Adriano Alatri

Ultrasound-Assisted Versus Conventional Catheter-Directed Thrombolysis for Acute Iliofemoral Deep Vein Thrombosis

Background—For patients with acute iliofemoral deep vein thrombosis, it remains unclear whether the addition of intravascular high-frequency, low-power ultrasound energy facilitates the resolution of thrombosis during catheter-directed thrombolysis. Methods and Results—In a controlled clinical trial, 48 patients (mean age 50±21 years, 52% women) with acute iliofemoral deep vein thrombosis were randomized to receive ultrasound-assisted catheter-directed thrombolysis (N=24) or conventional catheter-directed thrombolysis (N=24). Thrombolysis regimen (20 mg r-tPA over 15 hours) was identical in all patients. The primary efficacy end point was the percentage of thrombus load reduction from baseline to 15 hours according to the length-adjusted thrombus score, obtained from standardized venograms and evaluated by a core laboratory blinded to group assignment. The percentage of thrombus load reduction was 55%±27% in the ultrasound-assisted catheter-directed thrombolysis group and 54%±27% in the conventional catheter-directed thrombolysis group (P=0.91). Adjunctive angioplasty and stenting was performed in 19 (80%) patients and in 20 (83%) patients, respectively (P>0.99). Treatment-related complications occurred in 3 (12%) and 2 (8%) patients, respectively (P>0.99). At 3-month follow-up, primary venous patency was 100% in the ultrasound-assisted catheter-directed thrombolysis group and 96% in the conventional catheter-directed thrombolysis group (P=0.33), and there was no difference in the severity of the post-thrombotic syndrome (mean Villalta score: 3.0±3.9 [range 0–15] versus 1.9±1.9 [range 0–7]; P=0.21), respectively. Conclusions—In this randomized controlled clinical trial of patients with acute iliofemoral deep vein thrombosis treated with a fixed-dose catheter thrombolysis regimen, the addition of intravascular ultrasound did not facilitate thrombus resolution. Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifier: NCT01482273.

Diagnosis and management of acute deep vein thrombosis: a joint consensus document from the European society of cardiology working groups of aorta and peripheral circulation and pulmonary circulation and right ventricular function.

Division of Angiology, Heart and Vessel Department, Lausanne University Hospital, Ch du Mont-Paisible 18, 1011 Lausanne, Switzerland; Department of Cardiology, Dupuytren University Hospital, and, Inserm 1098, Tropical Neuroepidemiology, School of Medicine, 2 avenue martin Luther-King 87042 Limoges cedex, France; Department of Clinical and Experimental Medicine, University of Insubria, Via Ravasi 2, 21100 Varese, Italy; Internal and Cardiovascular Medicine Stroke Unit, University of Perugia, S. Andrea delle Fratte, 06156 Perugia, Italy; Department of Vascular Medicine, Klinikum Darmstadt GmbH, Grafenstraße 9, 64283 Darmstadt, Germany; Center for Thrombosis and Hemostasis, University Medical Center Mainz, Langenbeckstr. 1, 55131 Mainz, Germany; Department of Vascular Medicine, Academic Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands; Cardiology and Vascular Medicine, Toulon Hospital Centre, 54 Rue Henri Sainte-Claire Deville, 83100 Toulon, France; Assistance PubliqueHôpitaux de Paris, Saint-Louis Hospital, Internal Medicine and Vascular Disease Unit and Groupe Francophone on Thrombosis and Cancer, Paris 7 Diderot University, Sorbonne Paris Cité, 1, Avenue Claude Vellefaux, 75010 Paris, France; Department of Cardiology, Democritus University of Thrace, Greece; Cardiovascular Diseases, University of Bologna, Via Albertoni 15, 40138 Bologna, Italy; Department of Cardiovascular Sciences, Vascular Medicine Unit, University of Padua, Via Nicol o Giustiniani, 2, 35121 Padua, Italy; Division of Angiology and Hemostasis, Department of Medical Specialties, Geneva University Hospital, Rue Gabrielle Perret-Gentil 4, 1205 Geneva, Switzerland; Department of Pulmonary Circulation and Thromboembolic Diseases, Medical Center for Postgraduate Education, ul Plocka 26, 01-138, Warszawa, Otwock, Poland; Department of Cardiology, Athens Medical School, Profiti elia 24, 14575 Athens, Greece; and Division of Angiology, Medical University Graz, Graz, Austria

