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Diagnostic efficacy of serum presepsin for postoperative infectious complications: a meta-analysis
Chun-Ying Lu1, Chia-Li Kao2, Kuo-Chuan Hung1
1Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan.
Background:
Postoperative infectious complications (PICs) are major concerns. Early and accurate diagnosis is critical for timely treatment and improved outcomes. Presepsin is an emerging biomarker for bacterial infections. However, its diagnostic efficacy for PICs across surgical specialties remains unclear.
Methods:
In this study, a systematic search on MEDLINE, Embase, Google Scholar, and Cochrane Library was performed on September 30, 2023, to identify studies that evaluated presepsin for diagnosing PICs. PIC is defined as the development of surgical site infection or remote infection. Pooled sensitivity, specificity, and hierarchical summary receiver operating characteristic (HSROC) curves were calculated. The primary outcome was the assessment of the efficacy of presepsin for PIC diagnosis, and the secondary outcome was the investigation of the reliability of procalcitonin or C-reactive protein (CRP) in the diagnosis of PICs.
Results:
This meta-analysis included eight studies (n = 984) and revealed that the pooled sensitivity and specificity of presepsin for PIC diagnosis were 76% (95% confidence interval [CI] 68%-82%) and 83% (95% CI 75%-89%), respectively. The HSROC curve yielded an area under the curve (AUC) of 0.77 (95% CI 0.73-0.81). Analysis of six studies on procalcitonin showed a combined sensitivity of 78% and specificity of 77%, with an AUC of 0.83 derived from the HSROC. Meanwhile, data from five studies on CRP indicated pooled sensitivity of 84% and specificity of 79%, with the HSROC curve yielding an AUC of 0.89.
Conclusion:
Presepsin exhibits moderate diagnostic accuracy for PIC across surgical disciplines. Based on the HSROC-derived AUC, CRP has the highest diagnostic efficacy for PICs, followed by procalcitonin and presepsin. Nonetheless, presepsin demonstrated greater specificity than the other biomarkers. Further study is warranted to validate the utility of and optimize the cutoff values for presepsin.
Systematic Review Registration:
https://www.crd.york.ac.uk/prospero/, identifier CRD42023468358.
Insights
Presepsin shows moderate accuracy in diagnosing postoperative infectious complications (PICs). C-reactive protein (CRP) demonstrated the highest diagnostic efficacy, followed by procalcitonin and presepsin, though presepsin offered greater specificity.
Area of Science:
- Medical diagnostics
- Biomarker research
- Surgical outcomes
Background:
- Postoperative infectious complications (PICs) are a significant clinical concern.
- Early and accurate diagnosis of PICs is crucial for effective treatment and improved patient outcomes.
- Presepsin is an emerging biomarker for bacterial infections, but its diagnostic value for PICs across various surgical fields is not well-established.
Approach:
- A systematic literature search was conducted across major databases (MEDLINE, Embase, Google Scholar, Cochrane Library) up to September 30, 2023.
- Meta-analysis techniques were employed to calculate pooled sensitivity, specificity, and hierarchical summary receiver operating characteristic (HSROC) curves.
- The study assessed the diagnostic efficacy of presepsin for PICs and compared it with procalcitonin and C-reactive protein (CRP).
Key Points:
- The meta-analysis included eight studies with 984 patients.
- Presepsin demonstrated a pooled sensitivity of 76% and specificity of 83% for PIC diagnosis (AUC = 0.77).
- C-reactive protein (CRP) showed the highest diagnostic efficacy (AUC = 0.89), followed by procalcitonin (AUC = 0.83), with presepsin at 0.77.
Conclusions:
- Presepsin demonstrates moderate diagnostic accuracy for PICs across surgical specialties.
- Based on AUC values, CRP exhibited the highest diagnostic efficacy, followed by procalcitonin, and then presepsin.
- Presepsin showed higher specificity compared to procalcitonin and CRP, suggesting potential utility, but further research is needed to optimize its diagnostic cutoff values.
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