Jiang Yaxin, Li Zimeng, Jiang Weiting, Wei Tingyu, Chen Bizhen
Department of Healthcare-Associated Infection Management, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China.
School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Front Oncol. 2024 Aug 5;14:1419633. doi: 10.3389/fonc.2024.1419633. eCollection 2024.
Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery.
PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.
A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%.
In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation.
https://www.crd.york.ac.uk/prospero, identifier CRD42024527085.
众多研究已开发或验证了预测模型，以估计食管癌（EC）患者术后肺炎（POP）的发生可能性。这些模型的质量以及它们在临床实践和未来研究中的适用性评估仍不清楚。本研究系统评估了食管癌手术患者发生POP的风险预测模型的偏倚风险和适用性。
检索了PubMed、Embase、Web of Science、Cochrane图书馆、护理及相关健康文献累积索引（CINAHL）、中国知网（CNKI）、中国科技期刊数据库（VIP）、万方数据库和中国生物医学文献数据库，检索时间从建库至2024年3月12日。两名研究者独立筛选文献并提取数据。采用预测模型偏倚风险评估工具（PROBAST）清单评估偏倚风险和适用性。
共纳入14项研究，涉及23个模型。这些研究主要发表于2014年至2023年之间。所有研究的适用性良好。然而，所有研究均表现出较高的偏倚风险，主要归因于数据来源不当、样本量不足、变量和缺失数据处理不合理以及缺乏模型验证。食管癌手术患者中POP的发生率在14.60%至39.26%之间。最常用的预测因素为吸烟、年龄、慢性阻塞性肺疾病（COPD）、糖尿病和开胸手术方式。模型间的鉴别度在0.627至0.850之间，灵敏度在60.7%至84.0%之间，特异度在59.1%至83.9%之间。
在所有纳入研究中，食管癌手术患者POP风险预测模型的鉴别度良好，表明模型性能稳定。然而，根据PROBAST清单，所有研究均有较高的偏倚风险。未来研究应使用预测模型评估工具来改进研究设计，并开发具有更大样本量和多中心外部验证的新模型。
