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Posts Tagged ‘Confidence interval’


Carotid Endarterectomy (CEA) vs. Carotid Artery Stenting (CAS): Comparison of CMMS high-risk criteria on the Outcomes after Surgery:  Analysis of the Society for Vascular Surgery (SVS) Vascular Registry Data

Writer and Curator: Larry H. Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN 

 
UPDATED on 8/20/2018

Transcarotid Artery Revascularization Shows Favorable Outcomes in Patients With Carotid Artery Disease

First large body of real-world clinical evidence showing benefits of TCAR versus surgery presented at SVS 2018 Annual Meeting

 

Transcarotid Artery Revascularization Shows Favorable Outcomes in Patients With Carotid Artery Disease

July 30, 2018 — Silk Road Medical Inc. recently announced the presentation of real-world data for the treatment of patients with carotid artery disease at risk for stroke at the Society for Vascular Surgery 2018 Vascular Annual Meeting (VAM), June 20-23 in Boston. In a headline presentation, Marc Schermerhorn, M.D., of Beth Israel Deaconess Medical Center (Boston) shared, for the first time, results from the ongoing TransCarotid Artery Revascularization (TCAR) Surveillance Project, a key initiative of the Society for Vascular Surgery’s Vascular Quality Initiative (VQI).

The trial evaluated patients over a two-year period, with 1,182 patients receiving TCAR compared to 10,797 patients receiving carotid endarterectomy (CEA).

“Our overall findings showed that while patients receiving TCAR were sicker and more likely to be symptomatic with a higher degree of stenosis, the stroke and death rate compared to CEA was the same,” Schermerhorn said. “With TCAR, there were significantly lower cranial nerve injuries, less time spent in the operating room and fewer patients with a prolonged length of stay. I believe that clinicians should more widely adopt the TCAR technology as it has demonstrated both safety and efficacy and is an excellent alternative to CEA.”

Significant findings from the study showed TCAR to have:

  • Comparable rates of in-hospital stroke or death to CEA (TCAR, 1.6 percent; CEA, 1.4 percent, p=.33);
  • Lower rates of acute cranial nerve injury (TCAR, 0.6 percent; CEA, 1.8 percent, p<.001);
  • Shorter operative times (TCAR, 78 min; CEA, 111 min, p<.001); and
  • Shorter hospital stays, despite patients being older and sicker (percent of hospitals stays longer than one night: TCAR, 27%; CEA, 30%, p=0.046).

TCAR is a clinically proven procedure combining surgical principles of neuroprotection with minimally invasive endovascular techniques to treat blockages in the carotid artery at risk of causing a stroke. The TCAR Surveillance Project is the largest single body of evidence reported since the launch of TCAR in 2016.

Additional TCAR presentations highlighted at SVS VAM 2018 demonstrated similar results:

“Vascular Live: Latest Stroke Prevention Data Signals Standard of Care Potential in Carotid Revascularization” provided an interim update on the ROADSTER 2 Per Protocol data set. The ROADSTER 2 trial is a post-market study intended to enroll a minimum of 600 patients and with at least 70 percent enrollment completed by newly trained operators. Peter Schneider, M.D., of Kaiser Permanente (Honolulu) and co-principal investigator for the ROADSTER 2 trial, presented interim results on 470 patients. Schneider highlighted a 30-day stroke rate of 0.6 percent and a stroke/death rate of 0.9 percent, consistent with the outcomes seen in the pivotal ROADSTER trial.

“A Multi-Institutional Analysis of Contemporary Outcomes after TransCarotid Artery Revascularization versus Carotid Endarterectomy” compared outcomes of TCAR to CEA across four institutions. Alex King of University Hospitals Cleveland Medical Center (Ohio) presented results showing that patients undergoing TCAR (n=292), had similar 30-day stroke rates (TCAR, 1 percent; CEA, 1.1 percent, p=1.00) compared with patients undergoing CEA (n=371), despite being more likely to have significant comorbidities. Acute (TCAR, 0.3 percent; CEA, 4.1 percent, p<.01) and six-month cranial nerve injury rates (TCAR, 0 percent; CEA: 1.9 percent, p=0.02) were shown to be lower with TCAR vs CEA.

