Diagnostic Utility of Pleural C-Reactive Protein and Procalcitonin for Parapneumonic Pleural Effusion: A Head-to-Head Comparison Study
Bibliographic record
Abstract
Qian Yang,1,2,* Su-Na Cha,1,2,* Yan Niu,3 Jian-Xun Wen,3 Li Yan,2,4 Ling Hai,5,6 Ying-Jun Wang,1,2 Wen-Hui Gao,1 Feng Zhou,7 Qianghua Zhou,8 Zhi-De Hu,1,2 Wen-Qi Zheng1,2 1Department of Laboratory Medicine, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 2Key Laboratory for Biomarkers, Inner Mongolia Medical University, Hohhot, People’s Republic of China; 3Medical Experiment Center, The College of Basic Medicine, Inner Mongolia Medical University, Hohhot, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 5Department of Pathology, The College of Basic Medical, Inner Mongolia Medical University, Hohhot, People’s Republic of China; 6Department of Pathology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 7Department of Blood Transfusion, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, People’s Republic of China; 8Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada*These authors contributed equally to this workCorrespondence: Zhi-De Hu, Email hzdlj81@163.com; Wen-Qi Zheng, Email zhengwenqi2011@163.comIntroduction: The diagnostic utility of pleural fluid C-reactive protein (CRP) and procalcitonin (PCT) for parapneumonic pleural effusion (PPE) is a subject of ongoing investigation. There remains lack studies comparing their diagnostic accuracy in a head-to-head manner. Furthermore, the incremental diagnostic value of their combination over a single marker and the net benefit of them remains unknown.Methods: This prospective study enrolled participants presenting with undiagnosed pleural effusion, subsequently measuring their pleural levels of CRP and PCT. A diagnostic model that integrated both biomarkers was constructed using logistic regression analysis. The diagnostic performance and net benefit of CRP, PCT, and the composite model were assessed through receiver-operating characteristic (ROC) curve analysis and decision curve analysis (DCA).Results: The study included 32 PPE patients and 121 patients without PPE. The area under the ROC curve (AUC) for CRP was 0.73 (95% confidence interval [CI]: 0.63– 0.83), with a sensitivity of 0.71 (95% CI: 0.55– 0.87) and a specificity of 0.68 (95% CI: 0.59– 0.77) at a threshold of 10 mg/L. In contrast, the AUC for PCT was 0.58 (95% CI: 0.46– 0.69), with sensitivity and specificity rates of 0.50 (95% CI: 0.33– 0.67) and 0.65 (95% CI: 0.56– 0.74) at a threshold of 0.1 ng/mL, respectively. Notably, the AUC for the diagnostic model was comparable to that of CRP alone at 0.73 (95% CI: 0.63– 0.82). DCA showed that applying CRP provided a net clinical benefit, while PCT did not.Conclusion: Pleural fluid CRP possesses moderate diagnostic capability for PPE, while PCT exhibits limited diagnostic utility. Additionally, the combined application of CRP and PCT does not confer any significant enhancement in diagnostic accuracy over the use of CRP alone.Keywords: C-reactive protein, diagnostic test accuracy, parapneumonic pleural effusion, procalcitonin
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".