Division of Spine, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Shanghai 200072, China.
Division of Spine, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Shanghai 200072, China; Institute of Spinal and Spinal Cord Injury, Tongji University School of Medicine, Shanghai 200065, China.
Int Immunopharmacol. 2024 Dec 5;142(Pt B):113246. doi: 10.1016/j.intimp.2024.113246. Epub 2024 Sep 27.
Postoperative pulmonary infection (POI) of patients with cervical spinal cord injury (CSCI) is highly heterogeneous, while the potential endotypes and related risk factors remain unclear.
A retrospective collection of 290 CSCI patients was conducted from January 2010 to July 2024 using 1:1 propensity score matching to compare POI (n = 145) and non-POI (n = 145) groups. We generated laboratory examination data from admission patients and identified endotypes using unsupervised consensus clustering and machine learning. CSCI patients were randomly assigned to the training set (n = 203) and internal validation set (n = 87). A separate cohort comprising 245 CSCI patients were used for external validation. Independent predictors for POI were identified using univariate and multivariate logistic regression. A nomogram and an online calculator were developed and validated, both internally and externally.
Two inflammation-related endotypes were identified: high inflammation endotype (endotype C1) and low inflammation endotype (endotype C2). Eight predictors for POI were identified (including age, operation duration, number of surgical segments, time between injury and surgery, preoperative steroid pulse, American Spinal Injury Association (ASIA) grade, smoking history, and inflammation-related endotype). A nomogram integrating the risk factors showed excellent discrimination in the training set (AUC, 0.976; 95% CI 0.956-0.996), internal validation set (AUC, 0.993; 95% CI 0.981-1.000), and external validation set (AUC, 0.799; 95%CI 0.744-0.854). Calibration curves demonstrated excellent fit, and decision curves highlighted its favorable clinical value. An online calculator (https://tjspine.shinyapps.io/dynnomapp/) was constructed to improve the convenience and efficiency of our prediction model.
We identified inflammation-related endotype and constructed a web-based calculator for predicting POI in patients with CSCI, exhibiting excellent clinical utility.
颈椎脊髓损伤（CSCI）患者术后肺部感染（POI）具有高度异质性，但其潜在的表型和相关的危险因素尚不清楚。
采用回顾性收集 2010 年 1 月至 2024 年 7 月的 290 例 CSCI 患者，采用 1:1 倾向评分匹配法比较 POI（n=145）和非 POI（n=145）组。我们从入院患者中生成实验室检查数据，并使用无监督一致性聚类和机器学习来识别表型。CSCI 患者被随机分配到训练集（n=203）和内部验证集（n=87）。另外一个由 245 例 CSCI 患者组成的队列用于外部验证。使用单因素和多因素逻辑回归确定 POI 的独立预测因素。使用内部和外部验证建立和验证了列线图和在线计算器。
确定了两种与炎症相关的表型：高炎症表型（表型 C1）和低炎症表型（表型 C2）。确定了 8 个 POI 的预测因素（包括年龄、手术时间、手术节段数、损伤与手术时间间隔、术前类固醇冲击、美国脊髓损伤协会（ASIA）分级、吸烟史和炎症相关表型）。一个整合了危险因素的列线图在训练集（AUC：0.976；95%CI：0.956-0.996）、内部验证集（AUC：0.993；95%CI：0.981-1.000）和外部验证集（AUC：0.799；95%CI：0.744-0.854）中均具有出色的区分能力。校准曲线显示出良好的拟合度，决策曲线突出了其良好的临床价值。构建了一个在线计算器（https://tjspine.shinyapps.io/dynnomapp/），以提高我们预测模型的便利性和效率。
我们确定了与炎症相关的表型，并构建了一个基于网络的计算器来预测 CSCI 患者的 POI，具有出色的临床实用性。
