Acute kidney injury (AKI) is associated with a poor prognosis after transcatheter aortic valve replacement (TAVR). A paucity of data exists regarding the incidence and effect of AKI after TAVR using ...the new recommended Valve Academic Research Consortium criteria. At Columbia University Medical Center, 218 TAVR procedures (64.2% transfemoral, 35.8% transapical) were performed from 2008 to July 2011. The creatinine level was evaluated daily until discharge. Using the Valve Academic Research Consortium definitions, the 30-day and 1-year outcomes were compared between patients with significant AKI (AKI stage 2 or 3) and those without significant AKI (AKI stage 0 or 1). Significant AKI occurred in 18 patients (8.3%). Of these 18 patients, 10 (55.6%) had AKI stage 3 and 9 (50%) required dialysis. AKI was associated with a lower baseline mean transvalvular gradient (37.6 ± 11.4 vs 45.6 ± 14.8 mm Hg for no AKI, p = 0.03). After TAVR, the AKI group had a greater hemoglobin decrease (3.6 ± 2.0 vs 2.4 ± 1.3 g/dl, p = 0.01), greater white blood cell elevation at 72 hours (21.09 ± 12.99 vs 13.18 ± 4.82 × 103 /μl, p = 0.001), a more severe platelet decrease (118 ± 40 vs 75 ± 43 × 103 /μl, p <0.0001), and longer hospitalization (10.7 ± 6.4 vs 7.7 ± 8.5 days, p <0.001). One stroke (5.6%) occurred in the AKI group compared with 3 (1.5%) in the group without AKI (p = 0.29). The 30-day and 1-year rates of death were significantly greater in the AKI group than in the no-AKI group (44.4% vs 3.0%, hazard ratio 18.1, 95% confidence interval 6.25 to 52.20, p <0.0001; and 55.6% vs 16.0%, hazard ratio 6.32, 95% confidence interval 3.06 to 13.10, p <0.0001, respectively). Periprocedural life-threatening bleeding was the strongest predictor of AKI after TAVR. In conclusion, the occurrence of AKI, as defined by the Valve Academic Research Consortium criteria, is associated with periprocedural complications and a poor prognosis after TAVR.
Multicenter clinical trials use echocardiographic core laboratories to ensure expertise and consistency in the assessment of imaging eligibility criteria, as well as safety and efficacy end points. ...The aim of this study was to report the real-world implementation of guidelines for best practices in echocardiographic core laboratories, including their feasibility and quality results, in a large, international multicenter trial.
Processes and procedures were developed to optimize the acquisition and analysis of echocardiograms for the Placement of Aortic Transcatheter Valves (PARTNER) I trial of percutaneous aortic valve replacement for aortic stenosis. Comparison of baseline findings in the operative and nonoperative cohorts and reproducibility analyses were performed.
Echocardiography was performed in 1,055 patients (mean age, 83 years; 54% men) The average peak and mean aortic valve gradients were 73 ± 24 and 43 ± 15 mm Hg, and the average aortic valve area was 0.64 ± 0.20 cm(2). The average ejection fraction was 52 ± 13% by visual estimation and 53 ± 14% by biplane planimetry. The mean left ventricular mass index was 151 ± 42 g/m(2). The inoperable cohort had lower left ventricular mass and mass indexes and tended to have more severe mitral regurgitation. Core lab reproducibility was excellent, with intraclass correlation coefficients ranging from 0.92 to 0.99 and κ statistics from 0.58 to 0.85 for key variables. The image acquisition quality improvement process brought measurability to >85%, which was maintained for the duration of the study.
This real-world echocardiographic core lab experience in the PARTNER I trial demonstrates that a high standard of measurability and reproducibility can result from extensive quality assurance efforts in both image acquisition and analysis. These results and the echocardiographic data reported here provide a reference for future studies of aortic stenosis patients and should encourage the wider use of echocardiography in clinical research.
Branching processes in random environments have been widely studied and applied to population growth systems to model the spread of epidemics, infectious diseases, cancerous tumor growth, and social ...network traffic. However, Ebola virus, tuberculosis infections, and avian flu grow or change at rates that vary with time—at peak rates during pandemic time periods, while at low rates when near extinction. The branching processes in generalized autoregressive conditional environments we propose provide a novel approach to branching processes that allows for such time-varying random environments and instances of peak growth and near extinction-type rates. Offspring distributions we consider to illustrate the model include the generalized Poisson, binomial, and negative binomial integer-valued GARCH models. We establish conditions on the environmental process that guarantee stationarity and ergodicity of the mean offspring number and environmental processes and provide equations from which their variances, autocorrelation, and cross-correlation functions can be deduced. Furthermore, we present results on fundamental questions of importance to these processes—the survival-extinction dichotomy, growth behavior, necessary and sufficient conditions for noncertain extinction, characterization of the phase transition between the subcritical and supercritical regimes, and survival behavior in each phase and at criticality.
