Learning Effective Treatment Pathways for Type-2 Diabetes from a clinical data warehouse

AMIA Annu Symp Proc. 2017 Feb 10;2016:2036-2042. eCollection 2016.

Abstract

Treatment guidelines for management of type-2 diabetes mellitus (T2DM) are controversial because existing evidence from randomized clinical trials do not address many important clinical questions. Data from Electronic Medical Records (EMRs) has been used to profile first line therapy choices, but this work did not elucidate the factors underlying deviations from current treatment guidelines and the relative efficacy of different treatment options. We have used data from the Stanford Hospital to attempt to address these issues. Clinical features associated with the initial choice of treatment were effectively re-discovered using a machine learning approach. In addition, the efficacies of first and second line treatments were evaluated using Cox proportional hazard models for control of Hemoglobin A1c. Factors such as acute kidney disorder and liver disorder were predictive of first line therapy choices. Sitagliptin was the most effective second-line therapy, and as effective as metformin as a first line therapy.

Keywords: Learning Health Systems; Second-line treatment options; Treatment Pathways; Type-2 Diabetes.

MeSH terms

  • Adult
  • Aged
  • Consolidation Chemotherapy
  • Critical Pathways*
  • Diabetes Mellitus, Type 2 / drug therapy*
  • Electronic Health Records*
  • Female
  • Glycated Hemoglobin A / analysis
  • Humans
  • Hypoglycemic Agents / therapeutic use*
  • Machine Learning*
  • Male
  • Metformin / therapeutic use
  • Middle Aged
  • Proportional Hazards Models
  • Sitagliptin Phosphate / therapeutic use

Substances

  • Glycated Hemoglobin A
  • Hypoglycemic Agents
  • Metformin
  • Sitagliptin Phosphate