Using a postoperative pain trajectory to predict pain at 1 year after total knee arthroplasty
Introduction
Most patients who have undergone a total knee arthroplasty (TKA) experience pain relief within 6–12 weeks after the TKA, but 8–34% experience chronic post-surgical pain (CPSP) [1], [2]. A method is needed that can be used to predict and detect CPSP early in the patient’s course of treatment, as it would help healthcare providers identify patients who are likely to experience continued pain earlier and thus initiate treatment earlier, before the pain becomes chronic.
Regarding the prediction of CPSP, the preoperative pain intensity [3] and occurrence of acute postsurgical pain [4] were described as modifiable risk factors for the development of CPSP. However, scores on a visual analogue scale (VAS) or a numeral rating scale (NRS), which are typically used to measure pain, are greatly affected by individual differences, making it extremely difficult to predict a patient’s prognosis with only a single point of pain intensity [5]. The VAS or NRS score on any one day (e.g., 1, 3, or 5 days post-surgery) is too imprecise to meaningfully characterize an individual patient’s postoperative pain [6]. Other prediction methods, i.e. growth mixture modeling (GMM) [7] and group-based trajectory modeling (GBTM) [8], have often been used to predict patients’ postoperative pain. However, GMM and GBTM are difficult to perform in clinical settings. For example, the GMM models are classified using long-term data and are susceptible to the last day’s assessment data [7]; thus, post-surgical medical providers cannot adequately predict CPSP by GMM.
To solve this problem, Imai et al. described a method for tracking the pain trajectory of postoperative patients with distal radial fractures [6]. They calculated a pain curve slope (i.e. the rate of pain resolution) and an intercept (the initial pain level post-surgery) using the patients’ pain intensity at 1, 3, 5, and 7 days post-surgery. They concluded that the patients’ prognoses at 1 month post-surgery could be predicted with this pain trajectory method, and their results suggested that a pain trajectory (i.e. the pain curve slope and intercept) could help healthcare providers predict postoperative pain [6]. However, because the Imai et al. study was limited to patients with distal radial fractures and had a follow up of only 1 month postoperatively, it is not known whether the pain trajectory can predict long-term pain intensity for patients who undergo a TKA. Their method is easily applied, but the values that could be used to predict CPSP were not established. If these issues could be resolved, early intervention to prevent the development of CPSP at clinical sites may be possible.
We conducted the present study to test the hypothesis that the pain trajectory method could be used to predict patients’ pain intensity at 1 year post-surgery (TKA). In addition, to make the predictions in clinical settings easier and to prevent the development of CPSP, we performed a decision tree analysis and calculated the cut-off values that can be used to predict CPSP.
Section snippets
Patients
Individuals with symptomatic knee osteoarthritis (OA) who were scheduled to undergo a TKA at a single facility (Kashiba Asahigaoka Hospital, Nara, Japan) were considered for inclusion in the study. Recruitment took place between February 2018 and March 2021. All patients underwent an X-ray examination and were screened for eligibility by an orthopedic surgeon. The inclusion criteria were as follows: age 50–90 years with radiographic knee OA (a score of ≥3 on the Kellgren–Lawrence (K-L)
Measurements
Pain intensity (walking pain) was assessed using a VAS sheet (0 indicating no pain, 100 indicating the highest possible degree of pain). The patients were asked to mark the sheet according to the level of subjective pain. The patients were instructed to record their pain intensity (walking pain) preoperatively and at 1, 3, 5, and 7 days post-surgery while in the hospital. They were also instructed to assess their walking pain at 1 year post-surgery. Thus, the pain intensity was recorded at six
The pain trajectory
With the use of the Excel spreadsheet (https://www.kawasakigakuen.ac.jp/files/xls/pain_trajectory_calculator.xlsx) created by Imai et al., we calculated the pain trajectory using the patients’ pain intensity values recorded on 1, 3, 5, and 7 days post-surgery [6]. This method has been shown to easily predict the pain intensity of postoperative patients. The pain trajectory is a longitudinal characterization of acute pain as a growth curve, normally resolving in intensity over a period of days,
Patient characteristics and clinical assessment
Fifty of the 287 recruited patients were excluded because they had severe chronic uncontrolled pain at a site other than the surgical knee before the surgery (n = 10), a stroke or other central nervous system disorder (n = 4), another surgery within the prior 3 months (n = 15), rheumatoid arthritis (n = 15), or dementia (n = 6); while 237 patients who had undergone a TKA surgery were then enrolled, 16 of these did not complete the evaluations. The final number of subjects for the analyses was
Discussion
The results of our SEM analysis suggest that the pain trajectory determined using both the pain curve slope and the intercept is useful for predicting postoperative pain at 1 year post-TKA. The CHAID results revealed that only 2.4% of patients with a pain curve slope less than −9.5, i.e. a highly negative slope or quickly declining VAS pain score in the week following surgery, experience chronic pain (>30 mm) at 1 year post surgery. The results also showed that 33.3% of those with a positive
Conclusions
Our present findings demonstrated that a patient’s self-reported pain intensity values over the first week after TKA surgery, i.e. the pain trajectory, can be used to predict the development of CPSP. When the pain curve slope is more than 2.8, or when the pain curve slope is between −9.5 and 2.8 and the patient is >77 years old, the improvement in pain intensity at 1 year after surgery is not certain. By considering the pain trajectory, clinicians may be able detect the risk of prolonged pain
Funding sources
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors wish to thank all of the patients examined in this study and the staff at the Department of Rehabilitation, Kashiba Asahigaoka Hospital, for their help with data collection.
Ethics Committee Letter
This study was approved by Institutional Ethics Committee of Kashiba Asahigaoka Hospital (2019051-002) and conformed to the guidelines of the 2008 Helsinki Declaration of Human Rights. We explained the purpose and protocol of the study to the participants and obtained their written informed consent.
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