Constructing a Risk Prediction Model for Intraoperative Hypothermia in Children Under General Anesthesia

Официальное название

Construction of a Risk Prediction Model for Hypothermia During Surgery in Pediatric Under General Anesthesia Based on Machine Learning Algorithm

Обзор

Intraoperative hypothermia refers to a core body temperature below 36.0°C during surgery, which is common in surgical patients. Due to the fact that children's body temperature regulation function is not yet fully developed, they are light in weight, and their blood vessels are superficial, children are very susceptible to the influence of environmental temperature. With the effects of anesthetic drugs, exposure of the surgical field, and disinfection of the surgical area, children face a higher risk of intraoperative hypothermia than adult patients. Studies have shown that the incidence of intraoperative hypothermia can be as high as 80%. Intraoperative hypothermia can lead to increased adverse cardiovascular events, poor coagulation, slower healing of surgical incisions, or wound infection, threatening the health of children, resulting in prolonged postoperative hospitalization and increased hospitalization costs. Therefore, there is an urgent need to develop a tool for predicting intraoperative hypothermia suitable for children, identify high-risk groups early, and take preventive measures as soon as possible, thereby reducing a series of complications caused by hypothermia. The purpose of this study is to clarify the current status and risk factors affecting intraoperative hypothermia in children, to provide a theoretical basis for clinical medical staff to provide intraoperative thermal insulation care for children, and on this basis to construct an intraoperative hypothermia risk prediction model to identify the probability of hypothermia in children at an early stage, so as to take targeted thermal insulation measures.

Подробное описание

The study first clarified the overall incidence and influencing factors of IOH in children through meta-analysis, combined with expert consultation to determine potential predictive variables, and developed a data questionnaire for this study. The second part of the study will collect data prospectively, use four algorithms in machine learning, including logistic regression, decision tree, random forest, and support vector machine, to build an excellent intraoperative hypothermia risk prediction model, and conduct internal verification of the model. The third part of the study prospectively collects sample data to complete external verification of time period and space. The constructed model still has good predictive ability in the new data set. Finally, some models are visualized to form intuitive and easy-to-understand charts or interfaces.

Первичные конечные точки

• Core body temperature [Срок оценки: The investigators will measure the child's core body temperature when the child enters the operating room, after induction of anesthesia, and then every 30 minutes until the end of the operation.]

Критерии участия

Критерии включения

• Age < 16 years old;
• Anesthesia includes general anesthesia;
• Anesthesia duration > 30 minutes.

Критерии исключения

• Cardiac surgery or other procedures that require lowering the child's body temperature;
• Refusing to participate in this study;
• ASA grade >Ⅳ;
• Abnormal basal body temperature, preoperative core body temperature <36.0℃ or >38.0℃;
• Hyperthyroidism or hypothyroidism and other diseases that affect body temperature;
• The core body temperature cannot be measured due to surgery or ear canal disease.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• The Sixth Affiliated Hospital of Harbin Medical University — Харбин

Идентификаторы

NCT: NCT06892795 · LC2024-141 · YJSCX2024-124HYD

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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