High C‐reactive protein to lymphocyte ratio predicts mortality outcomes of patients with severe fever with thrombocytopenia syndrome: A multicenter study in China

Qian, Fang; Zhou, Wei; Liu, Yuanni; Ge, Ziruo; Lai, Jianming; Zhao, Zhenghua; Feng, Yang; Li, Gang; Shen, Yi; Zhang, Zhonglu; Zhang, Wei; Fan, Tianli; Zhao, Yongxiang; Chen, Zhihai

doi:10.1002/jmv.28546

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…Our research revealed that advanced age was a significant factor contributing to mortality from SFTS, aligning with findings from prior research studies 20,21 . Additionally, we observed a notable rise in the rate of bleeding among those in the deceased group.…”

Section: Discussionsupporting

confidence: 91%

“…Our research revealed that advanced age was a significant factor contributing to mortality from SFTS, aligning with findings from prior research studies. 20,21 Additionally, we observed a notable rise in the rate of bleeding among those in the deceased group. APTT and other coagulation in the death group were significantly increased, and APTT was an independent risk factor for SFTS.…”

Section: Discussionmentioning

confidence: 60%

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Blood urea nitrogen to albumin ratio is a novel predictor of fatal outcome for patients with severe fever with thrombocytopenia syndrome

Wang,

Qin,

Zhang

et al. 2024

Journal of Medical Virology

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Severe fever with thrombocytopenia syndrome (SFTS) is associated with a high death rate and lacks a targeted therapy plan. The ratio of blood urea nitrogen to albumin, known as BAR, is a valuable method for assessing the outlook of various infectious diseases. The objective of this research was to evaluate the effectiveness of BAR in forecasting the outcome of individuals with SFTS. Four hundred and thirty‐seven patients with SFTS from two clinical centers were included in this study according to inclusion and exclusion criteria. Clinical characteristics and test parameters of SFTS patients were analyzed between survival and fatal groups. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression suggested that BAR might serve as a standalone prognostic indicator for patients with SFTS in the initial phase (hazard ratio = 18.669, 95% confidence interval [CI]: 8.558–40.725, p < 0.001). And BAR had a better predictive effectiveness in clinical outcomes in patients with SFTS with an AUC of 0.832 (95% CI: 0.788–0.876, p < 0.001), a cutoff value of 0.19, a sensitivity of 0.812, and a specificity of 0.726 compared to C‐reactive protein, procalcitonin, and platelet to lymphocyte ratio via receiver operating characteristic curve. KM (Kaplan Meier) curves demonstrated that high level of BAR was associated with poor survival condition in patients with SFTS. Furthermore, the high level of BAR was associated with long hospital stays and test paraments of kidney, liver, and coagulation function in survival patients. So, BAR could be used as a promising early warning biomarker of adverse outcomes in patients with SFTS.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 60%

Blood urea nitrogen to albumin ratio is a novel predictor of fatal outcome for patients with severe fever with thrombocytopenia syndrome

Wang,

Qin,

Zhang

et al. 2024

Journal of Medical Virology

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“…Nevertheless, this investigation also indicated that advanced age was a significant contributor to SFTS mortality, consistent with previous reports. Qian et al (20) constructed a risk model to forecast fatalities in patients with SFTS based on three high-risk variables: age, APTT, and CRP to lymphocyte ratio. According to research by Jung et al (21), older age was linked to a higher 30 days mortality rate in patients with SFTS (adjusted hazard ratio: 1.10; 95% CI:1.04-1.17).…”

Section: Discussionmentioning

confidence: 99%

Novel nomograms to predict risk and prognosis in hospitalized patients with severe fever with thrombocytopenia syndrome

Li,

Zhang,

Chen

2023

Front. Med.

