European journal of medical research · 2025
Predictive model for meningitis after pituitary tumor resection by endoscopic nasal trans-sphenoidal sinus approach.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhou Peiyun, Shi Jianan, Long Zongke, Zhang Bingyan, Qu Wenran, Wei Huimin, Zhang Simeng, Luan Xiaorong
Аннотация
BACKGROUND: Meningitis is a significant complication following nasal trans-sphenoidal surgery for pituitary tumor resection. Meningitis increases hospital stays and costs, posing a burden to both patients and healthcare systems. This study aimed to develop a perioperative predictive model for meningitis based on key factors, such as the operation duration, tumor diameter, and intraoperative cerebrospinal fluid (CSF) leakage.
METHODS: A retrospective analysis was conducted on patients undergoing pituitary tumor resection via the nasal trans-sphenoidal approach. Predictive factors for meningitis, including operation duration, tumor diameter, and intraoperative CSF leakage, were analyzed. The model's predictive efficacy was evaluated using the collected data.
RESULTS: Meningitis occurred in 8.7% of cases (35/401). Intraoperative CSF leakage, observed in 24.2% of cases, significantly increased the risk of infection. The tumor diameter was also linked to higher infection rates. The constructed model demonstrated good predictive performance, allowing for early risk identification.
CONCLUSIONS: This study developed a predictive model for Meningitis after pituitary tumor resection using the operation duration, tumor diameter, and CSF leakage. The model provides healthcare professionals with an effective tool to assess infection risk and implement timely intervention strategies to improve patient outcomes.
METHODS: A retrospective analysis was conducted on patients undergoing pituitary tumor resection via the nasal trans-sphenoidal approach. Predictive factors for meningitis, including operation duration, tumor diameter, and intraoperative CSF leakage, were analyzed. The model's predictive efficacy was evaluated using the collected data.
RESULTS: Meningitis occurred in 8.7% of cases (35/401). Intraoperative CSF leakage, observed in 24.2% of cases, significantly increased the risk of infection. The tumor diameter was also linked to higher infection rates. The constructed model demonstrated good predictive performance, allowing for early risk identification.
CONCLUSIONS: This study developed a predictive model for Meningitis after pituitary tumor resection using the operation duration, tumor diameter, and CSF leakage. The model provides healthcare professionals with an effective tool to assess infection risk and implement timely intervention strategies to improve patient outcomes.