DOI: https://doi.org/10.21203/rs.3.rs-1606355/v1
This retrospective study was conducted to identify predictors for developing hand-foot syndrome (HFS) and to determine new strategies for improving quality of life (QoL) in patients undergoing chemotherapy. Between April 2014 and August 2018, we enrolled 165 cancer patients at our outpatient chemotherapy center who were undergoing capecitabine chemotherapy. Variables related to the development of HFS were extracted from the clinical records of patients for use in regression analysis. HFS severity was assessed at the time of completing capecitabine chemotherapy. The degree of HFS was classified in accordance with the National Cancer Institute Common Terminology Criteria for Adverse Events version 5. Multivariate ordered logistic regression analysis was performed to identify predictors for the development of HFS. Significant predictors for the development of HFS included concomitant use of a renin angiotensin system (RAS) inhibitor (odds ratio [OR] = 2.85, 95% confidence interval [CI] = 1.20–6.79; P = 0.018), body surface area (BSA) (high) (OR = 12.7, 95%CI = 2.29–70.94; P = 0.004) and albumin (low) (OR = 0.44, 95%CI = 0.20–0.96; P = 0.040). In conclusion, concomitant use of RAS inhibitor, high BSA, and low albumin were identified as significant predictors for the development of HFS.
Hand-foot syndrome (HFS), also known as palmoplantar erythrodysesthesia, is a common adverse effect of the fluoropyrimidine chemotherapeutic agent capecitabine [1–3]. HFS of any grade is reported to affect 43–71% of patients treated with capecitabine monotherapy [3–5]. Moreover, 15–21% of these patients show HFS of Grade 3 or more [4, 5]. Although not life-threatening, this condition can have adverse effects on quality of life (QoL) and the daily living activities of a patient. The dose interruptions and reductions required after observation of HFS can also affect dose intensity and treatment outcomes. Despite its common occurrence, the pathophysiology of HFS is not well understood. Some reports have considered the accumulation of anticancer drugs in the eccrine glands of the palms and soles as one cause [6, 7]. Sweat is secreted by the release of acetylcholine from sympathetic nerves into the sweat gland, so anticholinergic drugs are said to be able to suppress perspiration [8]. Other studies have reported preventive effects on HFS for celecoxib [9, 10], angiotensin-converting enzyme inhibitors [11] and proton pump inhibitors (PPIs) [12]. However, no preventive measures against capecitabine-induced HFS have been established.
This retrospective study was conducted to identify predictors for the development of capecitabine-induced HFS, to facilitate the development of strategies to improve QoL among patients undergoing chemotherapy with capecitabine.
Of the 189 cancer patients who underwent chemotherapy with capecitabine, 24 were excluded from this study due to difficulties in the evaluations. Fourteen patients discontinued treatment after only one cycle, due to adverse events in 13 patients and due to progressive disease in 1 patient. Adverse events caused by chemotherapy included general fatigue in 4 patients, cholangitis in 1, chemotherapy-induced nausea and vomiting in 1, diarrhea in 1, allergy caused by oxaliplatin in 1, infusion reaction caused by trastuzumab in 1, esophagitis in 1, grade 2 HFS due to panituzumab in 1, anorexia in 1, and drug eruption in 1. Other reasons for exclusion were transfer to another hospital in 2 patients, non-compliance with capecitabine medication in 3 and insufficient data for the remaining. Table 1 presents the clinical characteristics of the 165 enrolled patients, the variables considered potentially related to the development of HFS, and the results of univariate analyses. The stepwise selection procedure identified three variables: renin angiotensin system (RAS) inhibitors (angiotensin-converting enzyme inhibitors [ACE-Is] or angiotensin receptor blockers [ARBs]), body surface area (BSA); and albumin. These variables were then used for multivariate ordered logistic regression analysis. Factors identified as significantly correlated to the development of HFS included concomitant use of RAS inhibitor (odds ratio [OR] = 2.85, 95% confidence interval [CI] = 1.20–6.79; P = 0.018), high BSA (OR = 12.7, 95%CI = 2.29–70.94; P = 0.004) and low albumin (OR = 0.44, 95%CI = 0.20–0.96; P = 0.040). None of PPIs, anticholinergic drugs or celecoxib were predictive factors. The accuracy of our model, determined as the ratio of patients for whom the expected value was equal to the observed value, was 112/165 (Table 2). Receiver operating characteristic curve (ROC) [13] analysis of the group likely to develop HFS of grade ≥ 2 revealed thresholds for BSA and albumin of 1.68 m2 and 3.9 g/dL, with sensitivities of 45.8% and 58.3% and specificities of 78.7% and 63.1% (areas under the curve = 0.5703 and 0.6113), respectively. Univariate analysis also revealed stomatitis as significantly likely to develop at the onset of HFS.
