The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model
Serum C-reactive protein (CRP) is a useful biomarker reflecting the efficacy of clinical treatments for infectious and autoimmune diseases. Accurate prediction of the serum CRP concentration of a patient through accurate initial clinical evaluation must be preceded to promptly cope with inflammatory...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10818686/ |
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| author | Suk Joo Bae Gyu Ri Kim Sun Geu Chae Yeesuk Kim |
| author_facet | Suk Joo Bae Gyu Ri Kim Sun Geu Chae Yeesuk Kim |
| author_sort | Suk Joo Bae |
| collection | DOAJ |
| description | Serum C-reactive protein (CRP) is a useful biomarker reflecting the efficacy of clinical treatments for infectious and autoimmune diseases. Accurate prediction of the serum CRP concentration of a patient through accurate initial clinical evaluation must be preceded to promptly cope with inflammatory diseases. In general, serum CRP concentration rises sharply right after hip surgery and then falls down at a certain period of time. Such patterns can be used as a meaningful indicator to estimate recovery tendency of individual patients. This study proposes a nonlinear mixed-effects (NME) model to describe nonlinear patterns of serum CRP concentration over time for patients suffering from hip arthroplasty through an observational study. The bi-exponential model with random effects is applied to predict temporal CRP concentrations in patients after hip surgery. Analytical results show that the proposed model accurately predicts serum CRP concentrations over time by effectively capturing individual variation in serum CRP concentrations through random effects. Based on the estimated model, we derive the distribution of normalized concentration times using the Monte Carlo (MC) simulation. The NME model will be expected to support future research on best practices for intraoperative and postoperative management of patients with hip surgery, based on various levels of predicted risks of infection. |
| format | Article |
| id | doaj-art-7943d7bede03466b89f0fc5a93442de9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7943d7bede03466b89f0fc5a93442de92025-01-14T00:02:41ZengIEEEIEEE Access2169-35362025-01-01136507651410.1109/ACCESS.2024.352447110818686The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects ModelSuk Joo Bae0https://orcid.org/0000-0002-9938-7406Gyu Ri Kim1Sun Geu Chae2Yeesuk Kim3Department of Industrial Engineering, Hanyang University, Seoul, Republic of KoreaSamsung Electronics, Flash Product Engineering Team, Memory Business, Hwaseong-si, Republic of KoreaDepartment of Industrial Engineering, Hanyang University, Seoul, Republic of KoreaDepartment of Orthopaedic Surgery, Hanyang University, Seoul, Republic of KoreaSerum C-reactive protein (CRP) is a useful biomarker reflecting the efficacy of clinical treatments for infectious and autoimmune diseases. Accurate prediction of the serum CRP concentration of a patient through accurate initial clinical evaluation must be preceded to promptly cope with inflammatory diseases. In general, serum CRP concentration rises sharply right after hip surgery and then falls down at a certain period of time. Such patterns can be used as a meaningful indicator to estimate recovery tendency of individual patients. This study proposes a nonlinear mixed-effects (NME) model to describe nonlinear patterns of serum CRP concentration over time for patients suffering from hip arthroplasty through an observational study. The bi-exponential model with random effects is applied to predict temporal CRP concentrations in patients after hip surgery. Analytical results show that the proposed model accurately predicts serum CRP concentrations over time by effectively capturing individual variation in serum CRP concentrations through random effects. Based on the estimated model, we derive the distribution of normalized concentration times using the Monte Carlo (MC) simulation. The NME model will be expected to support future research on best practices for intraoperative and postoperative management of patients with hip surgery, based on various levels of predicted risks of infection.https://ieeexplore.ieee.org/document/10818686/Bi-exponential modelcompartment theorylikelihood ratio testMonte Carlo (MC) simulation |
| spellingShingle | Suk Joo Bae Gyu Ri Kim Sun Geu Chae Yeesuk Kim The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model IEEE Access Bi-exponential model compartment theory likelihood ratio test Monte Carlo (MC) simulation |
| title | The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model |
| title_full | The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model |
| title_fullStr | The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model |
| title_full_unstemmed | The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model |
| title_short | The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model |
| title_sort | prediction of serum c reactive protein concentration using nonlinear mixed effects model |
| topic | Bi-exponential model compartment theory likelihood ratio test Monte Carlo (MC) simulation |
| url | https://ieeexplore.ieee.org/document/10818686/ |
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