Contemporary Mortality Risk Prediction for Percutaneous Coronary Intervention: Results from 588,398 Procedures in the National Cardiovascular Data Registry

Abstract

Objective

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 two 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 STEMI 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 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, and policy applications.

Percutaneous coronary intervention (PCI) has become one of the most widely applied treatments in current-day cardiology, facilitating the relief of angina and (in the setting of acute ST-elevation myocardial infarction [STEMI]) saving lives (1). While the peri-procedural complications of PCI have declined over time, tangible risks remain. Estimating patients’ PCI mortality risk is important for several reasons. At the individual patient level, knowing one's procedural risk can help physicians and patients make informed clinical decisions (2). Identification and quantification of clinical factors associated with procedural risk can also facilitate observational comparative effectiveness research (3). Finally, at a policy level, predicted risk estimates can help “level the playing field” of provider outcome metrics, helping to adjust for potential differences in cases treated (4).

To date, several PCI mortality risk models have been published. Yet many have become outdated and do not reflect contemporary care or outcomes (5-13). Other risk models were developed on select populations and may not be generalizable (7-9,11,14-19). Additionally, many models failed to consider angiographic features that are associated with procedural risk (9,20,21). The National Cardiovascular Data Registry (NCDR) for Catheterization Percutaneous Coronary Intervention (CathPCI) provides the ideal infrastructure to derive procedure risk models in a national representative contemporary US sample. This database has a very large patient population, contains rich and complete clinical information, and is reflective of contemporary practice.

Using the NCDR CathPCI database, our goals were to: 1) develop PCI risk tools for estimating mortality risks for both elective and primary PCI; 2) determine the incremental prognostic value of angiographic details beyond pre-procedural risk factors; 3) develop a simplified, user-friendly, PCI risk score; 4) internally validate the PCI risk model and risk score in important sub-populations; and 5) assess the models’ ability to estimate 30-day PCI mortality risk among Medicare patients whose status is defined via claims data.

Methods

The NCDR CathPCI Registry database

The NCDR CathPCI Registry is co-sponsored by the American College of Cardiology and the Society for Cardiovascular Angiography and Interventions (22,23). The registry catalogs data on patient characteristics, clinical features, angiographic and procedural details, and in-hospital outcomes. Participating centers agree to submit complete information and outcomes from consecutive interventional cases performed at their institution. The NCDR also has a comprehensive data quality program, including data abstraction training, data quality thresholds for inclusion, site data quality feedback reports, independent auditing, and data validation (22). Data elements and definitions are available at: http://www.acc.org/ncdr/cathlab.htm. The Duke Clinical Research Institute (DCRI) serves as the primary analytic center for the CathPCI Registry, and performed the analyses for this manuscript.

Variable selection

The NCDR established a Risk Adjustment Model (RAM) committee of ACC volunteers to provide oversight for model development, including input on candidate variable selection and review of the model results. This group strictly adhered to current standards of model creation (24). The outcome of interest for these models was all-cause inhospital mortality. Candidate variables were selected based on their relevance, as identified in prior research, or as identified in the committee's clinical experience.

Missing data

The rates of overall missing data in the NCDR CathPCI database are very low. Of the final model variables, only ejection fraction (EF) percentage had more than a 5% rate of missing. For those few cases that contained missing information, the following imputation rules were used: 1) for elements dealing with a patient's past medical history, use of a pre-procedural intra-aortic balloon pump (IABP), presence of subacute thrombosis, and coronary lesion with highest risk lesion, missing data was imputed to “no”; 2) for body mass index (BMI), missing values were imputed to the gender-specific median; 3) for glomerular filtration rate (GFR), missing values were imputed to the gender-, prior renal failure-, and STEMI-specific median; and 4) for EF, missing was imputed by stratifying the population based on a history of congestive heart failure (CHF), prior myocardial infarction (MI), pre-procedural cardiogenic shock, and the presence of STEMI. Neither age nor the Society for Cardiovascular and Angiography and Interventions (SCAI) Lesion Class were imputed. We also performed a sensitivity analysis using multiple imputation methods. However, these results were nearly identical to the overall findings and are therefore, not presented.

