ssi after primary joint arthroplasty 1205
100 90 80 70 60 50 40 30 20 10 0
100 80 60
0.95 0.96 0.97 0.98 0.99 1.00
40 0.94 0 20 500 1000 Times since primary arthroplasty (days) 0
fig4. Risk of hip or knee arthroplasty infection over the period 2008–2012, according to the prosthesis location.
Survival delay Knee PJI (N) Hip PJI (N) Follow-up without SSI (%)
fig2. Distribution of surgical site infection (SSI), by location according to the survival delay, in parallel with the cohort follow-up (prosthetic joint infection [PJI] among the arthroplasties, by location). The black curve represents the proportion of patients remaining in the cohort over time.
1.00 0.99 0.98 0.97 0.96 0.95 0.94 0.93
Times since primary operation (days)
fig3. Risk of hip or knee arthroplasty infection (HKAI) over the period 2008–2012.
study recently published that a secular decrease in death caused by hip replacement has occurred owing to specific management acting on comorbitidies.14 A comorbidity score predicting infection after arthroplasty could be imagined based on hospital discharge cohort studies. By developing a method for classifying comorbid conditions that might alter the risk of infection, a weighted index taking into account the number of comorbidities could be built.32 This composite criterion could be proposed to assess the risk of PJI at replacement, compared with the index of
Charlson, predicting patient mortality according to a range of comorbidities. The Charlson score has already been assessed using International Statistical Classification of Disease, Tenth Revision, diagnosis codes in patients undergoing urological surgery in cancer.32,33 The PJI score could be calculated by the surgeon and/or the anesthetist before planning joint replacement (eg, an increasing score would be linked to the presence of components of interest—ulcer sore, undernutrition, liver dis- eases). This score could represent an indirect way to estimate the risk of PJI and it could be shared with patients.34 Like the Charlson score, this method of classifying comorbidity could provide a simple, readily applicable, and valid method of esti- mating PJI risk from chronic diseases found in hospital discharge database studies. Moreover, detection of PJI and highlighting risk factors could allow care adjustment, proposing new strategies for hip or knee replacement in France; this could represent a monitoring model, such as the cumulative monitoring methods that have shown promises in United Kingdom. However, the major question will remain: who determines the threshold of the score beyond which the surgery must not be performed? This kind of score would help streamline the decision to
1500
Hip prosthesis Knee prosthesis
undergo replacement.5,7,17,25,35–37 Moreover, it could help deter- mine new indicators for themanagement of joint replacement in aging patients with more comorbidities. The same investigation can be performed on other surgeries currently monitored in surveillance systems, such as colorectal or gynecological surgery (eg, building and validating an algorithm with professionals, and applying the case definition in a cohort to estimate the risk linked to predictors in different types of surgery).
Study Limitations
There were some limitations, particularly as concerns the quality of the hospital discharge data coded and thus the reporting of PJI.38,39 The reliability of the coding system used in the hospital discharges remains debatable because data are coded by different healthcare professionals.5,17,19,40 But the robustness of the method was previously demonstrated and
Patient Follow-up (%)
Hazard rate (HKAI per day)
SSI Patients (N)
Hazard rate
1 Year
Replacement stay
1 month 3 month One year > One year
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
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