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Objective: To explore a new algorithm and strategy for rhythm analysis during chest compressions (CCs), and to improve the efficiency of cardiopulmonary resuscitation (CPR) by minimizing interruptions., Methods: The clinical data and ECG of patients with sudden cardiac arrest (CA) from three hospitals in China were collected with Philips MRx monitor/defibrillators. The length of each analyzed ECG segment was 23 s, the first 11.5 s was selected to contain CPR compressions, the next 5 s had no compressions, and the last 6.5 s had no requirement. Three experienced emergency doctors annotated the ECG segments without compression artifacts. A two-step analysis through CPR (ATC) algorithm was applied to the selected data. The first step was analysis during chest compressions. If a shockable rhythm was not detected, compression-free analysis followed. The results of the ATC algorithm were compared with the annotations by the physicians, to determine the sensitivity and specificity of the algorithm., Results: In total 166 CA patients were included with 100 out-of-hospital cardiac arrest (OHCA) patients and 66 in-hospital cardiac arrest (IHCA) patients. A total of 1578 ECG segments were analyzed, including 115 (7.3%) shockable rhythms, 1278 (81.0%) non-shockable rhythms, and 185 (11.7%) intermediate/unknown rhythms. The specificity of all non-shockable rhythms was 99.8% at the end of chest compressions, and 99.5% after analysis without compression artifact. 70.5% of ventricular fibrillation (VF) rhythms were detected by the end of chest compressions. After the CC-free analysis, 93.6% of VF was identified., Conclusion: The ATC algorithm achieved sensitivity of 93.6% and specificity of 99.5% after the two-step analysis, and 70.5% of the patients with shockable rhythms did not require CC-free analysis. Such an approach has the potential to substantially reduce CC interruptions when identifying shockable rhythms., (Copyright © 2019 Elsevier B.V. All rights reserved.)

This paper generalizes the structural Markov properties for undirected decomposable graphs to arbitrary ones. This helps us to exploit the conditional independence properties of joint prior laws to analyze and compare multiple graphical structures, while being able to take advantage of the common conditional independence constraints. This work provides a theoretical support for full Bayesian posterior updating about the structure of a graph using data from a certain distribution. We further investigate the ratio of graph law so as to simplify the acceptance probability of the Metropolis–Hastings sampling algorithms. [ABSTRACT FROM AUTHOR]

Spontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model's outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783–0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784–0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP. [ABSTRACT FROM AUTHOR]

Estimation of low or high conditional quantiles is called for in many applications, but commonly encountered data sparsity at the tails of distributions makes this a challenging task. We develop a Bayesian joint-quantile regression method to borrow information across tail quantiles through a linear approximation of quantile coefficients. Motivated by a working likelihood linked to the asymmetric Laplace distributions, we propose a new Bayesian estimator for high quantiles by using a delayed rejection and adaptive Metropolis and Gibbs algorithm. We demonstrate through numerical studies that the proposed estimator is generally more stable and efficient than conventional methods for estimating tail quantiles, especially at small and modest sample sizes. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the risk factors of peripherally inserted central catheter (PICC) related thrombosis (PICC-RT) in tumor patients mediated by ultrasound images under minimum variance (MV) algorithm and put forward the corresponding nursing intervention methods based on the risk factors. The smoothing algorithm, diagonal loading algorithm, and coherence factor algorithm were optimized via MV algorithm. The optimized algorithm was compared with other algorithms to analyze its image quality and image processing speed. Literature retrieval was conducted to analyze the risk factors of PICC-RT in tumor patients. Tumor patients who received PICC for chemotherapy in the hospital from June 2018 to December 2019 were selected. Patients who were accepted before the experiment were taken as the controls, and there were control group (89 cases) and observation group (91 cases). Exercise, average flow rate of axillary vein per unit time, and PICC-RT were compared between the two groups. The results showed that the optimized algorithm (MV-N) had better image resolution, image contrast, and calculation speed than other algorithms. Gender, body mass index (BMI), pathological type, clinical stage, disease history, fibrinogen (FIB), and use of anticoagulant drugs were risk factors for PICC-RT in tumor patients. The number of PICC-RT and complications in the observation group was notably lower in contrast to the control group (P < 0.05). It indicated that a novel algorithm was successfully established, which could increase the ultrasonic image quality and computing speed, and upper limb exercise could reduce the incidence of PICC-RT and its complications in tumor patients. [ABSTRACT FROM AUTHOR]