Population definition

Two separate patient populations were identified: one for model development and one for prospective validation. For the model development phase, patients were included if they received their first PCI procedure at any of the 470 hospitals submitting PCI records between January 1, 2004 and March 30, 2006. Patients were excluded if they transferred-out or were missing more than 2 candidate variables (Figure 1). The model development population was further randomly allocated to an initial model development dataset (60% of total) and a second group (40% of total) was used for an initial validation sample. A second prospective validation sample was identified from cases performed at the 608 NCDR hospitals submitting PCI cases between March 31, 2006 and March 30, 2007, using the same inclusion and exclusion criteria as noted above (Figure 1).

Figure 1. Population Flow Diagram.

Between January 2004 and March 2007, 600713 PCI admissions were recorded in the NCDR CathPCI Registry. Following exclusions, 588398 total patients were included in the overall model development and validation cohort.

Additionally, we examined the robustness of our models to predict 30-day mortality, as opposed to in-hospital mortality, in a Medicare-eligible population (25). Since outcomes beyond the initial hospital stay are not routinely collected in the NCDR, we linked NCDR records for those aged 65 years or older to the national Center for Medicare and Medicaid Services (CMS) inpatient claims data. The process used to do this has been previously described (26). For this specific linkage to occur, we began with Medicare-eligible NCDR CathPCI patients undergoing a PCI procedure between January 2005 and December 2006 (the last available data from CMS). Of the possible 348,370 records, we linked 253,081 records (72.7%), representing 204,111 unique patients. Baseline characteristics of the linked population and unlinked records were similar.

Statistical methods

An initial candidate variable list was generated using clinical judgment and prior known PCI risk factors. Univariate analysis was then used to identify which of the potential candidate variables had a statistical association with in-hospital mortality (e.g. p-value < 0.05). Based on this univariate analysis, the RAM committee selected the most clinically meaningful variables as potential candidates for inclusion in the multivariable model. Multivariate logistic regression with a backward selection method (p < 0.05 to remain in model) was then performed to identify independent predictors of outcomes.

Three separate models were developed. First, a “full” model was created, which included all candidate variables (e.g. demographic, pre-catheterization clinical variables, and angiographic variables). Second, we contrasted this “full” model with a second “pre-cath” model, excluding detailed NCDR angiographic data. This second model assessed the incremental value of angiographic information for mortality prediction. Finally, we developed a “limited” pre-cath risk prediction model, which included only those variables with the strongest explanatory power based on their Wald chi square value (Table 3). The regression coefficients from the simplified pre-cath model were then converted into whole integers to create an NCDR CathPCI Risk Prediction Score (Table 4) (27).

Table 3.

