Development and validation of simplified predictive index for major complications in contemporary percutaneous transluminal coronary angioplasty practice. The Registry Committee of the Society for Cardiac Angiography and Interventions

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.

A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study

BACKGROUND

A suitable multivariate predictor for predicting mortality following percutaneous coronary intervention (PCI) remains undetermined. We used a nationwide database to construct mortality prediction models to find the appropriate model.

METHODS

Data were analyzed from the Taiwan National Health Insurance Research Database (NHIRD) covering the period from 2004 to 2013. The study cohort was composed of 3,421 patients with acute myocardial infarction (AMI) diagnosis undergoing PCI. The dataset of enrolled patients was used to construct multivariate prediction models. Of these, 3,079 and 342 patients were included in the training and test groups, respectively. Each patient had 22 input features and 2 output features that represented mortality. This study implemented an artificial neural network model (ANN), a decision tree (DT), a linear discriminant analysis classifier (LDA), a logistic regression model (LR), a naïve Bayes classifier (NB), and a support vector machine (SVM) to predict post-PCI patient mortality.

RESULTS

The DT model was found to be the most suitable in terms of performance and real-world applicability. The DT model achieved an area under receiving operating characteristic of 0.895 (95% confidence interval: 0.865-0.925), F1 of 0.969, precision of 0.971, and recall of 0.974.

CONCLUSIONS

The DT model constructed using data from the NHIRD exhibited effective 30-day mortality prediction for patients with AMI following PCI.

Percutaneous Coronary Intervention: Relationship Between Procedural Volume and Outcomes

Percutaneous coronary intervention (PCI) is an integral treatment modality for acute coronary syndromes (ACS) as well as chronic stable coronary artery disease (CAD) not responsive to optimal medical therapy. This coupled with studies on the feasibility and safety of performing PCI in centers without on-site surgical backup led to widespread growth of PCI centers. However, this has been accompanied by a recent steep decline in the volume of PCIs at both the operator and hospital level, which raises concerns regarding minimal procedural volumes required to maintain necessary skills and favorable clinical outcomes. The 2011 ACC/AHA/SCAI competency statement required PCI be performed by operators with a minimal procedural volume of >75 PCIs annually at high-volume centers with >400 PCIs per year, a number which was relaxed in the 2013 ACC/AHA/SCAI update to >50 PCIs/operator/year in hospitals with >200 PCIs annually to coincide with reduction in national PCI volume. Recent data suggests that many hospitals do not meet these thresholds. We review data on the importance of volume as a vital quality metric at both an operator and hospital level in determining procedural outcomes following PCI.

Development and validation of a simple risk score to predict 30-day readmission after percutaneous coronary intervention in a cohort of medicare patients

OBJECTIVES

To develop a risk model that can be used to identify PCI patients at higher risk of readmission who may benefit from additional resources at the time of discharge.

BACKGROUND

A high proportion of patients undergoing PCI are readmitted within 30 days of discharge.

METHODS

The sample comprised patients aged ≥65 years who underwent PCI at a CathPCI Registry®-participating hospital and could be linked with 100% Medicare fee-for-service claims between 01/2007 and 12/2009. The sample (n = 388,078) was randomly divided into risk score development (n = 193,899) and validation (n = 194,179) cohorts. We did not count as readmissions those associated with staged revascularization procedures. Multivariable logistic regression models using stepwise selection models were estimated to identify variables independently associated with all-cause 30-day readmission.

RESULTS

The mean 30-day readmission rates for the development (11.36%) and validation (11.35%) cohorts were similar. In total, 19 variables were significantly associated with risk of 30-day readmission (P < 0.05), and model c-statistics were similar in the development (0.67) and validation (0.66) cohorts. The simple risk score based on 14 variables identified patients at high and low risk of readmission. Patients with a score of ≥13 (15.4% of sample) had more than an 18.5% risk of readmission, while patients with a score ≤6 (41.9% of sample) had less than an 8% risk of readmission.

