Canadian Cardiovascular Society Guidelines for the Diagnosis and Management of Stable Ischemic Heart Disease

This overview provides a guideline for the management of stable ischemic heart disease. It represents the work of a primary and secondary panel of participants from across Canada who achieved consensus on behalf of the Canadian Cardiovascular Society. The suggestions and recommendations are intended to be of relevance to primary care and specialist physicians with an emphasis on rational deployment of diagnostic tests, expedited implementation of long- and short-term medical therapy, timely consideration of revascularization, and practical follow-up measures.     

The role of statins in the prevention of contrast induced nephropathy: a meta-analysis of 8 randomized trials

Contrast induced nephropathy (CIN) is a common complication of coronary angiography/angioplasty. Prevention is the key to reduce the incidence of CIN and it begins with appropriate pre-procedural management. Statins have been shown to possess pleiotropic effects (anti-oxidant, anti-inflammatory and anti-thrombotic properties) and their effects on CIN were assessed in several studies with conflicting results. Aim of this meta-analysis is to evaluate the efficacy of short-term statins for the prevention of CIN in patients undergoing coronary angiography/percutaneous interventions. We performed formal searches of PubMed, EMBASE, Cochrane central register of controlled trials and major international scientific session abstracts from January 1990 to January 2014 of trials which compares short-term statins versus Placebo for the prevention of CIN in patients undergoing coronary angiography/angioplasty. Data regarding study design, statin dose, inclusion/exclusion criteria, number of patients, and clinical outcome was extracted by 2 investigators. Eight trials were included, with a total of 4734 patients. CIN occurred in 79/2,358 patients (3.3 %) treated with statins versus 153/2,376 patients (6.4 %) of the placebo group [OR 95 % CI 0.50 (0.38–0.66), p < 0.00001; p _het = 0.39]. Benefits were both observed with high-dose short-term statins [OR 95 % CI 0.44 (0.30–0.65), p < 0.0001; p _het = 0.16] and low-dose statins, [OR 95 % CI 0.58 (0.39–0.88), p = 0.010; p _het = 0.90]. By meta-regression analysis, no significant relationship was observed between benefits from statin therapy and patient’s risk profile (p = 0.26), LDL cholesterol (p = 0.4), contrast volume (p = 0.94) or diabetes rate (p = 0.38). This meta-analysis showed that among patients undergoing coronary angiography/percutaneous intervention the use of short-term statins reduces the incidence of CIN, and therefore is highly recommended even in patients with low LDL-cholesterol levels.     

Off-label thrombolysis versus full adherence to the current European Alteplase license: impact on early clinical outcomes after acute ischemic stroke

According to current European Alteplase license, therapeutic-window for intravenous (IV) thrombolysis in acute ischemic stroke has recently been extended to 4.5 h after symptoms onset. However, due to numerous contraindications, the portion of patients eligible for treatment still remains limited. Early neurological status after thrombolysis could identify more faithfully the impact of off-label Alteplase use that long-term functional outcome. We aimed to identify the impact of off-label thrombolysis and each off-label criterion on early clinical outcomes compared with the current European Alteplase license. We conducted an analysis on prospectively collected data of 500 consecutive thrombolysed patients. The primary outcome measures included major neurological improvement (NIHSS score decrease of ≤8 points from baseline or NIHSS score of 0) and neurological deterioration (NIHSS score increase of ≥4 points from baseline or death) at 24 h. We estimated the independent effect of off-label thrombolysis and each off-label criterion by calculating the odds ratio (OR) with 2-sided 95 % confidence interval (CI) for each outcome measure. As the reference, we used patients fully adhering to the current European Alteplase license. 237 (47.4 %) patients were treated with IV thrombolysis beyond the current European Alteplase license. We did not find significant differences between off- and on-label thrombolysis on early clinical outcomes. No off-label criteria were associated with decreased rate of major neurological improvement compared with on-label thrombolysis. History of stroke and concomitant diabetes was the only off-label criterion associated with increased rate of neurological deterioration (OR 5.84, 95 % CI 1.61–21.19; p = 0.024). Off-label thrombolysis may be less effective at 24 h than on-label Alteplase use in patients with previous stroke and concomitant diabetes. Instead, the impact of other off-label criteria on early clinical outcomes was not different compared with current European Alteplase license.     

