AIMS: The complete blood count (CBC) and basic metabolic profile are common, low-cost blood tests, which have previously been used to create and validate the Intermountain Risk Score (IMRS) for mortality prediction. Mortality is the most definitive clinical endpoint, but medical care is more easily applied to modify morbidity and thereby prevent death. This study tested whether IMRS is associated with clinical morbidity endpoints. METHODS AND RESULTS: Patients seen for coronary angiography (n = 3927) were evaluated using a design similar to a genome-wide association study. The Bonferroni correction for 102 tests required a P-value of ≤ 4.9 × 10⁻⁴ for significance. A second set of angiography patients (n = 10 413) was used to validate significant findings from the first patient sample. In the first patient sample, IMRS predicted heart failure (HF) (P(trend) = 1.6 × 10(-26)), coronary disease (P(trend) = 2.6 × 10(-11)), myocardial infarction (MI) (P(trend) = 3.1 × 10(-25)), atrial fibrillation (P(trend) = 2.5 × 10(-20)), and chronic obstructive pulmonary disease (P(trend) = 4.7 × 10⁻⁴). Even more, IMRS predicted HF readmission [hazard ratio (HR) = 2.29/category, P(trend) = 1.2 × 10⁻⁶), incident HF (HR = 1.88/category, P(trend) = 0.02), and incident MI (HR = 1.56/category, P(trend) = 4.7 × 10⁻⁴). These findings were verified in the second patient sample. CONCLUSION: Intermountain Risk Score, a predictor of mortality, was associated with morbidity endpoints that often lead to mortality. Further research is required to fully characterize its clinical utility, but its low-cost CBC and basic metabolic profile composition may make it ideal for initial risk estimation and prevention of morbidity and mortality. An IMRS web calculator is freely available at http://intermountainhealthcare.org/IMRS.
AIMS: The complete blood count (CBC) and basic metabolic profile are common, low-cost blood tests, which have previously been used to create and validate the Intermountain Risk Score (IMRS) for mortality prediction. Mortality is the most definitive clinical endpoint, but medical care is more easily applied to modify morbidity and thereby prevent death. This study tested whether IMRS is associated with clinical morbidity endpoints. METHODS AND RESULTS:Patients seen for coronary angiography (n = 3927) were evaluated using a design similar to a genome-wide association study. The Bonferroni correction for 102 tests required a P-value of ≤ 4.9 × 10⁻⁴ for significance. A second set of angiography patients (n = 10 413) was used to validate significant findings from the first patient sample. In the first patient sample, IMRS predicted heart failure (HF) (P(trend) = 1.6 × 10(-26)), coronary disease (P(trend) = 2.6 × 10(-11)), myocardial infarction (MI) (P(trend) = 3.1 × 10(-25)), atrial fibrillation (P(trend) = 2.5 × 10(-20)), and chronic obstructive pulmonary disease (P(trend) = 4.7 × 10⁻⁴). Even more, IMRS predicted HF readmission [hazard ratio (HR) = 2.29/category, P(trend) = 1.2 × 10⁻⁶), incident HF (HR = 1.88/category, P(trend) = 0.02), and incident MI (HR = 1.56/category, P(trend) = 4.7 × 10⁻⁴). These findings were verified in the second patient sample. CONCLUSION: Intermountain Risk Score, a predictor of mortality, was associated with morbidity endpoints that often lead to mortality. Further research is required to fully characterize its clinical utility, but its low-cost CBC and basic metabolic profile composition may make it ideal for initial risk estimation and prevention of morbidity and mortality. An IMRS web calculator is freely available at http://intermountainhealthcare.org/IMRS.
Authors: Benjamin D Horne; Joseph B Muhlestein; Sterling T Bennett; Joseph Boone Muhlestein; Kurt R Jensen; Diane Marshall; Tami L Bair; Heidi T May; John F Carlquist; Matthew Hegewald; Stacey Knight; Viet T Le; T Jared Bunch; Donald L Lappé; Jeffrey L Anderson; Kirk U Knowlton Journal: JCI Insight Date: 2018-07-26
Authors: Lindsey Snyder; Scott M Stevens; Masarret Fazili; Emily L Wilson; James F Lloyd; Benjamin D Horne; Joseph Bledsoe; Scott C Woller Journal: Res Pract Thromb Haemost Date: 2020-05-20
Authors: Kevin Fiscella; Paul C Winters; Michael Mendoza; Gary J Noronha; Carlos M Swanger; John D Bisognano; Robert J Fortuna Journal: J Gen Intern Med Date: 2015-02 Impact factor: 5.128
Authors: Nasrien E Ibrahim; Asya Lyass; Hanna K Gaggin; Yuyin Liu; Roland R J van Kimmenade; Shweta R Motiwala; Noreen P Kelly; Parul U Gandhi; Mandy L Simon; Arianna M Belcher; Jamie E Harisiades; Joseph M Massaro; Ralph B D'Agostino; James L Januzzi Journal: ESC Heart Fail Date: 2018-02-09
Authors: Benjamin D Horne; Donald L Lappé; Joseph B Muhlestein; Heidi T May; Brianna S Ronnow; Kimberly D Brunisholz; Abdallah G Kfoury; T Jared Bunch; Rami Alharethi; Deborah Budge; Brian K Whisenant; Tami L Bair; Kurt R Jensen; Jeffrey L Anderson Journal: PLoS One Date: 2013-07-17 Impact factor: 3.240
Authors: C Arden Pope; Joseph B Muhlestein; Jeffrey L Anderson; John B Cannon; Nicholas M Hales; Kent G Meredith; Viet Le; Benjamin D Horne Journal: J Am Heart Assoc Date: 2015-12-08 Impact factor: 5.501
Authors: Benjamin D Horne; Matthew J Hegewald; Courtney Crim; Susan Rea; Tami L Bair; Denitza P Blagev Journal: Int J Chron Obstruct Pulmon Dis Date: 2020-07-20