Construction of risk prediction model for cleaning quality in disinfection supply center
消毒供应中心清洗质量风险预测模型的构建

摘要

目的 构建消毒供应中心清洗质量风险预测模型，提高医疗器械清洗质量合格率。方法 通过文献回顾确定影响清洗质量的多个因素，选取本院2025年01月-2025年05月消毒供应中心处理的医疗器械，次数为300次作为研究对象，利用随机数字，平均分为传统组和模型组。传统组采用常规清洗质量控制方法，模型组采用清洗质量风险预测模型。通过收集清洗过程中各个环节参数，采用多元逻辑回归分析构建预测模型，并比较两组清洗合格率、灭菌合格率、器械感染率以及临床满意度。结果 模型组清洗合格率和灭菌合格率显著高于传统组；模型组器械感染率低于传统组，且临床科室对模型组器械清洗质量的满意度也显著高于传统组，差异均具有统计学意义（p<0.05）。结论 消毒供应中心清洗质量风险预测模型能有效预测并控制清洗质量风险，显著提高清洗和灭菌合格率，降低医院感染风险，值得推广应用。

Abstract

Objective To develop a risk prediction model for the quality of cleaning in disinfection supply centers to improve the qualification rate of medical device cleaning.
Methods By reviewing literature, multiple factors affecting cleaning quality were identified. The study selected 300 cases of medical devices processed by the hospital's disinfection supply center from January to May 2025 as the research subjects. Using random numbers, these cases were evenly divided into a traditional group and a model group. The traditional group used conventional cleaning quality control methods, while the model group employed a cleaning quality risk prediction model. By collecting parameters from each stage of the cleaning process, a predictive model was constructed using multiple logistic regression analysis. The study compared the qualification rates of cleaning and sterilization, the infection rate of medical devices, and clinical satisfaction between the two groups.
Results The qualification rates of cleaning and sterilization in the model group were significantly higher than those in the traditional group. The infection rate of medical devices in the model group was lower than that in the traditional group, and clinical departments' satisfaction with the cleaning quality of medical devices in the model group was also significantly higher than that in the traditional group, with all differences being statistically significant (p<0.05).
Conclusion   The risk prediction model for cleaning quality in disinfection supply centers can effectively predict and control cleaning quality risks, significantly improve the qualification rates of cleaning and sterilization, and reduce hospital infection risks, making it worthy of promotion and application.