CONCLUSION

Among PCI patients, risk of readmission can be estimated using clinical factors present at the time of the procedure. This risk score may guide clinical decision-making and resource allocation for PCI patients at the time of hospital discharge. © 2016 Wiley Periodicals, Inc.

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.

Angiographic Lesion Complexity Score and In-Hospital Outcomes after Percutaneous Coronary Intervention

OBJECTIVE

We devised a percutaneous coronary intervention (PCI) scoring system based on angiographic lesion complexity and assessed its association with in-hospital complications.

BACKGROUND

Although PCI is finding increasing application in patients with coronary artery disease, lesion complexity can lead to in-hospital complications.

METHODS

Data from 3692 PCI patients were scored based on lesion complexity, defined by bifurcation, chronic total occlusion, type C, and left main lesion, along with acute thrombus in the presence of ST-segment elevation myocardial infarction (1 point assigned for each variable).

RESULTS

The patients' mean age was 67.5 +/- 10.8 years; 79.8% were male. About half of the patients (50.3%) presented with an acute coronary syndrome, and 2218 (60.1%) underwent PCI for at least one complex lesion. The patients in the higher-risk score groups were older (p < 0.001) and had present or previous heart failure (p = 0.02 and p = 0.01, respectively). Higher-risk score groups had significantly higher in-hospital event rates for death, heart failure, and cardiogenic shock (from 0 to 4 risk score; 1.7%, 4.5%, 6.3%, 7.1%, 40%, p < 0.001); bleeding with a hemoglobin decrease of >3.0 g/dL (3.1%, 11.0%, 13.1%, 10.3%, 28.6%, p < 0.001); and postoperative myocardial infarction (1.5%, 3.1%, 3.8%, 3.8%, 10%, p = 0.004), respectively. The association with adverse outcomes persisted after adjustment for known clinical predictors (odds ratio 1.72, p < 0.001).

CONCLUSION

The complexity score was cumulatively associated with in-hospital mortality and complication rate and could be used for event prediction in PCI patients.

Predictors of long term outcomes in 11,441 consecutive patients following percutaneous coronary interventions

Given the vicissitudes of percutaneous coronary intervention (PCI) technology, epidemiology, and mode of practice, the aim of this study was to define contemporary outcome predictors in a very large consecutive patient cohort. Data from 11,441 consecutive patients who underwent PCI at a tertiary medical center from April 2004 to September 2013 are presented. A comprehensive database was built using various data sources, with outcome end points defined as all-cause mortality and as a composite of death or nonfatal myocardial infarction during follow-up. Candidate variables to influence outcome were chosen a priori and were tested using multivariate time-dependent models to estimate each interaction. Mean follow-up was 5.5 years (range 3 months to 9.5 years). The cohort consisted of 75% men, 42% patients with diabetes, and 61% patients who underwent PCI in acute coronary syndrome settings and 7.8% for ST-elevation myocardial infarction. Drug-eluting stents were used in 43.4% of patients, bare-metal stents in 52%, and balloon angioplasty alone in 4.6%. In multivariate analysis, in addition to already well-recognized predictors of death or myocardial infarction such as advanced age (hazard ratio [HR] 1.031, p <0.001), female gender (HR 1.23, p <0.001), urgent setting (HR 1.23, p <0.001) and diabetes mellitus (HR 1.28, p <0.001), we particularly noted previous anemia (HR 1.55 p <0.001), previous chronic kidney injury (HR 1.93, p <0.001) and previous moderate to severe left ventricular dysfunction (HR 2.29, p <0.001). Drug-eluting stent placement was associated with better outcomes (HR 0.70, p <0.001). In conclusion, this analysis confirms the effect of some known predictors of PCI outcomes. However, the extent of their effect is modest, while other predictors may have a greater influence on outcomes. Risk stratification of PCI patients should take into account kidney injury, anemia, and left ventricular function. Drug-eluting stents provide sustained benefit.

Impact of annual operator and institutional volume on percutaneous coronary intervention outcomes: a 5-year United States experience (2005-2009)

BACKGROUND

The relationship between operator or institutional volume and outcomes among patients undergoing percutaneous coronary interventions (PCI) is unclear.

