Application of the New York State PTCA mortality model in patients undergoing stent implantation

A nomogram predicting 30-day mortality in patients undergoing percutaneous coronary intervention

Background and aims

Early detection of mortality after percutaneous coronary intervention (PCI) is crucial, whereas most risk prediction models are based on outdated cohorts before the year 2000. This study aimed to establish a nomogram predicting 30-day mortality after PCI.

Materials and methods

In total, 10,444 patients undergoing PCI in National Center for Cardiovascular Diseases in China were enrolled to establish a nomogram to predict 30-day mortality after PCI. The nomogram was generated by incorporating parameters selected by logistic regression with the stepwise backward method.

Results

Five features were selected to build the nomogram, including age, male sex, cardiac dysfunction, STEMI, and TIMI 0–2 after PCI. The performance of the nomogram was evaluated, and the area under the curves (AUC) was 0.881 (95% CI: 0.8–0.961). Our nomogram exhibited better performance than a previous risk model (AUC = 0.7, 95% CI: 0.586–0.813) established by Brener et al. The survival curve successfully stratified the patients above and below the median score of 4.

Conclusion

A novel nomogram for predicting 30-day mortality was established in unselected patients undergoing PCI, which may help risk stratification in clinical practice.

Multimorbidity and Mortality Models to Predict Complications Following Percutaneous Coronary Interventions

BACKGROUND

Previous percutaneous coronary intervention risk models were focused on single outcome, such as mortality or bleeding, etc, limiting their applicability. Our objective was to develop contemporary percutaneous coronary intervention risk models that not only determine in-hospital mortality but also predict postprocedure bleeding, acute kidney injury, and stroke from a common set of variables.

METHODS

We built risk models using logistic regression from first percutaneous coronary intervention for any indication per patient (n=19 322, 70.6% with acute coronary syndrome) using the Mayo Clinic registry from January 1, 2000 to December 31, 2016. Approval for the current study was obtained from the Mayo Foundation Institutional Review Board. Patients with missing outcomes (n=4183) and those under 18 (n=10) were removed resulting in a sample of 15 129. We built both models that included procedural and angiographic variables (Models A) and precatheterization model (Models B).

RESULTS

Death, bleeding, acute kidney injury, and stroke occurred in 247 (1.6%), 650 (4.3%), 1184 (7.8%), and 67 (0.4%), respectively. The C statistics from the test dataset for models A were 0.92, 0.70, 0.77, and 0.71 and for models B were 0.90, 0.67, 0.76, and 0.71 for in-hospital death, bleeding, acute kidney injury, and stroke, respectively. Bootstrap analysis indicated that the models were not overfit to the available dataset. The probabilities estimated from the models matched the observed data well, as indicated by the calibration curves. The models were robust across many subgroups, including women, elderly, acute coronary syndrome, cardiogenic shock, and diabetes.

CONCLUSIONS

The new risk scoring models based on precatheterization variables and models including procedural and angiographic variables accurately predict in-hospital mortality, bleeding, acute kidney injury, and stroke. The ease of its application will provide useful prognostic and therapeutic information to both patients and physicians.

Public reporting and coronary revascularization: risk and benefit

Since the 1980s, the evolution of public reporting of provider-specific and institution-specific clinical outcomes has historically been rooted in the field of cardiology. Although public reporting is not a novel concept, how we collect, analyze, report, and interpret outcome data remains a critical element in quality improvement and in the quest toward providing truly high-value care. In this review, we explore the emergence of public reporting within the scope of cardiovascular medicine, specifically as it relates to surgical and percutaneous coronary revascularization. We highlight both the advantages and the disadvantages of public reporting from the perspective of the patient, the practicing physician, the hospital, and the healthcare system. A discussion on the limitations of public reporting and specific strategies by which it can be improved is presented.

Risk scores for 30-day mortality after percutaneous coronary intervention: new insights into causes and risk of death

OBJECTIVE

To determine the causes and risk of death after percutaneous coronary interventions (PCIs) and to compare the discriminatory ability of the New York State Risk Score (NYSRS) with the Mayo Clinic Risk Score (MCRS).

PATIENTS AND METHODS

We studied in-hospital and 30-day mortality after PCI in 4898 patients treated at Mayo Clinic in Rochester, Minnesota, from January 1, 2007, through December 31, 2010, to validate the NYSRS equation with recalibrated predicted probabilities of death.

