1. No Financial Disclosure or Conflict of Interest
Validation of Mayo Clinic Risk Adjustment Model for In-Hospital Mortality following Percutaneous Coronary Interventions using the National Cardiovascular Data Registry
Mandeep Singh; Eric D. Peterson*; Sarah Milford-Beland*; John S. Rumsfeld,# John A. Spertus**
Mayo Clinic, Rochester, DCRI* (S.M-B, E.P.), Mid America Heart Institute** (J.A.S.), Denver VA Medical Center# (J.S.R.)
No Financial Disclosure or Conflict of Interest
CP Si ngh, M KK

2. BACKGROUND
Predictive models can assist patients and clinicians in decision-making and informed consent.
Existing PCI risk models include angiographic variables limiting routine clinical use.
Mayo Clinic Risk Score (MCRS) for in-hospital mortality is based on pre-procedural clinical and non-invasive assessment.
MCRS can potentially serve as a risk assessment aid to patients/physicians before coronary angiography for PCI.
CP Si ngh, M KK

3. Background External validation of the MCRS is lacking
The NCDR cath-PCI registry presents an ideal opportunity to validate the MCRS
Study population: Index PCI for 309,351 patients in NCDR participating hospital between January 2004 and March 2006.
Outcome: In-hospital mortality during the hospital admission following PCI.
CP Si ngh, M KK

4. Mayo Clinic Risk Score (MCRS)
Mortality 80 70 60 50 40 30 20 10 5 4 3 2 1
0.5
0.1
Points Score
Age (yr) See below ____
Creatinine (mg/dL) See below ____
LV ejection See below ____ fraction (%)
Preprocedural shock 9 ____
MI within 24 hours 4 ____
CHF on presentation 3 ____ (without AMI or shock)
Peripheral 2 ____ vascular disease
Total score ____
C-index=0.90
Estimated risk of death (%)
Risk score
Age (yr)
Creatinine (mg/dL)
LV ejection fraction (%) 20 30 40 50 60 70 80 90 1 2 3 4 5 6 7 8 9 10 11 20 40 60 80 2 1 1 2 3 4 5 1 1 2 3 4 5 6 4 3 2 1
CP Si ngh, M KK CP

5. Statistical Methods
Using the MCRS equation, predicted probabilities of death were calculated for each patient in the NCDR population.
Patients with the same predicted mortality score were grouped together, and within each group, the observed (O) mortality rate was calculated.
The O vs. E (expected) mortality rates for these groups were plotted and we used H-L method for calibration
Model discrimination was assessed using ROC, or c-statistic, for the entire population and within pre-specified subgroups.

6. Statistical Methods (Cont.)
The analysis was refined to include recalibration of the MCRS equation using the ACC population
For this recalibrated model, patients with the same predicted mortality score were again grouped together.
O vs. E mortality rates were plotted.
Calibration: Hosmer-Lemeshow method.
Internal validation of the new model using NCDR PCI patients April 2006, March 2007.

7. Patient Characteristics by In-Hospital Mortality in the NCDR
Variable Number (%) Mortality p
Age
<60Y 114,844 (37.12) <0.0001
≥80Y (11.11) 3.22
Congestive heart failure
Yes (8.73) <0.0001
No 282,321 (91.26) 0.84
Acute Myocardial infarction
Yes (22.02) <0.0001
No   241,128 (77.95) 0.60
Peripheral vascular disease
Yes (11.82) <0.0001
No 272,768 (88.17) 1.10
Cardiogenic shock
Yes (2.04) <0.0001
No 303,007 (97.95) 0.73
Renal failure
Yes (5.28) <0.0001
No 293,012 (94.72) 1.08

8. Frequency (%) Risk Score
Frequency of the Risk, based on the MCRS of Patients Undergoing PCI %
Frequency (%)
Risk Score

9. Discrimination of the MCRS
Group N MCRS (Min- Max) C-index
Overall 309,
Shock/ AMI
Age <
Age
CHF
Creatinine <
Creatinine >
Multivessel Dx
Female
Diabetes
CP Si ngh, M KK CP

10. Observed versus expected in-hospital mortality using the original MCRS prediction equation

11. Observed Mortality (%) Predicted Mortality (%)
O vs. E in-hospital mortality with recalibrated quadratic MCRS, internal validation sample (433,045)
Observed Mortality (%) 0% 5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
Predicted Mortality (%)
O=5,177; E=5,310 deaths (difference 2.5 per 100)
c index= 0.885

12. Summary and Conclusions
External validation of the MCRS using NCDR confirms its broader applicability.
The MCRS has high discrimination for in-hospital mortality using 7 clinical/non-invasive variables.
Most variables can be obtained at the time of first visit.
This may help the operator to individualize the risk of procedural death from PCI, and to counsel patients at the time of PCI.
External validation of the new, recalibrated MCRS model is, however, required.
CP Si ngh, M KK