The Enroute Transcarotid Stent is intended to be used in conjunction with the Enroute Transcarotid Neuroprotection System (NPS) during the TCAR procedure. The Enroute Transcarotid NPS is used to directly access the common carotid artery and initiate high rate temporary blood flow reversal to protect the brain from stroke while delivering and implanting the Enroute Transcarotid Stent.

For more information: www.silkroadmed.com

 

This is a review of the impact of the Centers for Medair and Medicaid Services on carotid artery endovascular outcomes carried out by the Division of Vascular and Endovascular Surgery at Harvard Medical School, Partners.

The impact of Centers for Medicare and Medicaid Services high-risk criteria on outcome after carotid endarterectomy and carotid artery stenting in the SVS Vascular Registry.

Schermerhorn ML, Fokkema M, Goodney P, Dillavou ED, Jim J, Kenwood CT, Siami FS, White RA; SVS Outcomes Committee.
 J Vasc Surg. 2013 May;57(5):1318-24.   http://dx.doi.org/10.1016/j.jvs.2012.10.107. Epub 2013 Feb 11.
The Centers for Medicare and Medicaid Services (CMS) require high-risk (HR) criteria for carotid artery stenting (CAS) reimbursement. The impact of these criteria on outcomes after carotid endarterectomy (CEA) and CAS remains uncertain. Additionally, if these HR criteria are associated with more adverse events after CAS, then existing comparative effectiveness analysis of CEA vs CAS may be biased. We sought to elucidate this using data from the SVS Vascular Registry.
We analyzed 10,107 patients undergoing CEA (6370) and CAS (3737), stratified by CMS HR criteria. The primary endpoint was composite death, stroke, and myocardial infarction (MI) (major adverse cardiovascular event [MACE]) at 30 days. We compared baseline characteristics and outcomes using univariate and multivariable analyses.
CAS patients were more likely than CEA to have
  • preoperative stroke (26% vs 21%) or
  • transient ischemic attack (23% vs 19%) .
Although age ≥ 80 years was similar, CAS patients were more likely to have all other HR criteria.
For CEA, HR patients had higher MACEs than normal risk in both
  • symptomatic (7.3% vs 4.6%; P < .01) and
  • asymptomatic patients (5% vs 2.2%; P < .0001).
For CAS, HR status was not associated with a significant increase in MACE for
  • symptomatic (9.1% vs 6.2%; P = .24) or
  • asymptomatic patients (5.4% vs 4.2%; P = .61).
All CAS patients had MACE rates similar to HR CEA. After multivariable risk adjustment, CAS had higher rates than CEA
  • for MACE (odds ratio [OR], 1.2; 95% confidence interval [CI], 1.0-1.5),
  • death (OR, 1.5; 95% CI, 1.0-2.2), and
  • stroke (OR, 1.3; 95% CI,1.0-1.7),
whereas there was no difference in MI (OR, 0.8; 95% CI, 0.6-1.3).
Among CEA patients, MACE was predicted by:
  • age ≥ 80 (OR, 1.4; 95% CI, 1.02-1.8),
  • congestive heart failure (OR, 1.7; 95% CI, 1.03-2.8),
  • EF <30% (OR, 3.5; 95% CI, 1.6-7.7),
  • angina (OR, 3.9; 95% CI, 1.6-9.9),
  • contralateral occlusion (OR, 3.2; 95% CI, 2.1-4.7), and
  • high anatomic lesion (OR, 2.7; 95% CI, 1.33-5.6).
Among CAS patients, recent MI (OR, 3.2; 95% CI, 1.5-7.0) was predictive, and
  • radiation (OR, 0.6; 95% CI, 0.4-0.8) and
  • restenosis (OR, 0.5; 95% CI, 0.3-0.96) …..were protective for MACE
Although CMS HR criteria can successfully discriminate a group of patients at HR for adverse events after CEA, certain CMS HR criteria are more important than others. However, CEA appears safer for the majority of patients with carotid disease. Among patients undergoing CAS, non-HR status may be limited to restenosis and radiation.
This study was preceded by another publication 5-years earlier involving ML Schermerhorn, of the study above.

