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Assessment of application of the new 2019 European Society of Cardiology/ European Atherosclerosis Society Guidelines for the Management of Dyslipidaemias in daily clinical practice — one center study

Patrycja Cecha Anna Chromik Ilona Piotrowska Michał Zabojszcz Magdalena Dolecka-Ślusarczyk Zbigniew Siudak
Folia Medica Cracoviensia | 2021 | Vol. 61 | No 3 | 43-54 | DOI: 10.24425/fmc.2021.138950
Keywords cardiovascular risk ESC/EAS guidelines lipid-lowering therapy PCSK9 inhibitors statins
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Abstract

Background: Cardiovascular diseases are the first cause of death globally. Hypercholester-olemia is the most important factor responsible for atherosclerotic plaque formation and increasing cardiovascular risk. Reduction of LDL-C level is the most relevant goal for reduction of cardiovascular risk.
Aims: Real life adherence to guidelines concerning statin therapy in one center study population. Methods: We analyzed data collected in the Department of Internal Diseases from September 2019 to February 2020, obtained from 238 patients hospitalized in this time period. We assessed application of the new 2019 ESC/EAS Guidelines for the Management of Dyslipidaemias in daily clinical practice and compared effectiveness of LLT according to 2016 and 2019 guidelines.
Results: Only 1 in 5 patients with dyslipideamia achieve the 2019 ESC/EAS guideline-recommended levels of LDL-C with relation to their TCVR. We noticed that 20 of patients who did not achieve proper 2019 LDL level, meet the therapy targets established in year 2016. We observed that higher patient TCVR resulted in better compliance with guidelines and ordination of proper LLT. Most patients were on monotherapy with statins.
Conclusions: It could be beneficial to start treatment with double or even triple therapy especially in group with the highest LDL-C levels.
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Authors and Affiliations

Patrycja Cecha
1
Anna Chromik
1
Ilona Piotrowska
1
Michał Zabojszcz
1
Magdalena Dolecka-Ślusarczyk
1
Zbigniew Siudak
1
  1. Collegium Medicum, Jan Kochanowski University, Kielce, Poland

Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease

Ewelina Majda-Zdancewicz Anna Potulska-Chromik Jacek Jakubowski Monika Nojszewska Anna Kostera-Pruszczyk
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137347 | DOI: 10.24425/bpasts.2021.137347
Keywords voice processing Parkinson’s disease non-linear analysis convolutional networks
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Abstract

Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
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Authors and Affiliations

Ewelina Majda-Zdancewicz
1
ORCID: ORCID
Anna Potulska-Chromik
2
ORCID: ORCID
Jacek Jakubowski
1
ORCID: ORCID
Monika Nojszewska
2
ORCID: ORCID
Anna Kostera-Pruszczyk
2
ORCID: ORCID
  1. Faculty of Electronics, Military University of Technology, ul. Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
  2. Department of Neurology, Medical University of Warsaw, ul. Banacha 1a, 02-097 Warsaw, Poland

Application of imaging techniques to objectify the Finger Tapping test used in the diagnosis of Parkinson's disease

Jacek Jakubowski Anna Potulska-Chromik Jolanta Chmielińska Monika Nojszewska Anna Kostera-Pruszczyk
Bulletin of the Polish Academy of Sciences Technical Sciences | 2023 | 71 | 2 | e144886 | DOI: 10.24425/bpasts.2023.144886
Keywords image processing medical diagnosis Parkinson’s disease finger tapping test
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Abstract

Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.
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Authors and Affiliations

Jacek Jakubowski
1
ORCID: ORCID
Anna Potulska-Chromik
2
ORCID: ORCID
Jolanta Chmielińska
1
ORCID: ORCID
Monika Nojszewska
2
ORCID: ORCID
Anna Kostera-Pruszczyk
2
ORCID: ORCID
  1. Faculty of Electronics, Military University of Technology, Warsaw, Poland
  2. Department of Neurology, Medical University of Warsaw, Warsaw, Poland

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