In hindsight, even a cursory look may have revealed substantial growth of the 2014 Ebola infection and death cases in West Africa before drastic interventions showed an effect in late 2014. Yet a ...timely assessment as to whether an intervention has a sufficient impact to stabilize and eventually end an outbreak is equally important as early detection and accurate prediction of the magnitude of the outbreak several months before it spins out of control. To this aim, we consider an intervention effect in the GARCE branching process model, proposed by Hueter, that was successful to early detect the magnitude of the outbreak when data became available in early 2014. This model provides a novel and simple approach to branching processes that allows for time-varying random environments and instances of peak growth and near extinction-type rates as seen in Ebola viruses, tuberculosis infections, and infectious diseases. We present results on the survival and extinction behaviours, characterization of the phase transition between the subcritical and supercritical phases, and a sufficient condition for escape from supercriticality upon a level shift intervention. Intervention analysis of the Ebola outbreak data are presented and findings on the outbreak’s estimated phase and intervention effect are discussed.
Survival rates for women with a diagnosis of breast cancer continue to improve. However, some women may experience physical, psychological and emotional effects post diagnosis, throughout treatment ...and beyond. Support groups can provide opportunities for people to share their experiences and learn from others. As the number of online support groups increases, more and more women with breast cancer will likely access them.
To assess effects of online support groups on the emotional distress, uncertainty, anxiety, depression and quality of life (QoL) of women with breast cancer.
We searched for trials in the Cochrane Breast Cancer Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL; 2016, Issue 4), MEDLINE, Embase and PsycINFO on 2 May 2016, and we handsearched journals and reference lists. We also searched the World Health Organization's International Clinical Trials Registry Platform (WHO ICTRP) search portal and clinicaltrials.gov on 2 May 2016.
We included randomised controlled trials (RCTs) assessing effects of online support groups on women with a diagnosis of breast cancer and women who have completed breast cancer treatment. We included studies comparing online support groups with a usual care group, and studies comparing two or more types of online support groups (without a usual care group).
Two review authors independently extracted data and assessed risk of bias. We presented outcome data using mean differences (MDs) and standardised mean differences (SMDs) along with 95% confidence intervals (CIs), and we used the fixed-effect model when appropriate. We assessed the quality of the body of evidence using the GRADE approach.
We included six studies (492 women) that assessed online support groups for women with breast cancer. Online support groups in these six trials lasted from six to 30 weeks. Women participated in these groups between 1.5 and 2.5 hours per week, and investigators conducted all studies in the USA. Participants were predominantly white and well educated and were moderate to high earners. Four studies compared an online support group versus a control group, and the other two compared a 'moderated' versus a 'peer-led' online support group, and a 'standard' versus an 'enhanced' online support group, respectively.None of the included studies measured 'emotional distress' or uncertainty. One study (78 women) for which data for analysis were missing reported no positive effects of online support on 'distress' and 'cancer-specific distress' versus support provided by a control group. Two studies measured anxiety: One study (72 women) found no difference in anxiety at the end of the intervention between the online support group and the control group (MD -0.40, 95% CI -6.42 to 5.62; low-quality evidence), and the second study (184 women) reported a reduction in anxiety levels at the end of the intervention when comparing the 'standard' support group (run by participants without prompting from health professionals) versus an 'enhanced' online support group (in which participants were specifically asked by the researcher to respond to one another's need for support).Five studies (414 women) measured depression. Three studies compared depression in the online support group with depression in the control group. Pooled data from two studies (120 women) showed a small to moderate reduction in depression in the online support group compared with control groups at the end of the intervention (SMD -0.37, 95% CI -0.75 to 0.00; very low-quality evidence). The third study, a pilot study (30 women), provided no data for analysis but reported no difference in depression between participants in support and control groups at the end of the intervention. Of the remaining two studies that measured depression, one study (60 women) provided no extractable data for comparison but reported no difference in depressive symptoms between a 'moderated' and a 'peer-led' support group; the other study (184 women) reported greater reduction in depression in the 'standard' support group than in the 'enhanced' online support group.Three studies measured quality of life. One pilot study (30 women) provided limited data for analysis but reported no change in quality of life at the end of the intervention. Only two studies (140 women) provided data for pooling and showed no positive effects on quality of life at four months post intervention compared with controls (SMD -0.11, 95% CI -0.47 to 0.24; very low-quality evidence). At 12 months post intervention, one study (78 women) reported that the intervention group did not attain better quality of life scores than the control group (MD -10.89, 95% CI -20.41 to -1.37; low-quality evidence).We found no data for subgroup analyses on stage of disease, treatment modality and types and doses of interventions. No studies measured adverse effects.
