Background & Aims

The end-stage treatment of knee osteoarthritis is total knee arthroplasty (TKA) surgery. Preoperative identification of patients at risk of high pain after TKA is warranted as this would allow for potential add-on therapies for these high-risk individuals.
Lifestyle factors, such as sleep quality, and cognitive factors, such as mood and symptoms of depression and anxiety, might be associated with pain after TKA surgery [1,2]. The number of comorbidities impacts chronic postoperative pain [3] but has not been studied in combination with cognitive factors and postoperative pain. Moreover, poor quality of sleep is often a consequence of surgery but the day-to-day interaction between postoperative sleep quality and clinical pain needs to be explored.
This study aimed to predict postoperative pain using measures of lifestyle, cognitive factors, and comorbidities. Additionally, we explored the interaction between postoperative sleep quality and clinical pain.

Methods

Patients scheduled for TKA surgery at Vejle Sygehus, Vejle, Denmark were contacted and asked to participate. Patients providing verbal consent to participate were sent a link to an online questionnaire preoperatively and then every day for the first seven postoperative days. The preoperative self-report assessments included the Fantastic Lifestyle Checklist (FANTASTIC), Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Scale (HADS), and Positive and Negative Affect Schedule (PANAS). Furthermore, baseline demographics, comorbidities, and clinical pain (Visual Analog Scale (VAS) 0-10) were assessed preoperatively. Every day for the first seven postoperative days the patients were inquired about their worst clinical pain (VAS 0-10) during the past 24 hours and their quality of sleep was noted as either good, neutral, or poor.

Results

A total of 97 patients (58 female) had complete data and were included in the analysis. The mean age of the participants was 66.1 (±8.6) with a mean BMI of 30.1 (±5.9) and 49% of the sample reported more than one comorbidity. A preliminary model using preoperative FANTASTIC, PSQI, HADS, PANAS, clinical pain, and the number of comorbidities predicted acute postoperative pain (P=0.05, R2=0.155). Utilizing backward elimination, a model combining preoperative pain and number of comorbidities predicted acute postoperative pain (P<0.01, R2=0.103). A total of 82 patients had data available for all postoperative days. At postoperative days 5, 6, and 7, patients reporting poor quality of sleep the previous night reported higher clinical pain the following days compared to patients with good quality of sleep (P<0.01).

Conclusions

The current exploratory analysis found that the number of comorbidities and preoperative pain explained approx. 10% of the variability of postoperative pain. In addition, a night with poor quality of sleep seems to be associated with higher postoperative pain the following day but only on postoperative days 5, 6, and 7.

References

[1]Boye Larsen D, Laursen M, Simonsen O, Arendt-Nielsen L, Petersen KK. The association between sleep quality, preoperative risk factors for chronic postoperative pain and postoperative pain intensity 12 months after knee and hip arthroplasty. Br J Pain 2021;15:486–96.
[2]Edwards RR, Haythornthwaite J a, Smith MT, Klick B, Katz JN. Catastrophizing and depressive symptoms as prospective predictors of outcomes following total knee replacement. Pain Res Manag 2009;14:307–11.
[3]Skrejborg P, Petersen KK, Kold S, Kappel A, Pedersen C, Østgaard SE, et al. Patients With High Chronic Postoperative Knee Pain 5 Years After Total Knee Replacement Demonstrate Low-grad Inflammation, Impairment of Function, and High Levels of Pain Catastrophizing. Clin J Pain 2021;37:161–7.

Presenting Author

Kristian Peterson

Poster Authors

Kristian Petersen, PhD

PhD, dr.med.

Aalborg University, Aalborg, Denmark

Lead Author

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Emma Hertel

Aalborg University

Lead Author

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Elisabeth Julie Poulsen

Aalborg University

Lead Author

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Maibrit Pape Bilenberg Sørensen

Aalborg University

Lead Author

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Jonas Loft Nielsen

Aalborg University

Lead Author

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Jacob Berthelsen Carlsbæk

Aalborg University

Lead Author

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Topics

  • Joint Pain