Abstract

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the “Treatment Pathways in Chronic Disease” protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.

Highlights

  • Chronic diseases are the main cause of death worldwide, with an annual death toll higher than the sum of deaths caused by all other diseases

  • We proved the feasibility of these Observational Health Data Sciences and Informatics (OHDSI) type studies in China

  • The percentage of patients with depression using monotherapy with paroxetine decreased from 88.10% in 2005 to 6.15% in 2015

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Summary

Introduction

Chronic diseases are the main cause of death worldwide, with an annual death toll higher than the sum of deaths caused by all other diseases. During 2011–2025, the cumulative economic losses due to non-communicable diseases (NCDs) under a Bbusiness as usual^ scenario in low- and middle-income countries was estimated at US$ 7 trillion. This sum far outweighs the annual US$ 11.2 billion cost of implementing a set of high-impact interventions to reduce the NCD burden. These chronic diseases are mainly cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases. Studies concerning the diagnosis, treatment, and interventions for chronic disease are increasingly important

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