The neutrophil-lymphocyte ratio to predict poor prognosis of critical acute myocardial infarction patients: a retrospective cohort study

IntroductionInflammation is closely related to adverse outcomes of acute myocardial infarction (AMI). This study aimed to evaluate whether neutrophil-lymphocyte ratio (NLR) can predict poor prognosis of critical AMI patients. Materials and methodsWe designed a retrospective cohort study and extracted AMI patients from the “Medical Information Mart for Intensive Care-III” database. The primary outcome was 1-year all-cause mortality. The secondary outcomes were 90-day and in-hospital all-cause mortalities, and acute kidney injury (AKI) incidence. The optimal cut-offs of NLR were picked by X-tile software according to the 1-year mortality and patient groups were created: low-NLR ( 21.1). Cox and modified Poisson regression models were used to evaluate the effect of NLR on outcomes in critically AMI patients. ResultsFinally, 782 critical AMI patients were enrolled in this study, and the 1-year mortality was 32% (249/782). The high- and very high-NLR groups had a higher incidence of outcomes than the low-NLR group (P < 0.05). The multivariate regression analyses found that the high- and very high-NLR groups had a higher risk of 1-year mortality (Hazard ratio (HR) = 1.59, 95% CI: 1.12 to 2.24, P = 0.009 and HR = 1.73, 95% CI: 1.09 to 2.73, P = 0.020), 90-day mortality (HR = 1.69, 95% CI: 1.13 to 2.54, P = 0.011 and HR = 1.90, 95% CI: 1.13 to 3.20, P = 0.016), in-hospital mortality (Relative risk (RR) = 1.77, 95% CI: 1.14 to 2.74, P = 0.010 and RR = 2.10, 95% CI: 1.23 to 3.58, P = 0.007), and AKI incidence (RR = 1.44, 95% CI: 1.06 to 1.95, P = 0.018 and RR = 1.34, 95% CI: 0.87 to 2.07, P = 0.180) compared with low-NLR group. NLR retained stable predictive ability in sensitivity analyses. ConclusionBaseline NLR is an independent risk factor for 1-year mortality, 90-day mortality, in-hospital mortality, and AKI incidence in AMI patients

Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset. Document type: Articl