Clinical Effects of Hypertension on the Mortality of Patients with Acute Myocardial Infarction

The incidence of ischemic heart disease has been increased rapidly in Korea. However, the clinical effects of antecedent hypertension on acute myocardial infarction have not been identified. We assessed the relationship between antecedent hypertension and clinical outcomes in 7,784 patients with acute myocardial infarction in the Korea Acute Myocardial Infarction Registry during one-year follow-up. Diabetes mellitus, hyperlipidemia, cerebrovascular disease, heart failure, and peripheral artery disease were more prevalent in hypertensives (n=3,775) than nonhypertensives (n=4,009). During hospitalization, hypertensive patients suffered from acute renal failure, shock, and cerebrovascular event more frequently than in nonhypertensives. During follow-up of one-year, the incidence of major adverse cardiac events was higher in hypertensives. In multi-variate adjustment, old age, Killip class ≥III, left ventricular ejection fraction <45%, systolic blood pressure <90 mmHg on admission, post procedural TIMI flow grade ≤2, female sex, and history of hypertension were independent predictors for in-hospital mortality. However antecedent hypertension was not significantly associated with one-year mortality. Hypertension at the time of acute myocardial infarction is associated with an increased rate of in-hospital mortality

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death

Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms

We proposed a method of label-free segmentation of cell nuclei by exploiting a deep learning (DL) framework. Over the years, fluorescent proteins and staining agents have been widely used to identify cell nuclei. However, the use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis and even interferes with intrinsic physiological conditions. Without any agents, the proposed method was applied to label-free optical diffraction tomography (ODT) of human breast cancer cells. A novel architecture with optimized training strategies was validated through cross-modality and cross-laboratory experiments. The nucleus volumes from the DL-based label-free ODT segmentation accurately agreed with those from fluorescent-based. Furthermore, the 4D cell nucleus segmentation was successfully performed for the time-lapse ODT images. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available