1. Mortality Prognostic Model for Peripheral Arterial Disease
Adelaide M. Arruda-Olson MD, PhD, Naveed Afzal PhD,
Homam Moussa Pacha MBBS, Ahmad Said MD,
Bradley Lewis MS, Christopher Scott MS, Kent Bailey PhD,
Hongfang Liu PhD, Iftikhar J. Kullo MD

2. Background Peripheral artery disease is common
High morbidity and mortality
Management is often suboptimal
Prognostic models for PAD not available
Gerhard-Herman et al, Circ 2016; Kullo & Rooke, NEJM, 2016
Feringa et al, Arch Int Med 2007; Goldstein, JAMIA 2017

3. Objectives
Extract risk factors from the integrated health information system of Rochester Epidemiology Project
Create a mortality prognostic model for PAD patients, which can be deployed at the point of care

4. Rochester Epidemiology Project
Unique identification numbers to each person
Matches medical records of participating institutions to specific individuals
Geographically defined population of Olmsted County
Olmsted County, Minnesota

5. Participating Institutions - REP

6. Billing Code Algorithms
PAD
Integer score for each code (ICD-9 or procedural codes)
Score ≥ 8 = PAD cases
Fan J, Arruda-Olson AM, et al. JAMIA , 2013
Comorbidities
12 comorbidities
ICD-9 billing codes Deyo et al. J Clin Epidemiol ,1992

7. Diagnostic Criteria for PAD
ABI ≤ 0.90 rest or post-exercise
≥20% decrease ABI after exercise
ABI >1.40 = poorly compressible arteries
Results - vascular laboratory dataset
Gerhard-Herman, Circulation 2016
Kullo & Rooke, NEJM, 2016

8. Outcome: All-Cause Mortality
Sources for death information
Electronic Minnesota state death certificates
National death index
Sauver JL et al. Int J Epidemiol 2012;41(6):

9. Cox Proportional Hazards Regression
Age, sex, prior revascularization, ABI
Forced into models
Age2
Non-linear association of age with mortality risk
Comorbidities
Chosen based on number of times present
in 10-fold cross validation of stepwise selection

10. Statistical Analysis Survival c-statistics
Summarize predictive ability of models
Based on cross-validation
Calibration
Defined risk groups in each derivation set
Applied to cross-validation sets.
Survival and hazard ratios estimated for each risk group

11. Olmsted residents with PAD n = 1676 72±13 yrs, 45% women
PAD by ABI
5-year follow-up or Died
n = 1565
593 deaths

12. Prognostic Model “A” for 5-Year Mortality
Parameter
Beta estimate HR
95% CI
p value
Age2
0.081
1.08
1.04
1.13
<0.0001
Female sex
-0.221
0.80
0.68
0.95
0.01
Cross Validation C-Statistic 0.70
95% CI:
Prior Revascularization
0.492
1.64
1.28
2.08
<0.0001
PCA
0.852
2.34
1.88
2.92
ABI Value (continuous) per 0.1
-0.089
0.92
0.88
0.95
Unknown ABI value
0.383
1.45
1.07
2.01
0.02

13. Prognostic Model “B” for 5-Year Mortality
Parameter
Beta estimate HR
95% CI
p value
Age2
0.076
1.08
1.04
1.12
0.0002
Female sex
-0.165
0.85
0.72
1.00
0.06
Prior Revascularization
0.461
1.59
1.24
2.02
0.0002
PCA
0.566
1.76
1.40
2.22
<0.0001
ABI Value (continuous) per 0.1
-0.074
0.93
0.89
0.97
0.0007
Cross Validation C-Statistic 0.75
95% CI 0.73, 0.77
Diabetes
0.321
1.38
1.16
1.64
0.0003
Lung disease
0.332
1.39
1.18
1.65
0.0001
Renal disease
0.414
1.51
1.27
1.80
<0.0001
History of heart failure
0.634
1.89
1.58
2.24
Dementia
0.562
1.76
1.43
2.16
Statin
-0.383
0.68
0.57
0.81

14. Risk Groups – Model “B” Risk Groups N deaths (N ) HR 95% CI p value
Low risk (score ≤ -0.17) 18
(268)
0.35
0.21
0.58
<0.0001
Low-intermediate (-0.17 ≤ score < 0.70)
104
(570)
reference
Intermediate-high
(0.70 ≤ score <1.85)
257
2.98
2.37
3.74
High
(score ≥ 1.85)
214
8.44
6.66
10.70

15. Kaplan-Meier curves by risk subgroups
Model A
Model B
Cumulative survival
Years
Years
Low risk derivation
Intermediate low risk derivation
Intermediate high risk derivation
High risk derivation
Low risk validation
Intermediate low risk validation
Intermediate high risk validation
High risk validation

16. Strengths and Limitations
Applied electronic phenotyping algorithms to integrated health information system of the REP and to vascular laboratory dataset
Community PAD patients, inpatient or outpatient
Robust internal validation
Future studies needed for external validation

17. Conclusions
Automated data mining of an integrated health information system generates prognostic models for death in PAD patients
2 models
Model A c-statistic = 0.70 ( )
Model B c-statistic = 0.75 ( )
Patients in the highest risk group had HR 8.44 ( ) compared to reference group

18. Clinical Implications
These models have potential for translation to patient care and could be used for automated risk calculators to be deployed at the point of care

19. Acknowledgements Grants NHLBI - K01HL124045
NHGRI - HG04599 and HG006379
NIA - R01AG034676
NIGMS - R01GM102283A1