Prevalence and risk factors for venous thromboembolic complications in the Swiss Inflammatory Bowel Disease Cohort.

Abstract Objective: Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is associated with the occurrence of venous thromboembolism (VTE) such as deep vein thrombosis (DVT) and pulmonary embolism (PE). We aimed to assess the prevalence and associated risk factors for VTE in a large national cohort of IBD patients. Material and methods: Data from patients of the Swiss IBD Cohort Study (SIBDCS) enrolled between 2006 and 2013 were analyzed. Results: A total of 2284 IBD patients were analyzed of which 1324 suffered from CD and 960 from UC. VTE prevalence was 3.9% (90/2284) overall and 3.4% (45/1324) in CD patients (whereof 2.4% suffered from DVT and 1.5% from PE) and 4.7% (45/960) in UC patients (whereof 3.2% suffered from DVT and 2.4% from PE). Median disease duration in CD patients with VTE was 12 years [IQR 8–23] compared to eight years [3–16] in CD patients without VTE (pu2009=u20090.001). Disease duration in UC patients with VTE was seven years [4–18] compared to six years [2–13] in UC patients without VTE (pu2009=u20090.051). Age at CD diagnosis ≥40 years (OR 1.851, pu2009=u20090.073) and disease duration >10 years (OR 1.771, pu2009=u20090.088) showed a trend to be associated with VTE. In UC patients, IBD-related surgery (OR 3.396, pu2009=u20090.004) and pancolitis (OR 1.927, pu2009=u20090.050) were significantly associated with VTE. Conclusions: VTE are prevalent in CD and UC patients. Pancolitis and UC-related surgery are significantly associated with VTE in UC patients.

Initiation of rivaroxaban in patients with nonvalvular atrial fibrillation at the primary care level: the Swiss Therapy in Atrial Fibrillation for the Regulation of Coagulation (STAR) Study.

BACKGROUNDnRivaroxaban has become an alternative to vitamin-K antagonists (VKA) for stroke prevention in non-valvular atrial fibrillation (AF) patients due to its favourable risk-benefit profile in the restrictive setting of a large randomized trial. However in the primary care setting, physicians motivation to begin with rivaroxaban, treatment satisfaction and the clinical event rate after the initiation of rivaroxaban are not known.nnnMETHODSnProspective data collection by 115 primary care physicians in Switzerland on consecutive nonvalvular AF patients with newly established rivaroxaban anticoagulation with 3-month follow-up.nnnRESULTSnWe enrolled 537 patients (73±11years, 57% men) with mean CHADS2 and HAS-BLED-scores of 2.2±1.3 and 2.4±1.1, respectively: 301(56%) were switched from VKA to rivaroxaban (STR-group) and 236(44%) were VKA-naïve (VN-group). Absence of routine coagulation monitoring (68%) and fixed-dose once-daily treatment (58%) were the most frequent criteria for physicians to initiate rivaroxaban. In the STR-group, patients satisfaction increased from 3.6±1.4 under VKA to 5.5±0.8 points (P<0.001), and overall physician satisfaction from 3.9±1.3 to 5.4±0.9 points (P<0.001) at 3months of rivaroxaban therapy (score from 1 to 6 with higher scores indicating greater satisfaction). In the VN-group, both patients (5.4±0.9) and physicians satisfaction (5.5±0.7) at follow-up were comparable to the STR-group. During follow-up, 1(0.19%; 95%CI, 0.01-1.03%) ischemic stroke, 2(0.37%; 95%CI, 0.05-1.34%) major non-fatal bleeding and 11(2.05%; 95%CI, 1.03-3.64%) minor bleeding complications occurred. Rivaroxaban was stopped in 30(5.6%) patients, with side effects being the most frequent reason.nnnCONCLUSIONnInitiation of rivaroxaban for patients with nonvalvular AF by primary care physicians was associated with a low clinical event rate and with high overall patients and physicians satisfaction.