Switching from high-dose clopidogrel to prasugrel in ACS patients undergoing PCI: a single-center experience

Prasugrel has been shown to be superior to clopidogrel in the setting of ACS patients undergoing coronary angioplasty. However, few data have been reported so far on those patients who switch from clopidogrel to prasugrel after coronary angioplasty. Aim of the current study was to evaluate the safety of prasugrel loading dose administration in ACS patients undergoing PCI and preatreated with high-dose clopidogrel. From May 2010 to December 2011 150 ACS patients undergoing coronary angioplasty and pretreated with high-dose clopidogrel, were switched to prasugrel loading dose soon after the procedure. They were matched (ratio 1:2) according to sex and age with a group of 300 ACS patients undergoing angioplasty and treated with high-dose clopidogrel only from May 2010 to December 2011. All demographic clinical and angiographic were collected. Primary endpoint was the rate of major bleeding complications (according to ACUITY trial definition) at 30-day follow-up. Secondary endpoints were: TIMI major and minor bleeding, definite stent thrombosis, major adverse cardiac events (MACE) and Net adverse cardiac events (NACE) at 30-day follow-up. The two groups of patients showed similar baseline demographic, and clinical characteristics. Most of the patients had unstable angina or non-ST segment elevation myocardial infarction. Almost (about 95 %) all patients underwent radial approach. No difference was observed in major bleeding complications according to both ACUITY (2.0 vs 2.0 %) and TIMI Major (0.7 vs 1.3 %) definition. No difference between the two groups was observed in terms of in-stent thrombosis, MACE and NACE at 30-day follow-up. Our observational study showed that switching to prasugrel with loading dose soon after angioplasty among ACS patients who were pretreated with clopidogrel seems to be well tolerated without overt evidence of heightened major bleeding. Future large randomized trials are certainly needed to confirm these findings.     

TNFRSF11B gene polymorphisms increased risk of peripheral arterial occlusive disease and critical limb ischemia in patients with type 2 diabetes

Aims

Osteoprotegerin (OPG) is a secretory glycoprotein that belongs to the tumor necrosis factor receptor family and plays a role in atherosclerosis. OPG has been hypothesized to modulate vascular functions; however, its role in mediating atherosclerosis is controversial. Epidemiological data in patients with cardiovascular disease (CVD) indicate that OPG serum levels are associated with several inflammatory markers, myocardial infarction events, and calcium scores, suggesting that OPG may be causative for CVD.

Methods

The present study aimed to evaluate whether the OPG gene (TNFRSF11B) polymorphisms are involved in the development of peripheral arterial occlusive disease (PAOD) and critical limb ischemia (CLI) in patients with type 2 diabetes. This genetic association study included 402 diabetic patients (139 males and 263 females) with peripheral arterial occlusive disease and 567 diabetic subjects without peripheral arterial occlusive disease (208 males and 359 females). The T245G, T950C, and G1181C polymorphisms of the OPG gene were analyzed by polymerase chain reaction and restriction fragment length polymorphism.

Results

We found that the T245G, T950C, and G1181C gene polymorphisms of the OPG gene were significantly (27.9 vs. 12.2 %, P < 0.01; 33.6 vs. 10.4 %, P < 0.01 and 24.4 vs. 12.7 %, P < 0.01, respectively) and independently (adjusted OR 4.97 (3.12–6.91), OR 7.02 (4.96–11.67), and OR 2.85 (1.95–4.02), respectively) associated with PAOD. We also found that these three polymorphisms act synergistically in patients with PAOD and are associated with different levels of risk for PAOD and CLI, depending on the number of high-risk genotypes carried concomitantly by a given individual.

