Perioperative Blood Biomarkers of Infectious and Non-Infectious Postoperative Pulmonary Complications: A Narrative Review

TL;DR

This review explores blood biomarkers that can help distinguish between infectious and non-infectious lung complications after surgery, aiming to improve patient outcomes.

Contribution

The paper provides a narrative synthesis of perioperative blood biomarker trajectories for distinguishing infectious from non-infectious postoperative pulmonary complications.

Findings

Serial patterns of biomarkers like C-reactive protein and procalcitonin help differentiate postoperative inflammation from infection.

Mechanistic biomarkers offer biological insights but face limitations in standardization and assay availability.

Omics signatures and machine learning models show promise but need validation for clinical use.

Abstract

Background/Objectives: Postoperative pulmonary complications (PPCs) remain frequent and increase morbidity, mortality, and resource use. Preoperative risk scores (ARISCAT, NSQIP-derived calculators) use mostly static variables and may miss the dynamic perioperative host response preceding respiratory deterioration or infection. We address the gap in clinically interpretable syntheses of perioperative blood biomarker trajectories that distinguish infectious from non-infectious PPCs and clarify bedside-ready versus exploratory markers. Methods: We conducted a narrative review with a structured Medline search (inception to 1 November 2025) plus reference screening. We included English-language adult surgical studies (observational or interventional) evaluating perioperative blood biomarkers in relation to PPCs or postoperative pulmonary infection; case reports, editorials, and reviews were excluded. No formal risk-of-bias assessment or quantitative meta-analysis was performed. Results: Across 298 cited publications, serial patterns of routinely available biomarkers (C-reactive protein, procalcitonin, lactate, albumin, and leukocyte-derived indices) were most consistently associated with PPC risk and helped separate expected postoperative inflammation from evolving infection when interpreted longitudinally rather than as single values. Mechanistic biomarkers (cytokines/immune-function assays, endothelial injury and coagulation/fibrinolysis markers, oxidative stress indicators) add biological insight but are limited by assay availability, heterogeneous sampling windows, and absent standardized cut-offs. Omics signatures and machine learning models combining biomarker kinetics with clinical variables are promising but require prospective, transportable validation. Conclusions: Key barriers to implementation include biological variability, non-specificity across postoperative syndromes, heterogeneous sampling windows, and lack of standardized cut-offs. Integrating multimarker panels into validated, dynamic predictive frameworks represents a promising direction for perioperative precision medicine.