Risk-adjusted 30-day outcomes of carotid stenting and endarterectomy: results from the SVS Vascular Registry.

Sidawy AN, Zwolak RM, White RA, Siami FS, Schermerhorn ML, Sicard GA; Outcomes Committee for the Society for Vascular Surgery.
Department of Surgery, Washington VA Medical Center, Washington, DC, USA.
J Vasc Surg. 2009 Jan;49(1):71-9. http:/dx.doi.org/10.1016/j.jvs.2008.08.039. Epub 2008 Nov 22.
As of December 26, 2007, 6403 procedures with discharge data were entered by 287 providers at 56 centers on 2763 CAS patients (1450 with 30-day outcomes, 52.5%) and 3259 CEA patients (1368 with 30-day outcomes, 42%).
Of the total cohort, 98% of CEA and 70.7% of CAS (P < .001) were performed for atherosclerotic disease.
  • Restenosis accounted for 22.3% and
  • post-radiation induced stenosis in 4.5% of CAS patients.
Preprocedure lateralizing neurologic symptoms were present in a greater proportion of – CAS patients (49.2%) than CEA patients (42.4%, P < .001).
CAS patients also had higher preprocedure prevalence of
  1. coronary artery disease (CAD),
  2. MI,
  3. congestive heart failure (CHF),
  4. chronic obstructive pulmonary disease (COPD), and
  5. cardiac arrhythmia.
For CAS, death/stroke/MI at 30 days was
  • 7.13% for symptomatic patients and 4.60% for asymptomatic patients (P = .04).
For CEA, death/stroke/MI at 30 days was
  • 3.75% in symptomatic patients and 1.97% in asymptomatic patients (P = .05).
After risk-adjustment for age, history of stroke, diabetes, and American Society of Anesthesiologists (ASA) grade (ie, factors found to be significant confounders in outcomes using backwards elimination),
logistic regression analysis suggested better outcomes following CEA.
When CAS and CEA were compared in the treatment of atherosclerotic disease only, the difference in outcomes between the two procedures was more pronounced, with
  • death/stroke/MI 6.42% after CAS vs 2.62% following CEA, P < .0001.
With continued enrollment and follow-up, analysis of SVS-VR will supplement randomized trials by providing real-world comparisons of CAS and CEA with sufficient numbers to serve as an outcome assessment tool of important patient subsets and across the spectrum of peripheral vascular procedures.
J Vasc Surg. 2012 May;55(5):1313-20; discussion 1321. doi: 10.1016/j.jvs.2011.11.128. Epub 2012 Mar 28.

Society for Vascular Surgery (SVS) Vascular Registry evaluation of comparative effectiveness of carotid revascularization procedures stratified by Medicare age.

Jim JRubin BGRicotta JJ 2ndKenwood CTSiami FSSicard GASVS Outcomes Committee.

Source

Washington University School of Medicine, St. Louis, Mo., USA.

Abstract

OBJECTIVE:

Recent randomized controlled trials have shown that age significantly affects the outcome of carotid revascularization procedures. This study used data from the Society for Vascular Surgery Vascular Registry (VR) to report the influence of age on the comparative effectiveness of carotid endarterectomy (CEA) and carotid artery stenting (CAS).

METHODS:

VR collects provider-reported data on patients using a Web-based database. Patients were stratified by age and symptoms. The primary end point was the composite outcome of death, stroke, or myocardial infarction (MI) at 30 days.

