严重肺炎支原体肺炎患儿支气管肺泡灌洗治疗时机的优化策略。
Optimization strategy for the early timing of bronchoalveolar lavage treatment for children with severe mycoplasma pneumoniae pneumonia.
机构信息
Department of Pediatrics, the First Affiliated Hospital of Xinxiang Medical University, Weihui, 453100, China.
The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510260, China.
出版信息
BMC Infect Dis. 2023 Oct 5;23(1):661. doi: 10.1186/s12879-023-08619-9.
BACKGROUND
Early evaluation of severe mycoplasma pneumoniae pneumonia (SMPP) and the prompt utilization of fiberoptic bronchoscopic manipulation can effectively alleviate complications and restrict the progression of sequelae. This study aim to establish a nomogram forecasting model for SMPP in children and explore an optimal early therapeutic bronchoalveolar lavage (TBAL) treatment strategy.
METHODS
This retrospective study included children with mycoplasma pneumoniae pneumonia (MPP) from January 2019 to December 2021. Multivariate logistic regression analysis was used to screen independent risk factors for SMPP and establish a nomogram model. The bootstrap method was employed and a receiver operator characteristic (ROC) curve was drawn to evaluate the accuracy and robustness of the model. Kaplan-Meier analysis was used to assess the effect of lavage and hospitalization times.
RESULTS
A total of 244 cases were enrolled in the study, among whom 68 with SMPP and 176 with non-SMPP (NSMPP). A prediction model with five independent risk factors: left upper lobe computed tomography (CT) score, sequential organ failure assessment (SOFA) score, acute physiology and chronic health assessment (APACHE) II score, bronchitis score (BS), and c-reactive protein (CRP) was established based on the multivariate logistic regression analysis. The ROC curve of the prediction model showed the area under ROC curve (AUC) was 0.985 (95% confidence interval (CI) 0.972-0.997). The Hosmer-Lemeshow goodness-of-fit test results showed that the nomogram model predicted the risk of SMPP well (χ2 = 2.127, P = 0.977). The log-rank result suggested that an early BAL treatment could shorten MPP hospitalization time (P = 0.0057).
CONCLUSION
This nomogram model, based on the left upper lobe CT score, SOFA score, APACHE II score, BS, and CRP level, represents a valuable tool to predict the risk of SMPP in children and optimize the timing of TBAL.
背景
早期评估严重肺炎支原体肺炎（SMPP）并及时进行纤维支气管镜操作，可有效缓解并发症，限制后遗症的进展。本研究旨在建立儿童 SMPP 的列线图预测模型，并探讨最佳的早期治疗性支气管肺泡灌洗（TBAL）治疗策略。
方法
本回顾性研究纳入了 2019 年 1 月至 2021 年 12 月患有肺炎支原体肺炎（MPP）的儿童。采用多变量逻辑回归分析筛选 SMPP 的独立危险因素，并建立列线图模型。采用自举法绘制受试者工作特征（ROC）曲线评估模型的准确性和稳健性。采用 Kaplan-Meier 分析评估灌洗和住院时间的效果。
结果
本研究共纳入 244 例患儿，其中 SMPP 患儿 68 例，非 SMPP（NSMPP）患儿 176 例。多变量逻辑回归分析筛选出左肺上叶 CT 评分、序贯器官衰竭评估（SOFA）评分、急性生理学和慢性健康评估（APACHE）Ⅱ评分、支气管炎评分（BS）和 C 反应蛋白（CRP）等 5 个独立危险因素，建立了预测模型。预测模型的 ROC 曲线显示，ROC 曲线下面积（AUC）为 0.985（95%置信区间（CI）0.972-0.997）。Hosmer-Lemeshow 拟合优度检验结果显示，列线图模型预测 SMPP 风险的拟合度良好（χ2=2.127，P=0.977）。Log-rank 结果表明，早期 BAL 治疗可缩短 MPP 住院时间（P=0.0057）。
结论
该列线图模型基于左肺上叶 CT 评分、SOFA 评分、APACHE II 评分、BS 和 CRP 水平，是预测儿童 SMPP 风险和优化 TBAL 时机的有价值工具。
Zotero 插件