Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model

Abstract

Background

Uterine leiomyoma (UL) is one of the most common benign tumors in women, and its incidence is gradually increasing in China. The clinical complications of UL have a negative impact on women’s health, and the cost of treatment poses a significant burden on patients. Diagnosis-related groups (DRG) are internationally recognized as advanced healthcare payment management methods that can effectively reduce costs. However, there are variations in the design and grouping rules of DRG policies across different regions. Therefore, this study aims to analyze the factors influencing the hospitalization costs of patients with UL and optimize the design of DRG grouping schemes to provide insights for the development of localized DRG grouping policies.

Methods

The Mann–Whitney U-test or the Kruskal–Wallis H-test was employed for univariate analysis, and multiple stepwise linear regression analysis was utilized to identify the primary influencing factors of hospitalization costs for UL. Case combination classification was conducted using the exhaustive chi-square automatic interactive detection (E-CHAID) algorithm within a decision tree framework.

Results

Age, occupation, number of hospitalizations, type of medical insurance, Transfer to other departments, length of stay (LOS), type of UL, admission condition, comorbidities and complications, type of primary procedure, other types of surgical procedures, and discharge method had a significant impact on hospitalization costs (P<0.05). Among them, the type of primary procedure, other types of surgical procedures, and LOS were the main factors influencing hospitalization costs. By incorporating the type of primary procedure, other types of surgical procedures, and LOS into the decision tree model, patients were divided into 11 DRG combinations.

Conclusion

Hospitalization costs for UL are mainly related to the type of primary procedure, other types of surgical procedures, and LOS. The DRG case combinations of UL based on E-CHAID algorithm are scientific and reasonable.

Keywords:

• uterine leiomyoma
• diagnosis-related groups
• decision tree

Ethics Approval

This study has been approved by the Ethics Committee of West China Second Hospital of Sichuan University (No.2023257) and all patients signed the informed consent form. Ethical principles of the Declaration of Helsinki were adhered to throughout this study.

Acknowledgments

We are indebted to all participants in this study for their cooperation. Special thanks to MW, SH, and PX for their writing assistance with manuscript formats and YX for funding support.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

This work was supported by the Postdoctoral Foundation of West China Hospital of Sichuan University (No. 2019HXBH006). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.