This review did not find the evidence required to show whether participation in online support groups was beneficial for women with breast cancer, because identified trials were small and of low or very low quality. Large, rigorous trials with ethnically and economically diverse participants are needed to provide robust evidence regarding the psychosocial outcomes selected for this review.
We give a central limit theorem for the number N_n of vertices of the convex hull of n independent and identically distributed random vectors, being sampled from a certain class of spherically ...symmetric distributions in \mathbb{R}^d ; (d> 1), that includes the normal family. Furthermore, we prove that, among these distributions, the variance of N_n exhibits the same order of magnitude as the expectation as n \rightarrow \infty. The main tools are Poisson approximation of the point process of vertices of the convex hull and (sub/super)-martingales.
Abstract Background Little is known about the incidence of prosthesis-patient mismatch (PPM) and its impact on outcomes after transcatheter aortic valve replacement (TAVR). Objectives The objectives ...of this study were: 1) to compare the incidence of PPM in the TAVR and surgical aortic valve replacement (SAVR) randomized control trial (RCT) arms of the PARTNER (Placement of AoRTic TraNscathetER Valves) I Trial cohort A; and 2) to assess the impact of PPM on regression of left ventricular (LV) hypertrophy and mortality in these 2 arms and in the TAVR nonrandomized continued access (NRCA) registry cohort. Methods The PARTNER Trial cohort A randomized patients 1:1 to TAVR or bioprosthetic SAVR. Postoperative PPM was defined as absent if the indexed effective orifice area (EOA) was >0.85 cm2 /m2 , moderate if the indexed EOA was ≥0.65 but ≤0.85 cm2 /m2 , or severe if the indexed EOA was <0.65 cm2 /m2 . LV mass regression and mortality were analyzed using the SAVR-RCT (n = 270), TAVR-RCT (n = 304), and TAVR-NRCA (n = 1,637) cohorts. Results The incidence of PPM was 60.0% (severe: 28.1%) in the SAVR-RCT cohort versus 46.4% (severe: 19.7%) in the TAVR-RCT cohort (p < 0.001) and 43.8% (severe: 13.6%) in the TAVR-NRCA cohort. In patients with an aortic annulus diameter <20 mm, severe PPM developed in 33.7% undergoing SAVR compared with 19.0% undergoing TAVR (p = 0.002). PPM was an independent predictor of less LV mass regression at 1 year in the SAVR-RCT (p = 0.017) and TAVR-NRCA (p = 0.012) cohorts but not in the TAVR-RCT cohort (p = 0.35). Severe PPM was an independent predictor of 2-year mortality in the SAVR-RCT cohort (hazard ratio HR: 1.78; p = 0.041) but not in the TAVR-RCT cohort (HR: 0.58; p = 0.11). In the TAVR-NRCA cohort, severe PPM was not a predictor of 1-year mortality in all patients (HR: 1.05; p = 0.60) but did independently predict mortality in the subset of patients with no post-procedural aortic regurgitation (HR: 1.88; p = 0.02). Conclusions In patients with severe aortic stenosis and high surgical risk, PPM is more frequent and more often severe after SAVR than TAVR. Patients with PPM after SAVR have worse survival and less LV mass regression than those without PPM. Severe PPM also has a significant impact on survival after TAVR in the subset of patients with no post-procedural aortic regurgitation. TAVR may be preferable to SAVR in patients with a small aortic annulus who are susceptible to PPM to avoid its adverse impact on LV mass regression and survival. (The PARTNER Trial: Placement of AoRTic TraNscathetER Valve Trial; NCT00530894 )
The prognosis and treatment of patients with low-flow (LF) severe aortic stenosis are controversial.
