(Circulation. 2000;102:517.)
© 2000 American Heart Association, Inc.
Clinical Investigation and Reports |
Application of the New York State PTCA Mortality Model in Patients Undergoing Stent Implantation
From the Division of Cardiovascular Diseases and Internal Medicine (D.R.H., P.B.B., K.N.G., V.M., M.R.B., G.W.B., S.T.H., L.N.H., C.S.R.) and the Section of Biostatistics (D.E.G.), Mayo Clinic and Mayo Foundation, Rochester, Minn.
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Abstract |
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Background—This study applied the New York State conventional coronary angioplasty (PTCA) model of clinical outcomes to evaluate whether it has relevance in the current era of stent implantation. The model was developed in 62 670 patients treated with conventional PTCA from 1991 to 1994 to risk adjust mortality and bypass surgery after PTCA. Since then, stents have become the dominant form of intervention. Whether that model remains relevant is uncertain.
Methods and Results—All patients undergoing stenting at the Mayo
Clinic from 1995 to 1998 were analyzed for in-hospital
mortality, bypass surgery performed after attempted stenting, and
longer-term mortality. No patients were excluded. The New York model
was used to risk adjust and predict in-hospital and follow-up
mortality. There were 3761 patients with 4063 procedural admissions for
stenting; 6472 target vessel segments were attempted, and 96.1% of
procedures were successful. With the New York multivariable risk
factor equation, 79 in-hospital deaths were expected (1.95%); 66
deaths (1.62%) were observed. The New York model risk score in a
logistic regression model was the most significant factor associated
with in-hospital mortality (OR, 1.86; P<0.001). During
a mean follow-up of 1.2±1.0 years, there were 154 deaths.
Multivariable analysis documented 6 factors associated with
subsequent mortality; New York risk score was the most significant
(
2=16.64, P=0.0001).
Conclusions—Although the New York mortality model was developed in an era of conventional angioplasty, it remains relevant in patients undergoing stenting. The risk score derived from that model is the variable most significantly associated with not only in-hospital but also longer-term outcome.
Key Words: angioplasty • coronary disease • stents
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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Outcome analysis has become an increasingly important component of the practice of interventional cardiology and has been used to evaluate the relationship between procedural complications and institutional and individual operator volume.1 2 3 4 5 6 7 8 9 With conventional PTCA, an inverse relationship between complications and case volume has been frequently, although not universally, identified.2 3 4 5 6 7 8 The inconsistent results of these analyses may relate to variations in case mix, referral patterns, or procedural techniques, all of which can complicate analyses. Although benchmarking raw outcome data is difficult, multivariable adjustment may facilitate outcome analysis and risk stratification. The New York State multivariable model was developed from a prospectively evaluated database of 62 670 patients treated with conventional PTCA from 1991 to 1994 to stratify the risk of procedure-related death.7 Since the development of this model, however, interventional cardiology has changed dramatically,10 11 12 particularly with the widespread use of stent implantation in 70% to 80% of patients and the use of glycoprotein IIb/IIIa antagonists in 30% to 50%. Data are limited on the relevance of the New York State model to current interventional practice. The purpose of this study was to determine whether this model, developed in patients treated with conventional balloon angioplasty, can predict procedural in-hospital or longer-term mortality after stent implantation.
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Methods |
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Under a protocol approved by the institutional review board, a prospective interventional database has been maintained at the Mayo Clinic since 1979; it includes demographic, clinical, angiographic, and procedural data. Angiographic characteristics are coded at the time of the interventional procedure; immediate and in-hospital events are identified and recorded by a team of independent research nurses and technicians, and all patients are subsequently contacted by telephone at 6 and 12 months and each year thereafter.
The study group included all patients undergoing stent implantation from 1995 to 1998. No subgroups of patients receiving a stent were excluded, except the 105 patients who denied use of their medical records for research purposes (according to Minnesota law, patients must consent to such use). During this time, various stents were used, including investigational stents and stents approved by the Food and Drug Administration. The specific stent and size used and adjunctive antiplatelet therapy were selected by the staff interventional cardiologist. Adjunctive therapy consisted of aspirin and either ticlopidine or clopidogrel. Warfarin was not used unless required for concomitant conditions; glycoprotein IIb/IIIa receptor antagonists were used at the discretion of each interventional cardiologist. Cardiac enzymes were not routinely monitored after the procedure but were measured if infarction was clinically suspected.
