Journal of thoracic disease · 2025
Establishment and validation of a risk prediction model for postoperative persistent cough in lung resection patients: a systematic review and meta-analysis-based 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.
Ye Lei, Xia Guanghui, Wu Guanghong, Ding Jiefang, Wang Qin
Аннотация
BACKGROUND: Postoperative cough is a prevalent complication in lung resection patients, exacerbating postoperative pain, impairing sleep and communication, and thereby reducing overall quality of life. Early identification of high-risk patients and targeted preventive measures are essential to reduce cough frequency, alleviate symptoms, facilitate recovery, and enhance quality of life. This study aimed to develop a predictive model to identify patients at risk of persistent cough following pulmonary resection.
METHODS: A systematic review and meta-analysis were performed to identify risk factors associated with persistent cough after pulmonary resection. In the predictive model, the natural logarithm of the pooled risk estimate for each factor was used as a coefficient to assign risk prediction scores. Data from 294 patients who underwent pulmonary resection between October and December 2024 were used to evaluate the predictive accuracy of the model.
RESULTS: The meta-analysis included 13 studies with 3,669 patients who underwent pulmonary resection, of whom 1,115 developed postoperative chronic cough. Seven significant predictors of persistent cough were identified: lobectomy, right-sided surgery, right upper lobectomy, subcarinal lymph node dissection, lymph node dissection around the bronchial tree, tracheal intubation time (≥172 min), and postoperative acid reflux. The model was validated using logistic regression analysis with an external cohort, yielding an area under the receiver operating characteristic curve of 0.87 (95% confidence interval: 0.82-0.91). A cutoff value of 23.25 was selected with a sensitivity of 0.735 and specificity of 0.849.
CONCLUSIONS: The predictive model for persistent cough following pulmonary resection, derived from meta-analysis, demonstrates strong predictive performance and shows potential as a valuable tool for clinical risk assessment.
METHODS: A systematic review and meta-analysis were performed to identify risk factors associated with persistent cough after pulmonary resection. In the predictive model, the natural logarithm of the pooled risk estimate for each factor was used as a coefficient to assign risk prediction scores. Data from 294 patients who underwent pulmonary resection between October and December 2024 were used to evaluate the predictive accuracy of the model.
RESULTS: The meta-analysis included 13 studies with 3,669 patients who underwent pulmonary resection, of whom 1,115 developed postoperative chronic cough. Seven significant predictors of persistent cough were identified: lobectomy, right-sided surgery, right upper lobectomy, subcarinal lymph node dissection, lymph node dissection around the bronchial tree, tracheal intubation time (≥172 min), and postoperative acid reflux. The model was validated using logistic regression analysis with an external cohort, yielding an area under the receiver operating characteristic curve of 0.87 (95% confidence interval: 0.82-0.91). A cutoff value of 23.25 was selected with a sensitivity of 0.735 and specificity of 0.849.
CONCLUSIONS: The predictive model for persistent cough following pulmonary resection, derived from meta-analysis, demonstrates strong predictive performance and shows potential as a valuable tool for clinical risk assessment.