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Pulmonary-Specific Intermountain Risk Score Predicts All-Cause Mortality via Spirometry, the Red Cell Distribution
Benjamin D Horne1, Matthew Hegewald2, Joseph B Muhlestein3
1Intermountain Heart Institute, Intermountain Medical Center Genetic Epidemiology Division, Department of Medicine, University of Utah benjamin.horne@imail.org.
Background:
Pulmonary function testing parameters predict cardiovascular and mortality outcomes. Previously, risk scores were created using the basic metabolic profile and complete blood count, including the Intermountain Risk Score (IMRS). This study sought to develop similar pulmonary-specific risk scores for mortality prediction.
Methods:
Subjects evaluated by spirometry at 5 Intermountain Healthcare hospitals (females: n = 2,943; males: n = 2,495) were randomly assigned to risk score derivation (70% of subjects) or an independent validation set (the remaining 30%). Sex-specific scores used spirometry, age, and metabolic and blood count laboratory data. Cox regression β-coefficients formed the basis of risk score weightings.
Results:
Among females, pulmonary IMRS was strongly associated with 5-y mortality in the validation set (hazard ratio = 1.24 per +1 risk score, CI 1.16-1.33, P trend < .001), with C-statistics of C = 0.835 and C = 0.757 for derivation and validation, respectively. Among males, validation results were similarly significant (hazard ratio = 1.20 per +1 risk score value, CI 1.11-1.28, P trend < .001), with C = 0.755 and C = 0.699 in derivation and validation sets, respectively. Results were stronger for pulmonary basic metabolic profile risk score, with females having C = 0.815 (derivation) and C = 0.806 (validation), whereas males had C = 0.734 and C = 0.731.
Conclusions:
Pulmonary-specific IMRS and pulmonary-specific basic metabolic profile risk score provided excellent discrimination of mortality among pulmonary subjects. These risk stratification tools combine familiar, relatively inexpensive, commonly-measured, standardized laboratory parameters with spirometry data. They may be electronically calculated and delivered at the point of care, providing meaningful risk information to assist clinicians in patient evaluations.
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