METHODS AND RESULTS

Cross-sectional study based on the Healthcare Cost and Utilization Project's Nationwide Inpatient Sample between 2005 to 2009. Subjects were identified by International Classification of Diseases, 9(th) Revision, Clinical Modification procedure code, 36.06 and 36.07. Annual operator and institutional volumes were calculated using unique identification numbers and then divided into quartiles. Three-level hierarchical multivariate mixed models were created. The primary outcome was in-hospital mortality; secondary outcome was a composite of in-hospital mortality and peri-procedural complications. A total of 457,498 PCIs were identified representing a total of 2,243,209 PCIs performed in the United States during the study period. In-hospital, all-cause mortality was 1.08%, and the overall complication rate was 7.10%. The primary and secondary outcomes of procedures performed by operators in 4(th) [annual procedural volume; primary and secondary outcomes] [>100; 0.59% and 5.51%], 3(rd) [45-100; 0.87% and 6.40%], and 2(nd) quartile [16-44; 1.15% and 7.75%] were significantly less (P<0.001) when compared with those by operators in the 1(st) quartile [≤15; 1.68% and 10.91%]. Spline analysis also showed significant operator and institutional volume outcome relationship. Similarly operators in the higher quartiles witnessed a significant reduction in length of hospital stay and cost of hospitalization (P<0.001).

CONCLUSIONS

Overall in-hospital mortality after PCI was low. An increase in operator and institutional volume of PCI was found to be associated with a decrease in adverse outcomes, length of hospital stay, and cost of hospitalization.

Simplifying clinical risk prediction for percutaneous coronary intervention of bifurcation lesions: the case for the ACEF (age, creatinine, ejection fraction) score

AIMS

We aimed to appraise the predictive accuracy of a novel and user-friendly risk score, the ACEF (age, creatinine, ejection fraction), in patients undergoing PCI for coronary bifurcations.

METHODS AND RESULTS

A multicentre, retrospective study was conducted enrolling consecutive patients undergoing bifurcation PCI between January 2002 and December 2006 in 22 Italian centres. Patients with complete data to enable computation of the ACEF score were divided into three groups according to tertiles of ACEF score. The primary endpoint was 30-day mortality. The discrimination of the ACEF score as a continuous variable was also appraised with area under the curve (AUC) of the receiver-operating characteristic. A total of 3,535 patients were included: 1,119 in the lowest tertile of ACEF score, 1,190 in the mid tertile, and 1,153 in the highest tertile. Increased ACEF score was associated with significantly different rates of 30-day mortality (0.1% in the lowest tertile vs. 0.5% in the mid tertile and 3.0% in the highest tertile, p<0.001), with similar differences in myocardial infarction (0.3% vs. 0.7% and 1.8%, p<0.001) and major adverse cardiac events (MACE, 0.5% vs. 1.2% and 4.3%, p<0.001). After an average follow-up of 24.4±15.1 months, increased ACEF score was still associated with a higher rate of all-cause death (1.3% vs. 2.4% and 11.0%, p<0.001), cardiac death (0.9% vs. 1.4% and 7.2%, p<0.001), myocardial infarction (3.4% vs. 2.7% and 5.7%, p<0.001), MACE (13.6% vs. 15.9% and 22.3%, p<0.001), and stent thrombosis (2.3% vs. 1.8% and 5.0%, p<0.001). Discrimination of ACEF score was satisfactory for 30-day mortality (AUC=0.82 [0.77-0.87], p<0.001), 30-day MACE (AUC=0.73 [0.67-0.78], p<0.001), long-term mortality (AUC=0.77 [0.74-0.81], p<0.001), and moderate for long-term MACE (AUC=0.60 [0.57-0.62], p<0.001).

CONCLUSIONS

The simple and extremely user-friendly ACEF score can accurately identify patients undergoing PCI for coronary bifurcation lesions at high risk of early fatal or non-fatal complications, as well as long-term fatality.

