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Online Appendix for the following JACC article
TITLE: Arterial Wave Reflections and Incident Cardiovascular Events and Heart Failure:
The Multiethnic Study of Atherosclerosis
AUTHORS: Julio A. Chirinos, MD,* Jan G. Kips, PHD,† David R. Jacobs Jr., PHD,‡ Lyndia
Brumback, PHD,§ Daniel A. Duprez, MD, PHD,¶ Richard Kronmal, PHD,§ David A. Bluemke,
MD,# Raymond R. Townsend, MD,* Sebastian Vermeersch, PHD,† Patrick Segers, PHD†
APPENDIX
Supplemental Methods
Pulse wave quality control
All pressure waveforms were visually inspected by an investigator (JAC) for quality and
physiologic consistency. We excluded averaged waveforms that met any of the following
criteria: (1) A non-physiologic appearance (usually from bigeminy, trigeminy, or contamination
of the signal average by aberrantly recorded complexes); (2) Cardiac cycle duration variation
≥10%; (3) Pulse height (beat-to-beat pulse pressure) variation ≥20%; (4) Less than 10 adequately
recorded cycles available for signal averaging.
Central pulse wave separation analysis
We used a physiologic flow waveform previously obtained from averaging empiric
measurements among men and women without cardiovascular disease as previously described
(1). Using the central pressure and the physiologic flow waveforms, linear wave separation
analysis was performed as first described by Westerhof et al (2). First, characteristic impedance,
Zc, which describes the relationship between pulsatile pressure and flow in the proximal aorta in
the absence of wave reflections, was calculated by averaging the modulus of the 3rd to 10th
harmonics of input impedance (i.e., pressure moduli divided by corresponding flow moduli).
Pressure and flow waveforms were then used for wave separation as follows:
Pf = (P + Zc*Q)/2
Pb = (P – Zc*Q)/2
Where P and Q denote harmonics derived from the measured pressure and flow waveform, using
Fourier decomposition. To obtain the total forward and backward wave, forward and backward
harmonics were summated. As described by Westerhof et al. (3), waveform calibration is not
necessary for wave separation. Given that Zc is expressed as P/Q, the product Zc*Q is
independent of flow amplitude (because the flow units in the numerator and denominator cancel
out).
Once Pf and Pb were computed, reflection magnitude was calculated as the ratio of their
amplitudes expressed as a percentage (100 * Pb amplitude/Pf amplitude).
Statistical Methods
Our statistical analyses were aimed at: (1) Assessing the independent association between central
pressure indices and incident cardiovascular events/heart failure; (2) Assessing the increase in
predictive performance achieved by adding central pressure indices to models containing
established risk factors; (3) Comparing the strength/performance of central pressure indices to
established risk factors (particularly brachial pressures) as predictors of incident cardiovascular
events/heart failure in multivariate analyses; (4) Assessing the population-attributable risk of
incident events/heart failure associated with central pressure indices and comparing it to that of
hypertension (the only currently well-established risk factor defined on the basis of arterial
pressure measurements).
Cumulative probability curves for the endpoints of interest were constructed by using the
Kaplan-Meier method, stratifying participants according to tertiles of the central pressure
variable of interest. The relations of central pressure variables to incident events were further
investigated with the use of Cox proportional-hazards regression in two sets of models: crude
models and models adjusted for established risk factors for cardiovascular disease and other
potential confounders. These included age, gender, systolic blood pressure, diastolic blood
pressure, antihypertensive medication use, body height and weight, diabetes mellitus, total
cholesterol, HDL-cholesterol, current smoking, heart rate and glomerular filtration rate
(estimated by the Modification of Diet in Renal Disease formula [4]).
The performance of various Cox models was assessed for statistical aims (2) and (3)
mentioned above. We used several indices of model performance, which in turn express several
aspects of fit and predictive ability. Overall model fit was compared with the Akaike information
criterion (AIC) and the Bayesian information criterion (BIC) (5,6), both of which are functions
of the log likelihood with an added penalty for the number of parameters. Lower values of the
AIC and BIC indicate better fit. The discriminative ability of the models was assessed with the
Harrel's c-index, which is analogous to the area under the receiver operator characteristic curve,
applied to survival data (5,7,8). Improvements in subject risk reclassification was further
assessed using the net reclassification improvement (NRI), as described by Pencina et al (8) and
applied to censored (survival) data (5,8). The NRI quantifies the amount of correct
reclassification achieved by using a new model compared to the classification achieved by using
a “base” model. The NRI depends on the difference between new and the base model in the
individual estimated probability that a subject will be classified as an event, given that the
individual is observed to be an event, minus the corresponding model difference estimated
probability of classification as an event, given that the individual is observed to be a nonevent.
