A critical appraisal of current models of risk stratification for percutaneous coronary interventions

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.

EuroHeart score for the evaluation of in-hospital mortality in patients undergoing percutaneous coronary intervention

AIMS

The applicability of currently available risk prediction models for patients undergoing percutaneous coronary interventions (PCIs) is limited. We aimed to develop a model for the prediction of in-hospital mortality after PCI that is based on contemporary and representative data from a European perspective.

METHODS AND RESULTS

Our analyses are based on the Euro Heart Survey of PCIs, which contains information on 46 064 consecutive patients who underwent PCI for different indications in 176 participating European centres during 2005-08. Patients were randomly divided into a training (n = 23 032) and a validation (n = 23 032) set with similar characteristics. In these sets, 339 (1.5%) and 305 (1.3%) patients died during hospitalization, respectively. On the basis of the training set, a logistic model was constructed that related 16 independent patient or lesion characteristics with mortality, including PCI indication, advanced age, haemodynamic instability, multivessel disease, and proximal LAD disease. In both the training and validation data sets, the model had a good performance in terms of discrimination (C-index 0.91 and 0.90, respectively) and calibration (Hosmer-Lemeshow P-value 0.39 and 0.18, respectively).

CONCLUSION

In-hospital mortality in PCI patients was well predicted by a risk score that contains 16 factors. The score has strong applicability for European practices.

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.

Comparison of long-term mortality after percutaneous coronary intervention in patients treated for acute ST-elevation myocardial infarction versus those with unstable and stable angina pectoris

Data remain limited regarding the comparative long-term mortality across the spectrum of patients with different indications for percutaneous coronary intervention (PCI). We evaluated early and late mortality in patients with ST-segment elevation myocardial infarction (STEMI) treated with primary PCI compared with early and late mortality in patients undergoing PCI for unstable angina (UA) or non-STEMI (NSTEMI) and stable angina. A total of 10,549 consecutive patients undergoing PCI from 1997 to 2005 at a single institution were followed up prospectively (median 3.2 years, interquartile range 1.5 to 5.6) to assess all-cause mortality. The indication for PCI was STEMI in 28%, UA/NSTEMI in 32%, and stable angina in 40%. The mortality rate at 6 years was 18.9% in patients with STEMI, 16.2% in patients with UA/NSTEMI, and 11.7% in those with stable angina. During the initial 6 months, patients with STEMI had an increased risk of death compared with patients with UA/NSTEMI (relative risk [RR] 3.09, 95% confidence interval [CI] 2.46 to 3.89) and stable angina (RR 5.82, 95% CI 4.45 to 7.62). However, between 6 months and 6 years, mortality accrued at an almost similar rate among patients with STEMI and those with stable angina (RR 1.06, 95% CI 0.86 to 1.32) and mortality was greatest in patients with UA/NSTEMI (UA/NSTEMI vs stable angina: RR 1.33, 95% CI 1.11 to 1.58; STEMI vs UA/NSTEMI: RR 0.80, 95% CI 0.65 to 0.99). In conclusion, we have demonstrated that the inferior survival rates in patients with STEMI after primary PCI are mainly attributed to greater mortality in the first months after the event. These observations highlight that new adjunctive therapeutic strategies should aim at mortality reduction in the first months after primary PCI.

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.

Risk-adjustment model in health outcomes evaluation: a contribution to strengthen assessment towards quality improvement in interventional cardiology

OBJECTIVE

The aim of this study was to develop a risk adjustment model for major adverse cardiac and cerebrovascular events following percutaneous coronary intervention (PCI), using data from a national registry, and to highlight the use of the risk adjustment when we evaluate the quality of care in interventional cardiology.

DESIGN

The

STUDY DESIGN

was based on a Coorte study. Bivariate and multivariate logistic regression models were used to identify independent risk factors for these major adverse events.

SETTING

A total of 19 hospitals from the Portuguese National Registry of Interventional Cardiology.

