{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T13:03:21Z","timestamp":1776431001320,"version":"3.51.2"},"reference-count":60,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T00:00:00Z","timestamp":1713657600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Obstet and Gynaecol"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this study was to develop a web\u2010based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The patients (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk\u2010factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk\u2010factors, and the variables with statistical significance were selected in multivariable logistic model building. The web\u2010based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer\u2013Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ultimately, a total of five predictors including patient\u2010controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98\u201312.44), motion sickness (OR, 4.80; 95% CI, 2.71\u20138.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41\u20137.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44\u20133.43), and pain response (OR, 1.64; 95% CI, 1.48\u20131.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC\u2010areas under the curve, 93.0%; 95% CI, 90.7%\u201395.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jog.15956","type":"journal-article","created":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T23:45:11Z","timestamp":1713743111000},"page":"1216-1228","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A web\u2010based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic 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