Research progress of pain cause analysis and pain prediction models after total knee arthroplasty

Abstract

Pain is the most common symptom after total knee arthroplasty (TKA) and is also the most cause of unsatisfactory. When surgeons judged the cause of pain, they paid more attention to the surgery related factors such as infection, prosthesis loosening and improper location of prosthesis, but without sufficient understanding of some causes of pain. In 2016, William and Craig updated the definition of pain, stressing that pain is the subjective feeling of patient, as well as the cognitive level and some social factors. International Association for the Study of Pain (IASP) divided chronic pain into nociceptive pain and non-nociceptive pain. Perioperative pain, acute infection and prosthesis loosening mostly belong to the former, neuropathic pain and idiopathic (psychogenic) pain, mostly as chronic pain, belongs to the latter. According to the criteria, we re-analyzed the potential causes of postoperative pain after TKA, especially the unexplained pain. We found that some of the factors, such as gender, psychosocial status, sleep et al, also have certain relationships with the postoperative pain. In recent years, researchers abroad have paid more attention to the patient-reported outcome measures (PROMs). Foreign studies have established some postoperative outcome prediction models after TKA based on PROMs and obtained preliminary verification. However, domestic studies have not paid enough attention to self-factors of patients with postoperative pain. Therefore, it is difficult to predict whether patients would feel pain after TKA. The purpose of the present review is to comprehensively summarize the causes of postoperative pain in TKA patients in a new viewpoint, and to summarize the progress of prediction models of postoperative pain for patients after TKA. Ultimately, the present review is to provide certain references for surgeons to re-understand the causes of postoperative pain in TKA patients and to establish a pain prediction model suitable for Chinese patients.