| Grade 0 (n = 32) | Grade 1 (n = 109) | Grade 2 (n = 24) | P | Odds ratio (95%CI) | |
|---|---|---|---|---|---|
| Demographic data | |||||
| Age (y), median (range) | 68 (41–86) | 67 (36–86) | 67 (43–79) | 0.844 | 1.00 (0.97–1.03) |
| Sex (male), n (%) | 14 (43.8) | 59 (54.1) | 14 (58.3) | 0.254 | 1.45 (0.77–2.74) |
| Physical/physiological parameters | |||||
| Height (cm), median (range) | 158.6 (140.7–178.4) | 161.8 (139.7–181) | 168.5 (145.8–181.1) | 0.024* | 1.04 (1.01–1.08) |
| Body weight (kg), median (range) | 51.2 (35–78) | 55.5 (32.6–90.4) | 61.5 (35.4–116) | 0.006* | 1.04 (1.01–1.06) |
| Body mass index (kg/m2), median (range) | 20.9 (13.4–27.6) | 21.7 (14.6–33.1) | 21.3 (16.4–37.0) | 0.050 | 1.09 (1.00–1.19) |
| Body surface area (m2), median (range) | 1.45 (1.18–1.84) | 1.54 (1.14–1.99) | 1.62 (1.18–2.27) | 0.005* | 11.19 (2.08–60.27) |
| ECOG-PS (at the beginning of therapy) (0/1/2/3) | 25/5/2 | 93/15/1/0 | 14/9/1/0 | 0.241 | 1.50 (0.76–2.93) |
| Cancer type | |||||
| Colon, n (%) | 28 (87.5) | 99 (90.8) | 22 (91.7) | 0.570 | 1.36 (0.47–3.92) |
| Gastric, n (%) | 4 (12.5) | 10 (9.2) | 2 (8.3) | 0.570 | 0.74 (0.26–2.12) |
| Comorbidity | |||||
| Diabetes mellitus, n (%) | 2 (6.3) | 21 (19.3) | 6 (25.0) | 0.062 | 2.23 (0.96–5.19) |
| Daily dose (mg) of capecitabine, median (range) | 2400 (1800–4800) | 3000 (1200–4200) | 3000 (1200–4200) | 0.836 | 1.02 (0.88–1.17) |
| Number of cycles, median (range) | 8 (2–13) | 8 (2–55) | 8 (2–40) | 0.118 | 1.04 (0.99–1.09) |
| log(Total dosage, mg) | 12.5 (11.1–13.7) | 12.6 (10.8–14.4) | 12.6 (11.2–14.3) | 0.256 | 1.37 (0.79–2.38) |
| Pre-treatment history of chemotherapy, n (%) | 1 (3.1) | 28 (25.7) | 6 (25.0) | 0.035* | 2.35 (1.06–5.18) |
| Regimen | |||||
| Capecitabine monotherapy, n (%) | 3 (9.4) | 11 (10.1) | 6 (25.0) | 0.089 | 2.33 (0.88–6.15) |
| XELOX, n (%) | 29 (90.6) | 97 (89.0) | 18 (75.0) | 0.095 | 0.44 (0.17–1.15) |
| Chemotherapy with bevacizumab, n (%) | 8 (25.0) | 43 (39.4) | 11 (45.8) | 0.097 | 1.76 (0.90–3.41) |
| Concomitant medication | |||||
| Renin-angiotensin system inhibitors, n (%) | 2 (6.3) | 19 (17.4) | 8 (33.3) | 0.009* | 3.10 (1.32–7.26) |
| Calcium channel blockers, n (%) | 3 (9.4) | 9 (8.3) | 5 (20.8) | 0.190 | 2.01 (0.71–5.68) |
| Cholinergic blocking agent, n (%) | 10 (31.3) | 37 (33.9) | 6 (25.0) | 0.698 | 0.87 (0.45–1.72) |
| Non-steroidal anti-inflammatory drugs, n (%) | 2 (6.3) | 9 (8.3) | 5 (20.8) | 0.075 | 2.63 (0.91–7.64) |
| Cyclooxygenase-2 inhibitor, n (%) | 1 (3.1) | 5 (4.6) | 3 (12.5) | 0.133 | 2.85 (0.73–11.20) |
| Celecoxib, n (%) | 1 (3.1) | 5 (4.6) | 2 (8.3) | 0.379 | 1.93 (0.45–8.33) |
| Proton pump inhibitor, n (%) | 9 (28.1) | 32 (29.4) | 8 (33.3) | 0.689 | 1.15 (0.58–2.30) |
| Laboratory test values | |||||
| Alanine aminotransferase, IU/L, median (range) | 14 (4–51) | 16 (5–131) | 16 (7–74) | 0.492 | 1.01 (0.99–1.03) |
| Total bilirubin, mg/dL, median (range) | 0.64 (0.33–1.25) | 0.69 (0.39–1.65) | 0.66 (0.34–1.89) | 0.262 | 1.99 0.60–6.65 |
| Serum creatinine, mg/dL, median (range) | 0.66 (0.38–1.21) | 0.73 (0.42–1.77) | 0.70 (0.27–1.13) | 0.582 | 1.50 (0.35–6.41) |
