Safety and Outcomes of Peripherally Administered Vasopressor Infusion in Patients Admitted with Shock to an Intensive Cardiac Care Unit-A Single-Center Prospective Study.

BACKGROUND: Vasopressors are frequently utilized for blood pressure stabilization in patients with cardiogenic shock (CS), although with a questionable benefit. Obtaining central venous access is time consuming and may be associated with serious complications. Hence, we thought to evaluate whether the administration of vasopressors through a peripheral venous catheter (PVC) is a safe and effective alternative for the management of patients with CS presenting to the intensive cardiovascular care unit (ICCU). METHODS: A prospective single-center study was conducted to compare the safety and outcomes of vasopressors administered via a PVC vs. a central venous catheter (CVC) in patients presenting with CS over a 12-month period. RESULTS: A total of 1100 patients were included; of them, 139 (12.6%) required a vasopressor treatment due to shock, with 108 (78%) treated via a PVC and 31 (22%) treated via a CVC according to the discretion of the treating physician. The duration of the vasopressor administration was shorter in the PVC group compared with the CVC group (2.5 days vs. 4.2 days, respectively, p < 0.05). Phlebitis and the extravasation of vasopressors occurred at similar rates in the PVC and CVC groups (5.7% vs. 3.3%, respectively, p = 0.33; 0.9% vs. 3.3%, respectively, p = 0.17). Nevertheless, the bleeding rate was higher in the CVC group compared with the PVC group (3% vs. 0%, p = 0.03). CONCLUSIONS: The administration of vasopressor infusions via PVC for the management of patients with CS is feasible and safe in patients with cardiogenic shock. Further studies are needed to establish this method of treatment.

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm.

Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular KNN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel KNN variant (KNNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The KNNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three KNN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

A study of an adaptive replication framework for orchestrated composite web services.

Replication is considered one of the most important techniques to improve the Quality of Services (QoS) of published Web Services. It has achieved impressive success in managing resource sharing and usage in order to moderate the energy consumed in IT environments. For a robust and successful replication process, attention should be paid to suitable time as well as the constraints and capabilities in which the process runs. The replication process is time-consuming since outsourcing some new replicas into other hosts is lengthy. Furthermore, nowadays, most of the business processes that might be implemented over the Web are composed of multiple Web services working together in two main styles: Orchestration and Choreography. Accomplishing a replication over such business processes is another challenge due to the complexity and flexibility involved. In this paper, we present an adaptive replication framework for regular and orchestrated composite Web services. The suggested framework includes a number of components for detecting unexpected and unhappy events that might occur when consuming the original published web services including failure or overloading. It also includes a specific replication controller to manage the replication process and select the best host that would encapsulate a new replica. In addition, it includes a component for predicting the incoming load in order to decrease the time needed for outsourcing new replicas, enhancing the performance greatly. A simulation environment has been created to measure the performance of the suggested framework. The results indicate that adaptive replication with prediction scenario is the best option for enhancing the performance of the replication process in an online business environment.