Any in-hospital death occurring after a percutaneous intervention was considered related to the procedure. Morbidity and mortality were prospectively monitored for each procedure and for each physician, and the results were reviewed by the Quality Assurance Program and the Cardiac Catheterization Laboratory director.
Lesion success was defined as achievement of a residual luminal
diameter stenosis of <50%, including a 
20% improvement by
visual estimation of biplane orthogonal views by 2 cardiologists.
Procedural success was defined as 1 successful lesion without
in-hospital death, Q-wave myocardial infarction, or emergency CABG.
Before the procedure, the operator defined lesion complexity according
to the modified criteria of the American Heart Association and the
American College of Cardiology
(AHA/ACC).13 A definite infarction was defined as the
development of new Q waves on ECG and prolonged pain or increased
enzyme values. A probable infarction was defined as new persistent ST-T
wave changes and prolonged ischemic pain or increased enzyme
values. Single-vessel disease was defined as >70% luminal diameter
stenosis in 1 major epicardial vessel, and multivessel disease
was defined as >70% stenosis in 1 major epicardial vessel
and > 50% stenosis in 1 other major
epicardial vessel.
Statistical Analysis
Results are presented as either mean±SD or as a
percentage of the total. Continuous data were compared with Student’s
t test; proportions were compared with Pearson’s
2 test. Several patients had multiple
interventions and >1 procedural admission, usually performed by a
different operator. Because the intent of the study was to examine
procedural mortality, the number of procedural admissions rather than
the number of patients was chosen as the most appropriate denominator
for percentage determinations. The statistical significance of the
baseline variables of the hospital survivors versus those who died
was tested with Student’s t test for continuing
variables or Pearson’s 2 test for
discrete variables.
The coefficients from the multivariable risk factor equation for
hospital deaths during or after PTCA in the New York State model (Table 1
) were used to calculate a risk
score and corresponding probability of death for each procedure. These
probabilities were summed to obtain an expected number of deaths for
the population on the basis of this model. The number of observed
deaths was then compared with the expected number of deaths for the
entire study group and for subsets of the study cohort. CIs were
calculated with the normal approximation to a binomial distribution.
The risk score based on the New York State model was used as a
covariate in a logistic regression model to assess this relationship
with in-hospital mortality and to adjust for important differences
between cases. The New York State model did not include specific
anatomic lesion characteristics, such as calcification; these were
added to the risk score and the logistic regression model for
in-hospital mortality. The ORs and CIs are given for these models. The
C statistic, which is equivalent to the area under a
receiver-operator characteristic curve, and the Hosmer-Lemeshow
goodness-of-fit statistic are provided for each model.
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Long-term follow-up of patients surviving the hospital phase is presented with the Kaplan-Meier product-limit method; 6-month and 1-year event rates are for hospital survivors. Cox proportional-hazards models were constructed with stepwise techniques.
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Results |
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During the period of this study, 3761 patients had a total of 4063 procedural admissions for stent implantation. A total of 6472 coronary segments were treated during these procedures. Most patients (71.2%) were male; the mean age of patients was 64.6 years (Table 2
). Most patients (68%) had
unstable angina, and 43.2% had pain at rest. A myocardial infarction
within 24 hours before stent implantation had been documented in 12.9%
of patients. Other high-risk features included a history of congestive
heart failure (12.4%) and a history of stroke or a transient
ischemic event (10.2%).
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Most patients (68.9%) had 2- or 3-vessel disease (Table 3). Multivessel intervention was
performed in 16%. Forty-nine percent of patients had 1 AHA/ACC type
C lesion. Other specific adverse characteristics included calcium
(37.2%) or thrombus (27.4%) at the treatment site and a bifurcation
lesion (10.9%). Abciximab was administered to 34% of all
patients.
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In-Hospital Outcome
Of the 6472 target vessel segments for which stenting was
attempted, 1 was stented successfully in 96.1% of patients. During
the index hospitalization, severe complications were infrequent.