Validation of the BCIS-1 myocardial jeopardy score using cardiac magnetic resonance perfusion imaging

The recently described angiographic BCIS-1 Myocardial Jeopardy Score (BCIS-JS) provides a semi-quantitative estimate of the extent of coronary artery disease (CAD). It is simple to use and applicable to all patients including those with bypass grafts. Our objective was to validate the BCIS-JS by evaluating its correlation with myocardial ischaemic burden and its accuracy at predicting a prognostic ischaemic threshold. Seventy-five patients with angina and known or suspected CAD referred for coronary angiography prospectively underwent high-resolution CMR perfusion imaging. There was good correlation between the BCIS-JS and myocardial ischaemic burden: r = 0·75, P<0·0001. Area under the ROC curve for BCIS-JS to detect ≥12% myocardial ischaemic burden was 0·87 (95% CI 0·78-0·96). BCIS-JS ≥6 predicted ≥12% myocardial ischaemic burden with a sensitivity of 68% and a specificity of 91%. The BCIS-JS correlates well with myocardial ischaemic burden. A BCIS-JS ≥6 predicts the prognostically important ischaemic threshold of 12% with high specificity. These findings demonstrate that the BCIS-JS has functional relevance and support its utility for classification of CAD burden in clinical trials and in clinical practice.

Prediction of long-term mortality after percutaneous coronary intervention in older adults: results from the National Cardiovascular Data Registry

BACKGROUND

The purpose of this study was to develop a long-term model to predict mortality after percutaneous coronary intervention in both patients with ST-segment elevation myocardial infarction and those with more stable coronary disease.

METHODS AND RESULTS

The American College of Cardiology Foundation CathPCI Registry data were linked to the Centers for Medicare and Medicaid Services 100% denominator file by probabilistic matching. Preprocedure demographic and clinical variables from the CathPCI Registry were used to predict the probability of death over 3 years as recorded in the Centers for Medicare and Medicaid Services database. Between 2004 and 2007, 343 466 patients (66%) of 518 195 patients aged ≥65 years undergoing first percutaneous coronary intervention in the CathPCI Registry were successfully linked to Centers for Medicare and Medicaid Services data. This study population was randomly divided into 60% derivation and 40% validation cohorts. Median follow-up was 15 months, with mortality of 3.0% at 30 days and 8.7%, 13.4%, and 18.7% at 1, 2, and 3 years, respectively. Twenty-four characteristics related to demographics, clinical comorbidity, prior history of disease, and indices of disease severity and acuity were identified as being associated with mortality. The C indices in the validation cohorts for patients with and without ST-segment elevation myocardial infarction were 0.79 and 0.78. The model calibrated well across a wide range of predicted probabilities.

CONCLUSIONS

On the basis of the large and nationally representative CathPCI Registry, we have developed a model that has excellent discrimination, calibration, and validation to predict survival up to 3 years after percutaneous coronary intervention.

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.

Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry

OBJECTIVES

We sought to create contemporary models for predicting mortality risk following percutaneous coronary intervention (PCI).

BACKGROUND

There is a need to identify PCI risk factors and accurately quantify procedural risks to facilitate comparative effectiveness research, provider comparisons, and informed patient decision making.

METHODS

Data from 181,775 procedures performed from January 2004 to March 2006 were used to develop risk models based on pre-procedural and/or angiographic factors using logistic regression. These models were independently evaluated in 2 validation cohorts: contemporary (n = 121,183, January 2004 to March 2006) and prospective (n = 285,440, March 2006 to March 2007).

RESULTS

Overall, PCI in-hospital mortality was 1.27%, ranging from 0.65% in elective PCI to 4.81% in ST-segment elevation myocardial infarction patients. Multiple pre-procedural clinical factors were significantly associated with in-hospital mortality. Angiographic variables provided only modest incremental information to pre-procedural risk assessments. The overall National Cardiovascular Data Registry (NCDR) model, as well as a simplified NCDR risk score (based on 8 key pre-procedure factors), had excellent discrimination (c-index: 0.93 and 0.91, respectively). Discrimination and calibration of both risk tools were retained among specific patient subgroups, in the validation samples, and when used to estimate 30-day mortality rates among Medicare patients.