RESULTS

Of the 4898 patients studied, 93 (1.9%) died during the index hospitalization, and 36 (0.7%) died within 30 days after discharge. For the in-hospital and 30-day mortality, respectively, the area under the receiver operating characteristic curve was 0.92 and 0.88 for the NYSRS and 0.93 and 0.90 for the MCRS, indicating excellent discrimination. The NYSRS model underpredicted event rates when applied in Mayo Clinic data (2.6% observed [127 of 4898 patients] vs 2.3% predicted [114 of 4898 patients]), even after recalibration. The instantaneous hazard over time revealed the highest risk of death in the first 3 days after PCI (daily probability, >0.2%), declined to 0.1% until about day 12, and then decreased below 0.1%. Cardiac causes (mainly myocardial infarction) dominated in the first week (83 of 85 deaths [97.6%]) and then decreased to 59.5% (25 of 42 deaths) between 8 and 30 days after PCI.

CONCLUSION

The discriminatory ability of the NYSRS and the MCRS for in-hospital and 30-day mortality after PCI is roughly interchangeable. The risk of death is highest during the first 2 weeks and is dominated by cardiac causes of death.

Prediction of length of stay following elective percutaneous coronary intervention

There have been published risk stratification approaches to predict complications following percutaneous coronary interventions (PCI). However, a formal assessment of such approaches with respect to predicting length of stay (LOS) is lacking. Therefore, we sought to assess the performance of, an easy-to-use, tree-structured prognostic classification model in predicting LOS among patients with elective PCI. The study is based on the New York State PCI database. The model was developed on data for 1999-2000, consisting of 67,766 procedures. Validation was carried out, with respect to LOS, using data for 2001-2002, consisting of 79,545 procedures. The risk groups identified by the model exhibited a strong progressively increasing relative risk pattern of longer LOS. The predicted average LOS ranged from 3 to 9 days. The performance of this model was comparable to other published risk scores. In conclusion, the tree-structured prognostic classification is a model which can be easily applied to aid practitioners early on in their decision process regarding the need for extra resources required for the management of more complicated patients following PCI, or to justify to payors the extra costs required for the management of patients who have required extended observation and care after PCI.

EuroSCORE refines the predictive ability of SYNTAX score in patients undergoing left main percutaneous coronary intervention

BACKGROUND

Whether SYNTAX score should be used as a stand-alone tool or whether its performance may be improved by the parallel use of clinical scores focusing on comorbidities, such as EuroSCORE, is a matter of debate.

METHODS

A combined risk model including both clinical and angiographic information was developed, and its performance tested on a contemporary population of 255 patients with left main disease undergoing percutaneous coronary intervention (PCI). A global risk classification (GRC) system was created by combination of SYNTAX score and EuroSCORE strata, and new classes of risk were defined.

RESULTS

When EuroSCORE was fitted into the SYNTAX score model, c-statistic increased from 0.681 to 0.732 for the prediction of cardiac mortality. The likelihood ratio test for the significance of adding the EuroSCORE term to the model was chi(2) = 4.109 (P = .043) with a net reclassification improvement of 26% (P = .002). GRC showed the best prediction and discriminative ability in terms of two-year cardiac mortality (HR 3.40, 95% CI 1.79-6.43, P < .001; c-statistic 0.756) as compared with SYNTAX score (HR 2.87, 95% CI 1.35-6.10, P = .006; c-statistic 0.747) and EuroSCORE (HR 3.04, 95% CI 1.41-6.57, P = .005; c-statistic 0.708) alone.

CONCLUSIONS

We found a significant improvement in the prediction of cardiac mortality with the inclusion of EuroSCORE in a SYNTAX score-based model. The degree of reclassification between treatment threshold categories indicates that clinical and angiographic information are both important for assessing individual risk of patients undergoing left main PCI.

Adverse events even after percutaneous coronary intervention: is there any way to predict it?

Since its development, the frequency and indication of percutaneous coronary intervention (PCI) have been increasing tremendously. Although generally the procedure shows promising results, some patients still develop cardiovascular events even after PCI. Some risk prediction models have been developed in order to identify this group of patients who possess higher risk of developing cardiovascular events after PCI. This article discusses the available risk prediction models, the difference between risk prediction models, choosing risk prediction models, and also limitations of the available risk prediction models.

A simple prognostic classification model for postprocedural complications after percutaneous coronary intervention for acute myocardial infarction (from the New York State percutaneous coronary intervention database)

Previous postprocedural complications risk scores have shown very good performance. However, the need for real-time risk score computation makes their implementation in an emergency situation challenging. Therefore, we developed an easy-to-use prognostic classification model for postprocedural complications after early percutaneous coronary intervention for acute myocardial infarction. The model was developed on the New York State percutaneous coronary intervention database for 1999 to 2000 (consisting of 5,385 procedures) and was validated using the subsequent 2001 to 2002 database (consisting of 7,414 procedures). Tree-structured prognostic classification identified 4 key presenting features: cardiogenic shock, congestive heart failure, age, and diabetes. In the validation database, the model identified patient groups with postprocedural complications rates ranging from 1.0% to 22.8%, >22-fold increased risk. The performance of this model was similar to the Mayo Clinic and another recently published risk scores with a discrimination capacity of 78% (95% confidence interval, 75%, 80%). In conclusion, patients undergoing percutaneous coronary intervention for acute myocardial infarction can be readily stratified into distinct prognostic classes using the tree-structured model.