Full and Pre-Cath Simplified Risk Models

Label	Odds Ratio	Full Model 95% Confidence Limits		Χ 2	Odds Ratio	Pre-Cath Model 95% Confidence Limits		Χ 2
Intercept				171.14				708.97
STEMI Patients				1.77				44.55
Cardiogenic Shock at Admission	8.35	7.40	9.44	1168.28	12.19	10.86	13.68	1804.73
PCI Status
for STEMI
- Urgent	1.09	0.64	1.83	0.09	1.25	0.75	2.07	0.71
- Emergency	2.07	1.30	3.31	9.24	2.65	1.68	4.18	17.58
- Salvage	14.55	8.39	25.21	91.08	21.45	12.57	36.61	126.36
for no STEMI
- Urgent	2.01	1.70	2.39	63.91	2.49	2.11	2.95	114.46
- Emergency	7.29	5.91	8.99	343.95	11.79	9.69	14.34	606.91
- Salvage	82.54	45.83	148.63	216.24	146.55	82.60	260.04	290.59
Age*
for age>70	1.71	1.57	1.88	125.80	1.76	1.60	1.91	150.93
for age≥70	1.55	1.44	1.69	115.33	1.52	1.40	1.64	107.92
GFR*
for STEMI	0.77	0.74	0.80	181.90	0.77	0.75	0.78	377.55
for no STEMI	0.82	0.78	0.85	100.96
NYHA Class IV
for no STEMI	1.74	1.50	2.02	52.82	1.61	1.46	1.79	81.71
for STEMI	1.21	1.05	1.39	6.74
Chronic Lung Disease	1.48	1.31	1.66	43.04	1.52	1.36	1.71	52.87
Peripheral Vascular Disease	1.53	1.35	1.74	42.39	1.67	1.48	1.89	67.78
Previous History - CHF	1.29	1.13	1.47	13.85	1.75	1.54	1.98	77.25
Ejection Fraction Percentage*	0.73	0.70	0.76	234.09
Highest Risk Lesion: SCAI Lesion Class
IV vs. I	2.05	1.70	2.47	57.40
II or III vs. I	1.47	1.29	1.67	33.84
Diabetes/Control
Insulin Diabetes vs. No	1.78	1.53	2.07	56.24
Diabetes
Non-Insulin Diabetes vs.	1.11	0.98	1.25	2.47
No Diabetes
Highest Risk Lesion - Segment Category
for STEMI
Left Main	5.54	3.43	8.93	49.26
pLAD	1.52	1.26	1.83	19.00
pRCA/mLAD/pCIRC	1.34	1.13	1.59	11.18
Previous PCI	0.69	0.61	0.78	36.59
BMI [kg/m2]†
for No STEMI	0.76	0.69	0.83	33.91
for STEMI	0.93	0.85	1.03	1.97
Pre-op IABP	3.14	2.12	4.65	32.64
for No STEMI
pLAD	1.65	1.38	1.98	29.257
Left Main	2.33	1.71	3.17	28.586
pRCA/mLAD/pCIRC	1.26	1.07	1.48	7.721
Subacute Thrombosis? Yes vs. No	1.96	1.41	2.72	16.21
Cerebrovascular Disease	1.26	1.11	1.44	12.02
Previous Vascular Disease	1.58	1.10	2.26	6.02
Highest Risk Pre-Procedure	1.19	1.02	1.38	4.84
TIMI Flow = 0 vs. Other

^*Per 10 unit increase.

^†Per 5 unit increase.

STEMI=ST-segment elevation myocardial infarction; GFR=glomerular filtration rate; NYHA=New York Heart Association; CHF=congestive heart failure; SCAI=Society for Cardiovascular Angiography and Interventions; pLAD=proximal left anterior descending artery; mLAD=mid left anterior descending artery; pRCA=proximal right coronary artery; pCIRC=proximal left circumflex artery; PCI=percutaneous coronary intervention; BMI=body mass index; IABP=intra-aortic balloon pump; TIMI=Thrombolysis in MI

Table 4.

NCDR CathPCI Risk Score System

Variable	Scoring Response Categories				Total Points	Risk of In-patient Mortality
Age	<60	≥60,<70	>70,≥80	≥80	0	0.0%
	0	4	8	14	5	0.1%
Cardiogenic Shock	No	Yes			10	0.1%
	0	25			15	0.2%
Prior CHF	No	Yes			20	0.3%
	0	5			25	0.6%
PVD	No	Yes			30	1.1%
	0	5			35	2.0%
CLD	No	Yes			40	3.6%
	0	4			45	6.3%
GFR	<30	30-60	60-90	>90	50	10.9%
	18	10	6	0	55	18.3%
NYHA Class IV	No	Yes			60	29.0%
	0	4			65	42.7%
PCI Status (STEMI)	Elective	Urgent	Emergent	Salvage	70	57.6%
	12	15	20	38	75	71.2%
PCI Status (No STEMI)	Elective	Urgent	Emergent	Salvage	80	81.%
	0	8	20	42	85	89.2%
					90	93.8%
					95	96.5%
					100	98.0%

