Assessing Markers From Ambulatory Laboratory Tests for Predicting High-Risk Patients

Published: August 10, 2018
Category: Bibliography > Papers
Authors: Hadi Kharrazi; MD; PhD; MHI, Jonathan P. Weiner; DrPH, Kimberly A. Gudzune; MD; MPH, Klaus W. Lemke; PhD
Countries: United States, USA
Language: English
Types: Care coordination, Care Management, Performance Analysis, Population Health
Settings: Academic, Government, Health Plan, Hospital


Objectives: This exploratory study used outpatient laboratory test results from electronic health records (EHRs) for patient risk assessment and evaluated whether risk markers based on laboratory results improve the performance of diagnosis- and pharmacy-based predictive models for healthcare outcomes.

Study Design: Observational study of a patient cohort over 2 years.

Methods: We used administrative claims and EHR data over a 2-year period for a population of continuously insured patients in an integrated health system who had at least 1 ambulatory visit during the first year. We performed regression tree analyses to develop risk markers from frequently ordered outpatient laboratory tests. We added these risk markers to demographic and Charlson Comorbidity Index models and 3 models from the Johns Hopkins Adjusted Clinical Groups system to predict individual cost, inpatient admission, and high-cost patients. We evaluated the predictive and discriminatory performance of 5 lab-enhanced models.

Results: Our study population included 120,844 patients. Adding laboratory markers to base models improved R2 predictions of costs by 0.1% to 3.7%, identification of high-cost patients by 3.4% to 121%, and identification of patients with inpatient admissions by 1.0% to 188% for the demographic model. The addition of laboratory risk markers to comprehensive risk models, compared with simpler models, resulted in smaller improvements in predictive power.

Conclusions: The addition of laboratory risk markers can significantly improve the identification of high-risk patients using models that include age, gender, and a limited number of morbidities; however, models that use comprehensive risk measures may be only marginally improved.

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