PARTICIPANTS

Data from 10.641 consecutives procedures collected between June 30, 2003 and June 30, 2006.

INTERVENTION

Build a risk adjustment model for these major adverse events, following percutaneous coronary intervention.

MAIN OUTCOME MEASURE

Factors that were associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention.

RESULTS

The rate of in-hospital major adverse cardiac and cerebrovascular events was 1.9%. Factors associated with major adverse cardiac and cerebrovascular events included, among others: age >80 years (adjusted odds ratio = 3.91); female gender (1.72); and cardiogenic shock (6.05). Overall, a good discrimination was achieved with receiver operating characteristics curve = 0.84 and Hosmer-Lemeshow goodness of fit statistic across groups of risk was not significant (P = 0.18) indicating little departure from a perfect fit.

CONCLUSIONS

These findings will represent an important contribution to quality and safety improvement and should help driving new research and innovative approaches to different subgroups of patients who have higher chances of having an adverse event or poorer outcomes following this intervention.

Predicting major adverse cardiac events after percutaneous coronary intervention: the Texas Heart Institute risk score

BACKGROUND

Many models have been devised in the past to predict adverse outcomes after PCI, but with rapid advancements in this field, a new risk-prediction model may be needed. The purpose of our study was to identify the clinical and angiographic variables associated with adverse cardiac events after percutaneous coronary intervention (PCI) and to construct a simple bedside tool for risk stratification of PCI patients.

METHODS

Using our institution's database, we analyzed data from 9,494 patients who underwent PCI between January 1, 1996, and December 31, 2002 (ie, during the bare-metal stent era). Predictors of major adverse cardiac events--death, myocardial infarction, stroke, and repeat revascularization by emergent coronary artery bypass grafting or PCI--were identified by multivariate logistic regression analysis using baseline clinical, angiographic, and procedural variables. A simple integer score was constructed by multiplying the beta coefficient for each variable by a constant and rounding the result to the nearest integer. The score was validated in 5,545 patients who underwent PCI between January 1, 2003, and December 31, 2006 (ie, during the drug-eluting stent era).

RESULTS

Multivariate regression analysis identified emergent procedure, urgent procedure, unstable angina, acute myocardial infarction, renal insufficiency, hypertension, congestive heart failure, peripheral vascular disease, type C lesion, presence of thrombus, and number of stents placed as independent predictors of adverse events after PCI. The model had good overall discrimination (area under the receiver operator characteristic curve 0.701), and the model fitted the validation cohort adequately.

CONCLUSIONS

Risk of complications after PCI can be assessed with this simple tool, which may permit comparisons between different operators as well as different hospitals.

Survival advantage in Medicare patients receiving drug-eluting stents compared with bare metal stents: real or artefactual?

BACKGROUND

Concerns have been raised regarding late mortality, particularly from late stent thrombosis, from drug-eluting stents (DES). Randomized clinical trials have shown that DES decrease restenosis but do not decrease mortality compared with bare metal stents (BMS). These studies utilized well-defined clinical and angiographic subsets. In the "real world" drug-eluting stents are used in a much broader crosssection of patients. We evaluated mortality in the first year after implantation of DES, specifically the sirolimus-eluting stent (SES), Cypher vs. BMS in "real world" older patients using the Medicare claims database.

METHODS AND RESULTS

Data for the years 2002 (n = 6,890; pre-DES) and 2003 (n = 7,566; first year of DES use) (May through December of each year) were analyzed. BMS and DES groups had similar baseline characteristics except for small but significant differences with BMS patients being somewhat older, having more males and African Americans, and a higher percentage of peripheral artery disease and heart failure while DES patients had a higher percentage of diabetics and patients with prior revascularization procedures. A significant improvement in mortality using both unadjusted and adjusted analyses was observed for DES (6.0% vs. 11.4%, P < 0.0001; hazard ratio 1.98, 95% CI 1.68-2.34). Controlling for comorbidity, extent of disease, and other characteristics by multivariable analysis or by propensity analysis had little impact on these results. On the other hand, there was no change in overall mortality in all stented patients in 2003 compared with all stented patients in 2002.

