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. 2021 Nov 26:17:1239-1248.
doi: 10.2147/TCRM.S337040. eCollection 2021.

A Prognostic Model Incorporating Red Cell Distribution Width to Platelet Ratio for Patients with Traumatic Brain Injury

Ruoran Wang  1 , Min He  2 , Jing Zhang  1 , Shaobo Wang  3 , Jianguo Xu  1
Affiliations

A Prognostic Model Incorporating Red Cell Distribution Width to Platelet Ratio for Patients with Traumatic Brain Injury

Ruoran Wang et al. Ther Clin Risk Manag. .

Abstract

Background: As an inflammation-based marker, red cell distribution width to platelet ratio (RPR) has been verified to be associated with disease severity and outcome in many clinical settings. We designed this study to evaluate the prognostic value of RPR in patients with traumatic brain injury (TBI).

Methods: A total of 420 patients admitted with TBI were included in this study. Laboratory and clinical data were collected from an electronic medical record system. Univariate and multivariate logistic regression analyses were sequentially performed to discover risk factors of in-hospital mortality. Receiver operating characteristic (ROC) curves were drawn to confirm the predictive value of different markers including RPR in training set and testing set.

Results: Non-survivors had higher level of RPR than survivors (P<0.001). Logistic regression analysis showed that RPR was significantly associated with mortality even after adjusting for confounding factors (P<0.001). The area under the ROC curve (AUC) value of Glasgow Coma Scale (GCS) for predicting mortality was 0.761 and 0775 in training set and testing set, respectively. And the constructed predictive model incorporating RPR had the highest AUC value of 0.858 and 0.884 in training set and testing set.

Conclusion: RPR is significantly associated with mortality in TBI patients. Utilizing RPR to construct a predictive model is valuable to evaluate prognosis of TBI patients.

Keywords: marker; prognosis; red cell distribution to platelet ratio; traumatic brain injury.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
(A) ROC curve of single predictive markers in training cohort; (B) ROC curve of constructed prognostic models in training cohort; (C) ROC curve of single predictive markers in testing cohort; (D) ROC curve of constructed prognostic models in testing cohort.
Figure 2
Figure 2
(A) Decision curve analysis of constructed models in training cohort; (B) decision curve analysis of constructed models in testing cohort.
See this image and copyright information in PMC

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