Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection
| Autor: | Timothy G Costales, Ingwon Yeo, Young-Min Kwon, Christian Klemt, Yasamin Habibi, Jillian C Burns, Samuel Laurencin, Akachimere Cosmas Uzosike |
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| Rok vydání: | 2021 |
| Předmět: |
Reoperation
Arthritis Infectious Recurrent infections medicine.medical_specialty Prosthesis-Related Infections business.industry Potential risk Patient demographics Periprosthetic Surgery Machine Learning Treatment Outcome Decision curve analysis Reinfection Cohort medicine Humans Orthopedics and Sports Medicine In patient Arthroplasty Replacement Knee business Revision total knee arthroplasty Retrospective Studies |
| Zdroj: | Knee Surgery, Sports Traumatology, Arthroscopy. 30:2582-2590 |
| ISSN: | 1433-7347 0942-2056 |
| Popis: | PURPOSE This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. METHODS A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. RESULTS The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p 4 prior open surgeries (p |
| Databáze: | OpenAIRE |
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