Clinical research studyExceptional Mortality Prediction by Risk Scores from Common Laboratory Tests
Section snippets
Study Populations and End Points
This study's primary aim was to develop and validate risk scores for mortality that aggregate all of the independent risk information contained within the complete blood count and the basic metabolic profile into a useful and intuitive metric for clinicians.
The primary outcome of this study was incident all-cause mortality. Death outcomes were determined from Intermountain Healthcare electronic records (covering an integrated delivery system of 22 hospitals and many clinics and employed
Training and Test Populations
Demographics of the training population were as follows: age, 55.0 ± 20.0 years (range: 18-103 years); female, 58.4%; inpatient, 48.5%; outpatient, 40.3%; and emergency, 11.2%. Results were similar for the test population for age (55.2 ± 19.9 years), sex (58.0% were female), patient care setting, and laboratory test component values (all P >.12 compared with the training population).
Sex-specific risk score values are provided in Table 2 (overall values are in Supplemental Table 1, available
Discussion
Optimally, a general medical risk score would be intuitive to clinicians, would apply to any patient, group, or individual, and would not complicate (by time or expense) the care-delivery process—being computed outside of the clinical setting and provided on standard clinical reports. The components of this ideal risk score would not have qualitative or subjective measures, would be simple to obtain, would use commonly obtained well-characterized tests, and would not use specialty-specific risk
Study Strengths and Limitations
Because the study was observational, it might be limited by confounders and unmeasured variables may have influenced study findings. The study used only a baseline measurement of complete blood count and basic metabolic profile, so repeated measurement over time may provide additional research ability to stratify risk and clinical opportunities to ameliorate risk. Use of quintiles of component measures instead of other statistical methods of categorization may have made the risk scores more
Conclusions
In large, independent patient populations across a large integrated health care system and in a nationally representative general US population, the Intermountain Risk Score provided exceptional stratification of mortality by simultaneously modeling the components of the complete blood count and basic metabolic profile together with age and sex. Although only a few of the components of these panels are routinely used clinically, each component provided meaningful contribution to risk, including
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Funding: This study was funded by internal institutional funds.
Conflict of Interest: BDH, HTM, BSR, and JLA are named as inventors on a patent protecting the risk scores; the authors have no other potential conflicts of interest to report.
Authorship: All authors had access to the data and played a role in writing this manuscript.
Trial Registration: Database registry of the Intermountain Heart Collaborative Study: NCT00406185 (ClinicalTrials.gov).
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Current address: Clinical Development and Medical Affairs, GlaxoSmithKline, 5 Moore Drive, Research Triangle Park, NC 27709.