The Placement of Aortic Transcatheter Valves (PARTNER) trial randomized patients with severe ...aortic stenosis to medical management versus transcatheter aortic valve replacement (TAVR; inoperable cohort) and surgical aortic valve replacement versus TAVR (high-risk cohort). Among 971 patients with evaluable echocardiograms (92%), LF (stroke volume index ≤35 mL/m(2)) was observed in 530 (55%); LF and low ejection fraction (<50%) in 225 (23%); and LF, low ejection fraction, and low mean gradient (<40 mm Hg) in 147 (15%). Two-year mortality was significantly higher in patients with LF compared with those with normal stroke volume index (47% versus 34%; hazard ratio, 1.5; 95% confidence interval, 1.25-1.89; P=0.006). In the inoperable cohort, patients with LF had higher mortality than those with normal flow, but both groups improved with TAVR (46% versus 76% with LF and 38% versus 53% with normal flow; P<0.001). In the high-risk cohort, there was no difference between TAVR and surgical aortic valve replacement. In patients with paradoxical LF and low gradient (preserved ejection fraction), TAVR reduced 1-year mortality from 66% to 35% (hazard ratio, 0.38; P=0.02). LF was an independent predictor of mortality in all patient cohorts (hazard ratio, ≈1.5), whereas ejection fraction and gradient were not.
LF is common in severe aortic stenosis and independently predicts mortality. Survival is improved with TAVR compared with medical management and similar with TAVR and surgical aortic valve replacement. A measure of flow (stroke volume index) should be included in the evaluation and therapeutic decision making of patients with severe aortic stenosis.
URL: http://www.clinicaltrial.gov. Unique identifier: NCT0053089.4.
Little is known about the incidence of prosthesis-patient mismatch (PPM) and its impact on outcomes after transcatheter aortic valve replacement (TAVR).
The objectives of this study were: 1) to ...compare the incidence of PPM in the TAVR and surgical aortic valve replacement (SAVR) randomized control trial (RCT) arms of the PARTNER (Placement of AoRTic TraNscathetER Valves) I Trial cohort A; and 2) to assess the impact of PPM on regression of left ventricular (LV) hypertrophy and mortality in these 2 arms and in the TAVR nonrandomized continued access (NRCA) registry cohort.
The PARTNER Trial cohort A randomized patients 1:1 to TAVR or bioprosthetic SAVR. Postoperative PPM was defined as absent if the indexed effective orifice area (EOA) was >0.85 cm(2)/m(2), moderate if the indexed EOA was ≥0.65 but ≤0.85 cm(2)/m(2), or severe if the indexed EOA was <0.65 cm(2)/m(2). LV mass regression and mortality were analyzed using the SAVR-RCT (n = 270), TAVR-RCT (n = 304), and TAVR-NRCA (n = 1,637) cohorts.
The incidence of PPM was 60.0% (severe: 28.1%) in the SAVR-RCT cohort versus 46.4% (severe: 19.7%) in the TAVR-RCT cohort (p < 0.001) and 43.8% (severe: 13.6%) in the TAVR-NRCA cohort. In patients with an aortic annulus diameter <20 mm, severe PPM developed in 33.7% undergoing SAVR compared with 19.0% undergoing TAVR (p = 0.002). PPM was an independent predictor of less LV mass regression at 1 year in the SAVR-RCT (p = 0.017) and TAVR-NRCA (p = 0.012) cohorts but not in the TAVR-RCT cohort (p = 0.35). Severe PPM was an independent predictor of 2-year mortality in the SAVR-RCT cohort (hazard ratio HR: 1.78; p = 0.041) but not in the TAVR-RCT cohort (HR: 0.58; p = 0.11). In the TAVR-NRCA cohort, severe PPM was not a predictor of 1-year mortality in all patients (HR: 1.05; p = 0.60) but did independently predict mortality in the subset of patients with no post-procedural aortic regurgitation (HR: 1.88; p = 0.02).
In patients with severe aortic stenosis and high surgical risk, PPM is more frequent and more often severe after SAVR than TAVR. Patients with PPM after SAVR have worse survival and less LV mass regression than those without PPM. Severe PPM also has a significant impact on survival after TAVR in the subset of patients with no post-procedural aortic regurgitation. TAVR may be preferable to SAVR in patients with a small aortic annulus who are susceptible to PPM to avoid its adverse impact on LV mass regression and survival. (The PARTNER Trial: Placement of AoRTic TraNscathetER Valve Trial; NCT00530894).
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