Prevalence, Clinical Characteristics, and Factors Associated with Capecitabine-Induced Hand-Foot Syndrome in Patients with Cancer: Evidence from Sudan.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY
Ola K Obeid, Bashir A Yousef, Yousif B Hamadalneel, Mawahib A Mustafa, Amani Abdelaziz Mahmoud, Elkhanssa Abdelhameed Ahmed Elhag
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引用次数: 0

Abstract

Background: Hand‒foot syndrome (HFS) is a common cutaneous toxicity of capecitabine that can impair treatment continuity and quality of life. This study aimed to assess the prevalence, clinical characteristics, and factors associated with capecitabine-induced HFS.

Methods: A cross-sectional analytical study was conducted at Khartoum Oncology Hospital, Sudan (June-October 2022), among adult patients with cancer receiving capecitabine monotherapy. Hand‒foot syndrome was evaluated using the National Cancer Institute Common Terminology Criteria for Adverse Events and a modified Black Patient Scale. Multivariable logistic regression was used to identify factors associated with HFS, with adjusted odds ratios (aORs) and 95% confidence intervals (CIs) reported. Data analysis was performed using SPSS version 27.

Results: A total of 135 patients were included in the analysis, 90 (66.7%) of whom were female and 52 (38.5%) were aged 56-70 years. The most common cancer type was breast cancer (n = 56) [41.5%]. The overall prevalence of HFS was 74 (54.8%), with 37 (50.0%) involving the hands only and 28 (37.8%) involving both the hands and feet. Onset occurred within 7-21 days in 47 (63.5%) patients. Pain was reported by 41 (30.4%), predominantly moderate by 20 (48.8). Grade I was the most frequent by National Cancer Institute criteria (n = 33) [44.5%] and the Black Patient Scale (n = 33) [44.5%]. Hand‒foot syndrome was more prevalent in female patients (n = 58) [64.4%, p = 0.001], patients with breast cancer (n = 35) [62.5%] (p = 0.049), and those receiving > 6 cycles (n = 6) [75%] (p = 0.006). After adjustment, female sex (aOR = 0.224, 95% CI 0.074-0.681; p = 0.008), patients without concomitant diseases (aOR = 2.389, 95% CI 1.011-5.646; p = 0.047), and patients receiving 4-6 cycles compared with those receiving 1-3 cycles (aOR = 0.359, 95% CI 0.155-0.828; p = 0.016) were independent predictors.

Conclusions: Capecitabine-induced HFS affects more than half of patients and generally occurs early in onset. After adjustment, female patients and those who received 4-6 chemotherapy cycles were less likely to experience HFS, whereas the absence of concomitant diseases was associated with a greater likelihood of developing HFS. Early monitoring and proactive supportive care remain essential to optimize treatment continuity.

癌症患者卡培他滨诱发手足综合征的患病率、临床特征和相关因素:来自苏丹的证据
背景:手足综合征(HFS)是卡培他滨常见的皮肤毒性,可影响治疗的连续性和生活质量。本研究旨在评估卡培他滨诱导HFS的患病率、临床特征和相关因素。方法:在苏丹喀土穆肿瘤医院(2022年6月至10月)对接受卡培他滨单药治疗的成年癌症患者进行横断面分析研究。手足综合征采用国家癌症研究所不良事件通用术语标准和修改后的黑人患者量表进行评估。采用多变量logistic回归确定与HFS相关的因素,并报告调整优势比(aORs)和95%置信区间(CIs)。数据分析采用SPSS 27版。结果:共纳入135例患者,其中女性90例(66.7%),年龄56 ~ 70岁52例(38.5%)。最常见的癌症类型是乳腺癌(n = 56)[41.5%]。HFS的总患病率为74例(54.8%),其中37例(50.0%)仅累及手部,28例(37.8%)累及手脚。47例(63.5%)患者在7 ~ 21天内发病。41例(30.4%)报告疼痛,20例(48.8%)报告中度疼痛。根据美国国家癌症研究所的标准(n = 33)[44.5%]和黑人患者量表(n = 33) [44.5%], I级是最常见的。手足综合征以女性患者(n = 58) [64.4%, p = 0.001]、乳腺癌患者(n = 35) [62.5%] (p = 0.049)和bbb6周期患者(n = 6) [75%] (p = 0.006)最为常见。调整后,女性(aOR = 0.224, 95% CI 0.074-0.681; p = 0.008)、无合并疾病患者(aOR = 2.389, 95% CI 1.011-5.646; p = 0.047)、接受4-6个周期与接受1-3个周期患者(aOR = 0.359, 95% CI 0.155-0.828; p = 0.016)为独立预测因子。结论:卡培他滨诱导的HFS影响超过一半的患者,通常发生在发病早期。调整后,女性患者和接受4-6个化疗周期的患者发生HFS的可能性较小,而无伴发疾病的患者发生HFS的可能性较大。早期监测和积极的支持性护理仍然是优化治疗连续性的关键。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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