Sixty-six patients (1.6%) died, and a definite or probable myocardial
infarction occurred in 3.6%. A Q-wave myocardial infarction was
infrequent, occurring in 0.9%. CABG was performed within 24 hours in
0.5% (20 patients) and was performed at any time during the index
hospitalization in only 0.7%. Other complications were also infrequent
and included subacute closure in 0.9%, renal failure in 0.9%, and
transient ischemic attack or cerebral vascular accident in
0.3%.
Patients who died were older (70.5 versus 64.5 years, P=0.0001), had higher prevalences of prior CABG (45.5% versus 22.6%, P=0.0001) and prior myocardial infarction (83.1% versus 51.6%, P=0.0001), more often had congestive heart failure on presentation (34.9% versus 7.5%, P=0.0001), had more 3-vessel disease (59.4% versus 29.8%, P=0.00001), more frequently had a myocardial infarction within 24 hours (30.3% versus 12.6%, P=0.0001), and were more often in shock at baseline (25.8% versus 2.9%, P=0.0001).
Risk Adjustment
The baseline clinical risk characteristics identified in the New
York State database (Table 1
) were used to predict risk-adjusted
mortality in the current data set. The relationship between discrete
variables and outcome is given in Table 4. On the basis of the multivariable
risk factor equation, 79 deaths (1.95%) were expected, whereas 66
deaths (1.62%) were observed.
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The CI of the difference between the 2 rates was -0.695 to 0.048
(P=0.09). Logistic regression results from the models for
in-hospital mortality are given in Table 5. The C statistic is
presented to describe model fit and was good when only the New
York score was included in the model (C=0.80). The
Hosmer-Lemeshow formula also indicated good model fit
(2=2.8 with 8 df,
P=0.95). The New York State risk model did not include
lesion characteristics. In our data set, these were added to the New
York score. With use of a backward selection process of logistic
regression, in addition to the New York score
(2=86.6, P<0.001), treatment of a
type C lesion was the only other variable independently associated
with in-hospital mortality (2=5.89,
P=0.02). Additionally, we used a forward logistic regression
model with all of the individual variables that contributed to the
New York score plus the New York score and the lesion variable. The
New York score was the most significant factor associated with
in-hospital mortality (OR, 1.86; P=0.001;
2=92.0); the presence of
peripheral vascular disease (OR, 2.07; P=0.009)
and prior cardiac surgery (OR, 2.23; P=0.002) also were
associated with in-hospital death above and beyond their contribution
to the New York score.
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Follow-Up Mortality
The mean duration of follow-up was 1.2±1.0 years in all hospital
survivors. During follow-up, there were an additional 154 deaths.
Multivariable analysis of factors associated with
subsequent mortality identified 6 factors (Table 6). These included diabetes mellitus,
prior cardiac surgery, history of shock, renal failure, New York score,
and decreased left ventricular function. The most
significant factor was the New York score (Wald
2=16.64; P=0.0001; relative risk,
1.46).
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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This study documents that the New York State multivariable model for risk stratification developed in a patient population treated solely with conventional balloon angioplasty remains relevant for patients treated with stent implantation. In a consecutive series of 3761 patients undergoing 4063 stent procedures, the New York State model predicted 79 deaths (1.95%), whereas 66 (1.62%) were observed. With logistic regression, the New York score was the variable most strongly associated with in-hospital mortality (Wald
2=92.03). The second major finding is that
although the New York State model was used for in-hospital mortality,
for the first time it was found to be the variable also most
strongly associated with late mortality, a finding that has not been
previously reported. During a mean follow-up of 1.2±1.0 years,
although other variables were associated with mortality, the
association was strongest with the New York score (Wald
2=16.639). Variables used in outcome analysis models must be objective and easily obtained and have a high degree of intraobserver and interobserver correlation on repeated measurements. Several models have been developed, generally for assessing the relationship between operator or institutional volume and outcome.1 2 4 7 8 These studies have usually found an inverse (albeit modest) relationship. In the California State database, rates of CABG after dilation were lowest in hospitals that performed the most procedures.14 In the Medicare database, in-hospital mortality rates were lower in patients with higher institutional procedural volumes, although risk-adjusted mortality rates were not calculated.2 In a voluntary registry study, major complications of conventional angioplasty were highest in institutions with lower volumes.1
In contrast to these multicenter or registry studies, Ellis et
al8 assessed operator-specific outcomes in 4860 procedures
at a single institution and developed models using multivariable
logistic regression for the combined end point of death, infarction,
bypass grafting, and procedural success. Although the ability of the
model to discriminate between operator outcomes was limited, there were
7 multivariable correlates of in-hospital death, including acute
infarction, age 75 years, ejection fraction of 
40%, 3-vessel
disease, modified AHA/ACC lesion score of B2 or
C, creatinine value of 2.0 mg/dL, and proximal left
anterior descending target site.