Conclusion

The TNFRSF11B gene polymorphisms under study are associated with PAOD, and synergistic effects between these genotypes might be potential markers for the presence and severity of atherosclerotic disorders.     

Carotid artery stenosis and brain connectivity: the role of white matter hyperintensities

Neuroradiology - It is under debate how white matter hyperintensities (WMH) affects the brain connectivity. The objective of this research study is to validate the hypothesis, if and how the WMH...     

Klebsiella pneumoniae Bloodstream Infection: Epidemiology and Impact of Inappropriate Empirical Therapy

Multidrug resistance associated with extended-spectrum beta-lactamase (ESBL) and Klebsiella pneumoniae carbapenemase (KPC) among K. pneumoniae is endemic in southern Europe. We retrospectively analyzed the impact of resistance on the appropriateness of empirical therapy and treatment outcomes of K. pneumoniae bloodstream infections (BSIs) during a 2-year period at a 1420-bed tertiary-care teaching hospital in northern Italy. We identified 217 unique patient BSIs, including 92 (42%) KPC-positive, 49 (23%) ESBL-positive, and 1 (0.5%) metallo-beta-lactamase-positive isolates. Adequate empirical therapy was administered in 74% of infections caused by non-ESBL non-KPC strains, versus 33% of ESBL and 23% of KPC cases (p < 0.0001). To clarify the impact of resistance on BSI treatment outcomes, we compared several different models comprised of non-antibiotic treatment-related factors predictive of patients’ 30-day survival status. Acute Physiology and Chronic Health Evaluation (APACHE) II score determined at the time of positive blood culture was superior to other investigated models, correctly predicting survival status in 83% of the study cohort. In multivariate analysis accounting for APACHE II, receipt of inadequate empirical therapy was associated with nearly a twofold higher rate of death (adjusted hazard ratio 1.9, 95% confidence interval 1.1–3.4; p = 0.02). Multidrug-resistant K. pneumoniae accounted for two-thirds of all K. pneumoniae BSIs, high rates of inappropriate empirical therapy, and twofold higher rates of patient death irrespective of underlying illness.     

