Background: Pain is one of the most terrifying symptoms for cancer patients. Although most patients with cancer pain need opioids, complete relief of pain is hard to achieve. This study investigated the factors influencing persistent pain-free survival (PPFS) and opioid efficiency. Materials and Met...
Background: Pain is one of the most terrifying symptoms for cancer patients. Although most patients with cancer pain need opioids, complete relief of pain is hard to achieve. This study investigated the factors influencing persistent pain-free survival (PPFS) and opioid efficiency. Materials and Methods: A prospective study was conducted on 100 patients with cancer pain, hospitalized at the medical oncology clinic of Akdeniz University. Patient records were collected including patient demographics, the disease, treatment characteristics, and details of opioid usage. Pain intensity was measured using a patient self-reported visual analogue scale (VAS). The area under the curve (AUC) reflecting the pain load was calculated from daily VAS tables. PPFS, the primary measure of opioid efficacy, was described as the duration for which a patient reported a greater than or equal to two-point decline in their VAS for pain. Predictors of opioid efficacy were analysed using a multivariate analysis. Results: In the multivariate analysis, PPFS was associated with the AUC for pain (Exp (B)=0.39 (0.23-0.67), P=0.001), the cumulative opioid dosage used during hospitalisation (Exp (B)=1.00(0.99-1.00), P=0.003) and changes in the opioid dosage (Exp (B)=1.01 (1.00-1.01), P=0.016). The change in VAS score over the standard dosage of opioids was strongly associated with current cancer treatment (chemotherapy vs. others) (${\beta}=-0.31$, T=-2.81, P=0.007) and the VAS for pain at the time of hospitalisation (${\beta}=-0.34$, T=-3.07, P= 0.003). Conclusions: The pain load, opioid dosage, concurrent usage of chemotherapy and initial pain intensity correlate with the benefit received from opioids in cancer patients.
Background: Pain is one of the most terrifying symptoms for cancer patients. Although most patients with cancer pain need opioids, complete relief of pain is hard to achieve. This study investigated the factors influencing persistent pain-free survival (PPFS) and opioid efficiency. Materials and Methods: A prospective study was conducted on 100 patients with cancer pain, hospitalized at the medical oncology clinic of Akdeniz University. Patient records were collected including patient demographics, the disease, treatment characteristics, and details of opioid usage. Pain intensity was measured using a patient self-reported visual analogue scale (VAS). The area under the curve (AUC) reflecting the pain load was calculated from daily VAS tables. PPFS, the primary measure of opioid efficacy, was described as the duration for which a patient reported a greater than or equal to two-point decline in their VAS for pain. Predictors of opioid efficacy were analysed using a multivariate analysis. Results: In the multivariate analysis, PPFS was associated with the AUC for pain (Exp (B)=0.39 (0.23-0.67), P=0.001), the cumulative opioid dosage used during hospitalisation (Exp (B)=1.00(0.99-1.00), P=0.003) and changes in the opioid dosage (Exp (B)=1.01 (1.00-1.01), P=0.016). The change in VAS score over the standard dosage of opioids was strongly associated with current cancer treatment (chemotherapy vs. others) (${\beta}=-0.31$, T=-2.81, P=0.007) and the VAS for pain at the time of hospitalisation (${\beta}=-0.34$, T=-3.07, P= 0.003). Conclusions: The pain load, opioid dosage, concurrent usage of chemotherapy and initial pain intensity correlate with the benefit received from opioids in cancer patients.
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
문제 정의
However, pain complexity and the heterogeneity of patient response to opioid analgesics are important barriers for optimal pain control in cancer patients. Therefore, the objective of this study is to identify additional factors that may influence pain-free survival as well as factors that are predictive of patient response to opioid analgesics.
제안 방법
Patients were included in the study, whether they were hospitalized for pain palliation or another reason, if they reported cancer related pain regardless of pain medications they were using. Patient records were collected for information regarding patient age, gender, occupation, social support, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, nutritional status, cancer type, stage, details of previous therapies, reason for hospitalisation, pain localisation, pain treatment details and opioid dosage used.
대상 데이터
Of the 100 patients included in the present study, 63 were male, 37 were female, and the median age of all patients was 56. The most common cancer diagnoses were lung (30%), gastrointestinal (28%), breast (9%) and gynaecologic (9%) cancers, and 89% of patients were diagnosed with tumour metastasis.
The study population included 100 cancer patients who were hospitalised at the medical oncology clinic of Akdeniz University between August 2009 and January 2010. Patients were included in the study, whether they were hospitalized for pain palliation or another reason, if they reported cancer related pain regardless of pain medications they were using.
데이터처리
The statistical significance of the associations between the PPFS and the analysed factors was evaluated by univariate analysis. Factors with P-values less than 0.10 were then tested in a multivariate Cox-regression analysis using a forward stepwise procedure. BMI and AUC were logarithmically transformed prior to their inclusion in the analysis.
