Huang Siting, Liu Aiqin, Yu Xiaoruo, Qiu Zhifeng, Weng Guizhen, Liu Dun, Wang Yan, Zhuo Yan, Yao Liuqing, Yang Mei, Lin Hui, Ke Xi
Department of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350122, Fujian Province, China.
Department of Nursing, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
Sci Rep. 2025 Apr 25;15(1):14415. doi: 10.1038/s41598-025-90814-6.
The incidence of moderate to severe pain after chemotherapy with primary hepatic carcinoma (PHC) patients is high. Although standardized treatment can effectively relieve pain, the control effect is poor. More attention should be paid to the prevention of pain at the beginning of symptoms, so as to reduce the incidence of pain and promote the health of patients. However, there are lack of a prospective design to predict pain before it occurs. The study is a prospective case‒control study. Population was PHC patients who received chemotherapy from April to August to 2024 in three grade 3 and first-class hospital. Data were collected in two periods (on the day of admission and within 24 h of chemotherapy). According to the Brief Pain Inventory, the patients were divided into case group and control group. Then the patients were randomly divided into a training group and an internal validation group at a 2:1 ratio. Single-factor logistics regression was used to analyze the risk factors, and the back-propagation artificial neural network (BP-ANN) model was constructed and verified. A total of 467 patients consisting of 312 training samples and 155 validation samples. BP-ANN model showed the AUC, sensitivity, specificity, and accuracy of prediction were 0.808, 70.6%, 81.7%, 93%, respectively. Internal verification also indicated these indicators were 0.783, 78.8%, 70.8%, and 94.2%, respectively. Significant predictors identified were age > 57.5, BMI > 19.9, symptoms of insomnia prior to illness, worker, Renvastinib, Child-Pugh = B, glutamic oxalacetic transaminase, other platinum drugs, cancer staging of IV, ECOG = 2, NRS-2002 = 3, Oxaliplatin, and Donafenib. The BP-ANN model holds high predictive value for the moderate to severe pain of PHC patients after chemotherapy. In the future, the model can be further visualized to facilitate clinical screening and to provide a basis for subsequent intervention.
原发性肝癌（PHC）患者化疗后中重度疼痛的发生率较高。虽然规范化治疗能有效缓解疼痛，但控制效果不佳。应在症状初期更加重视疼痛的预防，以降低疼痛发生率，促进患者健康。然而，目前缺乏前瞻性设计来在疼痛发生前进行预测。本研究是一项前瞻性病例对照研究。研究对象为2024年4月至8月在3家三级甲等医院接受化疗的PHC患者。在两个时间段（入院当天和化疗后24小时内）收集数据。根据简明疼痛量表，将患者分为病例组和对照组。然后将患者按2:1的比例随机分为训练组和内部验证组。采用单因素逻辑回归分析危险因素，并构建和验证反向传播人工神经网络（BP-ANN）模型。共有467例患者，其中312例为训练样本，155例为验证样本。BP-ANN模型显示预测的AUC、敏感性、特异性和准确性分别为0.808、70.6%、81.7%、93%。内部验证也表明这些指标分别为0.783、78.8%、70.8%和94.2%。确定的显著预测因素为年龄>57.5、BMI>19.9、病前失眠症状、职业、仑伐替尼、Child-Pugh=B级、谷草转氨酶、其他铂类药物、IV期癌症分期、ECOG=2、NRS-2002=3、奥沙利铂和多纳非尼。BP-ANN模型对PHC患者化疗后中重度疼痛具有较高的预测价值。未来，该模型可进一步可视化，以方便临床筛查，并为后续干预提供依据。
