Dataset Information

Nomogram for predicting opioid-induced nausea and vomiting for cancer pain patients.

ABSTRACT:

Objective

Opioid-induced nausea and vomiting are frequently observed as an adverse effect in the treatment of cancer-related pain. The factors that affect OINV in cancer patients remain unclear. In this study, we developed a nomogram for predicting the occurrence of OINV in this population using retrospective clinical data.

Methods

We collected data from 416 cancer pain patients, 70% of whom used the training set to analyze demographic and clinical variables. We used multivariate logistic regression to identify significant factors associated with OINV. Then, we construct a prediction nomogram. The validation set comprises the remaining 30%. The reliability of the nomogram is evaluated by bootstrap resampling.

Results

Using multivariate logistic regression, we identified five significant factors associated with OINV. The C-index was 0.835 (95% confidence interval [CI], 0.828-0.842) for the training set and 0.810 (95% CI, 0.793-0.826) for the validation set. The calibrated curves show a good agreement between the predicted and actual occurrence of OINV.

Conclusion

In a retrospective study based on five saliency-found variables, we developed and proved a reliable nomogram model to predict OINV in cancer pain patients. Future prospective studies should assess the model's reliability and usefulness in clinical practice.

SUBMITTER: Kong L

PROVIDER: S-EPMC10620250 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nomogram for predicting opioid-induced nausea and vomiting for cancer pain patients.

Kong Lingping L Wang Jing J Guan Shasha S Chen Xiaochen X Li Meiqing M Gao Liming L Zhong Diansheng D Zhang Linlin L

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer 20231102 12

<h4>Objective</h4>Opioid-induced nausea and vomiting are frequently observed as an adverse effect in the treatment of cancer-related pain. The factors that affect OINV in cancer patients remain unclear. In this study, we developed a nomogram for predicting the occurrence of OINV in this population using retrospective clinical data.<h4>Methods</h4>We collected data from 416 cancer pain patients, 70% of whom used the training set to analyze demographic and clinical variables. We used multivariate ...[more]

PMID: 37914831

Similar Datasets

Project description:ObjectiveThis study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction.MethodsData from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross-validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model-Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score.ResultsThe deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780-0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non-standard antiemetic regimen.ConclusionThe deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC.

Project description:BackgroundDue to the worldwide rise in cancer incidence, and therefore the rise in the need for antineoplastic chemotherapy, it is important for both healthcare professionals and patients alike that the side effects of chemotherapy, such as chemotherapy-induced nausea and vomiting (CINV), are treated and prevented. Auriculotherapy is a type of acupuncture and may be a low-cost and safe antiemetic measure to control the side effects of chemotherapy. The goal of this systematic review is to synthesize the available evidence in the literature regarding the auriculotherapy effects to treat CINV in people with cancer.MethodsThe review will only include randomized controlled trials (RCTs) that compare the clinical effects of the auriculotherapy intervention (used alone or as an add-on), with sham auriculotherapy, routine treatment with antiemetic drugs, or other non-pharmacological interventions in patients with cancer with CINV who are undergoing chemotherapy. The outcomes to be evaluated are nausea and vomiting: in acute, delayed, or anticipated stages, when induced by chemotherapy. A comprehensive search for studies will be carried out in these databases: MEDLINE via PubMed, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, ICTRP, LILACS, CUMED, IBECS, BVS MTCI Americas, Web of Science, Scopus, PEDro, CNKI, and CBMdisc up until December 31, 2018. Only articles in English, Portuguese, and Spanish will be selected. Two independent reviewers will evaluate full texts, extract data, and assess the risk of bias of eligible articles. The quality of evidence will be assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). A meta-analysis will be undertaken to assess the interventions and outcomes' homogeneity, assessing statistical heterogeneity using the Cochran's Q test and quantified using Higgins' inconsistency index. If there is insufficient data for a meta-analysis, a narrative synthesis will be presented. This protocol has been prepared according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.DiscussionThe results of this systematic review will summarize the strength of evidence for the use of auriculotherapy in the control of CINV of patients with cancer and will be used to identify evidence gaps.Systematic review registrationPROSPERO CRD42018117513.

Dataset Information

Nomogram for predicting opioid-induced nausea and vomiting for cancer pain patients.

Objective

Methods

Results

Conclusion

Publications

Nomogram for predicting opioid-induced nausea and vomiting for cancer pain patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets