| Title: | Using supervised machine learning to predict the post-operative infection after total knee or hip arthroplasty and phenome-wide association study to examine the causal relationship |
| Lead Institution: | Tzu-Chi University |
| Principal investigator: | Professor Shu-Hui Wen |
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About
Post-operative infection after total knee or hip arthroplasty is rare but causes a significant burden financially for patients and healthcare system. Patients with more comorbidities such as diabetes or anemia would be more likely to have post-operative infection. The aim of this study is to examine the causal relationship between pre-existing comorbidities and post-operative infection. To build up a prediction model of post-operative infection, we use the National Health Insurance Research Database in Taiwan and identify the comprehensive pre-existing comorbidities as predictors. We plan to use the UK Biobank data for two main goals: (1) to replicate our findings in an independent dataset and (2) to identify the genetic effect by using a genetic risk score for the top-associating comorbidities and test its association with all available disease phenotypes. The duration of the project will be a period of 3 years. The completion of this study will help us to understand the causal relationship between pre-existing comorbidities and post-operative infection. We anticipate that this will enable the early prevention of post-operative infection more precisely.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12108 | Integrating Genome-wide Polygenic Risk Scores with Non-genetic Models to Predict Surgical Site Infection after Total Knee Arthroplasty Using United Kingdom Biobank Data | Pei-Yu Chen (+1) | 2024 | The Journal of Arthroplasty |
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