CONCLUSIONS

Risks for early mortality following PCI can be accurately predicted in contemporary practice. Incorporation of such risk tools should facilitate research, clinical decisions, and policy applications.

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.

Validation of the Mayo Clinic Risk Score for In-Hospital Mortality After Percutaneous Coronary Interventions Using the National Cardiovascular Data Registry

Background— We sought to validate the recently developed Mayo Clinic Risk Score model for in-hospital mortality after percutaneous coronary intervention using an independent data set. The Mayo Clinic Risk Score has 7 simple clinical and noninvasive variables, available before coronary angiography, for prediction of in-hospital mortality. External validation using an independent data set would support broader applicability of the model.

Methods and Results— In-hospital mortality after percutaneous coronary intervention on 309 351 patients from the National Cardiovascular Data Registry admitted from January 1, 2004, to March, 30, 2006, was studied. Using the Mayo Clinic Risk Score equation, we assigned predicted probabilities of death to each patient. The area under the receiver-operating characteristics curve was 0.884, indicating excellent discrimination overall as well as among subgroups, including gender, diabetes mellitus, renal failure, low ejection fraction, different age groups, and multivessel disease. Ninety-seven percent of patients undergoing percutaneous coronary intervention had a Mayo Clinic Risk Score <10, indicating low to intermediate risk. The Mayo Clinic Risk Score model initially slightly underpredicted event rates when applied in National Cardiovascular Data Registry data (observed 1.23% versus predicted 1.10%), but this underprediction was corrected after recalibration. The recalibrated risk score discriminated (c index=0.885) and calibrated well in an National Cardiovascular Data Registry validation data set consisting of procedures performed between April 1, 2006, and March 30, 2007.

Conclusions— Seven variables can be combined into a convenient risk scoring system before coronary angiography is performed to predict in-hospital mortality after percutaneous coronary intervention. This model may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process before percutaneous coronary intervention.

Cumulative funnel plots for the early detection of interoperator variation: retrospective database analysis of observed versus predicted results of percutaneous coronary intervention

OBJECTIVE

To use funnel plots and cumulative funnel plots to compare in-hospital outcome data for operators undertaking percutaneous coronary interventions with predicted results derived from a validated risk score to allow for early detection of variation in performance.

DESIGN

Analysis of prospectively collected data.

SETTING

Tertiary centre NHS hospital in the north east of England.

PARTICIPANTS

Five cardiologists carrying out percutaneous coronary interventions between January 2003 and December 2006.

MAIN OUTCOME MEASURES

In-hospital major adverse cardiovascular and cerebrovascular events (in-hospital death, Q wave myocardial infarction, emergency coronary artery bypass graft surgery, and cerebrovascular accident) analysed against the logistic north west quality improvement programme predicted risk, for each operator. Results are displayed as funnel plots summarising overall performance for each operator and cumulative funnel plots for an individual operator's performance on a case series basis.

RESULTS

The funnel plots for 5198 patients undergoing percutaneous coronary interventions showed an average observed rate for major adverse cardiovascular and cerebrovascular events of 1.96% overall. This was below the predicted risk of 2.06% by the logistic north west quality improvement programme risk score. Rates of in-hospital major adverse cardiovascular and cerebrovascular events for all operators were within the 3sigma upper control limit of 2.75% and 2sigma upper warning limit of 2.49%.

CONCLUSION

The overall in-hospital major adverse cardiovascular and cerebrovascular events rates were under the predicted event rate. In-hospital rates after percutaneous coronary intervention procedure can be monitored successfully using funnel and cumulative funnel plots with 3sigma control limits to display and publish each operator's outcomes. The upper warning limit (2sigma control limit) could be used for internal monitoring. The main advantage of these charts is their transparency, as they show observed and predicted events separately. By this approach individual operators can monitor their own performance, using the predicted risk for their patients but in a way that is compatible with benchmarking to colleagues, encapsulated by the funnel plot. This methodology is applicable regardless of variations in individual operator case volume and case mix.

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).

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.

