Rationale, Summary, Significance
We aimed to test the feasibility of applying common data model (CDM) in the orthopedic field and analyzed risk factors for periprosthetic joint infection (PJI) after total joint arthroplasty. Variables such as demographic/social factors, medical history, laboratory results and admission days could be converted into CDM, but the others such as intraoperative factors, observation duration, and venous thromboembolism prophylaxis could not be converted to CDM. When analyzed by using CDM, we found that hypertension and urinary tract infection were risk factors of PJI after THA, and age bracket of 70 to 79 years, male, anemia, steroid use and urinary tract infection were risk factors of PJI after TKA. This study demonstrates orthopedic researches using CDM is feasible although data converting to CDM was possible for limited factors.
Conversion of EMR parameters to CDM
The CDM is designed to include all observational data derived from the EMR to support the generation of reliable evidence [11, 25]. It is important to obtain what we want from the study by properly designing the algorithm with the parameters currently available in the CDM. Creating mappings the variable EMR data into the target CDM concepts is also crucial to improve patient data standardization [14, 22]. Thus, in previous studies, cohort studies have been mainly focused on the pharmacoepidemiological research as treatment of diseases and epidemiological analysis of deaths from certain diseases [11, 12, 19, 23, 32, 33]. In our case, we focused on parameters related to risk factors for PJI after TJA and constructed the algorithm directly using SQL, not through programs already created within the CDM, to achieve the desired results in our study. Of course, the code mapping process was not easy. Four authors reviewed the code mapping, but because of incomplete concepts matching and difference between the coding systems, a little information loss was inevitable. In addition, the data in EMR are typically expressed in non-standard terms, and the textual variable values are often in free-style using different local expressions, we could not standardize these terms and the textual values into standard concepts in this study.
The main advantages of research using CDM is that such studies can be conducted on a larger scale, against lower costs, and within shorter time frames than traditional studies [5]. Also, it protects the privacy and security of patients in research because not the information of a certain patient but the information of a certain result is used in CDM tool [25]. In our study, to maintain patient confidentiality privacy and security, the original patient identifications were removed when the patient data were converted to the CDM. The CDM is also an important part of multi-organization collaborative research [19, 22]. Because each hospital has a different structure in patient information, it is necessary to cooperate with multiple hospitals to provide information for standardization of patient information through CDM tool. However, differences in data structures and coding system are still major barriers to standardize data in CDM tool [31].
Risk factors of PJI
In this study, hypertension was identified as a significant risk factor of PJI after THA, which is concurrent with some studies [1, 2, 6, 30]. The studies demonstrated that hypertension is associated with delayed wound healing following TJA.
Urinary tract infection (UTI) was a significant risk factor of PJI after both THA and TKA in this study. Usually, UTI is more common in women than in men and the reported prevalence of UTI in women undergoing primary TJA ranges from 5.1–36% [4, 6, 9, 26]. Therefore, symptomatic UTI should be treated before proceeding TJA.
We found an association between age and risk of revision, which is consistent with previous findings [5, 7, 17–21]. Although older patient age would seem to coincide with poorer nutritional status and thus elevated infection risk, some studies reported an increased risk of revision for relatively younger patients [7, 17, 18, 22].
This study found that a male sex was a significant risk factor of PJI after TKA, which coincides with some studies [3, 6, 15, 27, 29]. A study suggested that men can get a greater degree of surgical trauma and tissue necrosis than in women [27]. Also, men have a more active life-style than women after TJA. Therefore, differences in exercise volume can cause overuse differences after TJA, which may result in revision surgery.
In this study, preoperative anemia was also associated with risk factors of PJI after TKA. Anemia is usually associated with a patient’s poor nutritional status. Previous literatures have shown that primary TJA patients who have preoperative anemia are more likely to receive blood transfusions, which are associated with an increased risk of postoperative infection [2, 4, 8–10]. Therefore, patients should be preoperatively evaluated for causes of anemia, such as iron deficiency, and considered for recombinant human erythropoietin treatment in order to decrease the risk of PJI [8, 10].
We also found steroid use as a risk factor of PJI after TKA, which is consistent with previous literatures [2, 6, 18, 28]. The association between steroid use and PJI is likely to be mediated at least in part by impaired wound healing due to the anti-inflammatory and immuno-suppressive effects of steroids [20]. In addition, steroid use can cause problems of calcium and vitamin D metabolism, zinc deficiency, and most importantly an accelerated bone mineral loss [16].
Limitations of study
There are several limitations to our study that must be noted. First, although the study objective was to utilize a CDM to identify risk factors of PJI after TJA, we couldn't analyze all of them that have been reported in the literature. We couldn’t use non-matching EMR code in CDM. In our further study, we will continue improving the scalability of the converting variable data to CDM. Further data transforming technologies need to be developed to analyze more factors relevant to orthopedic area, such as intraoperative factors and imaging findings. Second, the subjects were from a single institution and our methodology has not been tested with other uses. The research of CDM designed for one use might lack credibility in terms of methodology. Therefore, the generalizability still needs to be confirmed. We will conduct subsequent research to use multi-center data for large-scale analysis and further validate our methods.