D-dimer testing and recurrent venous thromboembolism after unprovoked pulmonary embolism: A post-hoc analysis of the prolong extension study

D-dimer testing and recurrent venous thromboembolism after unprovoked pulmonary embolism: A post-hoc analysis of the prolong extension study -

The Modified Ottawa Score and Clinical Events in Hospitalized Patients with Cancer-Associated Thrombosis from the Swiss VTE Registry.

Abstract The modified Ottawa score (MOS) predicted venous thromboembolism (VTE) recurrence in a cohort of patients with cancer‐associated thrombosis mainly managed on an outpatient basis. We aimed to assess the prognostic value of the MOS in hospitalized patients with cancer‐associated thrombosis. In 383 hospitalized patients with cancer‐associated VTE from the SWIss VTE Registry, 98 (25%) were classified as low risk, 175 (46%) as intermediate risk, and 110 (29%) as high risk for VTE recurrence based on the MOS. Clinical end points were recurrent VTE, fatal VTE, major bleeding, and overall mortality at 90 days. Overall, 179 (47%) patients were female, 172 (45%) had metastatic disease, and 72 (19%) prior VTE. The primary site of cancer was lung in 48 (13%) patients and breast in 43 (11%). According to the MOS, the rate of VTE recurrence was 4.1% for low, 6.3% intermediate, and 5.5% high risk (p = 0.75); the rate of fatal VTE was 0.8, 1.9, and 2.0% (p = 0.69); the rate of major bleeding was 3.1, 4.1, and 3.6% (p = 0.92); and the rate of death was 6.1, 12.0, and 28.2% (p < 0.001), respectively. None of the MOS items was associated with VTE recurrence: female gender hazard ratio (HR) 1.26 (95% confidence interval [CI], 0.53‐2.96), lung cancer HR 1.17 (95% CI, 0.35‐3.98), prior VTE HR 0.44 (95% CI, 0.10‐1.91), breast cancer HR 0.83 (95% CI, 0.19‐3.58), and absence of metastases HR 0.74 (95% CI, 0.31‐1.74). In hospitalized patients with cancer‐associated VTE, the MOS failed to predict VTE recurrence at 3 months but was associated with early mortality.

Accuracy of in-patients ankle-brachial index measurement by medical students.

BACKGROUNDnAnkle brachial index (ABI) is a first line non-invasive screening tool for peripheral arterial disease (PAD) in at risk populations. The need to extend ABI use in large population screening has urged its use by professionals other than vascular physicians. As advocated by the American Heart Association, ABI teaching is part of medical curriculum in several countries. We determine accuracy in ABI measurement by trained medical students compared with an experienced angiologist.nnnMETHODSnTwelve 6th year medical students underwent 9 days of training at Lausanne University Hospital. Students and an experienced angiologist, blinded to students results, screened consecutive hospitalised patients aged ≥ 65 or ≥ 50 with at least one cardiovascular risk factor during a 6-week period.nnnRESULTSnA total of 249 patients were screened of whom 59 (23.7%) met the inclusion criteria. Median age was 80, 45.8% were women, and 6.8% were symptomatic. In total, 116 ABIs were available for analysis. Agreement between students and angiologist was moderate with a k-value of 0.498 (95% confidence interval: 0.389-0.606). Overall accuracy and precision of PAD screening performed by students showed sensitivity of 73.2% and specificity of 88.0%. Positive and negative predictive values were 76.9% and 85.7%, respectively; positive and negative likelihood ratios were 6.3 and 3, respectively.nnnCONCLUSIONSnA nine day training program on ABI measurement is not sufficient for inexperienced medical students to achieve an acceptable diagnostic accuracy in detecting PAD in at risk populations.