The Toronto score for in-hospital mortality after percutaneous coronary interventions

BACKGROUND

Benchmarking the performance of providers is an increasing priority in many health care economies. In-hospital mortality represents an important and uniformly assessed measure on which to examine the outcome of percutaneous coronary intervention (PCI). Most existing prediction models of in-hospital mortality after PCI were derived from 1990s data, and their current relevance is uncertain.

METHODS

From consecutive PCIs performed during 2000-2008, derivation and validation cohorts of 10,694 and 5,347 patients, respectively, were analyzed. Logistic regression for in-hospital death yielded integer risk weights for each independent predictor variable. These were summed for each patient to create the Toronto PCI risk score.

RESULTS

Death occurred in 1.3% of patients. Independent predictors with associated risk weights in parentheses were as follows: age 40 to 49 y (1), 50 to 59 y (2), 60 to 69 y (3), 70 to 79 y (4), and > or =80 y (5); diabetes (2); renal insufficiency (2); New York Heart Association class 4 (3); left ventricular ejection fraction <20% (3); myocardial infarction in the previous month (3); multivessel disease (1); left main disease (2); rescue or facilitated PCI (3); primary PCI (4); and shock (6). The model had a receiver operator curve of 0.96 and Hosmer-Lemeshow goodness-of-fit P = .16 in the validation set. Four previously published external models were tested in the entire data set. Three models had ROC curves significantly less than the Toronto PCI score, and all 4 showed significant levels of imprecision.

CONCLUSIONS

The Toronto PCI mortality score is an accurate and contemporary predictive tool that permits evaluation of risk-stratified outcomes and aids counseling of patients undergoing PCI.

Comorbid conditions and outcomes after percutaneous coronary intervention

OBJECTIVE

To evaluate whether adding comorbid conditions to a risk model can help predict in-hospital outcome and long-term mortality after percutaneous coronary intervention (PCI).

DESIGN

Retrospective chart review

SETTING

Academic medical centre.

PATIENTS

7659 patients who had 9032 PCIs.

INTERVENTIONS

PCI performed at Mayo Clinic between 1 January 1999 and 30 June 2004.

MAIN OUTCOME MEASURES

The Mayo Clinic Risk Score (MCRS) and the coronary artery disease (CAD)-specific index for determination of comorbid conditions in all patients.

RESULTS

The mean (SD) MCRS score was 6.5 (2.9). The CAD-specific index was 0 or 1 in 46%, 2 or 3 in 30% and 4 or higher in 24%. The rate of in-hospital major adverse cardiovascular events (MACE) increased with higher MCRS and CAD-specific index (Cochran-Armitage test, p<0.001 for both models). The c-statistic for the MCRS for in-hospital MACE was 0.78; adding the CAD-specific index did not improve its discriminatory ability for in-hospital MACE (c-statistic = 0.78; likelihood ratio test, p = 0.29). A total of 707 deaths after dismissal occurred after 7253 successful procedures. The c-statistic for all-cause mortality was 0.69 for the MCRS model alone and 0.75 for the MCRS and CAD-specific indices together (likelihood ratio test, p<0.001), indicating significant improvement in the discriminatory ability.

CONCLUSIONS

Addition of comorbid conditions to the MCRS adds significant prognostic information for post-dismissal mortality but adds little prognostic information about in-hospital complications after PCI. Such health-status measures should be included in future risk stratification models that predict long-term mortality after PCI.

Tree-structured risk stratification of in-hospital mortality after percutaneous coronary intervention for acute myocardial infarction: a report from the New York State percutaneous coronary intervention database

BACKGROUND

Previous risk scores have shown excellent performance. However, the need for real-time risk score computation makes their implementation in an emergent situation challenging. A more simplified approach can provide practitioners with a practical bedside risk stratification tool.

METHODS

We developed an easy-to-use tree-structured risk stratification model for patients undergoing early percutaneous coronary intervention (PCI) for acute myocardial infarction. The model was developed on the New York State PCI database for 1999 to 2000 (consisting of 5385 procedures) and was validated using the subsequent 2001 to 2002 database (consisting of 7414 procedures).

