Wang Yuqiang, Zhu Shihui, Liu Xiaoli, Zhao Bochao, Zhang Xiu, Luo Zeruxin, Liu Peizhao, Guo Yingqiang, Zhang Zhengbo, Yu Pengming
Cardiovascular Surgery Research Laboratory, West China Hospital, Sichuan University, Chengdu, China.
Mailman School of Public Health, Columbia University, New York, NY, United States.
Front Cardiovasc Med. 2024 Mar 26;11:1342586. doi: 10.3389/fcvm.2024.1342586. eCollection 2024.
Prolonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI.
We conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques.
The incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111-1.157], lactate level (OR, 1.131; 95% CI, 1.067-1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115-1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061-1.132), central venous pressure (OR, 1.052; 95% CI, 1.033-1.073), anion gap (OR, 1.075; 95% CI, 1.043-1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047-1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23-1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893-0.964), pH value (OR, 0.757; 95% CI, 0.629-0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003-1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840-0.865) in the training cohort, 0.867 (95% CI, 0.853-0.882) in the testing cohort, and 0.704 (95% CI, 0.679-0.727) in the external validation cohort.
Through multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.
长时间插管（PI）是心脏手术（CS）患者中常见的严重并发症。仅关注术前数据，而没有充分考虑手术、麻醉和体外循环程序对后续呼吸系统功能的持续影响，可能会影响疾病预后的预测准确性。针对这一挑战，我们制定并外部验证了一个适用于CS患者的易懂预测模型，利用术前信息和早期重症监护病房（ICU）数据，以促进对PI的早期预防。
我们进行了一项回顾性队列研究，分析接受CS的成年患者，并利用两个公开可用的ICU数据库的数据，即重症监护医学信息集市和eICU协作研究数据库。PI定义为需要插管超过24小时。使用多变量逻辑回归构建预测模型。对模型的预测性能进行外部验证，并通过可视化技术阐明结果。
训练、测试和外部验证队列中PI的发生率分别为11.8%、12.1%和17.5%。我们确定了11个与CS后PI相关的预测因素：平台压[比值比（OR），1.133；95%置信区间（CI），1.111 - 1.157]、乳酸水平（OR，1.131；95% CI，1.067 - 1.2）、Charlson合并症指数（OR，1.166；95% CI，1.115 - 1.219）、序贯器官衰竭评估评分（OR，1.096；95% CI，1.061 - 1.132）、中心静脉压（OR，1.052；95% CI，1.033 - 1.073）、阴离子间隙（OR，1.075；95% CI，1.043 - 1.107）、呼气末正压（OR，1.087；95% CI，1.047 - 1.129）、血管升压药使用情况（OR，1.521；95% CI，1.23 - 1.879）、视觉模拟评分（OR，0.928；95% CI，0.893 - 0.964）、pH值（OR，0.757；95% CI，0.629 - 0.913）和血尿素氮水平（OR，1.011；95% CI，1.003 - 1.02）。该模型在训练队列中的受试者操作特征曲线下面积（AUROC）为0.853（95% CI，0.840 - 0.865），在测试队列中为0.867（95% CI，0.853 - 0.882），在外部验证队列中为0.704（95% CI，0.679 - 0.727）。
通过多中心内部和外部验证，我们整合早期ICU数据和术前信息的模型表现出出色的辨别能力。这种整合能够在CS后的初始阶段准确评估PI风险，便于及时采取干预措施以减轻不良后果。
