sFlt-1/PIGF no Diagnóstico Preditivo de Pré-eclâmpsia: Estudo de Impacto Económico em Portugal

Overview and Aims: The last decade brought relevant insights into the pathophysiology of preeclampsia (PE), namely the role of the circulating levels of placental growth factor (PlGF) and soluble Fms-like tyrosine kinase-1 (sFlt-1). The purpose of this study is to estimate the financial impact of introducing the sFlt-1/PlGF ratio for the evaluation of women with suspicion of PE in the Portuguese National Healthcare System (SNS). Study Design: budget impact study evaluating short-term costs associated with the introduction of the sFlt-1/PlGF ratio from the SNS payer’s perspective. The time horizon for the study is 1 year. Population: The target population consists of women presenting to the healthcare system with signs or symptoms su ggestive of preeclampsia (estimated in 8500 subjects). Methods: A decision-tree model was used to estimate the budget impact of the introduction of the sFlt-1/PlGF ratio in the SNS. The model compares the management costs in the current clinical practice (“no test” scenario) vs. current diagnostic procedures plus the sFlt-1/PlGF ratio (“test” scenario). Clinical inputs have been derived primarily from literature review and, where data was unavailable, expert opinion. Resources and unit costs have been obtained from Portugal-specific sources. Results: In the current standard practice (no test), total costs were estimated to be €9 863 264 (€1160 per patient), with unnecessary admissions representing about €3,5 million. Total costs in the test scenario sum up to €9 781 194 (€1150 per patient), representing a cost saving to the system of €82 070 (€10 per patient), mainly due to a reduction of false positives and related unnecessary hospitalizations of women not developing PE. Conclusions: There is favorable economic evidence about the introduction of the sFlt- 1/PlGF ratio in the SNS. The generated savings appear to offset the costs related to the test.info:eu-repo/semantics/publishedVersio

Acute kidney injury in pregnancy: a clinical challenge

The incidence of acute kidney injury in pregnancy declined significantly over the second half of the 20th century; however, it is still associated with major maternal and perinatal morbidity and mortality. A set of systemic and renal physiological adaptive mechanisms occur during a normal gestation that will constrain several changes in laboratory parameters of renal function, electrolytes, fluid and acid-base balances. The diagnosis of acute kidney injury in pregnancy is based on the serum creatinine increase. The usual formulas for estimating glomerular filtration rate are not validated in this population. During the first trimester of gestation, acute kidney injury develops most often due to hyperemesis gravidarum or septic abortion. In the third trimester, the differential diagnosis is more challenging for the obstetrician and the nephrologist and comprises some pathologies that are reviewed in this article: preeclampsia/HELLP syndrome, acute fatty liver of pregnancy and thrombotic microangiopathies

INSTRUCTIONAL DESIGN AND ASSESSMENT An Elective Course in Adult Acute Care Medicine Using a Hybrid Delivery System

Objective. To develop and assess the effectiveness of an elective course modeled after activities students encounter on internal medicine advanced pharmacy practice experiences (APPEs). Design. This hybrid elective course used a Web-based course management system linking pre-class lectures and assignments, classroom discussions, and projects to promote active student learning. Assessment. Assessment of student performance was based on assignments, quizzes, and participation in classroom discussions. Students were surveyed to ascertain their opinion of the elective. Conclusion. This elective in adult acute care medicine increased student exposure to inpatient settings and provided students additional opportunities to communicate effectively, evaluate medical literature, and think critically

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

We agree with Ling-Yun et al. [5] and Zhang and Duan comments [2] about the typing error in equation (9) of the manuscript [8]. The correct formula was initially proposed in [6, 7]. The formula adopted in our algorithms discussed in our papers [1, 3, 4, 8] is, in fact, the following: ..

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

A sensor fusion layer to cope with reduced visibility in SLAM

Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained

Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation

A novel multiscale mathematical and computational model of the pulmonary circulation is presented and used to analyse both arterial and venous pressure and flow. This work is a major advance over previous studies by Olufsen et al. (Ann Biomed Eng 28:1281–1299, 2012) which only considered the arterial circulation. For the first three generations of vessels within the pulmonary circulation, geometry is specified from patient-specific measurements obtained using magnetic resonance imaging (MRI). Blood flow and pressure in the larger arteries and veins are predicted using a nonlinear, cross-sectional-area-averaged system of equations for a Newtonian fluid in an elastic tube. Inflow into the main pulmonary artery is obtained from MRI measurements, while pressure entering the left atrium from the main pulmonary vein is kept constant at the normal mean value of 2 mmHg. Each terminal vessel in the network of ‘large’ arteries is connected to its corresponding terminal vein via a network of vessels representing the vascular bed of smaller arteries and veins. We develop and implement an algorithm to calculate the admittance of each vascular bed, using bifurcating structured trees and recursion. The structured-tree models take into account the geometry and material properties of the ‘smaller’ arteries and veins of radii ≥ 50 μ m. We study the effects on flow and pressure associated with three classes of pulmonary hypertension expressed via stiffening of larger and smaller vessels, and vascular rarefaction. The results of simulating these pathological conditions are in agreement with clinical observations, showing that the model has potential for assisting with diagnosis and treatment for circulatory diseases within the lung