BMI and AUC were logarithmically transformed prior to their inclusion in the analysis. Similarly, changes in the VAS while receiving the standard dosage of opioids and its relationship with the factors tested was first evaluated by univariate linear regression analysis, followed by multivariate linear regression analysis using a forward stepwise procedure for factors with P-values less than 0.10. Factors with P-values ≤0.
The statistical significance of the associations between the PPFS and the analysed factors was evaluated by univariate analysis. Factors with P-values less than 0.
이론/모형
Patients asked to report their pain intensity every morning by the nursing staff during the hospitalisation period. The level of pain perception was measured by a patient self-reported visual analogue scale (VAS) for pain intensity on a scale from 0 to 10. Pain burden was defined as the area under the curve (AUC) calculated from VAS-time tables (Parruti et al.
성능/효과
The multivariate model demonstrated that VAS for pain at the time of hospitalisation (ß=-0.34, T=-3.07, P=0.003) and the current cancer treatment regimen (chemotherapy vs. others) (ß=-0.31, T=-2.81, P=0.007) were independently associated with the outcome.
참고문헌 (21)
Aslan FE, Kayis A, Inanir I et al (2011). Prevelance of cancer pain in outpatients registered to a cancer chemotherapy center in Turkey. Asian Pac J Cancer Prev, 12, 1373-5.
Azevedo Sao Leao Ferreira K, Kimura M, Jacobsen Teixeira M (2006). The WHO analgesic ladder for cancer pain control, twenty years of use. How much pain relief does one get from using it? Support Care Cancer, 14, 1086-93.
Brant JM (2010). The global experience of cancer pain. Asian Pac J Cancer Prev, 1, 7-12.
Budkaew J, Chumworathayi B (2013). Knowledge and attitudes toward palliative terminal cancer care among Thai generalists. Asian Pac J Cancer Prev, 14, 6173-80.
Colak D, Oguz A, Yazilitas D, Imamoglu IG, Altinbas M (2014). Morphine: patient knowledge and attitudes in the central Anatolia part of Turkey. Asian Pac J Cancer Prev, 15, 4983-8.
Fainsinger RL, Fairchild A, Nekolaichuk C et al (2009). Is pain intensity a predictor of the complexity of cancer pain management? J Clin Oncol, 27, 585-90.
Fainsinger RL, Nekolaichuk CL, Lawlor PG et al (2005). A multicenter study of the revised Edmonton Staging System for classifying cancer pain in advanced cancer patients. J Pain Symptom Manage, 29, 224-7.
Fainsinger RL, Nekolaichuk CL (2008). A "TNM" classification system for cancer pain: the Edmonton Classification System for Cancer Pain (ECS-CP). Support Care Cancer, 16, 547-55.
Green CR, Hart-Johnson T, Loeffler DR (2011). Cancer-related chronic pain: examining quality of life in diverse cancer survivors. Cancer, 117, 1994-2003.
Jokela R, Ahonen J, Tallgren M et al (2008). Premedication with pregabalin 75 or 150 mg with ibuprofen to control pain after day-case gynaecological laparoscopic surgery. Br J Anaesth, 100, 834-40.
Kanbayashi Y, Hosokawa T, Okamoto K et al (2011). Factors predicting requirement of high-dose transdermal fentanyl in opioid switching from oral morphine or oxycodone in patients with cancer pain. Clin J Pain, 27, 664-7.
Knudsen AK, Brunelli C, Kaasa S et al (2011). Which variables are associated with pain intensity and treatment response in advanced cancer patients?--Implications for a future classification system for cancer pain. Eur J Pain, 15, 320-7.
Liang SY, Chen KP, Tsay SL et al (2013). Relationship between belief about analgesics, analgesic adherence and pain experience in Taiwanese cancer outpatients. Asian Pac J Cancer Prev, 14, 713-6.
Liang SY, Wang TJ, Wu SF et al (2013). Gender differences associated with pain characteristics and treatment in Taiwanese oncology outpatients. Asian Pac J Cancer Prev, 14, 4077-82.
Parruti G, Tontodonati M, Rebuzzi C et al (2010). Predictors of pain intensity and persistence in a prospective Italian cohort of patients with herpes zoster: relevance of smoking, trauma and antiviral therapy. BMC Med, 8, 58.
van den Beuken-van Everdingen MHJ, de Rijke JM, Kessels AG et al (2007). Prevalence of pain in patients with cancer: a systematic review of the past 40 years. Ann Oncol, 18, 1437-49.
Vandervaart S, Berger H, Tam C et al (2011). The effect of distant reiki on pain in women after elective Caesarean section: a double-blinded randomised controlled trial. BMJ, 1, 21.
AI-Helper
※ AI-Helper는 부적절한 답변을 할 수 있습니다.