Relation of preprocedural statin therapy to in-hospital procedural complications following percutaneous coronary interventions in patients with hyperlipidemia

We investigated whether the observed protective effect of hyperlipidemia is stronger in patients who take statins and, if so, whether that effect is likely due to patient characteristics associated with statin use. In-hospital complications and long-term outcomes of patients with hyperlipidemia (cholesterol level > or = 240 mg/dl) undergoing percutaneous coronary interventions (PCI) on statins (group Ia, n = 2,052) and not on statins (group Ib, n = 1,650) were compared with 726 patients with lower cholesterol levels (group II). Despite a higher prevalence of co-morbidities and worse angiographic characteristics in patients with hyperlipidemia, patients in group Ia had significantly lower in-hospital mortality (0% vs 2% in the other 2 groups, p < 0.001), a lower increase in the postprocedure creatine kinase-MB fraction (14% vs 27% in group Ib and 28% in group II, p < 0.001), and fewer PCI complications (15% vs 30% in groups Ib and II, p < 0.001). After adjustment, patients in group Ia had a significant decrease in complications (odds ratio 0.72, 95% confidence interval 0.65 to 0.92, p = 0.009). In contrast, those in group Ib had outcomes similar to those of patients with lower cholesterol. After application of propensity analysis to adjust for the likelihood of receiving statins based on clinical, angiographic, and procedural characteristics, group Ia had fewer in-hospital complications (odds ratio 0.75, 95% confidence interval 0.62 to 0.90, p = 0.002) and lower in-hospital mortality (odds ratio 0.32, 95% confidence interval 0.12 to 0.84, p = 0.021). After successful PCI, overall survival after dismissal and survival free of myocardial infarction and target vessel revascularization were similar. In conclusion, hyperlipidemia per se is not associated with lower in-hospital complications after PCI. The benefit is largely limited to patients on statin treatment.

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.

Simplified scoring system for predicting mortality after percutaneous coronary intervention

OBJECTIVES

We sought to develop a simplified scoring system based on pre-intervention clinical characteristics to predict in-hospital mortality after percutaneous coronary intervention (PCI).

BACKGROUND

Percutaneous coronary intervention is associated with variety of complications, including the risk of death. Factors leading to poor outcomes need to be identified. Currently available indexes are cumbersome and therefore seldom used.

METHODS

Crude mortality and univariate odds ratios (ORs) for mortality associated with multiple clinical characteristics were calculated for 9,954 patients undergoing PCI at the William Beaumont Hospital during 1996 to 1998. Based on the OR, each factor was assigned a weighted score. Using these scores, a classification was constructed to determine the probability of death after PCI, with classes I through IV representing an increasing probability of procedural mortality. This classification was validated in a separate group of patients.

RESULTS

The factors with the highest univariate odds of dying and their scores were: myocardial infarction <14 days = 7; elevated creatinine = 4; multivessel disease = 4; and age >65 years = 3. Classes were created based on the presence of these factors in a given patient. The odds of dying and mortality increased significantly with each class. These results were reproduced in the validation subset.

CONCLUSIONS

Preprocedural clinical risk factors have a differential influence on the probability of death after PCI. Risk classification based on these factors can be used to accurately predict the procedural outcome. This simple classification can be used by interventionalists to assist in management decisions, to provide an estimate of procedural risk to the patients and relatives, and for quality assurance.

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.

Poor functional recovery may indicate restenosis in patients after coronary angioplasty

OBJECTIVE

To investigate whether poor response to exercise training can detect restenosis in asymptomatic patients after percutaneous transluminal coronary angioplasty (PTCA).

DESIGN

Case-control study.

SETTING

A hospital-based outpatient cardiac rehabilitation program in Taiwan.

PARTICIPANTS

Sixteen patients aged 49.7+/-7.8 years who had undergone PTCA and completed a 3-month exercise program. Patients were separated into a restenosis group (n=7; age, 46.4+/-9.8y) and a nonrestenosis group (n=9; age, 52.3+/-12.9y), according to their angiography follow-up results. The interval between PTCA and angiography ranged from 6 months to 2 years.

