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\begin{document}

\title{Nomogram for Predicting Chemotherapy-induced Nausea and Vomiting for
Breast Cancer Patients}



\author[1]{xinjuan Huang}%
\author[1]{Jinhua Li}%
\author[1]{Zheyu Hu}%
\author[1]{Lu Luo}%
\author[1]{Yan Tan}%
\author[1]{Hongyun Chen}%
\author[1]{Rongrong Fan}%
\author[1]{Tongyu Wang}%
\author[1]{Lingqi Meng}%
\author[1]{Tao Wei}%
\author[1]{Xuying Li}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 07, 2024}


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\begin{abstract}
Purpose: Chemotherapy-induced nausea and vomiting (CINV) is a common
side effect of cancer treatment. The factors influencing CINV in breast
cancer patients remain unclear. In this study we developed a nomogram
for predicting the occurrence of CINV in this group using prospective
clinical data from multiple sources Methods: We pooled data from several
studies conducted in China on CINV caused by emetogenic chemotherapy.
Demographic and clinical variables of 334 breast cancer patients at
Hunan Cancer Hospital (training set) were analyzed to identify factors
associated with CINV by multivariate logistic regression and construct a
prediction nomogram. The external validation set comprised an additional
66 patients. The reliability of the nomogram was assessed by bootstrap
resampling, and C-index and receiver operating characteristics curve
(ROC) analyses were carried out to assess its discriminatory power.
Results: Four risk factors were associated with CINV: history of CINV,
chemotherapy target regimen, metastasis, and symptoms of distress. The
C-index was 0.69 (95\% confidence interval {[}CI{]}, 0.63--0.75) for the
training set and 0.83 (95\% CI, 0.73--0.93) for the validation set. The
area under the ROC curve indicated that the model had a specificity of
68.5\% and 86.1\% and sensitivity of 57.7\% and 56.7\%, for the training
and validation sets, respectively. Calibration curves showed good
concordance between predicted and actual occurrence of CINV.
Conclusions: The developed nomogram can reliably predict the occurrence
of CINV in breast cancer patients based on 4 variables, which can aid in
clinical decision-making.%
\end{abstract}%



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