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

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

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

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

Participating Institutions - REP

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

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

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):1614-24

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

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

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

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: 0.68 - 0.73 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

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

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

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

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

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 (0.68 - 0.73) Model B c-statistic = 0.75 (0.73 - 0.77) Patients in the highest risk group had HR 8.44 (6.66 - 10.70) compared to reference group

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

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

ArrudaOlson.Adelaide@mayo.edu