| Summary: | 博士 === 國立臺灣大學 === 健康政策與管理研究所 === 100 === Background
Hospital-acquired infections are an important issue in the current health care society. Several large-scale studies have been conducted to verify factors that could affect hospital-acquired infections, including those where the relationship between service volume and infection was examined, although it remained controversial. Possible reasons for this equivocal relationship include inconsistent definition of service volume, limitations in data analysis methods, and inappropriate approaches adopted for identifying infection cases. Therefore, the purpose of this study is to develop a model which can be applied in the National Health Insurance (NHI) Research Database of Taiwan for identifying surgical site infection following CABG surgery. The model is further applied in later studies to examine the relationship between hospital and surgeon services volume and surgical site infection.
Study design
A restrospective study design.
Materials and method
Through literature review of domestic studies and those from abroad, the study collected models for infection identification which were developed upon registry or administrative data. Using the registry data of CABG surgeries between 2005 and 2008 from a medical center in Taiwan and information collected by the hospital surveillance system, the study established an alternative model for identifying surgical site infection following CABG surgery. This model was compared with the traditional model based on ICD-9 CM codes and other alternatives. Then, the derived optimal model was applied in the registry data of CABG surgery between 2005 and 2009 in Taiwan for infection case identification. The model was also used in later studies for exploring the service volume-infection relationship. For statistical analyses, service volume was divided into shorten monthly hospital/surgeon services volume and long-term cumulative services volume during the past 12 months of a surgery. The generalized additive model, k-means clustering, and quartiles were used for stratifying volume groups, and multi-level analysis was performed to examine the relationship between hospital and surgeon services volume and surgical site infection.
Results
The study results revealed that the decision tree model owned the highest positive predictive value (PPV) of 77.78 % and was qualified with good sensitivity, specificity, and negative predictive value. Furthermore, the study found that almost every alternative model had a PPV better than that of the traditional ICD-9-based model. Considering the homogeneity of data, the study only conducted its identification and analysis of infection cases in the NHI registry data of medical centers from 2006 to 2008. The study results showed an infection rate of 1.5 % in medical centers between 2006 and 2008, accompanied with a decreasing trend of antibiotics and healthcare services utilization. The study also found that different categorizations of service volume yielded different results, where a categorization using the generalized additive model brought the best goodness of fit of the model (AIC=1083.34). The study results suggested that greater cumulative physician services volume during the past 12 months of a surgery led to reduced risk of infection in patients (OR=0.9888). Regarding hospital services volume, no matter monthly or cumulative volume was adopted, middle-volume hospitals showed higher risk of infection in patients than low-volume and high-volume hospitals, where the OR were 1.9058 and 1.7510, respectively.
Conclusion
Compared to ICD-9 CM codes in claim data, a decision tree-based model can identify infection cases more precisely, which reduces bias in studies. When conducting studies regarding service volume and infection, the definition and categorization of service volume merit concern as results could vary accordingly. The study analyzes and compares among different categorizations of service volume, and the findings could serve as references for future studies.
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