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Development of a Prediction Model for Intraoperative Blood Pressure Variability

NCT05698433 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 47520

Last updated 2023-06-22

No results posted yet for this study

Summary

Objective: The aim of this study was to use machine learning to predict and interpret intraoperative high blood pressure variability（IHBPV）.

Design: Retrospective cohort study. Setting: Beijing Tsinghua Chang Gung Hospital . Data resources: 47520 operations performed under general anesthesia in the central operating room from March 2016 to April 2022.

Interventions: None. Measurements: investigators collected data on preoperative baseline information and intraoperative variables. The model was constructed with python and run using the following models: XGBoost, random forest, LGBoost, and logistic regression.

Conditions

  • Blood Pressure Immeasurable

Interventions

OTHER

No intervention

No intervention

Sponsors & Collaborators

  • Beijing Tsinghua Chang Gung Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-02
Primary Completion
2023-04-01
Completion
2023-04-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05698433 on ClinicalTrials.gov