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J Am Coll Cardiol, 2003; 42:1890-1895, doi:10.1016/j.jacc.2003.06.014
© 2003 by the American College of Cardiology Foundation
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CLINICAL RESEARCH: INTERVENTIONAL CARDIOLOGY

Simplified scoring system for predicting mortality after percutaneous coronary intervention

Mansoor A. Qureshi, MD*, Robert D. Safian, MD*, Cindy L. Grines, MD*, James A. Goldstein, MD*, Douglas C. Westveer, MD*, Susan Glazier, RN, BSN*, Mamtha Balasubramanian, BS* and William W. O'Neill, MD*,*

* Division of Cardiology, William Beaumont Hospital, Royal Oak, Michigan, USA

Manuscript received June 25, 2002; revised manuscript received May 20, 2003, accepted June 10, 2003.

* Reprint requests and correspondence: Dr. William W. O'Neill, Division of Cardiology, William Beaumont Hospital, 3601 West Thirteen Mile Road, Royal Oak, Michigan 48073-6769, USA.
woneill{at}beaumont.edu


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
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.

Abbreviations and Acronyms
  CABG = coronary artery bypass graft surgery
  CS = cardiogenic shock
  MI = myocardial infarction
  MVD = multivessel disease
  OR = odds ratio
  PCI = percutaneous coronary intervention
  PVD = peripheral vascular disease
  ROC = receiver operating characteristic

Since the introduction of balloon angioplasty in the late 1970s (1), there have been dramatic advances in the success and safety (2–5) of percutaneous coronary intervention (PCI). This has been coupled with an exponential increase in the number of patients undergoing percutaneous treatment for stable angina, as well as acute coronary syndromes (6–14). As technical refinements, including stent use (15) and glycoprotein receptor antagonist use (16,17), have become widespread, anatomic lesion characteristics have become less predictive of a poor outcome. At present, clinical characteristics such as patient age, renal function, and the presence of acute coronary syndrome have taken a predominant role in determining the procedural risk of death (18–20).

Estimation of operative mortality is important for patients seeking health care, physicians making management decisions, and institutions for quality assurance purposes. Investigators have utilized clinical and angiographic characteristics to assess the risk of major adverse events in various patient cohorts undergoing PCI (18,21–25). Most of these methods either evaluate multiple clinical and angiographic features or use complex mathematical equations, and thus are difficult to use to risk stratify patients.

The goal of this study was to derive a simple scoring system based solely on preprocedural clinical features, which would provide risk classes predictive of mortality after PCI. Furthermore, we attempted to validate this scoring system on a different patient population. Most of the predictors of major complications identified in this study have been described previously (2,19,26–37).


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Patient population.   All patients undergoing PCI at the William Beaumont Hospital between January 1996 to December 1998 were studied (n = 9,954). Demographic, clinical, and procedural data were recorded for all patients in a dedicated database. The in-hospital outcome was carefully monitored and recorded by quality assurance nursing personnel trained and dedicated to catheterization laboratory and chart review. Data were prospectively collected on age, gender, coronary risk factors, renal function (serum creatinine), the presence of coronary artery disease (>50% diameter stenosis by angiography), a recent myocardial infarction (MI) (PCI within 14 days of MI), a history of coronary artery bypass graft surgery (CABG), and a history of peripheral vascular disease (PVD). (For the diagnosis of MI, two of the following criteria had to be present: chest pain >30 min, creatine kinase elevation >2 times the upper limit of normal, and electrocardiographic changes consistent with MI. If there was a history of lower extremity vascular disease or carotid artery disease, PVD was considered present.) Procedures were performed by interventional cardiologists who are required to be American Board of Internal Medicine cardiology board certified and who must meet minimal proficiency criteria of performance of ≥75 interventional cases per year. Percutaneous transluminal coronary angioplasty and stent placement were the predominant modes of revascularization. Procedural activated clotting times were maintained at 300 to 350 s, unless glycoprotein IIb/IIIa agents were used. Patients undergoing PCI from 1999 to November 2001 (n = 12,005) at the William Beaumont Hospital were used as a validation subset.

End point.   In-hospital mortality was the primary end point of the study. This included all-cause mortality in the catheterization laboratory and during the hospital stay after the procedure.

Statistical analysis.   Crude mortality and the univariate odd ratio (OR) of mortality for various clinical factors were calculated. Individual risk factors that were significant on the univariate analysis were entered into a stepwise, multiple logistic regression model to determine the most parsimonious model that best predicted mortality. To simplify the analysis, four clinical factors that were most prevalent and had the highest impact on mortality were used for analysis: a history of a recent MI (patients undergoing PCI within 14 days of MI), multivessel coronary artery disease (>50% stenosis in more than one coronary artery), elevated creatinine (>1.5 mg/dl), and age >65 years. Each of these factors was assigned a "weighted" score based on the odds of dying. The univariate OR rounded to the nearest integer constituted the score for each factor. Risk classes (1 to 4) were created based on the total scores obtained by the various combinations of risk factors. Crude mortality and OR were calculated for various classes for the entire data set and individual years. The odds of dying for individual classes were compared with class I. Classification system discrimination was assessed by use of the area under the receiver operating characteristic (ROC) curve (38). The area under the ROC curves was calculated separately for all statistically significant factors, four select factors that were considered for the classification, and the individual classes. These were also tested for goodness of fit by use of the Hosmer-Lemeshow statistic (39).