RESULTS:

As of December 7, 2010, there were 1347 CEA and 861 CAS patients aged < 65 years and 4169 CEA and 2536 CAS patients aged ≥ 65 years. CAS patients in both age groups were more likely to have a disease etiology of radiation or restenosis, be symptomatic, and have more cardiac comorbidities. In patients aged <65 years, the primary end point (5.23% CAS vs 3.56% CEA; P = .065) did not reach statistical significance. Subgroup analyses showed that CAS had a higher combined death/stroke/MI rate (4.44% vs 2.10%; P < .031) in asymptomatic patients but there was no difference in the symptomatic (6.00% vs 5.47%; P = .79) group. In patients aged ≥ 65 years, CEA had lower rates of death (0.91% vs 1.97%; P < .01), stroke (2.52% vs 4.89%; P < .01), and composite death/stroke/MI (4.27% vs 7.14%; P < .01). CEA in patients aged ≥ 65 years was associated with lower rates of the primary end point in symptomatic (5.27% vs 9.52%; P < .01) and asymptomatic (3.31% vs 5.27%; P < .01) subgroups. After risk adjustment, CAS patients aged ≥ 65 years were more likely to reach the primary end point.

CONCLUSIONS:

Compared with CEA, CAS resulted in inferior 30-day outcomes in symptomatic and asymptomatic patients aged ≥ 65 years. These findings do not support the widespread use of CAS in patients aged ≥ 65 years.

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English: FIG. 513 – The internal carotid and vertebral arteries. Right side. Deutsch: Rechte Arteria carotis (Photo credit: Wikipedia)

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Carotid Plaque Atherosclerotic plaque from a carotid endarterectomy specimen. This shows the bifurcation of the common into the internal and external carotid arteries. (Photo credit: Wikipedia)

Right common carotid artery - The Anatomy of t...

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Normality and the Parametric Paradigm

Larry H. Bernstein, MD

plbern@yahoo.com

This article is about the measure of central tendancy and dispersion of values around the center (mean or median), the underpinning of parametric methods of comparison of 2 or more sets of data.  More importantly, it is the beginning of a statistical journey.  The clinical laboratory deals with large volumes of patient data.  The use of a parametric approach is limited and is prone to problems introduced in the clinical domain.  Consequently, Galen and Gambino introduced the concept of predictive value and the effect of prevalence in a Bayesian context in Beyond Normality. These calculations work off of tables, the same tables that are used for sensitivity and specificity, and are used to calculate a chi squared probability.  The subsequent influence of epidemiology went further in introducing odds and odds ratios.  The third and last article will address the more recent advances beyond, beyond normality.  These improvements have all come about by the development of a powerful statistical methodology that is not constraint by the parametric paradigm and is well developed for hypothesis generation and validation, not just testing of simple hypotheses.

We have grown up with the normal curve and have incorporated it in our thinking, not just our work.  Even the use of the term Six Sigma for reduction of errors has reference to the classical “normal curve” introduced by Johann Carl Friedrich Gauss (1777–1855).  The normal or “bell shaped” curve is a plot of numerical values along the x-axis and the frequency of the occurrence on the y-axis.  If the set of measurements occurs as a random and independent event, we refer to this as parametric, and the distribution of the values is a bell shaped curve with all but 2.5% of the values included within both ends, with the mean or arithmetic average at the center, and with 67% of the sample contained within 1 standard deviation of the mean.   The reference to normality has been used with respect to student test scores, with respect to coin flipping and games of chance, with respect to investment, and in our experience with respect to errors of quality controlled measurements.  The expected value we refer to as the mean (closest to the true value), and the distance from the mean (or scatter) we refer to as dispersion, measured as the standard deviation.  Viewed in this light, we can convert the curve from a standard curve with an actual mean to a standard normal curve with a mean at the center of “0”, and with distances from “0” in standard deviations.   A bad example of this is the distribution of serum AST measurements of a large unselected population enrolled in a clinical trial.  The AST values tend to have many high values, which we call skewness to the right of the curve, so the behavior we are looking for is better described by a log transformation of the values to minimize nonlinearities in the measurement.  This is illustrated by the comparison of AST and log(AST) in Figure 1.