RESULTS

Tree-structured modeling identified 3 key presenting features: cardiogenic shock, congestive heart failure, and age. In the validation data set, this risk stratification model identified patient groups with in-hospital mortality ranging from 0.5% to 20.6%, more than a 20-fold increased risk. The performance of this model was similar to the Mayo Clinic Risk Score with a discriminative capacity of 82% (95% confidence interval, 79%-84%) versus 80% (95% confidence interval, 77%-82%), respectively.

CONCLUSION

Patients undergoing PCI for acute myocardial infarction can be readily stratified into risk categories using the tree-structured model. This provides practicing cardiologists with an internally validated and easy-to-use scheme for in-hospital mortality risk stratification.

Risk stratification following acute myocardial infarction

This article reviews the current risk assessment models available for patients presenting with myocardial infarction (MI). These practical tools enhance the health care provider's ability to rapidly and accurately assess patient risk from the event or revascularization therapy, and are of paramount importance in managing patients presenting with MI. This article highlights the models used for ST-elevation MI (STEMI) and non-ST elevation MI (NSTEMI) and provides an additional description of models used to assess risks after primary angioplasty (ie, angioplasty performed for STEMI).

External validation of a percutaneous coronary intervention mortality prediction model in patients with acute coronary syndromes

BACKGROUND

The recently published Michigan outcome prediction model (MM) for inhospital mortality was developed and validated on a series of consecutive patients undergoing percutaneous coronary intervention (PCI). Our purpose was to externally validate the performance of the MM in 2 separate cohorts of patients with acute coronary syndrome (ACS) undergoing PCI in Canada.

METHODS

A validation of the MM and development of an extended MM were performed on data describing 10,050 patients from the APPROACH prospective cohort study between January 1995 and December 2000. Performance of both models was assessed on an external data set of 3259 PCI cases from the British Columbia Cardiac Registries. Only patients with a diagnosis of ACS were included in the study.

RESULTS

The original MM predicted death rates ranging from 0.1% to 60.6%, but lacked accuracy to predict inhospital mortality as severity increased. The extended MM predicted death rates more widely from 0.0% to a high of 91.0% with better accuracy to predict inhospital death in patients with ACS undergoing PCI. The areas under the receiver operating characteristic curve for the MM and the extended MM on the external validation data set were 0.93 and 0.95, respectively.

CONCLUSION

The MM predicts death after PCI in patients with ACS and identifies a clear gradient of risk. However, the enhanced MM developed specifically for the subset of patients with ACS demonstrated better prediction and cross-validated performance. These prediction rules can be useful for risk-adjustment analyses and for prognostication for individual patients.

Treatment of stable angina: medical and invasive therapy--implications for the elderly

Currently available therapies for chronic stable angina are reviewed. Revascularization, i.e., coronary artery bypass surgery and percutaneous transluminal coronary angioplasty, is summarized briefly, with short- and long-term results summarized from several large registries and review articles. Advancing age is a risk factor for both coronary artery bypass surgery and percutaneous transluminal coronary angioplasty, but risks of coronary events are also higher without interventions in the elderly. In-hospital mortality for coronary artery bypass surgery is about 8% for patients over age 80 in one large national registry and not much different in elective coronary artery bypass surgery in highly-selected patients over age 90 in one institution. The few randomized trials of invasive vs. noninvasive therapy for stable coronary artery disease are described. Although patient numbers in available studies are too small to be conclusive as to which type of therapy is generally better, data appear to suggest that higher-risk patients have better outcomes with revascularization. Methods of risk stratification are discussed. Finally, unusual therapies for angina are briefly noted, including transmyocardial revascularization, external counterpulsation, and gene therapy.

Discrimination and calibration of mortality risk prediction models in interventional cardiology

OBJECTIVES

Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration.

BACKGROUND

Accurate risk prediction is important for a number of reasons including physician decision support, quality of care assessment, and patient education. Current evidence on the value of applying PCI risk models to individual cases drawn from a different population is controversial.

METHODS

Data were collected from January 01, 2002 to September 30, 2004 on 5216 consecutive percutaneous coronary interventions at Brigham and Women's Hospital (Boston, MA). Logistic regression was used to create a local risk model for in-hospital mortality in these procedures, and a number of statistical methods were used to compare the discrimination and calibration of this new and old local risk models, as well as the Northern New England Cooperative Group, New York State (1992 and 1997), University of Michigan consortium, American College of Cardiology-National Cardiovascular Data Registry, and The Cleveland Clinic Foundation risk prediction models. Areas under the ROC (AUC) curves were used to evaluate discrimination, and the Hosmer-Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases.