| Albumin, g/dL, median (range) | 4.1 (2.9–4.6) | 4.0 (2.7–4.9) | 3.9 (2.3–4.5) | 0.048 | 0.47 (0.22–0.99) |
| Creatinine clearance, mL/min, median (range) | 67.4 (37.7–124.8) | 75.0 (22.3–145.2) | 76.9 (44.0–220.1) | 0.025* | 1.01 (1.00–1.02) |
| C-reactive protein, mg/L, median (range) | 0.08 (0.01–1.71) | 0.11 (0.01–10.11) | 0.18 (0.01–3.35) | 0.305 | 1.19 (0.85–1.67) |
| Hemoglobin, g/dL, median (range) | 12.6 (9–16.3) | 12.1 (7.6–16.4) | 11.6 (9.4–15.2) | 0.379 | 0.91 (0.74–1.12) |
| Concomitant symptoms | |||||
| Stomatitis (0/1/2/3) ** | 29/3/0/0 | 76/24/9/0 | 12/4/8/0 | < .0001* | 2.84 (1.71–4.73) |
| Diarrhea (0/1/2/3) ** | 26/3/3/0 | 95/11/2/1 | 17/4/2/1 | 0.341 | 1.30 (0.76–2.25) |
| General fatigue (0/1/2/3) ** | 16/13/3/0 | 38/67/4/0 | 8/8/8/0 | 0.012* | 1.95 (1.16–3.28) |
| CI, confidence interval; ECOG-PS, Eastern Cooperative Oncology Group performance status; XELOX, capecitabine and oxaliplatin | |||||
| *P < 0.05 | |||||
| **Evaluated by National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) | |||||
| Variable | P | Odds ratio | 95%CI | |
|---|---|---|---|---|
| Lower 95% | Upper 95% | |||
| Renin-angiotensin system inhibitors | 0.018* | 2.85 | 1.20 | 6.79 |
| BSA | 0.004* | 12.7 | 2.29 | 70.94 |
| Albumin | 0.040* | 0.44 | 0.20 | 0.96 |
| CI, confidence interval; BSA, body surface area | ||||
| *P < 0.05 | ||||
The multivariate ordered logistic regression analysis performed in this study clarified that significant predictors for the development of HFS included concomitant use of RAS inhibitor, high BSA, and low albumin. PPIs, anticholinergic drugs and celecoxib were not extracted as predictors. The combined use of anticholinergic agents was associated with a decrease in the incidence of capecitabine-induced HFS in univariate analysis, but was not significant in multivariate analysis. ROC curve analysis of factors potentially responsible for the development of HFS Grade ≥ 2 indicated thresholds of BSA ≥ 1.68 m2 and albumin ≤ 3.9 g/dL.
This study determined that the optimal BSA cut-off value for the occurrence of HFS was ≥ 1.68 m2. The dose of capecitabine is determined based on the BSA. These results are therefore consistent with the findings of previous studies that reported the development of HFS as dose-dependent [10, 13, 14]. Clinicians thus need to be alert to the fact that HFS is likely to occur in patients receiving high doses of capecitabine, particularly patients with BSA ≥ 1.68 m2. The predictive model of HFS in patients receiving capecitabine allows the prediction of toxicity risk based on cumulative capecitabine dose [15]. In this study, HFS also tended to develop in a dose-dependent manner, but the actual dose of capecitabine was not significant. In addition, weight loading may contribute to the exacerbation of HFS in the foot.
The present study suggested an albumin cut-off for the occurrence of HFS of ≤ 3.9 g/dL. Previous studies have clarified that skin inflammation is difficult to heal if nutritional status is poor [16]. Our results agree on this point. When administering chemotherapy, good nutritional status is important.