What has not been said is that we view a reference range in terms of a homogeneous population.  This means that while all values might not be the same, the values are scattered within a distance from the mean that becomes less frequent as the distance is larger so that we can describe a mean and a 95% confidence interval around the mean.  In mineralogy we can measure physical elements that have structure defined by a relationship of structure to spectral lines.  Hence, the scatter about the mean is very small because of the precise measurements, even though the quantity may be very small.  This is not necessarily the case with clinical laboratory measurement because of hidden variables, such as – age, diurnal variation, racial factors, and disease.  One way to level the playing field is to compose uniform specimens for quality control that are representative of a population for comparison of laboratory measurements among many laboratories, which is established practice.  What is assumed is that a “normal” population is that population that is found after we remove bias, or contamination of the population by the hidden variable effects mentioned above.  Therefore, parametric statistics is actually a comparison of one or more populations that are to be compared with the hypothetical normal population.   The test of significance is a comparison of A and B with the assumption that they are sampled from the same population, but when they are found to have different means and confidence intervals by a t-test or an analysis of variance, we reject the “null hypothesis” and conclude that they are different based on a p (significance) less than 5%.   There are basic assumptions that are required when we use the parametric paradigm.   The distributions of the samples are the same, normality, the variances are the same, and errors are independent.  Consequently, when comparing 2 samples, as for a placebo and a test drug, these assumptions must hold (which is inherent in the logistic regression).   When we run quality control material, the confidence lines that we use are equivalent to a normal curve turned on its side.  When doing the t-test, the parametric limitations have to be followed.  A result of this is that a minimum of 40 samples are required because as N approaches 40 and over the fit of the data to a normal distribution is more likely.  This is a daily phenomenon in laboratories globally – it takes about 10 – 14 days to be confident about the reference range for a new lot of quality control material, regardless of high, low or normal.  Nevertheless, we have to ask whether we can use a small sample size to validate the reference range of a population sample.  The answer is not so simple.  One can minimize sampling bias by taking a sample of blood donors who are prescreened for serious medical conditions.  The use of laboratory staff donors historically introduced selection bias when the staff was uniformly younger. On the other hand, the amount of computing power readily available to the average practitioner has substantially improved in the last 5 years, and middleware may offer a further opportunity for improvement.  One can download a file with two weeks of results for any test and review and exclude outliers to the established values for the method.  The substantial remaining sample has at least 1,000 patients to work with.  Another method would use a nonparametric adjustment of the data by randomly removing a patient at a time and recalculating. We are not here concerned with distributional assumptions and population parameters. We work only with the data, and we observe the effects of recalculation.   That is an uncommon and unfamiliar approach.

We proceed to the important problem of comparing 2 variables.  Figure 1 is a bivariate plot of data with log(AST) and log(ALT) on each axis.  The result is a scattergram with 95 and 99 percent confidence limits for a reference range formed from two liver tests that meet the parametric constraints.   The scattergram shown in Figure 2 may show correlation, method A and method B, distinctly different, but having a linear association between them.  The parametric assumption holds, and the confidence interval along the so called regression line is determined by ordinary least square regression (OLS).  The subject of regression is a subject worthy of a separate topic.

The next topic is comparing two classes of subjects that we expect to be different because of effects on each group.  This can be represented by the plot of means and standard deviations between patients with ovarian cancer who underwent chemotherapy and either had no or short remission, or had a remission of 20 months, defining treatment success (Figure 2).   The result of means comparison is significant at p < 0.01 using the t-test (Figure 3).   But what if we were to take the same data and compare the patients with no remission, small remission, and complete remission?  One would do the one-way analysis of variance (ANOVA1), which uses the F test (Fisher’s variance ratio).  F is the same as t squared, or t is the square root of F.  The result would again be significant at p < 0.01.

This is a light review of very important methods used in both clinical and research laboratory studies.  They have a history of widespread use going back at least 5 decades, and certainly in experimental physics before biology, although it is from biological observations that we have Fisher’s discriminant function, which gives a linear distance between classified variable, i.e., petal length and petal width.  The discussion to follow will be concerned with tables and the chi squared distribution.

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