INTERVENTION

Bicycle exercise workouts were conducted 3 times a week during rehabilitation, with exercise intensity adjusted to each patient's ventilatory threshold.

MAIN OUTCOME MEASURES

A graded exercise test with gas analysis was conducted before training, at 6 weeks and at 3 months after training, to evaluate the sequential changes of cardiorespiratory function.

RESULTS

After 3 months of training, the nonrestenosis group showed an increase of 30.4% in peak oxygen uptake (Vo(2)peak, P<.05), 13.7% in peak oxygen pulse (P<.05), 22.2% in peak rate-pressure product (P<.05), and 13.6% in peak work rate (P<.05). Most of the improvement occurred within the first 6 weeks of training. The restenosis group did not show significant increase in these variables. At the ventilatory threshold, the nonrestenosis group also displayed a significant increase of Vdot;o(2), oxygen pulse, and work rate. However, the restenosis group showed no improvement after training.

CONCLUSION

Functional recovery appears to be a good indicator of restenosis for patients after PTCA. A poor response to exercise can be noted within 6 weeks of training in PTCA patients with restenosis.

Modeling and risk prediction in the current era of interventional cardiology: a report from the National Heart, Lung, and Blood Institute Dynamic Registry

BACKGROUND

Validation of in-hospital mortality models after percutaneous coronary interventions using multicenter data remains limited.

METHODS AND RESULTS

This study evaluated whether multivariable mortality models developed during the pre-stent era by New York State, American College of Cardiology (ACC)-National Cardiovascular Data Registry, Northern New England Cooperative Group, Cleveland Clinic Foundation, and the University of Michigan are relevant in patients undergoing percutaneous coronary intervention in the 1997 to 1999 National Heart, Lung, and Blood Institute Dynamic Registry. Of 4448 Dynamic Registry patients, 73% received > or =1 stent and 28% received a IIB/IIIA receptor inhibitor. In-hospital mortality occurred in 64 patients (1.4%). The New York state model predicted mortality in 69 patients (1.5%; 95% confidence bounds [CI], 0.89% to 1.70%); Northern New England predicted mortality in 60 patients (1.3%; 95% CI, 1.0% to 1.7%); and Cleveland Clinic predicted mortality in 76 patients (1.7%; 95% CI, 1.3% to 2.1%). Among high-risk subgroups, with these 3 models, observed and predicted in-hospital mortality rates in general were not different. The other 2 models yielded different results. The University of Michigan predicted fewer deaths (n=47; 1.1%; 95% CI, 0.7% to 1.3%), and the ACC Registry model predicted 603 deaths (13.5%; 95% CI, 12.6% to 14.4%). Using the ACC Registry model, predicted mortality was higher than observed in each subgroup.

CONCLUSIONS

Application of 5 mortality risk models developed from different data sets to patients undergoing percutaneous coronary intervention in the Dynamic Registry predicted, in 3 models, mortality rates that were not significantly different than those observed. In both high and low risk subgroups, the University of Michigan slightly underpredicted mortality, and the ACC Registry predicted significantly higher mortality than that observed.

Major adverse outcomes after percutaneous transluminal coronary angioplasty: a clinical prediction rule

In this study, we developed and internally validated a clinical model for predicting major adverse outcomes in patients undergoing percutaneous transluminal coronary angioplasty (PTCA) using a multi-institutional prospective cohort study involving all adult patients who underwent PTCA at 12 participating institutions from August 1993 to October 1995. A major adverse outcome, defined as death, renal failure, myocardial infarction, cardiac arrest, stroke, or coma, occurred in 3.3 and 3.2% of patients in the derivation and validation sets, respectively. Death occurred in 1.5% in both sets. Fourteen variables were independently correlated with major adverse outcomes. The rule, which stratifies PTCA patients into six levels of risk based on the severity score, showed excellent discrimination (receiver-operating characteristic curve area 0.82) and calibration (Hosmer-Lemeshow chi-square statistic P =.90) and performed well on internal validation. This rule allows accurate preprocedure stratification of PTCA candidates according to their risk of suffering a major adverse outcome.