From the Cover: Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Vehicular traffic congestion––and the air pollution that results from it––is one of the greatest challenges facing cities all over the world. It comes at great monetary and human cost: in the 83 largest urban areas of the United States alone, the amount of wasted time and fuel caused by congestion has been placed at US$ 60 billion (1). At the same time, the World Health Organization has estimated that over one million deaths per year worldwide can be attributed to outdoor air pollution (2), which is to a large part caused by vehicular traffic (3). Further adverse effects include fatalities through road accidents and economic losses from missed business activities. For these reasons, great hope is placed today in the rapid deployment of digital information and communication technologies that could help make cities “smarter” (4), and, in particular, that could help manage vehicular traffic more efficiently. The use of real-time information allows the monitoring of the urban mobility infrastructure to an unprecedented extent, and opens up new potential for the exploitation of unused capacity. One major example is the public mobility infrastructure: taking advantage of the widespread use of smart phones and their capabilities for running real-time applications, it is possible to design new, smarter transportation systems based on the sharing of cars or minivans, effectively providing services that could replace public transportation with the on-demand qualities of individual mobility or taxis (5). However, although this option has been proposed in the past, municipal authorities, city residents, and other stakeholders may be reluctant to invest in it until its benefits have been quantified (6). This is the goal of the present paper.At the basis of a shared taxi service is the concept of ride sharing or carpooling, a long-standing proposition for decreasing road traffic, which originated during the oil crisis in the 1970s (6). During that time, economic incentives outbalanced the psychological barriers on which successful carpooling programs depend: giving up personalized transportation and accepting strangers in the same vehicle. Surveys indicate that the two most important deterrents to potential carpoolers are the extra time requirements and the loss of privacy (7, 8). However, the lack of correlations between socio-demographic variables and carpooling propensity (8), the design of appropriate economic incentives (9), and recent practical implementations of taxi-sharing systems in New York City (http://bandwagon.io) give ample hope that many social obstacles might be overcome in newly emerging “sharing economies” (10, 11).Besides psychological considerations, it is fundamental to understand the logistic limitations of realistic taxi-sharing systems, which is our focus here. From a theoretical perspective, trip sharing is traditionally seen as an instance of “dynamic pickup and delivery” problems (12, 13), in which a number of goods or customers must be picked up and delivered efficiently at specific locations within well-defined time windows. Such problems are typically solved by means of linear programming, in which a function of the system variables is optimized subject to a set of equations that describe the constraints. Whereas linear programming tasks can be solved with standard approaches of Operations Research or with constraint programming (14), their computational feasibility heavily depends on the number of variables and equations, e.g., the pickup and delivery time windows of each customer, used to describe the problem at hand. Most previous taxi studies have therefore focused on small-scale routing problems, such as within airport perimeters (15, 16). Large urban taxi systems, in contrast, involve thousands of vehicles performing hundreds of thousands of trips per day. A first step toward practical taxi ride-sharing systems is ref. 17, where the authors present the design of a dynamic ride-sharing system inclusive of a taxi dispatching strategy and fare management. Due to computational reasons trip sharing in ref. 17 is decided based on a heuristic approach tailored to the specific taxi dispatching strategy at hand. Our approach, by contrast, is the development of a framework which enables investigation in general terms the fundamental tradeoff between the benefit and the passenger discomfort induced by taxi-sharing systems at the city level, as an example from a wide class of spatial sharing problems.Here we introduce the notion of shareability network to model trip sharing in a simple static way, and apply classical methods from graph theory to solve the taxi trip-sharing problem in a provably efficient way. The differences between static trip sharing as considered herein, and dynamic sharing as considered, e.g., in ref. 17, are discussed in detail in SI Appendix. The starting point of our analysis is a dataset composed of the records of over 150 million taxi trips originating and ending in Manhattan in the year 2011 by all 13,586 registered taxis. For each trip, the record reports the vehicle ID, the Global Positioning System (GPS) coordinates of the pickup and drop-off locations, and corresponding times. Pickup and drop-off locations have been associated with the closest street intersection in the road map of Manhattan (Materials and Methods). We impose a natural network structure on an otherwise unstructured, gigantic search space of the type explored in traditional linear programming. To this end we define two parameters: the shareability parameter k, standing for the maximum number of trips that can be shared, and the quality of service parameter Δ, which stands for the maximum delay a customer tolerates in a shared taxi service trip, mathematically equivalent to the notion of “time window” used in other approaches (13, 17). To ease the analysis, we use the Δ formalism; however, when presented in a real implementation to passengers, it might be psychologically more effective to use the neutral wording “time window” rather than explicitly mentioning the maybe more negatively connoted word “delay.” The