score calculation. Conclusions: The estimated prevalence of FCS was 19.4 per million in our region, which exceeds the prevalence data of other European countries. Analysis of larger regional and country-wide data might increase the number of FCS cases. Although FCS score is an excellent tool in identifying potential FCS patients, consideration of some other features may improve its accuracy.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk data mining familial chylomicronemia syndrome FCS score machine learning screening
Megjelenés:	Journal of Clinical Medicine. - 11 (2022), p. 1-14. -
További szerzők:	Harangi Mariann (1974-) (belgyógyász, endokrinológus) Daróczy Bálint (1984-) (informatikus, matematikus) Juhász Lilla (1990-) (általános orvos) Paragh György (1953-) (belgyógyász) Fülöp Péter (1974-) (belgyógyász, endokrinológus, lipidológus)
Pályázati támogatás:	GINOP-2.3.2-15-2016-00005 GINOP Bridging Fund Egyéb MTA Premium Postdoctoral Grant 2018 MTA
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