Abstract
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%).
