Validation of the Intermountain Risk Score and Get with the Guidelines-Heart Failure Score in predicting mortality.

Objective: The Intermountain Risk Score (IMRS) was evaluated for validation as a mortality predictor and compared with the American Heart Association's Get With The Guidelines-Heart Failure (GWTG-HF) risk score in a rural heart failure (HF) population.
Background: IMRS predicts mortality in general populations using common, inexpensive laboratory tests, patient age and sex, but requires validation in patients with HF.
Methods: Individuals were selected from the GWTG-HF registry at Essentia Health. This included consecutive HF inpatients age ≥18 years admitted July 2017-June 2019. IMRS was calculated using sex-specific weightings of the complete blood count, basic metabolic profile, and age.
Results: A total of 703 individuals (mean age: 74.12, 44.38% female) were studied. The 30-day IMRS predicted 30-day mortality for both sexes (females n=312: OR=1.19 (95% CI 1.08 to 1.32) per +1, p<0.001; males n=391: OR=1.23 (CI 1.12 to 1.36) per +1, p<0.001). The GWTG-HF risk score (only available in n=300, 42.7%) was independent of IMRS for 30-day mortality (OR=1.11 (CI 1.06 to 1.16) per +1, p<0.001). Using thresholds in bivariate modelling, IMRS (high vs low risk, OR=8.25 (CI 2.19 to 31.09), p=0.002) and the GWTG-HF score (tertile 3 vs 1: OR=2.18 (CI 0.84 to 5.68), p=0.11) independently predicted mortality. In multivariable analyses including covariables, IMRS (high vs low risk: OR=6.69 (CI 1.75 to 25.60), p=0.005) and the GWTG-HF score (tertile 3 vs 1: OR=2.62 (CI 0.96 to 7.12), p=0.06) remained predictors of mortality. Results were similar for 1-year mortality.
Conclusions: The IMRS and GWTG-HF scores predicted mortality of patients with HF in a large rural healthcare system. Future study of these scores as initial clinical risk estimators for evaluating their utility in improving patient health outcomes and increasing cost effectiveness is warranted.
Competing Interests: Competing interests: BDH is an inventor of clinical decision tools that are licensed to CareCentra and Alluceo, the PI of grants involving clinical decision tools that were funded by the Intermountain Research and Medical Foundation, CareCentra, GlaxoSmithKline, and AstraZeneca, a member of the scientific advisory board of Labme.ai, and a co-investigator on a grant funded by the Patient-Centered Outcomes Research Institute (PCORI). The authors declare that no other conflicts of interest or disclosures exist.
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