Jiajia Yang,1,* Weiyuan Bao,2,* Hongmei Wang,1 Jie Zhou,3 Qiang Hu,4 Ying Wang,1 Yuancheng Li5 

1Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215008, People’s Republic of China; 2Department of Logistics Support, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215008, People’s Republic of China; 3Intensive care unit, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215008, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215008, People’s Republic of China; 5Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, 210042, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Ying Wang, Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, No. 242, Guangji Road, Suzhou, 215008, People’s Republic of China, Email wangy830224@sina.com Yuancheng Li, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, Jiangsu, 210042, People’s Republic of China, Email ycli@pumcderm.cams.cn

Purpose: To construct a risk nomogram model of ventilator-associated pneumonia (VAP) patients with mechanical ventilation in the intensive care unit (ICU) based on peripheral blood inflammatory indicators and to evaluate its diagnostic value.
Patients and Methods: A matched 1:2 case: control study was conducted. Fifty-five mechanically ventilated patients with VAP and 113 patients without VAP were admitted to the ICU of Suzhou City Hospital with mechanical ventilation from January 2022 to June 2023 and were retrospectively included as study subjects. Clinical data and laboratory indicators of all patients were collected; the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immunoinflammatory index (SII), and systemic inflammatory response index (SIRI) were calculated, and endotracheal aspirate (ETA) culture results of VAP patients were recorded.
Results: There were 61 pathogenic bacteria cultured in the ETA samples of 55 VAP patients, including 56 gram-negative bacilli, 4 gram-positive cocci, and 1 fungus. The proportions of hypoproteinemia, procalcitonin (PCT), NLR, PLR, SII, and SIRI in VAP patients were significantly higher than those in non-VAP patients, with statistical significance (P < 0.05). Univariate and multivariate logistic regression analyses showed that hypoproteinemia, PCT, NLR, PLR, and SIRI were independent influencing factors for VAP in ICU patients (P < 0.05). The ROC curve analysis results showed that the area under the curve of the model for diagnosing VAP in ICU patients was 0.894 [(95% CI = 0.844– 0.945), P < 0.001], and the sensitivity and specificity were 87.3% and 74.3%, respectively. The calibration curve shows that the model has good accuracy, and the clinical decision curve indicates that the clinical net benefit rate is higher when the model is used to diagnose VAP.
Conclusion: Hypoproteinemia, PCT, NLR, PLR, and SIRI are the independent risk factors for VAP in ICU patients. The nomogram model constructed based on these easily accessible indicators may provide a promising tool for the early diagnosis of VAP in ICU patients, while requires further refinement for routine clinical use.

Keywords: ventilator-associated pneumonia, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, systemic inflammatory response index, nomogram model, intensive care unit