This classification system was applied on the validation subset to calculate mortality and ORs for individual classes. The areas under the ROC curves were also calculated in the validation set.


   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
Patient population.   Baseline clinical characteristics are shown in Table 1. During the study period, data were collected on 9,954 patients. None of the patients were excluded from the analysis. Overall mortality was 1.4%. Females constituted about one-third of the patients (32%); 59% of the patients had multivessel disease (MVD); 22% had a recent MI (22%); previous CABG had been performed in 21.4%; and about 9% of patients had creatinine >1.5 mg/dl at the time of the procedure. It can be noticed that the derivation subset was more likely to have had patients with previous CABG, a past cerebrovascular accident, and recent smokers. On the other hand, patients in the validation subset were more likely to be older, with MVD, to have elevated creatinine, and to have been recent smokers.


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  		Table 1 Baseline Clinical Characteristics in the Derivation and Validation Subsets

 
Influence of various risk factors and the classification system in the derivation subset.   Overall mortality for patients undergoing PCI from 1996 to 1998 (n = 9,954) was 1.4%. The univariate ORs for mortality for some of the clinical factors studied are shown in Table 2. It can be noticed that the highest individual univariate ORs are for MI <14 days (7.4), elevated creatinine (4.4), MVD (4.1), age >65 years (3.0). (Patients with cardiogenic shock [CS] were not analyzed separately and were included in the patients with MI <14 days. However, the univariate OR of patients who presented with MI <14 days and CS was 52.6, which is higher than any of the other factors studied separately. On the other hand, for those with MI <14 days without CS, the OR was 5.3.) As described earlier, these factors were given individual scores based on univariate OR: MI <14 days = 7; elevated creatinine = 4; MVD = 4; and age >65 years = 3. The classes with their ORs and mortality and scores are shown in Table 3. Multiple logistic regression analysis was also performed on these clinical factors (Table 4). The area under the ROC curve was computed for statistically significant factors. The average area under the ROC curve for the multiple logistic regression analysis for all significant factors studied was 0.87. It dropped only to 0.84 when the top four risk factors were considered, showing that these select factors still predict the overall outcome. The Hosmer-Lemeshow statistic was not significant for either of these analyses, showing little departure from a perfect fit (Table 5).


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  		Table 2 Crude Mortality, Relative Risk, Univariate Odds Ratios, and Risk Scores for Clinical Risk Factors

 

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  		Table 3 Risk Classification

 

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  		Table 4 Multivariate Logistic Regression Model of In-Hospital Mortality

 

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  		Table 5 Area Under the Receiver Operating Characteristic Curve for All Significant Factors, Four Selected Factors, and Risk Classes for the Derivation and Validation Subsets

 
According to this classification, patients with no risk factors were in class I. Possible risk factor combinations that will put patients in different classes are shown in Table 6. The area under the ROC curve for this classification is 0.81, with an insignificant Hosmer-Lemeshow p value, implying that this model fit has little departure from a perfect fit (Table 5). There appears to be a definite gradation in mortality as the classes increase. An increase in the score seems to be associated with a striking increase in the odds of dying and a significant increase in the crude mortality. Thus, the classification appears to distinguish patients who are at an increased risk of dying after PCI.


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  		Table 6 Various Combinations of Risk Factors That Would Fit Individual Risk Classes

 
Figure 1 shows the distribution of patients in various classes. The majority of the patients fall in class II; class IV has the fewest number of patients.



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  		Figure 1 Distribution of patients into various risk classes in the derivation and validation subsets.

 
Validation.   We applied this scoring system for all patients undergoing PCI from January 1999 to November 2001 (n = 12,005). Figure 2 compares the crude mortality for individual classes between the derivation and validation subset. It is apparent that classes show similar gradation in mortality and OR, as seen in patients undergoing PCI over the period 1996 to 1998, who constituted the basis of our classification. The area under the ROC curve was 0.87 for the logistic regression model, which included all of the statistically significant factors, and 0.83 for the classification, consistent with excellent discrimination (Table 5).



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  		Figure 2 Comparison of crude mortality of the individual classes in the derivation and validation subsets.

 

   Discussion
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 Abstract
 Methods
 Results
 Discussion
References
 
This report presents the largest single-center database of patients undergoing PCI, which includes prospectively collected data on various important clinical variables that could influence the procedural outcome. It represents a wide variety of patients presenting to the catheterization laboratory with diverse clinical presentations, ranging from stable angina to CS. Because all patients were analyzed, this database provides outcome evaluation of a very contemporary interventional patient population.

