Risk prediction of future cardiac arrest by evaluation of a genetic risk score alone and in combination with traditional risk factors

Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

Recent developments and controversies in therapeutic hypothermia after cardiopulmonary resuscitation

Therapeutic hypothermia was recommended as the only neuroprotective treatment in comatose patients after return of spontaneous circulation (ROSC). With new evidence suggesting a similar neuroprotective effect of 36 °C and 33 °C, the term "therapeutic hypothermia" was substituted by "targeted temperature management" in 2011, which in turn was replaced by the term "temperature control" in 2022 because of new evidence of the similar effects of target normothermia and 33 °C. However, there is no clear consensus on the efficacy of therapeutic hypothermia. In this article, we provide an overview of the recent evidence from basic and clinical research related to therapeutic hypothermia and re-evaluate its application as a post-ROSC neuroprotective intervention in clinical settings.

Genetic Risk Score for Coronary Heart Disease: Review

The present review deals with the stages of creation, methods of calculation, and the use of a genetic risk score for coronary heart disease in various populations. The concept of risk factors is generally recognized on the basis of the results of epidemiological studies in the 20th century; according to this concept, the high prevalence of diseases of the circulatory system is due to lifestyle characteristics and associated risk factors. An important and relevant task for the healthcare system is to identify the population segments most susceptible to cardiovascular diseases (CVDs). The level of individual risk of an unfavorable cardiovascular prognosis is determined by genetic factors in addition to lifestyle factors.