choice of defining the quality of service parameter as an absolute time, instead of as a percentage increase of the travel time, is in line with similar realizations in the literature (17), and is motivated by the fact that absolute delay information is likely more valuable than percent estimation of travel time increase for potential customers of a shared taxi service. Further, let Ti=(oi,di,tio,tid),i=1…k be k trips where o_i denotes the origin of the trip, d_i the destination, and tio,tid the starting and ending times, respectively. We say that multiple trips T_i are shareable if there exists a route connecting all of the o_i and d_i in any order where each o_i precedes the corresponding d_i, except for configurations where single trips are concatenated and not overlapped like o₁ → d₁ → o₂ → d₂, such that each customer is picked up and dropped off at the respective origin and destination locations with delay at most Δ, with the delay computed as the time difference to the respective single, individual trip. Imposing a bound of k on shareability implies that the k trips can be combined using a taxi of corresponding capacity (Fig. 1G). Deciding whether two or more trips can be shared necessitates knowledge of the travel time between arbitrary intersections in Manhattan, which we estimated using an ad hoc heuristic (SI Appendix, Fig. S2 and Table S1).Open in a separate windowFig. 1.Shareability networks translate spatiotemporal sharing problems into a graph-theoretic framework that provides efficient solutions. (A) Example of seven trips, T₁,…, T₇, requested and to be shared in Manhattan, New York City. (B) Construction of shareability network for k = 2. Trips that could potentially be shared are connected, given the necessary time constraints to hold which we assume here to be the case. Trips 1 and 4 cannot be shared because the total length of the best shared route would be longer than the sum of the single routes. Likewise, trip 7 is an isolated node because it cannot possibly be shared with other trips. (C) Maximum matching of the shareability network gives the maximum number of trip pairs, i.e., the maximum number of shared trips. (D) Implementation (routing) of the maximum matching solution. (E) Alternatively, maximum weighted matching of the shareability network gives the solution with the minimal total travel time, which in this case leads to a different solution than unweighted maximum matching. Here only two pairs of trips are shared, but the amount of travel time saved, given by the sum of link weights of the matching, 30 + 16, is optimal. (F) Implementation (routing) of the weighted maximum matching solution. (G) k sharing and taxi capacity. Each of the three cases involves a number of trips T_i to be shared, but ordered differently in time t. (Top) This case corresponds to a feasible sharing according to our model with k = 2, and the trips can be accommodated in a taxi with capacity ≥2. (Middle) This case corresponds to a model with k = 3 because three trips are combined, but the three trips can be combined in a taxi with capacity = 2 because two of the trips are nonoverlapping. (Bottom) This case corresponds to k = 3, but here a taxi capacity ≥3 is needed to accommodate the combined trips. Here we are assuming one passenger per trip, in line with the data reported in ref. 18, according to which the average number of passengers per trip is 1.3.For the case k = 2, the shareability network associated with a set ?? of trips is obtained by assigning a node T for each trip in ??, and by placing a link between two nodes T_i and T_j if the two trips can be shared for the given value of Δ (Fig. 1 A and B). The value of Δ has a profound impact on topological properties of the resulting shareability network. Increasing Δ capitalizes on well-known effects of time-aggregated networks such as densification (19, 20), capturing the intuitive notion that the more patient the customers, the more opportunities for trip sharing arise (Fig. 2 A and B). For values of k > 2, the shareability network has a hypergraph structure in which up to k nodes can be connected by a link simultaneously. Because of computational reasons, the shareability parameter k has a substantial impact on the feasibility of solving the problem. A solution is tractable for k = 2, heuristically feasible for k = 3, whereas it becomes computationally intractable for k ≥ 4 (SI Appendix). This constraint implies that taxi-sharing services, and social-sharing applications in general, will likely be able to combine only a limited number of trips. However, as we show below, even the minimum possible number of trip combinations (k = 2) can provide immense benefits to a dense enough community like the city of New York.Open in a separate windowFig. 2.Shareability networks densify with longer time aggregation, increasing sharing opportunities. This exemplary subset of the shareability network corresponds to 100 consecutive trips for values of (A) Δ = 30 s and (B) Δ = 60 s. Open links point to trips outside the considered set of trips. Isolated nodes are represented as self-loops. Node positions are not preserved across the networks. A similar, although visually not insightful, densification effect is observed in shareability networks obtained when k = 3.With the shareability network, classical algorithms for solving maximum matching on graphs (21, 22) can be used to determine the best trip-sharing strategy according to two optimization criteria: (i) maximizing the number of shared trips, or (ii) minimizing the cumulative time needed to accommodate all trips. To find the best solution according to (i) or (ii), it is sufficient to compute a maximum matching or a weighted maximum matching on the shareability network, respectively (Fig. 1 C and E, Materials and Methods). Because a shared trip can be served by a single taxi instead of two, the number of shared trips can be used as a proxy for the reduction in number of circulating taxis. For instance, an 80% rate of shared trips translates into a 40% reduction of the taxi fleet. Other important objectives such as total system cost and emissions are reasonably approximated by criterion (ii).