Abstract
Aim
At present, a variety of clinical guidelines for treatment of periprosthetic joint infections (PJI) inevitably lead to a variety in outcomes by differing case management. Ideally a treatment algorithm should incorporate all components contributing to the decision-making process for a patient tailored solution in PJI. We aim to present a comprehensive and reproducible treatment algorithm based on a validated staging system, a thorough understanding of the host, the causative microbiome and implant complexity.
Method
The diagnosis of a PJI was defined according to major and minor criteria following revised International Consensus Symposium algorithm
The validated McPherson staging system was used in our university hospital from January 2015 until January 2019 in referred PJI patients. Standardised preoperative and postoperative survey documents were completed in order to register data from the patient's medical, social and surgical history. The complexity of the infected implant was taken into consideration, including quantity of preceding procedures, residual bone stock, type of fixation, magnitude of prosthetic components and presence or absence of reconstructive options. Further, preoperatively obtained bacteriological information by means of arthrocentesis or tissue/bone biopsies was categorized according to the mono- or polybacterial nature and to the qualification of virulence and difficulties to treat. Social and professional history, financial impediments and patient's functional outcome wishes were included in the joint decision making.
Results
We present our comprehensive PJI treatment algorithm. The ‘deTerminators’ we included are a validated staging system focused on the host, the amount of unsuccessful prior attempts, the difficult to treat character of the microbiome, the implant complexity, anatomical location and socioeconomic patient derived factors. Furthermore, we call for source control by minimally invasive means or late DAIR in complex case management combined with lifelong suppressive antibiotic therapy with maintenance of quality of life as the main outcome instead of curative intention.
Conclusions
We present a comprehensive treatment algorithm based on an expanded McPherson staging system coupled with bundled clinical, technical, social and psychological data which should assist the surgeon and the patient to make informed choices. We hope that usage and testing of our algorithm in other centers could further demonstrate its usefulness.
