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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT06445920
Other study ID # 2024ZSLYEC-201
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date May 30, 2024
Est. completion date June 15, 2024

Study information

Verified date June 2024
Source Sixth Affiliated Hospital, Sun Yat-sen University
Contact Zhinan Zheng, MD
Phone 0086-15915734893
Email zhengzhn5@mail.sysu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Postoperative nausea and vomiting (PONV) is a distressing and common complication after surgery. The concept of clinical important PONV (CI-PONV) assesses the impact of PONV on patient-reported outcomes. This research aims to conduct an analysis of the risk factors contributing to CI-PONV utilizing the least absolute shrinkage and selection operator (LASSO) and stepwise regression techniques. All 1154 patients participating in the FDP-PONV trial are included in this study and categorized into two groups: the CI-PONV group and the non-CI-PONV group. CI-PONV is defined as the occurrence of PONV with a simplified PONV impact scale score of 5 or higher within 24 hours after surgery. The LASSO method is employed to identify the most relevant variables from an initial set of 56 related variables and stepwise regression is used to further refine the selection of the ultimate predictors.


Description:

Drawing from prior studies, we conducted a sample size calculation for a predictive model using the website https://mvansmeden.shinyapps.io/BeyondEPV/. By setting the number of candidate predictors to 9, the events fraction to 0.14, and the criterion value for rMPSE to 0.04, we determined that a minimum total sample size of 900 is required, with a minimally expected event per variable of 13.9. All patients were classified into either the CI-PONV group or the non-CI-PONV group. All 56 perioperative clinical features, encompassing baseline characteristics, preoperative conditions, and intraoperative information, were considered as potential predictive factors. In the quest to uncover potential predictive factors associated with CI-PONV, we employed the least absolute shrinkage and selection operator (LASSO) to sift through clinically significant variables. Subsequently, we utilized stepwise regression based on the Akaike Information Criterion (AIC) to further refine the selection of the ultimate predictors.


Recruitment information / eligibility

Status Recruiting
Enrollment 1154
Est. completion date June 15, 2024
Est. primary completion date June 15, 2024
Accepts healthy volunteers No
Gender All
Age group 18 Years to 75 Years
Eligibility Inclusion Criteria: a) age between 18 and 75 years, b) having 3 or 4 Apfel risk factors, and c) scheduled to undergo laparoscopic gastrointestinal surgical procedures under general anesthesia. Exclusion Criteria: a) American Society of Anesthesiologists (ASA) physical status greater than 3, b) severe hepatic dysfunction, c) contraindications to fosaprepitant, 5-HT3 receptor antagonist, or dexamethasone, d) preoperative use of medications known to have antiemetic properties, e) presence of mental disorders or inability to communicate, and f) pregnant or nursing women.

Study Design


Related Conditions & MeSH terms


Intervention

Other:
No Intervention
No Intervention

Locations

Country Name City State
China Department of Anesthesia, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou Guangdong

Sponsors (1)

Lead Sponsor Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University

Country where clinical trial is conducted

China, 

References & Publications (14)

Apfel CC, Laara E, Koivuranta M, Greim CA, Roewer N. A simplified risk score for predicting postoperative nausea and vomiting: conclusions from cross-validations between two centers. Anesthesiology. 1999 Sep;91(3):693-700. doi: 10.1097/00000542-199909000-00022. — View Citation

Eberhart LH, Mauch M, Morin AM, Wulf H, Geldner G. Impact of a multimodal anti-emetic prophylaxis on patient satisfaction in high-risk patients for postoperative nausea and vomiting. Anaesthesia. 2002 Oct;57(10):1022-7. doi: 10.1046/j.1365-2044.2002.02822.x. — View Citation

Edgman-Levitan S, Schoenbaum SC. Patient-centered care: achieving higher quality by designing care through the patient's eyes. Isr J Health Policy Res. 2021 Mar 5;10(1):21. doi: 10.1186/s13584-021-00459-9. — View Citation

