Association Between Postoperative Pain and Inflammatory Biomarkers After Ovariohysterectomy in Dogs: A Pilot Study

Vet Med Sci. 2026 Sep;12(5):e71160. doi: 10.1002/vms3.71160.

ABSTRACT

BACKGROUND: Postoperative pain is a complex, multidimensional phenomenon and its relationship with systemic inflammatory and stress-related biomarkers remains unclear in veterinary patients.

OBJECTIVES: To investigate the temporal relationship between postoperative pain intensity and systemic inflammatory or stress-related biomarkers in dogs undergoing elective ovariohysterectomy (OHE).

STUDY DESIGN: Prospective clinical study.

METHODS: Twenty clinically healthy female dogs were evaluated using a numerical rating scale (0-27) for pain. Serial blood samples were collected at five timepoints: preoperative baseline (T₀), and 1 h (T₁), 3 h (T₂), 6 h (T₃) and Day 7 (T₄) postoperatively. Serum tumour necrosis factor-α (TNF-α), interleukin-6 (IL-6), haptoglobin (HP), C-reactive protein (CRP) and cortisol concentrations were measured using canine-specific ELISA kits. A standardized anaesthetic, surgical and analgesic protocol was applied to all animals. Temporal changes were analysed using the Friedman test, and associations between pain scores and biomarkers were assessed using Spearman's rank correlation.

RESULTS: All variables demonstrated significant time-dependent changes following OHE (p < 0.0001). Pain scores increased in the early postoperative period and returned toward baseline by Day 7. Similarly, TNF-α, IL-6, HP, CRP and cortisol showed transient postoperative elevations followed by a decline toward preoperative levels. No statistically significant correlations were detected between pain scores and any biomarker at any timepoint (all p > 0.05).

CONCLUSIONS: Commonly measured systemic inflammatory and stress-related biomarkers do not reliably reflect acute postoperative pain intensity in dogs undergoing routine soft tissue surgery. Multimodal assessment approaches integrating validated clinical pain scales with objective parameters are recommended over reliance on biomarker data alone.

PMID:42573230 | DOI:10.1002/vms3.71160