Precision medicine tailoring the patient pathway based on the risk, prognosis, and treatment response may bring benefits to the patients. To identify risk factors contributing to the early failure of treatment (development of events of interest) and when possible to change the prognosis via modifying these factors may improve the outcome and/or lower the risk of complications. There is an emerging goal to identify such parameters in total knee arthroplasty (TKA) thus lower the risk of revision surgery. The goal of this study was to identify factors explaining the risk for early revision of TKA using an artificial intelligence method appropriate for this task.

We applied a patient similarity network (PSN) for the identification of risk factors associated with early reoperations (n=109, 5.8%) in patients with TKA (n=1885). Next, an algorithm based on formal concept analysis was developed to support the patient decision on how to change modifying personal characteristics with respect to the estimated probability of reoperations.

The early reoperations were less frequent in women (4.4%, median time to reoperation 4.5 mo) than in men (8.2%, 10 mo), reaching the highest incidence in younger men (10.9%).

The most common reasons for total joint arthroplasty (TJA) failure are aseptic loosening (AL) and prosthetic joint infection (PJI). There is a big clinical challenge to identify the patients with high risk of AL/PJI before the TJA surgery. Although there is evidence that genetic factors contribute to the individual susceptibility to AL/PJI, a predictive model for identification of patients with a high genetic risk of TJA failure has not been developed yet.

We aimed to develop a risk evaluation tool utilising the AL/PJI-associated polymorphisms for identification of patients with high genetic risk of TJA failure based on inflammation-gene polymorphism panel.

Based on allele and genotype frequencies of twenty-five single nucleotide polymorphisms (SNPs) in TNF, IL2, IL6, IL10, IL1b, IL-1Ra, MBL2, MMP1, FTO genes and those influencing the serum levels of biomarkers of TJA outcomes (IL6, CCL2/MCP-1, CRP, ESR) in peripheral blood obtained from patients with TJA (AL, n=110; PJI, n=93; no complications, n=123), we calculated a hazard ratio and a relative entropy of alleles and genotypes associated with AL and PJI and their combinations in patient subgroups.

We conducted a risk evaluation tool based on the presence of risk alleles and genotypes in TNF (rs361525, rs1800629), DARC (rs12075), MBL2 (rs11003125) and FTO (rs9939609, rs9930506) genes associated with implant failure (AL/PJI). Of these, FTO gene variations (rs9939609, rs9930506) were associated mainly with PJI (P=0.001, OR=2.04, 95%CI=1.132–2.603; P=0.011, OR=1.72, 95%CI=1.338–3.096) and DARC (rs12075) with AL (P=0.005, OR=1.79, 95%CI=1.193–2.696). This tool calculates a hazard ratio of a combination of SNPs associated with AL and PJI for identification of patients with high and low risk of AL/PJI TJA failure.

We proposed a risk evaluation tool for stratification of patients before the TJA surgery based on the genetic risk of AL/PJI development. The effect size for each genotype combination described in the study is small. Further multiparametric data analysis and studies on an extended patient cohort and other non-genetic and genetic parameters are ongoing. Grant support: AZV MZ CR VES16-131852A, VES15-27726A, IGA LF UP_2016_011.

Aim

The diagnosis of periprosthetic joint infection (PJI) in total joint arthroplasty (TJA) remains a serious clinical challenge. Nowadays, limited biomarkers associated with PJI are available. We investigated therefore the utility of gene expression pattern of Toll-like receptors (TLR) and members of interleukin (IL)1/IL1R family, molecules critically involved in the innate immune response to invading pathogens, for detecting PJI in periprosthetic tissues around TJA.