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. 2022 Feb 26;12(3):360.
doi: 10.3390/jpm12030360.

Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma

Hsin-Jung Tsai  1   2 , Wen-Kuei Chang  1   2 , Fang-Yu Yen  2   3 , Shih-Pin Lin  1   2 , Tzu-Ping Lin  2   4 , Kuang-Yi Chang  1   2
Affiliations

Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma

Hsin-Jung Tsai et al. J Pers Med. .

Abstract

Background: Renal cell carcinoma (RCC) is the most common neoplasm in kidneys, and surgical resection remains the mainstay treatment. Few studies have investigated how the postoperative pain changes over time and what has affected its trajectory. This study aimed to characterize the variations in postoperative pain over time and investigate associated factors after RCC surgery. Methods: This retrospective study was conducted in a single medical center in Taiwan, where maximal pain scores in a numeric rating scale were recorded daily in the first five postoperative days (PODs) after RCC surgery. Latent curve models were developed, using two latent variables, intercept and slope, which represented the baseline pain and rate of pain resolution. These models explain the variations in postoperative pain scores over time. A predictive model for postoperative pain trajectories was also constructed. Results: There were 861 patients with 3850 pain observations included in the analysis. Latent curve analysis identified that female patients and those with advanced cancer (stage III and IV) tended to have increased baseline pain scores (p = 0.028 and 0.012, respectively). Furthermore, patients over 60 years, without PCA use (both p < 0.001), and with more surgical blood loss (p = 0.001) tended to have slower pain resolution. The final predictive model fit the collected data acceptably (RMSEA = 0.06, CFI = 0.95). Conclusion: Latent curve analysis identified influential factors of acute pain trajectories after RCC surgery. This study may also help elucidate the complex relationships between the variations in pain intensity over time and their determinants, and guide personalized pain management after surgery for RCC.

Keywords: epidural analgesia; latent curve model; pain trajectory; patient-controlled analgesia; renal cell carcinoma.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The basic latent curve model for pain trajectories after surgery for renal cell carcinoma. (A) Hypothetical basic model. (B) Actual basic model.
Figure 2
Figure 2
The observed maximal pain scores with their standard deviations and predicted values of the basic latent curve model after surgery for renal cell carcinoma.
Figure 3
Figure 3
The final predictive model for pain trajectories after surgery for renal cell carcinoma.* On base-2 logarithmic scale.
See this image and copyright information in PMC

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