A novel online calculator based on inflammation-related endotypes and clinical features to predict postoperative pulmonary infection in patients with cervical spinal cord injury.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

We identified inflammation-related endotype and constructed a web-based calculator for predicting POI in patients with CSCI, exhibiting excellent clinical utility.