Application of the New York State PTCA Mortality Model in Patients Undergoing Stent Implantation
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 (χ2=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.
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.
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.
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 χ2 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 χ2 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.
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.
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%).
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.
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).
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.
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 (χ2=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 (χ2=86.6, P<0.001), treatment of a type C lesion was the only other variable independently associated with in-hospital mortality (χ2=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; χ2=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.
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 χ2=16.64; P=0.0001; relative risk, 1.46).
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 χ2=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 χ2=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 (χ2=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 (χ2=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 χ2=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.
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.
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.
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.
- Copyright © 2000 by American Heart Association
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