{"title":"剖宫产术后恶心呕吐的预测:一种结合胃超声的nomogram模型。","authors":"Yingchao Liu, Huohu Zhong, Zhisen Dai, Yuxin Huang, Yibin Liu, Hefan He, Yuewen Liao, Weifeng Liu","doi":"10.1186/s12871-025-02936-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>To investigate the independent risk factors associated with postoperative nausea and vomiting (PONV) following Cesarean section procedures, and establish and validate a nomogram to predict them.</p><p><strong>Methods: </strong>The clinical data of 116 adult patients who underwent Cesarean section procedures between August 2022 and February 2023 were included. Participants were randomly divided into training (n = 87) and verification sets (n = 29) in a 3:1 ratio. Univariate and multivariate logistic regression were used to analyze the risk factors for PONV following Cesarean sections and the independent risk factors were then used for the prediction model. Simultaneously, 29 adult patients who underwent caesarean section between February 2023 and April 2023 were included in the hospital as a test set to conduct external verification of the nomogram and Apfel scoring models, and compare their diagnostic efficacy in predicting PONV after caesarean section.</p><p><strong>Results: </strong>A history of motion sickness, systolic blood pressure reduction > 20%, and gastric volume were independent risk factors for PONV and used to construct the model. The AUC for predicting the risk of PONV in the training and validation sets was 0.814 (95% confidence interval [CI] = 0.709-0.918) and 0.792 (95% CI = 0.621-0.962), respectively. In the test set, the AUCs of the nomogram and the Apfel scoring models were 0.779 (95% CI = 0.593-0.965) and 0.547 (95% CI = 0.350-0.745), respectively, with the former being significantly higher (Z = 2.165, P < 0.05).</p><p><strong>Conclusions: </strong>Our nomogram model was superior to the Apfel scoring model and may be helpful in formulating appropriate individualized management strategies for nausea and vomiting following Cesarean sections, to promote the rapid recovery of patients.</p>","PeriodicalId":9190,"journal":{"name":"BMC Anesthesiology","volume":"25 1","pages":"64"},"PeriodicalIF":2.3000,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806823/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting postoperative nausea and vomiting after cesarean section: a nomogram model combined with gastric ultrasound.\",\"authors\":\"Yingchao Liu, Huohu Zhong, Zhisen Dai, Yuxin Huang, Yibin Liu, Hefan He, Yuewen Liao, Weifeng Liu\",\"doi\":\"10.1186/s12871-025-02936-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>To investigate the independent risk factors associated with postoperative nausea and vomiting (PONV) following Cesarean section procedures, and establish and validate a nomogram to predict them.</p><p><strong>Methods: </strong>The clinical data of 116 adult patients who underwent Cesarean section procedures between August 2022 and February 2023 were included. Participants were randomly divided into training (n = 87) and verification sets (n = 29) in a 3:1 ratio. Univariate and multivariate logistic regression were used to analyze the risk factors for PONV following Cesarean sections and the independent risk factors were then used for the prediction model. Simultaneously, 29 adult patients who underwent caesarean section between February 2023 and April 2023 were included in the hospital as a test set to conduct external verification of the nomogram and Apfel scoring models, and compare their diagnostic efficacy in predicting PONV after caesarean section.</p><p><strong>Results: </strong>A history of motion sickness, systolic blood pressure reduction > 20%, and gastric volume were independent risk factors for PONV and used to construct the model. The AUC for predicting the risk of PONV in the training and validation sets was 0.814 (95% confidence interval [CI] = 0.709-0.918) and 0.792 (95% CI = 0.621-0.962), respectively. In the test set, the AUCs of the nomogram and the Apfel scoring models were 0.779 (95% CI = 0.593-0.965) and 0.547 (95% CI = 0.350-0.745), respectively, with the former being significantly higher (Z = 2.165, P < 0.05).</p><p><strong>Conclusions: </strong>Our nomogram model was superior to the Apfel scoring model and may be helpful in formulating appropriate individualized management strategies for nausea and vomiting following Cesarean sections, to promote the rapid recovery of patients.</p>\",\"PeriodicalId\":9190,\"journal\":{\"name\":\"BMC Anesthesiology\",\"volume\":\"25 1\",\"pages\":\"64\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11806823/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Anesthesiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12871-025-02936-z\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ANESTHESIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Anesthesiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12871-025-02936-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:探讨与剖宫产术后恶心呕吐(PONV)相关的独立危险因素,并建立和验证预测其的nomogram方法。方法:收集2022年8月至2023年2月期间116例剖宫产手术的临床资料。参与者按3:1的比例随机分为训练集(n = 87)和验证集(n = 29)。采用单因素和多因素logistic回归分析剖宫产术后PONV的危险因素,并采用独立危险因素建立预测模型。同时选取2023年2月至2023年4月住院的29例剖宫产成人患者作为试验组,对nomogram和Apfel评分模型进行外部验证,比较其对剖宫产术后PONV的诊断效果。结果:晕车史、收缩压降低20%、胃容量是PONV的独立危险因素,并用于构建模型。训练集和验证集预测PONV风险的AUC分别为0.814(95%可信区间[CI] = 0.709-0.918)和0.792 (95% CI = 0.621-0.962)。在检验集中,nomogram和Apfel评分模型的auc分别为0.779 (95% CI = 0.593-0.965)和0.547 (95% CI = 0.35 -0.745),其中前者显著高于Apfel评分模型(Z = 2.165, P)。结论:我们的nomogram模型优于Apfel评分模型,可能有助于制定合适的剖宫产术后恶心呕吐的个性化管理策略,促进患者的快速康复。
Predicting postoperative nausea and vomiting after cesarean section: a nomogram model combined with gastric ultrasound.
Background: To investigate the independent risk factors associated with postoperative nausea and vomiting (PONV) following Cesarean section procedures, and establish and validate a nomogram to predict them.
Methods: The clinical data of 116 adult patients who underwent Cesarean section procedures between August 2022 and February 2023 were included. Participants were randomly divided into training (n = 87) and verification sets (n = 29) in a 3:1 ratio. Univariate and multivariate logistic regression were used to analyze the risk factors for PONV following Cesarean sections and the independent risk factors were then used for the prediction model. Simultaneously, 29 adult patients who underwent caesarean section between February 2023 and April 2023 were included in the hospital as a test set to conduct external verification of the nomogram and Apfel scoring models, and compare their diagnostic efficacy in predicting PONV after caesarean section.
Results: A history of motion sickness, systolic blood pressure reduction > 20%, and gastric volume were independent risk factors for PONV and used to construct the model. The AUC for predicting the risk of PONV in the training and validation sets was 0.814 (95% confidence interval [CI] = 0.709-0.918) and 0.792 (95% CI = 0.621-0.962), respectively. In the test set, the AUCs of the nomogram and the Apfel scoring models were 0.779 (95% CI = 0.593-0.965) and 0.547 (95% CI = 0.350-0.745), respectively, with the former being significantly higher (Z = 2.165, P < 0.05).
Conclusions: Our nomogram model was superior to the Apfel scoring model and may be helpful in formulating appropriate individualized management strategies for nausea and vomiting following Cesarean sections, to promote the rapid recovery of patients.
期刊介绍:
BMC Anesthesiology is an open access, peer-reviewed journal that considers articles on all aspects of anesthesiology, critical care, perioperative care and pain management, including clinical and experimental research into anesthetic mechanisms, administration and efficacy, technology and monitoring, and associated economic issues.