Impact and Modification of the New PJI-TNM Classification for Periprosthetic Joint Infections.
Andre LunzBurkhard LehnerMoritz N VossKevin KnappeSebastian JaegerMoritz M InnmannTobias RenkawitzGeorg W OmlorPublished in: Journal of clinical medicine (2023)
The comprehensive "PJI-TNM classification" for the description of periprosthetic joint infections (PJI) was introduced in 2020. Its structure is based on the well-known oncological TNM classification to appreciate the complexity, severity, and diversity of PJIs. The main goal of this study is to implement the new PJI-TNM classification into the clinical setting to determine its therapeutic and prognostic value and suggest modifications to further improve the classification for clinical routine use. A retrospective cohort study was conducted at our institution between 2017 and 2020. A total of 80 consecutive patients treated with a two-stage revision for periprosthetic knee joint infection were included. We retrospectively assessed correlations between patients' preoperative PJI-TNM classification and their therapy and outcome and identified several statistically significant correlations for both classifications, the original and our modified version. We have demonstrated that both classifications provide reliable predictions already at the time of diagnosis regarding the invasiveness of surgery (duration of surgery, blood and bone loss during surgery), likelihood of reimplantation, and patient mortality during the first 12 months after diagnosis. Orthopedic surgeons can use the classification system preoperatively as an objective and comprehensive tool for therapeutic decisions and patient information (informed consent). In the future, comparisons between different treatment options for truly similar preoperative baseline situations can be obtained for the first time. Clinicians and researchers should be familiar with the new PJI-TNM classification and start implementing it into their routine practice. Our adjusted and simplified version ("PJI-pTNM") might be a more convenient alternative for the clinical setting.
Keyphrases
- deep learning
- machine learning
- minimally invasive
- total knee arthroplasty
- coronary artery bypass
- bone loss
- total hip arthroplasty
- end stage renal disease
- total hip
- healthcare
- primary care
- prostate cancer
- patients undergoing
- quality improvement
- chronic kidney disease
- palliative care
- cardiovascular events
- clinical practice
- cardiovascular disease
- coronary artery disease
- bone marrow
- acute coronary syndrome
- risk factors
- patient reported outcomes
- knee osteoarthritis