A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study
Abstract 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,...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-90814-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849712739986964480 |
|---|---|
| author | Siting Huang Aiqin Liu Xiaoruo Yu Zhifeng Qiu Guizhen Weng Dun Liu Yan Wang Yan Zhuo Liuqing Yao Mei Yang Hui Lin Xi Ke |
| author_facet | Siting Huang Aiqin Liu Xiaoruo Yu Zhifeng Qiu Guizhen Weng Dun Liu Yan Wang Yan Zhuo Liuqing Yao Mei Yang Hui Lin Xi Ke |
| author_sort | Siting Huang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-cf58cd31ad5b495c82171fb2c445a3f5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cf58cd31ad5b495c82171fb2c445a3f52025-08-20T03:14:10ZengNature PortfolioScientific Reports2045-23222025-04-0115111110.1038/s41598-025-90814-6A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control studySiting Huang0Aiqin Liu1Xiaoruo Yu2Zhifeng Qiu3Guizhen Weng4Dun Liu5Yan Wang6Yan Zhuo7Liuqing Yao8Mei Yang9Hui Lin10Xi Ke11Department of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Mengchao Hepatobiliary Hospital of Fujian Medical UniversityDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Oncology Nursing, Fujian Medical University Union HospitalThe School of Nursing, Fujian Medical UniversityDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Nursing, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalAbstract 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.https://doi.org/10.1038/s41598-025-90814-6Cancer-related painPrimary hepatic carcinomaBack-propagation artificial neural networksRisk factors |
| spellingShingle | Siting Huang Aiqin Liu Xiaoruo Yu Zhifeng Qiu Guizhen Weng Dun Liu Yan Wang Yan Zhuo Liuqing Yao Mei Yang Hui Lin Xi Ke A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study Scientific Reports Cancer-related pain Primary hepatic carcinoma Back-propagation artificial neural networks Risk factors |
| title | A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study |
| title_full | A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study |
| title_fullStr | A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study |
| title_full_unstemmed | A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study |
| title_short | A prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy: a multi-center prospective case‒control study |
| title_sort | prediction model for moderate to severe pain in primary hepatic carcinoma after chemotherapy a multi center prospective case control study |
| topic | Cancer-related pain Primary hepatic carcinoma Back-propagation artificial neural networks Risk factors |
| url | https://doi.org/10.1038/s41598-025-90814-6 |
| work_keys_str_mv | AT sitinghuang apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT aiqinliu apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT xiaoruoyu apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT zhifengqiu apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT guizhenweng apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT dunliu apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT yanwang apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT yanzhuo apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT liuqingyao apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT meiyang apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT huilin apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT xike apredictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT sitinghuang predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT aiqinliu predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT xiaoruoyu predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT zhifengqiu predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT guizhenweng predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT dunliu predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT yanwang predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT yanzhuo predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT liuqingyao predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT meiyang predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT huilin predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy AT xike predictionmodelformoderatetoseverepaininprimaryhepaticcarcinomaafterchemotherapyamulticenterprospectivecasecontrolstudy |