Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma
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 po...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4426/12/3/360 |
| _version_ | 1797446427764326400 |
|---|---|
| author | Hsin-Jung Tsai Wen-Kuei Chang Fang-Yu Yen Shih-Pin Lin Tzu-Ping Lin Kuang-Yi Chang |
| author_facet | Hsin-Jung Tsai Wen-Kuei Chang Fang-Yu Yen Shih-Pin Lin Tzu-Ping Lin Kuang-Yi Chang |
| author_sort | Hsin-Jung Tsai |
| collection | DOAJ |
| description | 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 (<i>p</i> = 0.028 and 0.012, respectively). Furthermore, patients over 60 years, without PCA use (both <i>p</i> < 0.001), and with more surgical blood loss (<i>p</i> = 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. |
| first_indexed | 2024-03-09T13:40:22Z |
| format | Article |
| id | doaj.art-7978c0d3a17a454e8e78db8401085a5a |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T13:40:22Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-7978c0d3a17a454e8e78db8401085a5a2023-11-30T21:07:22ZengMDPI AGJournal of Personalized Medicine2075-44262022-02-0112336010.3390/jpm12030360Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell CarcinomaHsin-Jung Tsai0Wen-Kuei Chang1Fang-Yu Yen2Shih-Pin Lin3Tzu-Ping Lin4Kuang-Yi Chang5Department of Anesthesiology, Taipei Veterans General Hospital, Taipei 112201, TaiwanDepartment of Anesthesiology, Taipei Veterans General Hospital, Taipei 112201, TaiwanSchool of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, TaiwanDepartment of Anesthesiology, Taipei Veterans General Hospital, Taipei 112201, TaiwanSchool of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, TaiwanDepartment of Anesthesiology, Taipei Veterans General Hospital, Taipei 112201, TaiwanBackground: 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 (<i>p</i> = 0.028 and 0.012, respectively). Furthermore, patients over 60 years, without PCA use (both <i>p</i> < 0.001), and with more surgical blood loss (<i>p</i> = 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.https://www.mdpi.com/2075-4426/12/3/360epidural analgesialatent curve modelpain trajectorypatient-controlled analgesiarenal cell carcinoma |
| spellingShingle | Hsin-Jung Tsai Wen-Kuei Chang Fang-Yu Yen Shih-Pin Lin Tzu-Ping Lin Kuang-Yi Chang Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma Journal of Personalized Medicine epidural analgesia latent curve model pain trajectory patient-controlled analgesia renal cell carcinoma |
| title | Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma |
| title_full | Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma |
| title_fullStr | Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma |
| title_full_unstemmed | Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma |
| title_short | Influential Factors and Personalized Prediction Model of Acute Pain Trajectories after Surgery for Renal Cell Carcinoma |
| title_sort | influential factors and personalized prediction model of acute pain trajectories after surgery for renal cell carcinoma |
| topic | epidural analgesia latent curve model pain trajectory patient-controlled analgesia renal cell carcinoma |
| url | https://www.mdpi.com/2075-4426/12/3/360 |
| work_keys_str_mv | AT hsinjungtsai influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma AT wenkueichang influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma AT fangyuyen influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma AT shihpinlin influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma AT tzupinglin influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma AT kuangyichang influentialfactorsandpersonalizedpredictionmodelofacutepaintrajectoriesaftersurgeryforrenalcellcarcinoma |