Frontiers in pediatrics · 2026
Prediction of revision adenoidectomy in pediatric patients: a 10-year retrospective study.
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.
Ge Yu-Ting, Yin Shi-Yao, Zhao Xing-He, Xu Yue, Zhu Hui-E
Аннотация
OBJECTIVE: This study aimed to identify risk factors for revision adenoidectomy in children and to develop and validate a clinical model for individualized risk prediction to support clinical decision-making.
METHODS: A total of 222 patients were selected via a nested case-control design from a source cohort of 16,457 children who underwent adenoidectomy at Children's Hospital of Soochow University. The dataset was randomly split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified through univariate and multivariate logistic regression analyses, and seven predictive models were constructed for comparison. The final model's performance was evaluated based on its discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration plot), and clinical utility (decision curve analysis, DCA).
RESULTS: Multivariate analysis identified six independent risk factors for revision adenoidectomy: adenoid size, surgical instrument type, concurrent tonsillotomy during the initial surgery, allergic rhinitis, otitis media with effusion, and perioperative antibiotic use. Among seven compared models, the logistic regression model showed the best discriminative ability with an AUC of 0.921 in the validation set. A nomogram and a freely accessible web application were developed to facilitate bedside risk calculation.
CONCLUSION: This study provides the first validated clinical prediction model for revision adenoidectomy in children. The model demonstrates excellent performance and can help clinicians accurately identify children at high risk of needing a revision surgery.
METHODS: A total of 222 patients were selected via a nested case-control design from a source cohort of 16,457 children who underwent adenoidectomy at Children's Hospital of Soochow University. The dataset was randomly split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified through univariate and multivariate logistic regression analyses, and seven predictive models were constructed for comparison. The final model's performance was evaluated based on its discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration plot), and clinical utility (decision curve analysis, DCA).
RESULTS: Multivariate analysis identified six independent risk factors for revision adenoidectomy: adenoid size, surgical instrument type, concurrent tonsillotomy during the initial surgery, allergic rhinitis, otitis media with effusion, and perioperative antibiotic use. Among seven compared models, the logistic regression model showed the best discriminative ability with an AUC of 0.921 in the validation set. A nomogram and a freely accessible web application were developed to facilitate bedside risk calculation.
CONCLUSION: This study provides the first validated clinical prediction model for revision adenoidectomy in children. The model demonstrates excellent performance and can help clinicians accurately identify children at high risk of needing a revision surgery.