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BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging and life-threatening infectious disease caused by SFTS virus. Although recent studies have reported the use of nomograms based on demographic and laboratory data to predict the prognosis of SFTS, no study has included viral load, which is an important factor that influences the prognosis, when compared with other risk factors. Therefore, this study aimed to develop a model that predicts SFTS prognosis before it reaches the critical illness stage and to compare the predictive ability of groups with and without viral load.MethodsTwo hundred patients with SFTS were enrolled between June 2018 and August 2023. Data were sourced from the first laboratory results at admission, and two nomograms for mortality risk were developed using multivariate logistic regression to identify the risk variables for poor prognosis in these patients. We calculated the area under the receiver operating characteristic curve (AUC) for the two nomograms to assess their discrimination, and predictive abilities were compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe multivariate logistic regression analysis identified four independent risk factors: age, bleeding manifestations, prolonged activated partial thromboplastin time, and viral load. Based on these factors, a final nomogram predicting mortality risk in patients with SFTS was constructed; in addition, a simplified nomogram was constructed excluding the viral load. The AUC [0.926, 95% confidence interval (CI): 0.882–0.970 vs. 0.882, 95% CI: 35 0.823–0.942], NRI (0.143, 95% CI, 0.036–0.285), and IDI (0.124, 95% CI, 0.061–0.186) were calculated and compared between the two models. The calibration curves of the two models showed excellent concordance, and decision curve analysis was used to quantify the net benefit at different threshold probabilities.ConclusionTwo critical risk nomograms were developed based on the indicators for early prediction of mortality risk in patients with SFTS, and enhanced predictive accuracy was observed in the model that incorporated the viral load. The models developed will provide frontline clinicians with a convenient tool for early identification of critically ill patients and initiation of a better personalized treatment in a timely manner.

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Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis

Liu,

Zhu,

Qin

et al. 2024

Immunity Inflam & Disease

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BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease primarily transmitted by ticks. The development of encephalitis in SFTS patients significantly increases the risk of adverse outcomes. However, the understanding of SFTS‐associated encephalitis (SFTSAE) is still limited. This study aimed to identify the clinical characteristics of SFTSAE and develop a predictive model for early detection.MethodsWe retrospectively collected data from 220 SFTS patients admitted to Nanjing Drum Tower Hospital between May 2019 and January 2024. The patients were first randomly divided into a training set (154 people, 70%) and a validation set (66 people, 30%). The patients in the training set were divided into SFTSAE and non‐SFTSAE groups according to the presence of encephalitis. A prediction model was constructed using the training set: important clinical parameters were selected using univariate logistic regression, and then multivariate logistic regression was performed to determine the independent risk factors for SFTSAE. A prediction model was constructed using these independent risk factors. Finally, the validation set was used to verify the predictive ability of the model.ResultsAge, C‐reactive protein, d‐dimer, and viral load were independent risk factors for SFTSAE (p < 0.05). A nomogram containing these four indicators was constructed, and the predictive performance of the nomogram was evaluated using the ROC curve. The AUC of the model was 0.846 (95% confidence interval [CI]: 0.770–0.921), which had good predictive ability for SFTSAE.ConclusionConclusion: The overall mortality rate of SFTS patients was 17.53%, and the mortality rate of encephalitis patients was 50%. Old age, high C‐reactive protein, elevated d‐dimer, and high viral load were independent risk factors for SFTSAE. The nomogram constructed based on these four indicators had good predictive ability and could be used as an evaluation tool for clinical treatment.

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High C‐reactive protein to lymphocyte ratio predicts mortality outcomes of patients with severe fever with thrombocytopenia syndrome: A multicenter study in China

Cited by 11 publications

References 41 publications

Blood urea nitrogen to albumin ratio is a novel predictor of fatal outcome for patients with severe fever with thrombocytopenia syndrome

Blood urea nitrogen to albumin ratio is a novel predictor of fatal outcome for patients with severe fever with thrombocytopenia syndrome

Novel nomograms to predict risk and prognosis in hospitalized patients with severe fever with thrombocytopenia syndrome

Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome‐Associated Encephalitis

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