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Opis: Background: Air pollution with increased concentrations of fine (< 2.5 μm) particulate matter (PM2.5) increases the risk of cardiovascular morbidity and mortality. Even short-term increase of PM2.5 may help trigger ST-elevation myocardial infarction (STEMI) and heart failure (HF) in susceptible individuals, even in areas with good air quality. Purpose: To evaluate the role of PM2.5 levels ≥ 20 μg/m3 in admission acute HF in STEMI patients. Materials and Methods: In 290 STEMI patients with the leading reperfusion strategy primary percutaneous coronary intervention (PPCI), we retrospectively studied independent predictors of admission acute HF and included admission demographic and clinical data as well as ambient PM2.5 levels ≥ 20 μg/m3. We defined admission acute HF in STEMI patients as classes II–IV by Killip Kimball classification. Results: Acute admission HF was observed in 34.5% of STEMI patients. PPCI was performed in 87.1% of acute admission HF patients and in 94.7% non-HF patients (p= 0.037). Significant independent predictors of acute admission HF were prior diabetes (OR 2.440, 95% CI 1.100 to 5.400, p=0.028), admission LBBB (OR 10.190, 95% CI 1.160 to 89.360, p=0.036), prior resuscitation (OR 2.530, 95% CI 1.010 to 6.340, p=0.048), admission troponin I≥ 5μg/l (OR 3.390, 95% CI 1.740 to 6.620, p< 0.001), admission eGFR levels (0.61, 95% CI 0.52 to 0.72, p < 0.001), and levels of PM2.5 ≥ 20 μg/m3 (OR 2.140, 95% CI 1.005 to 4.560, p=0.049) one day before admission. Conclusion: Temporary short-term increase in PM2.5 levels (≥ 20 μg/m3) one day prior to admission in an area with mainly good air quality was among significant independent predictors of acute admission HF in STEMI patients.
Ključne besede: admission acute heart failure, ST-elevation myocardial infarction, air pollution, PM2.5
Objavljeno v DKUM: 14.10.2024; Ogledov: 0; Prenosov: 1
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Opis: The COVID-19 pandemic has affected the outcomes of ST-elevation myocardial infarction (STEMI) patients in most countries. We aimed to retrospectively assess the admissions, treatments, complications, and mortality of STEMI patients in the northeast of Slovenia during the first (March–May 2020) and second waves (October–December 2020) of the COVID-19 pandemic and to compare them with data from 2019 (March–October). Comparing 2019 and both waves of the COVID-19 pandemic, we observed nonsignificant differences in the number of STEMI admissions, baseline characteristics, use of primary percutaneous coronary intervention (PCI) within the first 3 (40.5%* vs. 38.7% vs. 25%*, *p = 0.074) or 6 h, TIMI III flow after primary PCI, and hospital complications, as well as significant increases in hospital heart failure (23.3% vs. 42%, p = 0.015) and mitral regurgitation in the second wave (10% vs. 26.9%, p = 0.008) of the pandemic and a nonsignificant increase in hospital mortality (8.9% vs. 9.4% vs. 13.6%) during both waves of the pandemic. We conclude that, due to the increased severity of the COVID-19 pandemic in the second wave, there were longer delays to primary PCI in STEMI patients, resulting in significantly increased hospital heart failure and non-significantly increased hospital mortality.
Ključne besede: ST-elevation myocardial infarction, COVID-19, time delay, primary percutaneous coronary intervention, heart failure, mortality, pandemic
Objavljeno v DKUM: 04.07.2024; Ogledov: 134; Prenosov: 20
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Opis: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Ključne besede: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Objavljeno v DKUM: 14.03.2024; Ogledov: 299; Prenosov: 32
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Ključne besede: COVID-19 pandemic, ST-elevation myocardial infarction, bleeding, mortality, hospital complications
Objavljeno v DKUM: 21.12.2023; Ogledov: 289; Prenosov: 18
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Ključne besede: left ventricular diastolic dysfunction
Objavljeno v DKUM: 23.01.2023; Ogledov: 539; Prenosov: 60
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