CONCLUSION

An observed mortality benefit for DES compared with BMS in 2003 was observed, demonstrating the safety of DES, and suggesting the possibility of superiority in outcome in older patients with DES vs. BMS. However, the lack of improved survival from 2002 to 2003 in all stented patients suggests that the mortality advantage with DES finding may be due to unidentified selection biases. Our data suggest that DES in the Medicare population is as safe as, and possibly superior, to BMS for survival over the first year after implantation.

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).

Bedside estimation of risk from percutaneous coronary intervention: the new Mayo Clinic risk scores

OBJECTIVE

To derive risk models for percutaneous coronary intervention (PCI) outcomes from clinical and laboratory variables available before the procedure so they can be used for preprocedure risk stratification.

PATIENTS AND METHODS

Using the Mayo Clinic registry, we analyzed 9035 PCIs on 7640 unique patients from January 1, 2000, through April 30, 2005. We included only the first PCI per patient (n=7457). Logistic regression was used to model the calculated risk score and major procedural complications. Separate risk models were made for mortality and major adverse cardiovascular events (MACE) derived solely from baseline and laboratory characteristics. Final risk scores for procedural death, defined as any death during the index hospitalization, and MACE contained the same 7 variables (age, myocardial infarction less than or equal to 24 hours, preprocedural shock, serum creatinine level, left ventricular ejection fraction, congestive heart failure, and peripheral artery disease).

RESULTS

Models had adequate goodness of fit, and areas under the receiver operating characteristic curve were 0.74 and 0.89 for MACE and procedural death, respectively, indicating excellent overall discrimination. The model was robust across many subgroups, including those undergoing elective PCI, those having diabetes mellitus, and elderly patients. Bootstrap analysis indicated that the model was not overfit to the available data set.

CONCLUSION

Before coronary angiography is performed, a risk-scoring system based on 7 variables can be used conveniently to predict cardiovascular complications after PCI. This model may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process.

A risk score to predict in-hospital mortality for percutaneous coronary interventions

OBJECTIVES

Our purpose was to develop a risk score to predict in-hospital mortality for percutaneous coronary intervention (PCI) using a statewide population-based PCI registry.

BACKGROUND

Risk scores predicting adverse outcomes after PCI have been developed from a single or a small group of hospitals, and their abilities to be generalized to other patient populations might be affected.

METHODS

A logistic regression model was developed to predict in-hospital mortality for PCI using data from 46,090 procedures performed in 41 hospitals in the New York State Percutaneous Coronary Intervention Reporting System in 2002. A risk score was derived from this model and was validated using 2003 data from New York.

RESULTS

The risk score included nine significant risk factors (age, gender, hemodynamic state, ejection fraction, pre-procedural myocardial infarction, peripheral arterial disease, congestive heart disease, renal failure, and left main disease) that were consistent with other reports. The point values for risk factors range from 1 to 9, and the total risk score ranges from 0 to 40. The observed and recalibrated predicted risks in 2003 were highly correlated for all PCI patients as well as for those in the higher-risk subgroup who suffered myocardial infarctions within 24 h before the procedure. The total risk score for mortality is strongly associated with complication rates and length of stay in the 2003 PCI data.

CONCLUSIONS

The risk score accurately predicted in-hospital death for PCI procedures using future New York data. Its performance in other patient populations needs to be further studied.

Implementation and application of a continuous quality improvement (CQI) program for the cardiac catheterization laboratory: One institution's 10-year experience

There is consensus that a continuous quality improvement (CQI) program is essential in optimizing patient outcomes in the cardiovascular catheterization laboratory. A CQI method was described in guidelines produced by the Society for Cardiovascular Angiography and Interventions (SCAI) in 1993 and 1997. However, little information is available in the medical literature to determine the implementation and application of this approach in a modern catheterization program. This presentation describes the lessons learned from one institution's 10 year CQI experience by using the SCAI blueprint.