RESULTS

Multivariate risk factors included in the newly constructed local model were: age, prior intervention, diabetes, unstable angina, salvage versus elective procedure, cardiogenic shock, acute myocardial infarction (AMI), and left anterior descending artery intervention. The area under the ROC curve (AUC) was 0.929 (SE=0.017), and the p value for the Hosmer-Lemeshow (HL) goodness-of-fit was 0.473. This indicates good discrimination and calibration. Bootstrap re-sampling indicated AUC stability. Evaluation of the external models showed an AUC range from 0.82 to 0.90 indicating good discrimination across all models, but poor calibration (HL p value < or = 0.0001).

CONCLUSIONS

Validation of AUC values across all models suggests that certain risk factors have remained important over the last decade. However, the lack of calibration suggests that small changes in patient populations and data collection methods quickly reduce the accuracy of patient level estimations over time. Possible solutions to this problem involve either recalibration of models using local data or development of new local models.

Prediction of complications following nonemergency percutaneous coronary interventions

Previous models for prediction of complications after percutaneous coronary interventions (PCIs) have included in-hospital mortality and major in-hospital complications. In general, these models have excluded elevated cardiac biomarkers as a complication. We sought to determine whether a risk model could predict complications, including biomarker elevation, in patients undergoing nonemergency PCI. We examined the outcomes of nonemergency PCI performed on patients at Mayo Clinic from 2000 to 2003. The primary end point was in-hospital complications of death, myocardial infarction (MI) (Q-wave MI, or post-PCI creatine kinase-MB elevation >or=3 times the upper limit of normal), emergency coronary artery bypass grafting, or stroke. We used the Hosmer-Lemeshow test to demonstrate the adequacy of the model fit, and the c-index for discriminatory ability of the model. Of 2,894 nonemergency PCIs, the end point was noted in 232 (8%). The final prediction model included vein graft intervention (odds ratio [OR] 2.19), angiographic thrombus (OR 2.12), preprocedure stenosis of a minor (OR 1.98) or major (OR 1.62) side branch, and type C lesion (OR 1.48). The model had modest ability to discriminate between event and nonevent patients (c = 0.641). In the 500 bootstrap samples for internal validation, the c-index was 0.642 +/- 0.020, indicating only fair discriminatory ability. The average number of observed events was 232.0 +/- 14.7 compared with 232.1 +/- 2.5 expected events (average difference -0.06 +/- 14.5). In conclusion, the 5 risk variables associated with an increased risk of complications in patients undergoing elective PCI included vein graft intervention, presence of angiographic thrombus, stenosis of a major or minor side branch, and type C lesion; however, the discriminatory ability of the model derived from the variables was only modest.

Multivariate prediction of major adverse cardiac events after 9914 percutaneous coronary interventions in the north west of England

OBJECTIVE

To develop a multivariate prediction model for major adverse cardiac events (MACE) after percutaneous coronary interventions (PCIs) by using the North West Quality Improvement Programme in Cardiac Interventions (NWQIP) PCI Registry.

SETTING

All NHS centres undertaking adult PCIs in north west England.

METHODS

Retrospective analysis of prospectively collected data on 9914 consecutive patients undergoing adult PCI between 1 August 2001 and 31 December 2003. A multivariate logistic regression analysis was undertaken, with the forward stepwise technique, to identify independent risk factors for MACE. The area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness of fit statistic were calculated to assess the performance and calibration of the model, respectively. The statistical model was internally validated by using the technique of bootstrap resampling.

MAIN OUTCOME MEASURES

MACE, which were in-hospital mortality, Q wave myocardial infarction, emergency coronary artery bypass graft surgery, and cerebrovascular accidents.

RESULTS

Independent variables identified with an increased risk of developing MACE were advanced age, female sex, cerebrovascular disease, cardiogenic shock, priority, and treatment of the left main stem or graft lesions during PCI. The ROC curve for the predicted probability of MACE was 0.76, indicating a good discrimination power. The prediction equation was well calibrated, predicting well at all levels of risk. Bootstrapping showed that estimates were stable.

CONCLUSIONS

A contemporaneous multivariate prediction model for MACE after PCI was developed. The NWQIP tool allows calculation of the risk of MACE permitting meaningful risk adjusted comparisons of performance between hospitals and operators.

Public Reporting and Case Selection for Percutaneous Coronary Interventions

OBJECTIVES

The purpose of this research was to determine the potential effect of public reporting on case selection for percutaneous coronary intervention (PCI).

BACKGROUND

Previous studies have suggested that public reporting of coronary artery bypass graft surgery (CABG) mortality might result in case selection bias and in denial of care to or out migration of high-risk patients. The potential effect of public reporting on case selection for PCI is unknown.

METHODS

We compared demographics, indications, and outcomes of 11,374 patients included in a multicenter (eight hospitals) PCI database in Michigan where no public reporting is present, with 69,048 patients in a statewide (34 hospitals) PCI database in New York, where public reporting is present. The primary end point was in-hospital mortality.