Gan TJ, Belani KG, Bergese S, Chung F, Diemunsch P, Habib AS, Jin Z, Kovac AL, Meyer TA, Urman RD, Apfel CC, Ayad S, Beagley L, Candiotti K, Englesakis M, Hedrick TL, Kranke P, Lee S, Lipman D, Minkowitz HS, Morton J, Philip BK. Fourth Consensus Guidelines for the Management of Postoperative Nausea and Vomiting. Anesth Analg. 2020 Aug;131(2):411-448. doi: 10.1213/ANE.0000000000004833. Erratum In: Anesth Analg. 2020 Nov;131(5):e241. — View Citation

Habib AS, Chen YT, Taguchi A, Hu XH, Gan TJ. Postoperative nausea and vomiting following inpatient surgeries in a teaching hospital: a retrospective database analysis. Curr Med Res Opin. 2006 Jun;22(6):1093-9. doi: 10.1185/030079906X104830. — View Citation

Hubbard RA, Su YR, Bowles EJ, Ichikawa L, Kerlikowske K, Lowry KP, Miglioretti DL, Tosteson ANA, Wernli KJ, Lee JM. Predicting five-year interval second breast cancer risk in women with prior breast cancer. J Natl Cancer Inst. 2024 Mar 11:djae063. doi: 10.1093/jnci/djae063. Online ahead of print. — View Citation

Kim JH, Cheon BR, Kim MG, Hwang SM, Lim SY, Lee JJ, Kwon YS. Postoperative Nausea and Vomiting Prediction: Machine Learning Insights from a Comprehensive Analysis of Perioperative Data. Bioengineering (Basel). 2023 Oct 1;10(10):1152. doi: 10.3390/bioengineering10101152. — View Citation

Liu J, Yu Y, Qi W, Ma X, Han Y. Innovation and entrepreneurship of Chinese returning migrant workers in their home region. Heliyon. 2024 Apr 26;10(9):e30296. doi: 10.1016/j.heliyon.2024.e30296. eCollection 2024 May 15. — View Citation

Myles PS, Wengritzky R. Simplified postoperative nausea and vomiting impact scale for audit and post-discharge review. Br J Anaesth. 2012 Mar;108(3):423-9. doi: 10.1093/bja/aer505. Epub 2012 Jan 29. — View Citation

Myles PS, Williams DL, Hendrata M, Anderson H, Weeks AM. Patient satisfaction after anaesthesia and surgery: results of a prospective survey of 10,811 patients. Br J Anaesth. 2000 Jan;84(1):6-10. doi: 10.1093/oxfordjournals.bja.a013383. — View Citation

Parra-Sanchez I, Abdallah R, You J, Fu AZ, Grady M, Cummings K 3rd, Apfel C, Sessler DI. A time-motion economic analysis of postoperative nausea and vomiting in ambulatory surgery. Can J Anaesth. 2012 Apr;59(4):366-75. doi: 10.1007/s12630-011-9660-x. Epub 2012 Jan 6. — View Citation

Portet S. A primer on model selection using the Akaike Information Criterion. Infect Dis Model. 2020 Jan 7;5:111-128. doi: 10.1016/j.idm.2019.12.010. eCollection 2020. — View Citation

Shaikh SI, Nagarekha D, Hegade G, Marutheesh M. Postoperative nausea and vomiting: A simple yet complex problem. Anesth Essays Res. 2016 Sep-Dec;10(3):388-396. doi: 10.4103/0259-1162.179310. — View Citation

Zhou CM, Wang Y, Xue Q, Yang JJ, Zhu Y. Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms. BMC Med Res Methodol. 2023 May 31;23(1):133. doi: 10.1186/s12874-023-01955-z. — View Citation

* Note: There are 14 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Clinically important postoperative nausea and vomiting (CI-PONV) The occurrence of PONV with the simplified PONV impact scale score of 5 or more. PONV is defined as PONV as the experience of nausea, retching, or vomiting during the first 24 hours following surgery. The first 24 hours after surgery
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