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Preoperative Serum Raman Spectroscopy: A Rapid, Label-Free Predictor of Postoperative Pulmonary Infection After
Yuan Hui1, Jiaman She2, Tao Shi3
1Centers of Public Health, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Objective:
This study aimed to develop and validate a preoperative risk-stratification tool based on serum Raman spectroscopy to identify patients at high risk for postoperative pulmonary infection (PPI) following off-pump coronary artery bypass grafting (OPCABG).
Design:
A prospective diagnostic cohort study.
Setting:
The study was conducted at a single academic medical center.
Participants:
A total of 276 adult patients undergoing OPCABG were enrolled. Preoperative serum samples were collected from all participants.
Interventions:
Preoperative serum was analyzed using 532 nm confocal Raman spectroscopy (400-1,800 cm-1). Participants were randomly split 7:3 into training and test sets for model development and validation.
Measurements And Main Results:
PPI within 72 hours was adjudicated by Centers for Disease Control and Prevention/National Healthcare Safety Network criteria. Fifty-seven patients (20.7%) developed early PPI. After feature selection via 2-sample t-test, LASSO regularization, and multivariable logistic regression, a 2-peak Raman signature (664 cm-1 and 842 cm-1) was identified. The final logistic model demonstrated strong performance on the test set, with an area under the receiver operating characteristic curve of 0.794 (95% confidence interval: 0.676-0.912), sensitivity of 70.6%, specificity of 72.7%, and a Brier score of 0.133. SHapley Additive exPlanations analysis confirmed that elevated intensities at these peaks were associated with increased early PPI risk. Decision-curve analysis indicated net clinical benefit across threshold probabilities of 15% to 35%.
Conclusions:
A rapid reagent-free serum Raman signature comprising 2 peaks (664 cm-1 and 842 cm-1) provides an interpretable and effective preoperative prediction of early PPI risk in OPCABG patients, as demonstrated by internal validation.