CHF=congestive heart failure; PVD=peripheral vascular disease; CLD=chronic lung disease; GFR=glomerular filtration rate; NYHA=New York Heart Association; STEMI=ST-segment elevation myocardial infarction; PCI=percutaneous coronary intervention

Model performance characteristics

After development, we applied these three models to the prospective validation sample sets. Model discrimination was assessed using the c-index. A model c-index can range from 0.50 (no predictive value) to 1.0 (perfect prediction). To assess model calibration, patients were rank-ordered from lowest- to highest-predicted risk. Then, comparison was made of predicted versus observed event rates within risk strata. Model discrimination and calibration were assessed in the overall population, within the two validation samples, and among select subpopulations of both of these groups. Finally, we assessed the models’ discrimination among patients aged 65+ who had been linked to CMS data to assess both in-hospital and 30-day mortality.

Results

Between January 2004 and March 2007, 600,713 consecutive PCI admissions were recorded in the NCDR CathPCI Registry. Following exclusions, 588,398 total patients were included in our overall model development and validation cohort. From this population, a model development sample (n=181,775) was created from a random sample comprised of two-thirds the cases performed between January 2004 and March 2006. The final one-third of these cases was used to create a contemporary model validation sample (n=121,183). Cases performed between March 2006 and March 2007 were used as a prospective validation sample (n=285,440) (Figure 1).

Table 1 provides demographic, clinical, and angiographic features of those patients in the development set, as well as in the two validation sets. The mean patient age was 64 years, 33% were female, 32% had diabetes mellitus, and 10% had a prior history of CHF. Overall, 51% of the patients underwent non-elective procedures and 14% underwent multivessel PCI. The results were similar across the three samples, except that in-hospital mortality was slightly lower in the second prospective validation sample (1.17%), relative to the other two samples (1.24% and 1.27%).

Table 1.

Patient Clinical Characteristics

	Development (181,775)	1st Validation (121,183)	2nd Validation (285,440)
Patient Characteristics
Age	63.9±12.1	63.9±12.1	64.1±12.1
Female	33.4%	33.3%	33.3%
Caucasian	87.2%	87.1%	85.6%
BMI (kg/m2)	29.6±6.3	29.7±6.3	29.8±6.3
Prior MI (>7days)	29.1%	29.1%	27.3%
Prior CHF	10.1%	10.0%	9.9%
Diabetes
– Non-insulin	21.5%	21.7%	22.3%
– Insulin	10.0%	10.0%	10.3%
Mean GFR (MDRD)	73.6±30.5	73.5±29.0	73.2±28.1
Dialysis	1.6%	1.5%	1.5%
Cerebral Vascular Disease	10.9%	11.1%	11.1%
Peripheral Vascular Disease	11.7%	11.7%	11.9%
CLD	16.0%	16.0%	15.8%
Prior PCI	35.1%	35.4%	36.6%
NYHA Class IV	18.3%	18.3%	18.8%
Cardiogenic Shock	1.9%	1.8%	1.7%
Hospital Characteristics
Number of Beds	463±221	463±220	454±225
Location
- Rural	12.6%	12.6%	12.1%
- Urban	61.0%	61.3%	61.2%
Teaching	60.1%	60.0%	54.6%
Region
-West	14.1%	14.3%	16.2%
- Northeast	9.0%	9.9%	10.4%
- Midwest	36.9%	36.7%	35.8%
- South	36.5%	36.8%	37.6%
Mean Annual PCI Volume	666±550	668±550	679±573
Procedural Characteristics
LVEF	52.7±12.7	52.7±12.7	52.7±12.7
PCI Status
- Elective	49.3%	49.3%	50.2%
- Urgent	36.1%	35.6%	34.7%
- Emergency	14.4%	14.5%	15.0%
- Salvage	0.2%	0.2%	0.2%
Highest Risk Coronary Segment
- pLAD	18.2%	18.2%	18.2%
- Left Main	1.7%	1.8%	1.8%
TIMI 0 Flow	11.0%	10.7%	14.9%
Multivessel PCI	14.0%	13.9%	14.1%