SUBCUTANEOUS HEPARIN FOR DEEP VENOUS THROMBOSIS

In the first of two articles on evidence-based statistics (1), I outlined the inherent difficulties of the standard frequentist statistical approach to inference: problems with using the P value as a measure of evidence, internal inconsistencies of the combined hypothesis test- P value method, and how that method inhibits combining experimental results with background information. Here, I explore, as nonmathematically as possible, the Bayesian approach to measuring evidence and combining information and epistemologic uncertainties that affect all statistical approaches to inference. Some of this presentation may be new to clinical researchers, but most of it is based on ideas that have existed at least since the 1920s and, to some extent, centuries earlier (2). The Bayes Factor Alternative Bayesian inference is often described as a method of showing how belief is altered by data. Because of this, many researchers regard it as nonscientific; that is, they want to know what the data say, not what our belief should be after observing them (3). Comments such as the following, which appeared in response to an article proposing a Bayesian analysis of the GUSTO (Global Utilization of Streptokinase and tPA for Occluded Coronary Arteries) trial (4), are typical. When modern Bayesians include a prior probability distribution for the belief in the truth of a hypothesis, they are actually creating a metaphysical model of attitude change The result cannot be field-tested for its validity, other than that it feels reasonable to the consumer . The real problem is that neither classical nor Bayesian methods are able to provide the kind of answers clinicians want. That classical methods are flawed is undeniableI wish I had an alternative . (5) This comment reflects the widespread misperception that the only utility of the Bayesian approach is as a belief calculus. What is not appreciated is that Bayesian methods can instead be viewed as an evidential calculus. Bayes theorem has two componentsone that summarizes the data and one that represents belief. Here, I focus on the component related to the data: the Bayes factor, which in its simplest form is also called a likelihood ratio. In Bayes theorem, the Bayes factor is the index through which the data speak, and it is separate from the purely subjective part of the equation. It has also been called the relative betting odds, and its logarithm is sometimes referred to as the weight of the evidence (6, 7). The distinction between evidence and error is clear when it is recognized that the Bayes factor (evidence) is a measure of how much the probability of truth (that is, 1 prob[error], where prob is probability) is altered by the data. The equation is as follows: where Bayes factor= The Bayes factor is a comparison of how well two hypotheses predict the data. The hypothesis that predicts the observed data better is the one that is said to have more evidence supporting it. Unlike the P value, the Bayes factor has a sound theoretical foundation and an interpretation that allows it to be used in both inference and decision making. It links notions of objective probability, evidence, and subjective probability into a coherent package and is interpretable from all three perspectives. For example, if the Bayes factor for the null hypothesis compared with another hypothesis is 1/2, the meaning can be expressed in three ways. 1. Objective probability: The observed results are half as probable under the null hypothesis as they are under the alternative. 2. Inductive evidence: The evidence supports the null hypothesis half as strongly as it does the alternative. 3. Subjective probability: The odds of the null hypothesis relative to the alternative hypothesis after the experiment are half what they were before the experiment. The Bayes factor differs in many ways from a P value. First, the Bayes factor is not a probability itself but a ratio of probabilities, and it can vary from zero to infinity. It requires two hypotheses, making it clear that for evidence to be against the null hypothesis, it must be for some alternative. Second, the Bayes factor depends on the probability of the observed data alone, not including unobserved long run results that are part of the P value calculation. Thus, factors unrelated to the data that affect the P value, such as why an experiment was stopped, do not affect the Bayes factor (8, 9). Because we are so accustomed to thinking of evidence and the probability of error as synonymous, it may be difficult to know how to deal with a measure of evidence that is not a probability. It is helpful to think of it as analogous to the concept of energy. We know that energy is real, but because it is not directly observable, we infer the meaning of a given amount from how much it heats water, lifts a weight, lights a city, or cools a house. We begin to understand what a lot and a little mean through its effects. So it is with the Bayes factor: It modifies prior probabilities, and after seeing how much Bayes factors of certain sizes change various prior probabilities, we begin to understand what represents strong evidence, and weak evidence. Table 1 shows us how far various Bayes factors move prior probabilities, on the null hypothesis, of 90%, 50%, and 25%. These correspond, respectively, to high initial confidence in the null hypothesis, equivocal confidence, and moderate suspicion that the null hypothesis is not true. If one is highly convinced of no effect (90% prior probability of the null hypothesis) before starting the experiment, a Bayes factor of 1/10 will move one to being equivocal (47% probability on the null hypothesis), but if one is equivocal at the start (50% prior probability), that same amount of evidence will be moderately convincing that the null hypothesis is not true (9% posterior probability). A Bayes factor of 1/100 is strong enough to move one from being 90% sure of the null hypothesis to being only 8% sure. Table 1. Final (Posterior) Probability of the Null Hypothesis after Observing Various Bayes Factors, as a Function of the Prior Probability of the Null Hypothesis As the strength of the evidence increases, the data are more able to convert a skeptic into a believer or a tentative suggestion into an accepted truth. This means that as the experimental evidence gets stronger, the amount of external evidence needed to support a scientific claim decreases. Conversely, when there is little outside evidence supporting a claim, much stronger experimental evidence is required for it to be credible. This phenomenon can be observed empirically, in the medical communitys reluctance to accept the results of clinical trials that run counter to strong prior beliefs (10, 11). Bayes Factors and Meta-Analysis There are two dimensions to the evidence-based properties of Bayes factors. One is that they are a proper measure of quantitative evidence; this issue will be further explored shortly. The other is that they allow us to combine evidence from different experiments in a natural and intuitive way. To understand this, we must understand a little more of the theory underlying Bayes factors (12-14). Every hypothesis under which the observed data are not impossible can be said to have some evidence for it. The strength of this evidence is proportional to the probability of the data under that hypothesis and is called the likelihood of the hypothesis. This use of the term likelihood must not be confused with its common language meaning of probability (12, 13). Mathematical likelihoods have meaning only when compared to each other in the form of a ratio (hence, the likelihood ratio), a ratio that represents the comparative evidential support given to two hypotheses by the data. The likelihood ratio is the simplest form of Bayes factor. The hypothesis with the most evidence for it has the maximum mathematical likelihood, which means that it predicts the observed data best. If we observe a 10% difference between the cure rates of two treatments, the hypothesis with the maximum likelihood would be that the true difference was 10%. In other words, whatever effect we are measuring, the best-supported hypothesis is always that the unknown true effect is equal to the observed effect. Even when a true difference of 10% gets more support than any other hypothesis, a 10% observed difference also gives a true difference of 15% some support, albeit less than the maximum (Figure). Figure. Calculation of a Bayes factor (likelihood ratio) for the null hypothesis versus two other hypotheses: the maximally supported alternative hypothesis (change =10%) and an alternative hypothesis with less than the maximum support (=15%). This ideathat each experiment provides a certain amount of evidence for every underlying hypothesisis what makes meta-analysis straightforward under the Bayesian paradigm, and conceptually different than under standard methods. One merely combines the evidence provided by each experiment for each hypothesis. With log Bayes factors (or log likelihoods), this evidence can simply be added up (15-17). With standard methods, quantitative meta-analysis consists of taking a weighted average of the observed effects, with weights related to their precision. For example, if one experiment finds a 10% difference and another finds a 20% difference, we would average the numbers 10% and 20%, pool their standard errors, and calculate a new P value based on the average effect and pooled standard error. The cumulative evidence (P value) for the meta-analytic average has little relation to the P values for the individual effects, and averaging the numbers 10% and 20% obscures the fact that both experiments actually provide evidence for the same hypotheses, such as a true 15% difference. Although it might be noted that a 15% difference falls within the confidence intervals of both experiments, little can be done quantitatively or conceptually with that fact. So while meta-analy