RESULTS

Patients in Michigan more frequently underwent PCI for acute myocardial infarction (14.4% vs. 8.7%, p < 0.0001) and cardiogenic shock (2.56% vs. 0.38%, p < 0.0001) than those in New York. The Michigan cohort also had a higher prevalence of congestive heart failure and extracardiac vascular disease. The unadjusted in-hospital mortality rate was significantly lower in New York than in Michigan (0.83% vs. 1.54%, p < 0.0001; odds ratio [OR] 0.54, 95% confidence interval [CI] 0.45 to 0.63). However, after adjustment for comorbidities, there was no significant difference in mortality between the two groups (adjusted OR 1.05, 95% CI 0.84 to 1.31, p = 0.70, c-statistic 0.88).

CONCLUSIONS

There are significant differences in case mix between patients undergoing PCI in Michigan and New York that result in marked differences in unadjusted mortality rates. A propensity in New York toward not intervening on higher-risk patients because of fear of public reporting of high mortality rates is a possible explanation for these differences.

Exploration of a bayesian updating methodology to monitor the safety of interventional cardiovascular procedures

Appropriate methods for monitoring of the safety of medical devices introduced into clinical practice have been elusive to develop and implement. A novel approach is the application of Bayesian updating, which incorporates existing knowledge regarding event rates into the estimation of risk. This framework has been shown in other domains to be data efficient and to address some of the limitations of conventional statistical methods. In this article, the authors propose a methodologic framework for developing initial prior probability distributions in risk-stratified patient groups and a mechanism for incorporating accumulating procedure safety experience. In addition, they use this methodology to retrospectively analyze the clinical outcomes of 309 patients undergoing an infrequent interventional cardiology procedure, rotational atherectomy. These exploratory analyses demonstrate the feasibility of Bayesian updating applied to medical device safety evaluation and indicate that the methodology is capable of generating stable estimates of risk in a variety of patient risk groups.

Effect of peripheral arterial disease in patients undergoing percutaneous coronary intervention with intracoronary stents

OBJECTIVE

To compare the short-term and long-term outcomes of patients with coronary artery disease and peripheral arterial disease (PAD) who underwent intracoronary (IC) stent Implantation during percutaneous coronary intervention (PCI) with the outcomes of patients with isolated coronary artery disease but without PAD who underwent IC stent implantation.

PATIENTS AND METHODS

We analyzed the outcomes of 7696 patients who underwent IC stent implantation during PCI at the Mayo Clinic in Rochester, Minn, between January 1996 and December 2002. Outcomes of 6299 patients (82%) with Isolated coronary artery disease and without PAD who underwent IC stent implantation (group 1) were compared with outcomes of 1397 patients (18%) with coronary artery disease and PAD (group 2) who underwent PCI with IC stent implantation.

RESULTS

Patients in group 2 were older (71.1+/-10.2 years vs 65.0+/-12.0 years; P<.001) and had a higher prevalence of hypertension (79% vs 61%; P<.001), diabetes mellitus (33% vs 20%; P<.001), hyperlipidemia (76% vs 70%; P<.001), and history of smoking (70% vs 63%; P<.001) compared with group 1. Prevalence of multivessel disease was higher in group 2 (79% vs 68%; P<.001). Procedural success was significantly lower In group 2 (95% vs 97%; P<.001). In-hospital complications were higher in group 2: death (3% vs 1%; P<.001), any myocardial Infarction (MI) (8% vs 5%; P<.001), death/MI/coronary artery bypass grafting (CABG)/target vessel revascularization (11% vs 7%; P<.001), and blood loss requiring transfusion (11% vs 5.8%; P<.001). After adjustment for other risk factors, the odds ratio for in-hospital death was 1.84 (95% confidence interval [CI], 1.16-2.90; P=.009), and for death/MI/CABG/target vessel revascularization, the odds ratio was 1.25 (95% CI, 1.00-1.55; P=.048) in patients with PAD treated with IC stents. Median follow-up was 3.1 years. Six-month, 1-year, and 2-year Kaplan-Meier estimates of survival free of death/MI/CABG/target vessel revascularization were 84%, 77%, and 69%, respectively, for group 2 and were significantly worse compared with group 1 (89%, 85%, and 80%, respectively; P<.001). This effect remained after adjustment for other risk factors (hazard ratio, 1.36; 95% CI, 1.22-1.51).