BMI=body mass index; MI=myocardial infarction; GFR=glomerular filtration rate; MDRD=Modification of Diet in Renal Disease; CLD=chronic lung disease; PCI=percutaneous coronary intervention; NYHA=New York Heart Association; LVEF=left ventricular ejection fraction; pLAD=proximal left anterior descending artery; TIMI=Thrombolysis in MI

Risk factors for in-hospital mortality

Table 2 provides observed in-hospital mortality rates for various patient subgroups. These mortality rates ranged from 0.65% in the non-primary PCI population to 4.81% in the STEMI population (Table 2). Older patients, women, and diabetics experienced higher unadjusted in-hospital mortality rates than younger patients, men, and non-diabetic patients (2.25% vs. 0.76%, 1.63% vs. 1.04%, 1.44% vs. 1.15%, respectively).

Table 2.

Unadjusted Inhospital Mortality (%)

	Development (181,775)	1st Validation (121,183)	2nd Validation (285,440)
Overall Population	1.24	1.27	1.17
MI Status
-STEMI	4.81	4.79	4.69
-No STEMI	0.65	0.69	0.60
Gender
-Men	1.04	1.07	1.00
-Women	1.63	1.67	1.50
Age Group
-Age >70	2.25	2.32	2.02
-Age ≥ 70	0.76	0.77	0.76
Diabetes Status
-Diabetes	1.44	1.50	1.30
-No Diabetes	1.15	1.16	1.11

STEMI=ST elevation myocardial infarction

Table 3 provides the final full model, which includes 21 separate clinical variables, as well as interaction terms for STEMI/shock, BMI, GFR, dialysis, New York Heart Association (NYHA) class, highest-risk lesion segment category, and PCI status. When model Chi-squared value was used as the metric, cardiogenic shock was the most predictive of in-hospital mortality, followed by renal function (estimated glomerular filtration rate [eGFR]) and age. In contrast, angiographic predictors were generally less prognostic. The angiographic feature most highly associated with in-hospital mortality was lesion location (e.g. left main lesions and proximal left anterior descending [LAD] lesions).

NCDR PCI Bedside Risk Prediction Score

Predictors containing the strongest association with mortality are described in Table 3. These risk factors were then converted to an integer score (based on their relative magnitude of association with mortality), to create the NCDR CathPCI Risk Prediction Score (Table 4). Using this scoring system, the risk of in-hospital mortality can be estimated by summating point scores between 0 and 100.

Model performance

The full, NCDR CathPCI Mortality Risk Prediction model in the contemporary and prospective validation cohorts performed exceptionally well, with a c-index of 0.925 and 0.924, respectively. Additionally, the full model performed well in each of the eight predefined patient subgroups, with c-indices ranging from 0.892 to 0.930 (Table 5). Of note, the exclusion of angiographic details and EF from the full model resulted in only a slight decrement in the overall model accuracy. Similarly, there was limited loss in model discrimination when the model was transformed into the final, simplified NCDR CathPCI Risk Score, with c-indices of 0.901 and 0.905, respectively, in the validation samples. This simplified score also had good operating characteristics in all predefined patient subgroups.

Table 5.