Long-Term Outcome of Splanchnic Vein Thrombosis in Cirrhosis

Introduction: Little is known about the long‐term outcome of cirrhotic patients with splanchnic vein thrombosis (SVT). This prospective cohort study aimed to describe the clinical presentation, bleeding incidence, thrombotic events, and mortality in patients with SVT associated with cirrhosis. Methods: Among 604 consecutive patients with SVT enrolled over 2 years, 149 had cirrhosis. Major bleeding, thrombotic events, and all‐cause mortality were recorded during a 2‐year follow‐up. In a subgroup, the degree of recanalization with or without anticoagulation therapy, and the correlation between clinical events and liver disease severity were also investigated. Results: The most common thrombosis sites were the portal (88%) and mesenteric veins (34%). At presentation, 50% of patients were asymptomatic. Anticoagulation was administered to 92/149 patients for a median of 6.5 months. Vessel recanalization was documented in 47/98 patients with a radiological follow‐up. Anticoagulation was associated with a 3.33‐fold higher of recanalization rate, and a lower recurrent thrombosis rate, while patients with and without anticoagulation experienced a similar rate of major bleeding episodes. Mortality rates were 6.8 per 100 patient‐years for patients with thrombosis completely or partially resolving during the follow‐up, and 15.4 per 100 patient‐years for those with stable or progressing thrombosis. An impact of SVT on survival was only apparent in patients with more advanced liver disease (Child‐Pugh B‐C). Conclusions: Patients with SVT and cirrhosis have a substantial long‐term risk of recurrent thrombotic events, which is reduced by anticoagulation therapy without any increase in bleeding risk. Anticoagulation can improve the likelihood of vessel recanalization, and is associated with a lower risk of death for decompensated patients.

Derivation and Validation of a Prediction Model for Risk Stratification of Post-Thrombotic Syndrome in Elderly Patients with a First Deep Vein Thrombosis

BACKGROUNDnu2003Not all patients carry the same risk of developing a post-thrombotic syndrome (PTS), we therefore aimed to derive a prediction rule for risk stratification of PTS in patients with deep vein thrombosis (DVT).nnnMETHODSnu2003Our derivation sample included 276 patients with a first acute symptomatic lower limb DVT enrolled in a prospective cohort. We derived our prediction rule using regression analysis, with the occurrence of PTS within 24 months of a DVT based on the Villalta score as outcome, and 11 candidate variables as predictors. We used bootstrapping methods for internal validation.nnnRESULTSnu2003Overall, 161 patients (58.3%) developed a PTS within 24 months of a DVT. Our prediction rule was based on five predictors (age ≥ 75 years, prior varicose vein surgery, multi-level thrombosis, concomitant antiplatelet/non-steroidal anti-inflammatory drug therapy and the number of leg symptoms and signs). Overall, 16.3, 31.2 and 52.5% of patients were classified as low- (score, 0-3), moderate (score, 4-5) and high-risk (score, ≥ 6), for developing a PTS. Within 24 months of the index DVT, 24.4% of the patients in the low-risk category developed a PTS, 38.4% in the moderate and 80.7% in the high-risk category. The prediction model showed good predictive accuracy (area under the curve, 0.77; 95% confidence interval, 0.71-0.82, calibration slope, 0.90 and Brier score, 0.20).nnnCONCLUSIONnu2003This easy-to-use clinical prediction rule accurately identifies patients with DVT who are at high risk of developing PTS within 24 months who could potentially benefit from special educational or therapeutic measures to limit the risk of PTS.