These studies predated the contemporary practice of stent-based interventional cardiology. Recently, Kastrati et al15 evaluated operator volume and outcome in 3409 consecutive patients undergoing stent implantation. Patients with shock or those requiring ventilatory support were excluded. A composite end point of cardiac death, nonfatal infarction, and CABG was used. A classification and regression tree analysis model was used and identified procedural volume, lesion complexity, left ventricular function, and presence of unstable angina as independent risk factors for an adverse outcome.
One of the most robust models for risk adjustment is the New York State model, developed to predict in-hospital mortality and unplanned coronary surgery in a prospective database of 62 670 patients undergoing conventional PTCA from 1991 to 1994.7 This model incorporated demographics, comorbid conditions, and procedural variables; lesion characteristics were not included. In the 62 670 patients, in-hospital mortality was 0.9%, and the frequency of CABG performed the same day as attempted angioplasty was 3.43%.
The present study included all patients undergoing stent
implantation at our institution; no patient groups were excluded, even
those with cardiogenic shock or severe congestive heart failure. It
represents a higher-risk patient group than those in the New
York State experience; with that model, the predicted mortality in the
present study was 1.95% compared with 0.9% in the New York State
database. Although the projected mortality was 1.95%
(P=0.09), the observed mortality was 1.62%. Although this
difference did not reach statistical significance (P=0.09),
it may represent improved outcome with decreased mortality in
patients treated with stent implantation. The C statistic
used to describe the model fit was excellent (C=0.80), as
was the Hosmer-Lemeshow test (2=2.8 with 8
df, P=0.95).
The New York State model did not include information on specific lesion
characteristics that could affect outcome. After the addition of these
characteristics, the New York State score was still the variable
most closely associated with in-hospital mortality
(2=86.6, P<0.001). Although when
lesion variables were added to the score, treatment of 1 class C
lesion (P=0.01) also was correlated with in-hospital
mortality.
When the New York score was included with all the individual variables, which contributed to the New York score itself, the score as a covariate in the model remained the variable most significantly associated with in-hospital mortality. Although the observed rate of mortality in our practice is less than expected with use of the New York State model, the difference is not statistically significant despite stents being used in all patients. Several factors may be responsible. In some patients, such as those with severe left ventricular dysfunction or cardiogenic shock, mortality may still occur despite restoration of flow. In other patients, critical stenoses that either could not be treated or were not treated could have contributed to subsequent mortality. Alternatively, patients treated with stent implantation may have improved survival compared with those treated with conventional PTCA.
The New York State model has been used to predict in-hospital CABG after percutaneous intervention. In our experience, this procedure was performed in only 0.5% of patients; therefore, the number of events (25 patients) was too small to model. This surgical rate is similar to the 0.7% reported by Kastrati et al15 and reflects the ability of stents to either prevent or treat acute or threatened closure, thus avoiding urgent or emergency coronary surgery.
Identification of risk factors associated with mortality after
successful procedures has important implications. Identification of
patients at highest risk may encourage the application of other
approaches, such as more frequent surveillance, more intensive medical
management, repeated interventional procedures, or surgical procedures.
Before this present stent study, the New York State model had not
been applied to this question. In this study, at a mean follow-up of
1.2 years, there were 154 deaths. Multivariable analysis
documented 6 factors, the most significant of which was the New York
State score (Wald 2=16.64,
P=0.0001). Thus, although this score was not designed for
postdismissal mortality stratification, it is nevertheless a useful
tool for risk adjustment of hospital survivors.
Study Limitations
Although the New York State model was developed in a very large
data set, unmeasured or poorly defined variables may have an impact
on the adjustment process. The low number of some events may render
studies "underpowered" to detect significant differences.
