C-reactive protein concentration as a risk predictor of mortality in intensive care unit: a multicenter, prospective, observational study

Author:

Qu Rong,Hu Linhui,Ling Yun,Hou Yating,Fang Heng,Zhang Huidan,Liang Silin,He Zhimei,Fang Miaoxian,Li Jiaxin,Li Xu,Chen ChunboORCID

Abstract

Abstract Background It is not clear whether there are valuable inflammatory markers for prognosis judgment in the intensive care unit (ICU). We therefore conducted a multicenter, prospective, observational study to evaluate the prognostic role of inflammatory markers. Methods The clinical and laboratory data of patients at admission, including C-reactive protein (CRP), were collected in four general ICUs from September 1, 2018, to August 1, 2019. Multivariate logistic regression was used to identify factors independently associated with nonsurvival. The area under the receiver operating characteristic curve (AUC-ROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the effect size of different factors in predicting mortality during ICU stay. 3 -knots were used to assess whether alternative cut points for these biomarkers were more appropriate. Results A total of 813 patients were recruited, among whom 121 patients (14.88%) died during the ICU stay. The AUC-ROC values of PCT and CRP for discriminating ICU mortality were 0.696 (95% confidence interval [CI], 0.650–0.743) and 0.684 (95% CI, 0.633–0.735), respectively. In the multivariable analysis, only APACHE II score (odds ratio, 1.166; 95% CI, 1.129–1.203; P = 0.000) and CRP concentration > 62.8 mg/L (odds ratio, 2.145; 95% CI, 1.343–3.427; P = 0.001), were significantly associated with an increased risk of ICU mortality. Moreover, the combination of APACHE II score and CRP > 62.8 mg/L significantly improved risk reclassification over the APACHE II score alone, with NRI (0.556) and IDI (0.013). Restricted cubic spline analysis confirmed that CRP concentration > 62.8 mg/L was the optimal cut-off value for differentiating between surviving and nonsurviving patients. Conclusion CRP markedly improved risk reclassification for prognosis prediction.

Funder

Guangzhou Science, Technology and Innovation Commission

Guangdong Province High-level Hospital Construction Project of Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences

Emergent Science and Technology Project for Prevention and Treatment of Novel Coronavirus Pneumonia of Maoming City and High-level Hospital Construction Research Project of Maoming People's Hospital

Publisher

Springer Science and Business Media LLC

Subject

Anesthesiology and Pain Medicine

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