Risk Prediction Model for Radiation-induced Dermatitis in Patients with Cervical Carcinoma Undergoing Chemoradiotherapy.

PURPOSE: Radiation-induced dermatitis (RD) is a common side-effect of therapeutic ionizing radiation that can severely affect patient quality of life. This study aimed to develop a risk prediction model for the occurrence of RD in patients with cervical carcinoma undergoing chemoradiotherapy using electronic medical records (EMRs). METHODS: Using EMRs, the clinical data of patients who underwent simultaneous radiotherapy and chemotherapy at a tertiary cancer hospital between 2017 and 2022 were retrospectively collected, and the patients were divided into two groups: a training group and a validation group. A predictive model was constructed to predict the development of RD in patients who underwent concurrent radiotherapy and chemotherapy for cervical cancer. Finally, the model's efficacy was validated using a receiver operating characteristic curve. RESULTS: The incidence of radiation dermatitis was 89.5% (560/626) in the entire cohort, 88.6% (388/438) in the training group, and 91.5% (172/188) in the experimental group. The nomogram was established based on the following factors: age, the days between the beginning and conclusion of radiotherapy, the serum albumin after chemoradiotherapy, the use of single or multiple drugs for concurrent chemotherapy, and the total dose of afterloading radiotherapy. Internal and external verification indicated that the model had good discriminatory ability. Overall, the model achieved an area under the receiver operating characteristic curve of .66. CONCLUSIONS: The risk of RD in patients with cervical carcinoma undergoing chemoradiotherapy is high. A risk prediction model can be developed for RD in cervical carcinoma patients undergoing chemoradiotherapy, based on over 5 years of EMR data from a tertiary cancer hospital.

Corrigendum to "Evaluation of a whole process management model based on an information system for cancer patients with pain: A prospective nonrandomized controlled study" [Asia-Pacific J Oncol Nurs 9 (2022) 88-96].

[This corrects the article DOI: 10.1016/j.apjon.2021.12.017.].

Is Adjuvant Therapy a Better Option for Esophageal Squamous Cell Carcinoma Patients Treated With Esophagectomy? A Prognosis Prediction Model Based on Multicenter Real-World Data.

OBJECTIVE: To construct a prediction model for more precise evaluation of prognosis which will allow personalized treatment recommendations for adjuvant therapy in patients following resection of ESCC. BACKGROUND: Marked heterogeneity of patient prognosis and limited evidence regarding survival benefit of various adjuvant therapy regimens pose challenges in the clinical treatment of ESCC. METHODS: Based on comprehensive clinical data obtained from 4129 consecutive patients with resected ESCC in a high-risk region in China, we identified predictors for overall survival through a 2-phase selection based on Cox proportional hazard regression and minimization of Akaike information criterion. The model was internally validated using bootstrapping and externally validated in 1815 patients from a non-high-risk region in China. RESULTS: The final model incorporates 9 variables: age, sex, primary site, T stage, N stage, number of lymph nodes harvested, tumor size, adjuvant treatment, and hemoglobin level. A significant interaction was also observed between N stage and adjuvant treatment. N1+ stage patients were likely to benefit from addition of adjuvant therapy as opposed to surgery alone, but adjuvant therapy did not improve overall survival for N0 stage patients. The C -index of the model was 0.729 in the training cohort, 0.723 after bootstrapping, and 0.695 in the external validation cohort. This model outperformed the seventh edition American Joint Committee on Cancer staging system in prognostic prediction and risk stratification. CONCLUSIONS: The prediction model constructed in this study may facilitate precise prediction of survival and inform decision-making about adjuvant therapy according to N stage.

Evaluation of a whole process management model based on an information system for cancer patients with pain: A prospective nonrandomized controlled study.

Objective: The aim of this study was to evaluate the effects of whole process management model interventions based on information system benefits reported by patients with cancer pain. Methods: We performed a quantitative, prospective nonrandomized controlled design from June to October 2020. A total of 124 cancer patients with pain were enrolled. Patients in the experimental group received a whole process management model intervention based on an information system compared to the control group who received routine cancer pain management. Data were collected at baseline and after a four-week follow-up, acting as a test-retest control. The primary outcome was pain management quality, which was measured using the American Pain Society Patient Outcome Questionnaire-Chinese version (APS-POQ-C). Secondary outcomes were patient-related attitudinal barriers and analgesic adherence. The Barrier Questionnaire (BQ) and a single-item questionnaire were used. Chi-square tests were used to compare the pain intensity and analgesic adherence, independent sample t-test and Mann-Whitney U test were performed to test the differences in the pain management quality and patient-related attitudinal barriers between control and experimental groups. Results: Baseline characteristics and outcomes of the participants did not differ significantly (P â€‹> â€‹0.05). Primary outcomes were changes in four aspect of the quality of pain management (APS-POQ-C) between the two groups (P â€‹< â€‹0.05). Patients in the whole process management group reported significantly better pain control and perception of care than the control group. With respect to secondary endpoints, a significant difference in favor of the experimental group was found for barriers (P â€‹< â€‹0.05) and medication adherence (60.0% vs. 40.0%; P â€‹< â€‹0.05) after the interventions. Conclusions: The whole process management of patients with cancer pain effectively improves patient-reported quality of pain management, reduces patient-perceived barriers, enhances patient adherence to analgesic drugs and is worthy of clinical application.

An enrichment model using regular health examination data for early detection of colorectal cancer.

OBJECTIVE: Challenges remain in current practices of colorectal cancer (CRC) screening, such as low compliance, low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data. METHODS: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees (CART) algorithm. Receiver operating characteristic (ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening. RESULTS: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers (age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve (AUC) of the CART model was 0.88 [95% confidence interval (95% CI), 0.87-0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model's sensitivity was 62.2% (95% CI, 58.1%-66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value (1.6%) than fecal immunochemical test (0.3%). CONCLUSIONS: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare.

Demographic, occupational, and societal features associated with burnout among medical oncology staff members: Cross-sectional results of a Cancer Center in Beijing, China.

BACKGROUND: There is a growing recognition that medical staff members are exposed to job and life stressors that increase the risk of burnout. This study aimed to investigate the potential stressors among medical staff members working at a Cancer Center in Beijing and to explore the demographic, occupational, and societal features associated with burnout. METHODS: This was a cross-sectional study. The Maslach Burnout Inventory (MBI) survey was distributed to all medical staff members, along with an anonymous questionnaire to collect general information about demographic, occupational, and societal characteristics. The data were analyzed using T test, ANOVA, and multivariable linear regression. RESULTS: A total of 1096 of 1208 (91%) medical staff members completed the questionnaires, including 285 (26%) doctors and 572 (52%) nurses. The scores for emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) were 14.51 ± 9.78, 5.78 ± 5.59, and 35.07 ± 10.43, respectively. Domicile, being a nurse, working overtime, and low self-rated QoL were predictors of EE; Domicile, being a researcher, low self-rated health, low self-rated QoL, and bad colleague relationships were predictors of DP; Age, being a doctor or a nurse, low self-rated health, and low self-rated interpersonal relationships were predictors of low PA. CONCLUSION: Compared with the other occupations, doctors and nurses are more likely to experience burnout. Additionally, cultivating a better work environment, promoting the health and quality of life of staff, and improving rapport with colleagues may help to prevent burnout.