Conversely, events occurring at very low rates have less clinical
relevance to large populations, even if an intervention is proved to
reduce these rates further. In this regard, a difference between
clinically meaningful and statistically significant must be kept in
mind. Stent technology and adjunctive therapy, such as
glycoprotein IIb/IIIa agents and radiation, continue to
evolve and improve outcomes. This change may affect the ability of the
model to predict mortality in the future. Finally, although the fit of
the model was good, it was not perfect, with a C statistic
of 0.8.
Conclusions
Although the New York State mortality model was developed in an
era of complete reliance on conventional PTCA, it remains robust in the
current interventional practice that relies predominantly on stent
implantation. The risk score derived from that model remains the factor
most closely associated with in-hospital mortality. In addition, it is
closely associated with long-term survival. As such, use of this model
remains extremely valuable for outcome analyses.
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Footnotes |
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Reprint requests to David R. Holmes, Jr, MD, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
Received December 21, 1999; revision received February 14, 2000; accepted February 29, 2000.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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1. Kimmel SE, Berlin JA, Laskey WK, et al. The relationship between coronary angioplasty procedure volume and major complications. JAMA. 1995;274:1137–1142.
2.
Jollis JG, Peterson ED, DeLong ER, et al.
The relation between the volume of coronary angioplasty
procedures at hospitals treating Medicare beneficiaries and short-term
mortality. N Engl J Med. 1994;331:1625–1629.
3. Shook TL, Sun GW, Burstein S, et al. Comparison of percutaneous transluminal coronary angioplasty outcome and hospital costs for low-volume and high-volume operators. Am J Cardiol. 1996;77:331–336.[Medline] [Order article via Infotrieve]
4.
Ellis SG, Weintraub W, Holmes D, et al. Relation of
operator volume and experience to procedural outcome of
percutaneous coronary
revascularization at hospitals with high
interventional volumes. Circulation. 1997;95:2479–2484.
5.
McGrath PD, Wennberg DE, Malenka DJ, et al, for the
Northern New England Cardiovascular Disease Study
Group. Operator volume and outcomes in 12,998
percutaneous coronary interventions.
J Am Coll Cardiol. 1998;31:570–576.
6. Klein LW, Schaer GL, Calvin JE, et al. Does low individual operator coronary interventional procedural volume correlate with worse institutional procedural outcome? J Am Coll Cardiol. 1997;30:870–877.[Abstract]
7.
Hannan EL, Racz M, Ryan TJ, et al. Coronary
angioplasty volume-outcome relationships for hospitals and
cardiologists. JAMA. 1997;277:892–898.
8.
Ellis SG, Omoigui N, Bittl JA, et al. Analysis
and comparison of operator-specific outcomes in interventional
cardiology from a multicenter database of 4860
quality-controlled procedures. Circulation. 1996;93:431–439.
9.
Topol EJ, Califf RM. Scorecard
cardiovascular medicine: its impact and future
directions. Ann Intern Med. 1994;120:65–70.
10. Holmes DR Jr, Bell MR, Holmes DR III, et al. Interventional cardiology and intracoronary stents: a changing practice: approved vs. nonapproved indications. Cathet Cardiovasc Diagn. 1997;40:133–138.[Medline] [Order article via Infotrieve]
11.
Holmes DR Jr, Hirshfeld J Jr, Faxon D, et al. ACC
Expert Consensus document on coronary artery stents: document
of the American College of Cardiology. J Am
Coll Cardiol. 1998;32:1471–1482.
12.
Hasdai D, Berger PB, Bell MR, et al. The changing face
of coronary interventional practice: the Mayo Clinic
experience. Arch Intern Med. 1997;157:677–682.
13.
Ellis SG, Vandormael MG, Cowley MJ, et al, for the
Multivessel Angioplasty Prognosis Study Group. Coronary
morphologic and clinical determinants of procedural outcome with
angioplasty for multivessel coronary disease: implications for
patient selection. Circulation. 1990;82:1193–1202.
14.
Ritchie JL, Phillips KA, Luft HS. Coronary
angioplasty: statewide experience in California.
Circulation. 1993;88:2735–2743.
15.
Kastrati A, Neumann FJ, Schomig A. Operator volume and
outcome of patients undergoing coronary stent placement.
J Am Coll Cardiol. 1998;32:970–976.
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