Introduction Hemodynamic gain index (HGI) is a marker of hemodynamic reserve inversely associated with major adverse cardiovascular events in patients undergoing exercise stress testing. HGI and traditional exercise markers, such as metabolic equivalents (METS), are derived from patient data from two to six decades ago. Modern patients have increased comorbidities and cardiac medication use which may affect the applicability of those exercise markers. HGI’s value in comparison to traditional exercise markers in a contemporary population is not well-characterized. Methods This was a retrospective, single-center analysis of patients with exercise stress myocardial perfusion imaging from 2015-2017. Patients with >6 months follow-up and age >18 years were included. HGI was calculated with systolic blood pressure (SBP) and heart rate (HR): (SBPpeak x HRpeak – SBPrest x HRrest) / (SBPrest x HRrest). Other variables were calculated using prior literature: chronotropic incompetence (CI) = (HRpeak – HRrest) / (220 - age - HRrest) <0.8; impaired heart rate recovery (HRR) = HRpeak – HR1, min <12 or HRpeak – HR2, min <22; abnormal blood pressure response (ABPR) = Bruce protocol stage 2 SBP >210 (males), SBP >190 (females), diastolic BP >110, or SBP decrease compared to baseline; abnormal METS = METS <7. Cox proportional hazard analysis modeled subsequent death or nonfatal myocardial infarction (NFMI). Results The study included 1582 patients (mean age 62 years, 37% female, 16% non-white) with mean follow-up 4.8 years. Prevalence of coronary artery disease (CAD) and anti-hypertensive medication use was 32% and >50%, respectively. Exercise marker abnormality prevalence was: 24.4% impaired HGI (HGI <1.4), 32.7% CI, 24.2% HRR, 32.3% ABPR, 14.9% METS. Impaired HGI had odds ratio (OR) 1.97 (p<0.001) in multivariable logistic regression accounting for traditional risk factors (sex, tobacco use, diabetes mellitus, hypertension, chronic kidney disease, and CAD), and OR 2.15 (p<0.01) in multivariable modeling with the four other exercise markers. In Cox multivariable modeling, HGI (HR 1.75, p <0.001) outperformed CI (HR 1.63, p=0.001), HRR (HR 1.24, p=0.262), ABPR (HR 1.46, p=0.039), and METS (HR 1.30, p=0.163). Cox model assumptions were verified using Schoenfeld residuals. HGI models were superior to other marker models (likelihood ratio test p<0.01). Conclusion In a modern cohort with increased medication use and comorbidities, HGI outperforms established exercise markers in predicting death and NFMI. HGI can be easily calculated using vital signs and provides graded risk assessment beyond other markers. Greater application of this hemodynamic variable in exercise testing can augment ischemic heart disease prognostication.