An increased probability that observed events will be correctly classified as events and an
increased probability that observed nonevents will be correctly classified as nonevents imply
better prediction ability, whereas the opposite implies worse prediction ability. The NRI is
computed as the net proportion of events reclassified correctly plus the net proportion of nonevents reclassified correctly. We computed 2 versions of the NRI: a category-based NRI and a
category-free NRI. The category-based NRI was based on usual categories for 10-year coronary
heart disease risk (adapted at 5 years as <2.5%, 2.5-<5%, 5-<10% and ≥10%). Since no
established categories exist that guide clinical decisions for CHF risk, we primarily used
category-free reclassification measures for this endpoint, which are independent of arbitrarily
defined risk thresholds (9). The category-free NRI is computed as the net proportion of events in
whom the new model correctly predicts a higher probability of the event (compared to the
estimated probability of the base model) plus the net proportion of non-events in whom the new
model correctly predicts a lower probability of the event (compared to the estimated probability
of the base model).
Although the category-free NRI measures reclassification of case and control subjects to
a higher or lower predicted probability, it does not account for the magnitude of the difference of
predicted probabilities between cases and controls in the base and the new model. The difference
of predicted probabilities between cases and controls is called the discrimination slope. The
integrated discrimination improvement (IDI), expresses the absolute improvement in mean
discrimination slope, or the improvement in the probability of discrimination (calling an
observed event an event and observed nonevent a nonevent) between the base model and new
model (5,8–10). Because the IDI, as an absolute difference in probabilities, is highly sensitive to
event rates (which in our cohort are relatively low, particularly for the heart failure endpoint), its
absolute magnitude is difficult to interpret quantitatively. Therefore, it is useful to express IDI as
the relative IDI (10). The relative IDI expresses the relative increase in separation of events and
non-events from the separation achieved in the base model (i.e., the difference in discrimination
slopes is expressed as a proportion of the discrimination slope of the base model). For example,
if the addition of a new variable to a base model results in a relative IDI of 0.10, this indicates
that the addition of the new variable results in a 10% improvement in separation of events and
non-events relative to the separation that had been achieved by the joint effect of all variables
included in the base model. Confidence intervals for IDI and relative IDI were computed with
the bootstrap method. Larger positive IDI and relative IDI values indicate larger improvements
in model discrimination. We acknowledge that indices of model performance are under intense
development. Each index describes different aspects of model performance and at the present
time none can be stated to be a better or worse descriptor.
To assess model calibration, we used the Hosmer-Lemeshow test, which compares
observed and predicted risk according to deciles of predicted risk for various models.
Analyses were performed using SPSS for Windows v17 (SPSS Inc., Chicago, IL). All
statistical tests were 2-tailed and alpha was set at 0.05.
Adjustment for body size
To adjust for body size and obesity in initial analyses, body height and weight (rather than body
mass index), were used as separate terms, due to the known important relationship between
AIx/pulse pressure amplification and body height (requiring specific adjustment for body height
in the models). Although reflection magnitude is not importantly related to body height, it was
handled in an identical fashion in initial analyses to allow for direct comparisons with models
containing other central pressure indices. However, in additional models focused on reflection
magnitude, body mass index was used instead of body weight and height. Although this did not
appreciably change the hazard ratio or improvements in model performance associated with
reflection magnitude, it did allow for an intuitive assessment of the contribution of obesity
(expressed in units of body mass index of kg/m2) to event risk in the Cox models.
Adjudication of events
During follow-up, in addition to three on-site MESA examinations, a telephone interviewer
contacted each participant every 9 to 12 months to inquire about all interim hospital admissions,
cardiovascular outpatient diagnoses, and deaths. In order to verify self-reported diagnoses, copies
were requested of all death certificates and medical records for all hospitalizations and outpatient
cardiovascular diagnoses. Trained personnel abstracted any medical records suggesting possible
cardiovascular events. Two physicians from the MESA events committee independently
reviewed all medical records for blinded endpoint classification and assignment of incidence
dates using pre-specified criteria. If the reviewing physicians disagreed on the event
classification, they adjudicated differences. If disagreements persisted, the full events committee
made the final classification.