CONCLUSIONS

Compared with patients who had isolated coronary artery disease but no PAD, patients with coronary artery disease and PAD had lower procedural success and higher in-hospital major cardiovascular complications, including higher blood loss requiring transfusion, after PCI with stent Implantation. On follow-up, the short-term and long-term outcomes of patients with PAD were worse, with higher mortality, MI, and need for repeated target vessel revascularization.

Comparison of Mayo Clinic risk score and American College of Cardiology/American Heart Association lesion classification in the prediction of adverse cardiovascular outcome following percutaneous coronary interventions

OBJECTIVES

We compared American College of Cardiology/American Heart Association (ACC/AHA) lesion classification with the recently proposed Mayo Clinic risk score to predict complications following percutaneous coronary intervention (PCI).

BACKGROUND

The ability of the ACC/AHA classification system to predict complications following PCI has been modest. With the inclusion of patient demographics, acuity of presentation, and measure of left ventricular function, models with better discriminatory accuracy are presently available.

METHODS

The Mayo Clinic risk score is constructed by adding integer scores for the presence of eight variables. We mapped the lesion-specific risk levels to a patient level by counting the number of lesions in each class (A, B1, B2, C, and unknown).

RESULTS

In 5,064 PCIs, 183 patients (4%) had the primary end point (death, Q-wave myocardial infarction, stroke, emergency coronary artery bypass graft). Of the 7,632 treated lesions, 891 (12%) were unsuccessfully treated with PCI (residual stenosis >20%). The discriminatory ability of the Mayo Clinic risk score model for prediction of the primary end point, as measured by the c-statistic, was 0.78 (95% confidence interval [CI] 0.74 to 0.81). The Mayo Clinic risk score offered significantly better risk stratification than the ACC/AHA lesion classification counts (95% CI for c-statistic difference: 0.05 to 0.15). Regarding angiographic success, the ACC/AHA lesion classification was a better system (95% CI for c-statistic difference: -0.08 to -0.03 favoring ACC/AHA classification), although its absolute ability was modest (c = 0.58).

CONCLUSIONS

Mayo Clinic risk score offers significantly better prediction for cardiovascular complications than the ACC/AHA classification. However, lesion classification by ACC/AHA classification is a better predictor for angiographic success.

Impact of ST-segment resolution after primary angioplasty on outcomes after myocardial infarction in elderly patients: an analysis from the CADILLAC trial

BACKGROUND

Age is a strong independent predictor of outcomes after primary percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI). Whether lower rates of reperfusion success contribute to the poor prognosis in elderly patients is unknown.

METHODS

A formal ST-segment analysis substudy was performed in 695 patients undergoing primary PCI for AMI in the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications (CADILLAC) trial. Reperfusion success (determined by the magnitude of ST-segment elevation resolution [STR] after PCI) was evaluated in 4 age groups: <50 years (n = 163), >or=50 to <60 years (n = 187), >or=60 to <70 years (n = 194), and >or=70 years (n = 151).

RESULTS

There were no differences in the age groups for angiographic procedural success (>91% in all, P =.6), postprocedural Thrombolysis in Myocardial Infarction grade 3 flow (>94%, P =.8), and the proportions of patients with complete, partial, or absent STR (P >.8). However, rates of 30-day mortality (0.6%, 1.1%, 3.6%, 6.0%, respectively) and major adverse cardiac events (MACE; 2.5%, 4.8%, 6.2% 9.3%, respectively) increased with age. Rates of mortality and MACE were also inversely related to the magnitude of STR. Absent STR (hazard ratio, 3.00; 95% CI, 1.37-6.58; P =.006) and age (hazard ratio, 1.34; 95% CI, 1.01-1.77; P =.04) were independent predictors of 30-day MACE by using multivariable modeling.

CONCLUSIONS

Lack of effective myocardial reperfusion is not a contributory mechanism responsible for the high morbidity and mortality rates observed in elderly patients. Nevertheless, advanced age and absent STR are both independent predictors of adverse outcomes after primary PCI, emphasizing the importance of successful reperfusion in the elderly population.

From balloon angioplasty to drug-eluting stents: revolution or evolution?

Drug-eluting stenting is part of the evolution of interventional cardiology that started with Gruentzig's introduction of balloon angioplasty in 1977. Numerous advances in interventional cardiology technique have occurred since that time, and drug-eluting stents hold promise for reducing the restenosis rate but as yet have not been shown to influence survival or freedom from myocardial infarction. The ability of stents to influence these hard end points will be tested against the most difficult patient subset for interventional cardiology: persons with diabetes mellitus and multivessel disease. As more data are accumulated, prudent selective use of drug-eluting stenting seems most appropriate.