C-Indices for NCDR Models

	Sample N	Full Model (Pre-Cath + Cath Factors)	Pre-Cath Model Only	NCDR Simplifed Risk Score
Development	181,775	0.926	0.911	0.911
1st validation	121,183	0.925	0.905	0.901
2nd validation	285,440	0.924	0.910	0.905
Subgroups (in 2nd validation)
STEMI	39,889	0.902	0.890	0.884
No STEMI	245,551	0.892	0.896	0.862
Women	95,106	0.911	0.897	0.893
Men	190,334	0.930	0.916	0.911
Age>70	92,381	0.901	0.886	0.88
Age<=70	193,059	0.927	0.911	0.906
Diabetes	92,974	0.924	0.910	0.903
No Diabetes	192,466	0.923	0.910	0.906

Model calibration plots are shown in Figures 2 and 3. Notably, the majority of patients had a relatively low mortality risk (92.6% of patients had a predicted mortality risk between 0 and 2.5%). However, there was high concordance between model predicted risk and that which was actually observed. The simplified NCDR CathPCI Risk Score was also well-calibrated in both low and moderate risk populations with only a slight underestimation of predicted risk in high risk patients (Figure 3).

Figure 2. a. Calibration for the Full Model among STEMI Patients in the Validation Sample b. Calibration for the Full Model among Patients without STEMI in the Validation Sample.

Demonstrates observed versus predicted mortality estimates (and the 95% CI) for 10 equally sized risk groups of STEMI (2a) and NSTEMI (2b) patients, based on the full risk prediction model evaluated in the second validation sample.

Figure 3. Calibration of NCDR Bedside Risk Score in Validation Sample.

Based on their predicted risk, patients are grouped into eight risk groups, using the full risk prediction model, and then plotted again the observed mortality rates for these in the second validation sample.

Finally, we examined the full and simplified models’ ability to estimate 30-day mortality among patients aged 65 years or older who had been linked to CMS data. Among 204,111 Medicare patients, 4,068 (1.99%) died in-hospital and 6,011 (2.94%) died within 30 days of the procedure. C-indices for the full model in this population were: c=0.90 for in-hospital and c=0.86 for 30-day mortality, respectively. C-indices for the Simplified Risk Score in this population were: c=0.89 for in-hospital and c=0.83 for 30-day mortality, respectively.

Discussion

Despite tremendous advances in PCI over the past decade, early peri-procedural mortality remains a concern. Using data from the NDCR, we identified demographics, clinical factors, and angiographic features associated with PCI in-hospital mortality. These were summarized into a full risk model (with both pre-procedure and angiographic features) and a simplified eight-item NCDR CathPCI Risk Score, to support both robust hospital outcome comparisons and patient-level pre-procedural risk estimation, respectively. Both the full and simplified models retain their predictive accuracy in important patient subsets, in separate internal validation samples, and when estimating 30-day mortality in Medicare patients.

Several risk-adjustment models have been previously developed for the prediction of mortality following PCI. However, many of these were developed using data that predates the generalized use of stents and/or contemporary adjuvant antithrombotic therapy (5-13). Other models have been developed from select referral centers or regional populations and may not be as generalizable across the nation (7-9,11,14-19). Still, other models were developed using databases that included only elderly patients, or used administrative data which lacked the clinical details necessary to capture the important clinical and angiographic risks factors associated with peri-procedural mortality (9,20,21).

The models derived in this study expand on these prior models. First, the comprehensive and complete nature of the NCDR's clinical data allows for a more complete assessment of multiple risk predictors. For example, female sex has long been a feature predictive in many prior studies, yet this feature is no longer significantly associated with mortality after adjusting for multiple potential confounders (e.g. BMI, eGFR, etc.) and in the contemporary populations (28, 29). Additionally, we have demonstrated that the inclusion of angiographic details (as they are defined in the NCDR CathPCI Registry) to a pre-cath risk prediction model, add marginal overall improvements in our ability to predict in-hospital mortality. Rather, in-hospital mortality was driven primarily by pre-existing patient comorbidities and markers of clinical instability. This finding is consistent with the work of others (16) and has important clinical implications in that it allows patients and physicians to obtain a reasonable estimate of procedural risk, prior to angiography.

