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The American Journal of Managed Care June 2018
Prevalence and Predictors of Hypoglycemia in South Korea
Sun-Young Park, PhD; Eun Jin Jang, PhD; Ju-Young Shin, PhD; Min-Young Lee, PhD; Donguk Kim, PhD; and Eui-Kyung Lee, PhD
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Annie Lintzenich Andrews, MD, MSCR; Daniel Brinton, MHA, MAR; Kit N. Simpson, DrPH; and Annie N. Simpson, PhD
Physician Practice Variation Under Orthopedic Bundled Payment
Joshua M. Liao, MD, MSc; Ezekiel J. Emanuel, MD, PhD; Gary L. Whittington, BSBA; Dylan S. Small, PhD; Andrea B. Troxel, ScD; Jingsan Zhu, MS, MBA; Wenjun Zhong, PhD; and Amol S. Navathe, MD, PhD
Simply Delivered Meals: A Tale of Collaboration
Sarah L. Martin, PhD; Nancy Connelly, MBA; Cassandra Parsons, PharmD; and Katlyn Blackstone, MS, LSW
Placement of Selected New FDA-Approved Drugs in Medicare Part D Formularies, 2009-2013
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Currently Reading
Assessing Markers From Ambulatory Laboratory Tests for Predicting High-Risk Patients
Klaus W. Lemke, PhD; Kimberly A. Gudzune, MD, MPH; Hadi Kharrazi, MD, PhD, MHI; and Jonathan P. Weiner, DrPH
Cost Sharing for Antiepileptic Drugs: Medication Utilization and Health Plan Costs
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Assessing Markers From Ambulatory Laboratory Tests for Predicting High-Risk Patients

Klaus W. Lemke, PhD; Kimberly A. Gudzune, MD, MPH; Hadi Kharrazi, MD, PhD, MHI; and Jonathan P. Weiner, DrPH
An evaluation of the added value of risk markers derived from ambulatory laboratory tests in the prediction of healthcare costs and identification of high-risk patients.

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.

Am J Manag Care. 2018;24(6):e190-e195
Takeaway Points
  • Laboratory tests that are frequently ordered by physicians in outpatient practices contain valuable data for individual risk assessment.
  • Ranges of blood chemistries and hematology results define a set of model markers that have clinical face validity and potential utility for care management.
  • Adding the laboratory-based markers to risk levels derived from claims, prescriptions, and enrollment data improves the prediction of individual cost, the prediction of inpatient admission, and the prospective identification of high-cost patients.
  • For practices, a simple model that includes demographics and laboratory information may provide a basic tool to evaluate patient panels.
Most predictive models in healthcare have relied upon diagnosis information from health insurance claims or other administrative data. Such claims-based predictive models have been used extensively by health plans and government agencies for provider profiling and payment, underwriting, and prioritizing patients for care management.1 Although claims remain an important source of risk data, the widespread implementation of electronic health records (EHRs) and other clinical information technology systems offers a new source of data on disease severity and health status, as most EHRs contain information not captured in claims, such as laboratory values, vital signs, and clinical assessments.2

In the inpatient setting, laboratory tests have been used to assess the risk of mortality across a range of conditions, including acute myocardial infarction, congestive heart failure, diabetes, ischemic and hemorrhagic stroke, pneumonia, and septicemia.3-7 These predictive assessments of mortality risk have incorporated blood chemistries, hematology, and blood gases. Predictive models for mortality performed better after adding laboratory risk markers, but similar models predicting 30-day readmission did not improve as much.8

Another case for laboratory data has been made for case-mix adjustment of inpatient admissions using diagnosis-related groups (DRGs).9,10 Clinical laboratory results combined with inpatient administrative data incrementally improved the ability of DRGs to explain the length of inpatient stays; however, Medicare Severity DRGs and other DRG versions do not incorporate laboratory data for inpatient classification.

Laboratory tests can be powerful predictors among certain patient populations. For example, patients with diabetes who maintained reduced glycated hemoglobin (A1C) levels (ie, had better glycemic control) had lower annual costs than patients with higher levels.11

The goal of this study was to develop and evaluate an approach for transforming common outpatient laboratory tests into risk measures that could be useful when added to population-level predictive models. Our objective was to determine result ranges for several candidate blood tests that were associated with increased costs in the year after the tests were performed. We hypothesized that certain ranges of component results from blood tests in the base year would be associated with higher healthcare costs and increased inpatient utilization during the subsequent year. We also hypothesized that laboratory risk markers based on component ranges would improve predictive risk models for these outcomes, including models with demographic and Charlson Comorbidity Index (CCI) risk markers and 3 models from the Johns Hopkins Adjusted Clinical Groups (ACG) system.


Data Source and Study Population

We obtained data from HealthPartners, Inc (Bloomington, Minnesota), a health insurer and large integrated delivery system. Its database contains structured EHR data, including encounter diagnoses and laboratory test results; administrative data that included benefit eligibility files; and claims data with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnoses, Current Procedural Terminology (CPT) procedure codes from inpatient and outpatient settings, and filled prescriptions with National Drug Codes from outpatient pharmacies. HealthPartners provided these data for patients who were receiving care at facilities owned by the healthcare system.

Our study population included 120,844 patients who were continuously enrolled in 2012 and 2013 and had at least 1 visit to 1 of 5 HealthPartners outpatient clinics in the Minneapolis-St. Paul metropolitan area in 2012.

Data Preparation

To harmonize the coding of test orders across HealthPartners’ entities, we mapped the internal HealthPartners codes to Logical Observation Names Identifiers and Codes (LOINC). LOINC is a common language for identifying health measurements, observations, and documents, and it is commonly used for laboratory orders and findings.12

The assignment of LOINC was a 2-step process. We first used the Regenstrief LOINC Mapping Assistant to suggest potential LOINC, which were turned over to a pathologist for final review in the second step.13 All laboratory tests that we selected for this study were mapped to LOINC.

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