The endpoint of congestive heart failure (CHF), required "definitive" criteria, including
clinical symptoms (e.g., shortness of breath) or signs (e.g., edema), a physician diagnosis of CHF
and medical treatment for CHF in addition to: (a) pulmonary edema/congestion by chest X-ray
and/or (b) dilated ventricle or poor LV function by echocardiography or ventriculography, or
evidence of left ventricular diastolic dysfunction. Our composite endpoint of incident “hard
cardiovascular events” was met when any of the following occurred: myocardial infarction,
coronary heart disease death, resuscitated cardiac arrest, stroke, or stroke death. The endpoint of
"all cardiovascular events," was defined as any hard cardiovascular event, angina, other
atherosclerotic death, or other cardiovascular disease death. Therefore, hard cardiovascular
events and all cardiovascular events are related composite endpoints (all hard cardiovascular
events are included in the “all cardiovascular event” endpoint), whereas the definition of the
heart failure endpoint is independent of the other two endpoints.
Myocardial infarction was defined based on combinations of symptoms (e.g., chest pain),
ECG abnormalities, and cardiac biomarker levels. Coronary heart disease (CHD) death was
classified as present or absent based on hospital records and interviews with families. CHD death
required a myocardial infarction within 28 days of death, chest pain within the 72 hours before
death, or a history of CHD and the absence of a known non-atherosclerotic or non-cardiac cause
of death. Stroke required documented focal neurologic deficit lasting 24 hours or until death, or
if <24 hours, there was a clinically relevant lesion on brain imaging. Patients with focal
neurologic deficits secondary to brain trauma, tumor, infection, or other nonvascular cause were
excluded. Adjudicators classified angina based on their clinical judgment and documentation of
chest pain or anginal equivalents and objective evidence of reversible myocardial ischemia or
obstructive coronary artery disease (e.g., ≥70% coronary artery obstruction or positive stress
test).
Supplemental results
Reclassification improvement for heart failure risk achieved with reflection magnitude
The table below shows a detailed categorical reclassification table for 5-year heart failure risk.
These were adapted from the usual 10-year risk categories for cardiovascular risk as <2.5%, 2.5
to <5%, 5 to <10% and ≥10% at 5 years, respectively. The proportions of events and non-events
in each reclassification cell were computed using the 5-year Kaplan-Meier estimates within each
cell as described by Pencina et al. (9). The net reclassification improvement achieved by adding
reflection magnitude to a base model containing age, ethnicity, gender, body mass index,
diabetes mellitus, systolic blood pressure, diastolic blood pressure, antihypertensive medication
use, total cholesterol, HDL-cholesterol, current smoking, heart rate and estimated glomerular
filtration rate was 0.17 (P=0.005). Estimates computed with observed events within 5 years
(rather than Kaplan-Meier estimates in each cell) yielded very similar results.
Category-free indices were also computed. The category-free NRI was 0.38 (P<0.0001),
the integrated discrimination improvement was 0.018 (P<0.0001) and the relative integrated
discrimination improvement was 0.48 (P<0.0001). This relative IDI indicates that a 48% relative
increase in the discrimination slope achieved by the joint effect of all variables in the base model
was achieved by addition of reflection magnitude to the model.
Supplemental Table.
Probability predicted by new model
(all variables in base model + reflection magnitude)
Probability
Number
predicted by base
model
Number
(proportion) (proportion)
2.5 to
5 to
<5%
<10%
≥10%
<2.5%
Total
reclassified
reclassified
at lower
at higher
risk
risk
<2.5 %
Total n
Events, n
Nonevents, n
3787
308
14
0
4109
14
1
0
0
15
0 (0)
1 (0.07)
3773
307
14
0
4094
0 (0)
321 (0.08)
0.38%
0.32%
0%
0%
0.37%
232
607
248
12
1099
4
18
11
0
33
4 (0.12)
11 (0.33)
228
589
237
12
1066
228 (0.21)
249 (0.23)
1.78%
2.96%
4.33%
0%
2.98%
12
118
312
108
550
Kaplan-Meier 5 yr
estimate, %
2.5 to <5%
Total n
Events, n
Nonevents, n
Kaplan-Meier 5 yr
estimate, %
5 to <10%
Total n
Events, n
0
0
13
10
22
0 (0)
10 (0.45)
Nonevents, n
12
118
299
98
528
130 (0.25)
98 (0.19)
0%
0%
4.19%
8.81%
4.09%
Total n
0
3
37
136
176
Events, n
0
0
0
15
14
0 (0)
0 (0)
Nonevents, n
0
3
37
121
162
40 (0.25)
0 (0)
0%
0%
0%
10.68%
8.22%
4031
1036
611
256
5934
18
19
24
24
83
4 (0.05)
22 (0.27)
4013
1017
587
232
5851
398 (0.07)
668 (0.11)
0.46%
1.82%
3.91%
9.34%
1.40%
Kaplan-Meier 5 yr
estimate, %
≥10%
Kaplan-Meier 5 yr
estimate, %
Total
Events, n
Nonevents, n
Kaplan-Meier 5 yr
estimate, %
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