Concomitant use of RAS inhibitor was a significant risk factor for the occurrence of HFS. RAS inhibitors with ACE-Is or type II ARBs protect the vascular endothelium by decreasing the concentration of angiotensin II, which inhibits nitric oxide (NO) production and activity. RAS inhibitors prevent angiogenesis through increased NO production. NO has been suggested to be involved in dermatitis and inflammatory skin diseases caused by various inflammatory stimuli, such as exposure to ultraviolet light [17]. NO has also been reported to be involved in inflammatory skin diseases. NO regulates the expression of various genes involved in differentiation, proliferation, apoptosis, wound healing and angiogenesis [18–21]. On the other hand, NO promotes local accumulation of neutrophils [22], which not only help protect against infection but also promote local chronic inflammation (tissue damage) and exacerbate symptoms. RAS inhibitors may also dilate peripheral blood vessels and promote the transfer of capecitabine to peripheral tissues. Our results differed from those of previous studies in which ACE-I was a preventive factor for HFS [11]. Further verification is needed on this point.
Development of stomatitis showed a significant correlation with the onset of HFS. The mechanisms of onset for stomatitis and HFS may be similar, and further investigation of this issue is needed.
Several limitations of the present study need to be considered. First, the retrospective nature of our investigation may have decreased the reliability of the data collected. Second, as this study only involved patients treated at a single institute, the cohort size was relatively small. A larger, multicenter study is needed to confirm our findings. Third, potential confounding, selection, and information biases cannot be fully excluded in this study.
In conclusion, we used a statistical approach to identify significant predictors for the development of HFS, including concomitant use of RAS inhibitors, high BSA, and low albumin. However, these preliminary findings need to be confirmed in further studies. Nevertheless, the identification of potential predictors of HFS may assist in the development of strategies that can be used to improve QoL in patients receiving chemotherapy regimens that include capecitabine.
This retrospective study enrolled 189 cancer patients who had undergone capecitabine monotherapy (capecitabine, 1000 mg/m2 orally twice daily for 14 days followed by 7 days off), CapeOX (capecitabine, 1000 mg/m2 orally twice daily for 14 days followed by 7 days off; oxaliplatin, 130 mg/m2 intravenously on day 1 every 3 weeks) or XELIRI (irinotecan 200 mg/m2 on day 1 and oral capecitabine 1000 mg/m2 bid on days 1–14) every 3 weeks at our outpatient chemotherapy center between April 2014 and August 2018. The Medical Ethics Review Committee of the Kyoto Prefectural University of Medicine approved this study (approval no. ERB-C-867-4). All procedures were performed in accordance with the ethical standards of the Kyoto Prefectural University of Medicine Institutional Medical Ethics Review Committee and the 1964 Declaration of Helsinki and its later amendments. No prospective studies with human participants or animals were performed by any of the authors for this article as in our previous study [23]. Given the retrospective nature of this work, the need to obtain informed consent from the individual participants was waived by the Kyoto Prefectural University of Medicine Institutional Medical Ethics Review Committee.
Variables were extracted from clinical records and used for regression analysis. Variables extracted were factors potentially affecting HFS based on previous studies [7–12] and clinical significance, including demographic data (sex, age, height, weight, body mass index, and BSA), Eastern Cooperative Oncology Group performance status, cancer type, presence of comorbidity (diabetes mellitus), daily dose (in milligrams), number of cycles, total dosage, pre-treatment history of chemotherapy, regimen, concomitant medications, laboratory test values and concomitant symptoms.
Creatinine clearance was estimated using the Cockcroft and Gault equation based on serum creatinine, sex, age, and weight. Clinical information was extracted before the first dose of capecitabine. Concomitant medication was defined as administration of another drug for ≥2 weeks at the time of evaluation.
Severity of HFS was assessed at the completion of capecitabine therapy. The degree of HFS was classified in accordance with the National Cancer Institute Common Terminology Criteria for Adverse Events version 5.
Variables were examined for multicollinearity (correlation coefficient |r| ≥ 0.7), as correlations existing among variables can lead to the use of an inappropriate model. In the first step, we performed univariate ordered logistic regression analysis between the outcomes and each potential independent variable. We then constructed a multivariate ordered logistic regression model using the stepwise selection procedure among the potential candidate variables. P-levels for the model used a variable entry criterion of 0.25 and a variable retention criterion of 0.15. The degree of HFS was evaluated using a graded scale, as multiple factors could potentially be predictors for the development of HFS, thereby necessitating simultaneous evaluation of the factors. To overcome this issue, we performed an ordered logistic regression analysis. Threshold measurements were examined using an ROC curve [24]. All statistical analyses were performed using JMP® version 14.3.0 (SAS Institute, Cary, NC, USA) with a two-sided significance level of 0.05.
Data availability statement
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Data on individual patients are not available due to ethical restrictions.
Acknowledgements
We wish to thank all the patients and medical staff at University Hospital, Kyoto Prefectural University of Medicine who were involved in this study.
Author contributions
Y.K.: concept and design, data acquisition, data analysis, data interpretation, and manuscript writing; T.T. and T.I.: concept and design, data acquisition, data analysis, data interpretation; K.T.: concept and design, data interpretation and supervision of the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare no competing interests.