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Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study
  1. Assaf Gottlieb,
  2. Chen Yanover,
  3. Amos Cahan,
  4. Yaara Goldschmidt
  1. Machine Learning for Healthcare and Life Sciences, IBM Research, Haifa, Israel
  1. Correspondence to Prof Assaf Gottlieb; assaf.gottlieb{at}uth.tmc.edu and Dr Chen Yanover; cheny{at}il.ibm.com

Abstract

Objective Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records.

Study design and setting Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders.

Participants and exposures Our cohort consisted of more than 40 000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second-line drug classes—sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide-1 agonists—during the years 2000–2015. Roughly, 17 000 of these patients were followed for 12 months after being prescribed a second-line drug.

Main outcome measures HbA1c and BMI of these patients after 6 and 12 months following treatment.

Results We demonstrate that all four drug classes reduce HbA1c levels, but the effect of sulfonylureas after 6 and 12 months of treatment is less pronounced compared with other classes. We also estimate that DPP-4 inhibitors decrease body weight significantly more than sulfonylureas and thiazolidinediones.

Conclusion Our results are in line with current knowledge on second-line drug effectiveness and effect on BMI. They demonstrate that causal inference from electronic health records is an effective way for conducting multitreatment causal inference studies.

  • treatment efficacy
  • electronic medical records
  • type 2 diabetes
  • anti-diabetic drugs

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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Footnotes

  • Contributors AG and CY designed and performed the experiments. AC compiled domain expert confounders. AG, CY, AC and YG wrote the manuscript. AG and CY are guarantors of the paper.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No additional data are available.