While in the aggregate population angiographic risk factors added modest value, whereas in individual cases, their impact was more substantial. For example, the mean predicted PCI risk for patients with left main stenosis was 4.5% versus 2.4%, depending on whether or not the prediction included the angiographic left main risk feature. Other risk scores (such as the SYNTAX Score), which arguably focus more heavily on collecting exhaustive angiographic data, have found some additional benefit from these angiographic variables (30).

We also found that patients presenting for PCI in the setting of STEMI, faced substantially higher procedural risk. However, the scope and relative impact of risk factors needed to predict risk in acute versus elective cases, were quite similar. Based on this observation, we were able to develop a unified model of risk estimation for all PCI cases, as opposed to separate STEMI and elective models. This unified model (e.g. the simplified NCDR PCI Mortality Risk Score) accurately predicts mortality in both acute and elective cases.

Utility of risk models

The NCDR CathPCI risk prediction tools developed and validated in this analysis cover the broad spectrum of anticipated model uses and address the needs of researchers, administrators, physicians, and patients. The full NCDR model provides a comprehensive tool to: 1) permit the most accurate adjustment of both pre-procedural and angiographic features for research projects; and 2) “level the playing field” for provider-level mortality results comparisons. Yet the full model is complex, inclusive of multiple data elements, spline transformed continuous variables, and interaction terms – thus, the model is not practical to estimate patient's individual risk without computer assistance. Therefore, we also created the NCDR CathPCI Risk Score, whose simplified eight-item additive risk score can be used for bedside risk estimation.

Limitations

Participation in the NCDR CathPCI centers is voluntarily and slightly under-represents smaller clinical practices. With this said, the NCDR CathPCI Registry remains the largest, most generalizable US data source. Inpatient mortality, rather than 30-day mortality, has limitations as an endpoint (31). However, at the provider level, in-hospital and 30-day mortality results are highly correlated. Additionally, the only source of complete 30-day outcomes is Medicare data, which does not capture outcomes in those < 65 years of age. When our models were applied to predict 30-day mortality in the Medicare population, they retained good discrimination (c=0.86).

Future directions

As the practice of medicine continues to evolve, so will the use of risk prediction models. Clinically, computer-generated risk scores are being used to aid in the personalization of the procedural consent process (2). While mortality is clearly an important outcome, modeling other modifiable outcomes, such as myocardial infarction, renal failure, bleeding complications, restenosis, stent thrombosis, and angina relief, could further advance the Institute of Medicine's goals for evidence-based, patient-centered, medical care (2, 32). As advanced procedural support devices (e.g. hemodynamic support devices) continue to develop, risk prediction tools can be utilized to more clearly define the patient populations in which they will be maximally effective. From an administrative standpoint, the importance of these tool for provider-based risk-adjusted outcomes comparisons will continue to increase, as public reporting and pay-for-performance initiatives continue to grow in popularity. Finally, from a research perspective, these risk tools will be used to mitigate treatment selection bias when conducting comparative effectiveness analyses in observational data.

Conclusions

Using data from the NCDR CathPCI Registry, we have developed and validated contemporary models for assessing peri-procedural PCI mortality risk. Each of these has excellent predictive accuracy throughout the full spectrum of patient risk, and important patient subgroups. We anticipate that these models will have multiple applications (including bedside risk estimation using the simplified risk score, comparison of hospital performance, and risk adjustment).

Abbreviations

BMI

body mass index

CathPCI

Catheterization Percutaneous Coronary Intervention

CHF

congestive heart failure

EF

ejection fraction

GFR

glomerular filtration rate

IABP

intra-aortic balloon pump

MDRD

Modification of Diet in Renal Disease

MI

myocardial infarction

NCDR

National Cardiovascular Data Registry

NYHA

New York Heart Association

PCI

percutaneous coronary intervention

RAM

risk adjustment model

SBP

systolic blood pressure

STEMI

ST-elevation myocardial infarction

US

United States