Mortality data.   The yearly mortality of the patient population in the derivation ranges between 1.1% and 1.7% (mean 1.36%). This rate resembles those found in diverse patient cohorts reported by various investigators. Procedural mortality in contemporary PCI has generally remained low, despite the increasing complexity of procedures (2,19,30,40–44). Because a widely accepted risk-adjusted mortality rate does not exist, analysis of subgroups like those we report may provide meaningful information on operator and institutional outcomes.

Risk factors.   To establish risk factors that adversely influence mortality, we evaluated predictors that have been previously identified in various registry databases (2,19,21,26–36,45,46). Our goal was to use preprocedural clinical factors and not include procedural factors for the prediction of mortality. Recent MI and elevated creatinine were found to have the highest mortality and individual odds of dying. These findings are similar to the observations of Moscucci et al. (20). The presence of MVD and increasing age were the other factors with a strong influence on mortality.

As mentioned earlier, CS was found to be the strongest predictor of mortality, with an OR of 52.6 in the derivation group. However, these patients were not separated from the "MI <14 days" subgroup. This allowed us to evaluate the overall influence of MI <14 days on postprocedural mortality.

Risk classification.   Investigators have attempted to derive various predictive models for post–coronary interventional procedure outcomes. These include complex computerized models or indexes that consider both clinical and procedural factors. Kimmel et al. (18) described a predictive index that includes death, MI, and emergency CABG as procedure-related complications. Budde et al. (23) described a computer model (INTERVENT) that would predict postprocedural complications. This model relies on multiple clinical and procedural factors. Recently, Moscucci et al. (20) presented a predictive tool for in-hospital mortality. Their findings resemble ours to some extent, as increasing age, elevated creatinine, recent MI, and MVD appear to be the dominant factors influencing mortality. Similarly, this latter group also used weighted scores for individual risk factors. However, contrary to our analysis, a combination of demographics and procedural variables was used for risk assessment, which can make pre-PCI risk assessment somewhat difficult.

With our classification, use of only four clinical variables can give us an accurate idea about post-PCI risk of death. These calculations can be performed within seconds and do not require complex mathematical calculations.

Validation.   It is extremely important to validate predictive models in different patient populations, as technical advances, adjunctive pharmacology, and indications for PCI change rapidly. It is also important to assess the durability of this risk score. It can be noted that this classification provides similar results in a large validation subset (Fig. 2) with distinct patient cohorts that significantly differ in mortality. Despite differences in baseline characteristics (Table 1), the classification's ability to predict mortality does not seem to be decreased (Fig. 2).

Risk classification was made known to the interventionalists at our institution in early 1999, and concern was voiced regarding the mortality trends. This was followed by a distinct downward trend in procedural mortality (Fig. 3). This statistically significant decline in mortality could be due to a variety of reasons. Figure 1 shows that there is a distinct decrease in patients fitting into class IV in years 1999 to 2001 (7.0% to 5.9% in the derivation and validation subsets, respectively). In addition, there was a decrease in mortality for this class in the validation subset (9.1% vs. 7.1% in the derivation and validation subsets, respectively). Thus, a decrease in mortality in the validation subset could be related to both a decrease in PCI for high-risk cases and an improved outcome in these patients. Additional explanations may include increasing awareness of the risk resulting in the use of adjunctive medications (glycoprotein IIb/IIIa inhibitors) and devices, as well as elimination of factors that may lead to prolongation of the procedure. These factors are being currently evaluated.



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  		Figure 3 Comparison of overall crude mortality for the derivation and validation subsets.

 
Clinical implications.   We have shown that despite technical advancements, preprocedural clinical factors have a strong influence on mortality. These factors can be used to accurately predict postprocedural mortality at the bedside. This can help physicians make triage decisions and provide patients and families with accurate risk assessment.

This simplified classification can help in quality assurance, because mortality trends for individual classes can be tracked for the physicians and institutions. Finally, risk classes can also help interventionalists identify patients who may be candidates for adjuvant therapies, aggressive nursing, and hemodynamic monitoring.

Study limitations.   This classification uses a small group of risk factors which may underestimate the influence of other potentially important factors. Lesion characteristics and left ventricular function are not incorporated and may improve the precision of this tool.

Procedural factors such as contrast load also play a significant role in determining outcomes; however, our goal was to determine risk based on preprocedural clinical factors that were easy to obtain and available in all patients before initiation of the intervention.

This classification merely assigns a risk class to a patient and describes the probability of mortality; however, it does not predict individual patient outcome.

Another potential limitation is the fact that the data were derived from a single center, and one would have to be cautious in generalizing these findings.

Conclusions.   Preprocedural clinical risk factors strongly influence hospital mortality after PCI. These factors can be used to derive the probability of dying after PCI.

Using the presence or absence of four risk factors (i.e., MI <14 days, MVD, elevated creatinine [>1.5 mg/dl], age >65 years), patients can be classified into different risk classes, with mortality ranging from 0.2% to 9.1%. This risk classification provides quick and accurate assessment of postprocedural in-hospital mortality. It can be used for quality assurance and assessment of physician and institutional proficiency.


   References
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 Abstract
 Methods
 Results
 Discussion
 References
 

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