Risk stratification and interventional cardiology: Robert L. Frye Lecture

Risk stratification and risk-benefit ratios are extremely important in guiding patient-physician interactions as well as patient and family counseling. Risks associated with percutaneous transluminal coronary angioplasty are (1) compromise of the vessel lumen or vessel integrity, (2) unsuccessful procedure, and (3) restenosis. Predicting mortality risk depends on the specific patient population to be treated and on the specific mortality model used. The most common models are those from New York State, the American College of Cardiology, the Northern New England Cooperative Group, the University of Michigan, and The Cleveland Clinic Foundation. As more data and sophisticated analyses become available, risk stratification will become more accurate as long as the approach used is straightforward, makes intuitive sense, and is easy and efficient to apply.

Validation of Mayo clinic risk adjustment model for in-hospital complications after percutaneous coronary interventions, using the National Heart, Lung, and Blood Institute dynamic registry

OBJECTIVES

We sought to validate the recently proposed Mayo Clinic risk score model for complications after percutaneous coronary interventions (PCI), using an independent data set.

BACKGROUND

The Mayo Clinic risk score has eight simple clinical and angiographic variables for the prediction of complications defined as either death, Q-wave myocardial infarction, emergent or urgent coronary artery bypass graft surgery, or cerebrovascular accident after PCI. External validation using an independent data set is lacking.

METHODS

A total of 3,264 patients undergoing PCI at each of the 17 sites in the National Heart, Lung, and Blood Institute's Dynamic Registry during two enrollment periods (July 1997 to February 1998 and February to June 1999) were studied. Logistic regression was used to model the calculated risk score and major procedural complications. The expected number of complications, with 95% confidence bounds (CBs), was also calculated.

RESULTS

There were 96 (2.94%) observed procedural complications, and the Mayo Clinic risk score predicted 93.5 events (2.86%; 95% CB 2.32% to 3.41%; p = NS). The Hosmer-Lemeshow goodness-of-fit p value was 0.28, and the area under the receiver operating curve was 0.76, indicating excellent overall discrimination. There were no statistical differences between observed and predicted procedural complications using the Mayo Clinic risk score among the most selected high- and low-risk subgroups.

CONCLUSIONS

Eight variables were combined into a convenient risk scoring system that accurately predicts cardiovascular complications after PCI. The Mayo clinic predictive model for procedural complications yielded excellent results when applied to a multi-center external data set.

Correlates of procedural complications and a simple integer risk score for percutaneous coronary intervention

OBJECTIVES

Our goals were to identify clinical and angiographic risk factors associated with major cardiovascular complications of percutaneous coronary intervention (PCI) (in-hospital death, Q-wave myocardial infarction, urgent or emergent coronary artery bypass surgery and stroke) and to construct a simple score for risk stratification.

BACKGROUND

Both clinical and angiographic features influence risk of PCIs.

METHODS

Percutaneous coronary interventions performed between January 1, 1996, and December 31, 1999, were analyzed. Logistic regression and bootstrap methods were used to create an integer risk score for estimating the risk of procedural complications using baseline, angiographic and procedural characteristics. The risk score was tested in a validation-set consisting of all procedures performed in the year 2000.

RESULTS

Among 5,463 procedures, 5 clinical and 3 angiographic variables were significantly correlated with procedural complications: cardiogenic shock, left main coronary artery disease, severe renal disease, urgent or emergent procedure, congestive heart failure class III or higher, thrombus, multivessel disease and older age. In the validation-set, the model fitted the data adequately; the average receiver operating characteristic curve area was 0.782 (standard deviation, 0.018).

CONCLUSIONS

Eight variables were combined into a convenient bedside risk scoring system that estimates the risk of complications after PCIs.

Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention

The objectives of this analysis were to develop and validate simplified risk score models for predicting the risk of major in-hospital complications after percutaneous coronary intervention (PCI) in the era of widespread stenting and use of glycoprotein IIb/IIIa antagonists. We then sought to compare the performance of these simplified models with those of full logistic regression and neural network models. From January 1, 1997 to December 31, 1999, data were collected on 4,264 consecutive interventional procedures at a single center. Risk score models were derived from multiple logistic regression models using the first 2,804 cases and then validated on the final 1,460 cases. The area under the receiver operating characteristic (ROC) curve for the risk score model that predicted death was 0.86 compared with 0.85 for the multiple logistic model and 0.83 for the neural network model (validation set). For the combined end points of death, myocardial infarction, or bypass surgery, the corresponding areas under the ROC curves were 0.74, 0.78, and 0.81, respectively. Previously identified risk factors were confirmed in this analysis. The use of stents was associated with a decreased risk of in-hospital complications. Thus, risk score models can accurately predict the risk of major in-hospital complications after PCI. Their discriminatory power is comparable to those of logistic models and neural network models. Accurate bedside risk stratification may be achieved with these simple models.