Association Between Admission Blood Fibrinogen-To-Albumin Ratio and Clinical Outcomes After Acute Lacunar Stroke

Zheng, Lukai; Wang, Zhetao; Liu, Junfeng; Yang, Xue; Zhang, Shihong; Zeng, Hao; Liu, Ming; Wang, Deren

doi:10.2217/bmm-2019-0537

Cited by 12 publications

(12 citation statements)

References 36 publications

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“…Fibrinogen is closely related to thrombosis, and high fibrinogen levels are often regarded as biomarkers of vascular diseases such as myocardial infarction or stroke [ 24 ]. Some studies [ 25 – 27 ] have shown that high levels of fibrinogen can predict the occurrence and clinical prognosis of LCI, which further supports the results of this study, suggesting that fibrinogen can effectively predict the combination of LCI in elderly patients with a femoral neck fracture. Scr is one of the most commonly used indicators of kidney function.…”

Section: Discussionsupporting

confidence: 88%

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

Huang

Tan

et al. 2022

BMC Geriatr

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Background Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture. Methods Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models’ performance. Results The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance. Conclusion In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.

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Section: Discussionsupporting

confidence: 88%

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

Huang

Tan

et al. 2022

BMC Geriatr

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“… 30 Recently, Zheng et al specified that the FAR was associated with the severity and was a predictor of a poor 3-month outcome in patients with acute lacunar cerebral infarction. 10 Our study also showed that the FAR was associated with a poor 3-month clinical outcome of acute pontine infarction and further confirmed that the FAR was positively correlated with both the baseline NIHSS score and the mRS score. Patients with a high FAR had more severe neurological deficits and a worse outcome.…”

Section: Discussionsupporting

confidence: 83%

“…Zheng et al indicated that a FAR value ≥ 0.077 might be a predictor of poor 3-month clinical outcomes after acute lacunar cerebral infarction. 10 However, it is not clear whether an elevated FAR is associated with poor outcomes after pontine infarction. Therefore, the main purpose of this study was to elucidate the association between the FAR and the 3-month clinical outcomes of patients with acute pontine infarction.…”

Section: Introductionmentioning

confidence: 99%

The Relationship Between the Fibrinogen to Albumin Ratio and Early Outcomes in Patients with Acute Pontine Infarction

Zhai

Cao

et al. 2022

Clin Appl Thromb Hemost

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Background and purpose Previous studies have indicated that fibrinogen and low serum albumin levels are associated with poor outcomes of acute ischemic stroke. The role of the fibrinogen-to-albumin ratio (FAR) as a novel inflammatory and thrombotic biomarker in acute ischemic stroke is unclear. This study aims to investigate the relationship between the FAR and 3-month outcomes of acute pontine infarction. Methods: Patients with acute pontine infarction were consecutively included. All patients were followed up at 3 months after onset, and the 3-month outcome was evaluated using modified Rankin Scale (mRS) scores. A score of 0 to 2 was defined as a good outcome, and a score ≥ 3 was defined as a poor outcome. Receiver operating curve (ROC) analysis was used to calculate the optimal cutoff values for patients with acute pontine infarction. Then, a binary logistic regression model was used to evaluate the risk factors for a poor outcome after acute pontine infarction. Results: A total of 264 patients with acute pontine infarction were included. Eighty (30.3%) patients were included in the poor outcome group. The optimal cutoff value of the FAR for predicting the 3-month outcome of acute pontine infarction was 8.199. The FAR was independently associated with a poor outcome at 3 months in patients with acute pontine infarction (odds ratio [OR] = 1.293, 95% confidence interval [CI]: 1.150-1.453). Conclusions: We found that a high FAR predicted poor 3-month outcomes in patients with acute pontine infarction.

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“…The fibrinogen-to-albumin ratio (FAR) which combines coagulation with nutritional status, is a new vital inflammatory biomarker for a variety of diseases, such as cervical cancer, oligodendroglial gliomas, acute coronary syndrome, and stroke (22)(23)(24)(25). Zheng et al found that a high FAR level on admission was highly associated with 3-month mortality and disability in patients with acute lacunar stroke (22). Yet, the link between FAR and the risk of developing SAP in patients after AIS remains unclear.…”

Section: Introductionmentioning

confidence: 99%

High Fibrinogen to Albumin Ratio: A Novel Marker for Risk of Stroke-Associated Pneumonia?

Lin

Song

et al. 2022

Front. Neurol.

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Background: Stroke-associated pneumonia (SAP) is associated with poor prognosis after acute ischemic stroke (AIS).Purpose: This study aimed to describe the parameters of coagulation function and evaluate the association between the fibrinogen-to-albumin ratio (FAR) and SAP in patients with AIS.Patients and methods: A total of 932 consecutive patients with AIS were included. Coagulation parameters were measured at admission. All patients were classified into two groups according to the optimal cutoff FAR point at which the sum of the specificity and sensitivity was highest. Propensity score matching (PSM) was performed to balance potential confounding factors. Univariate and multivariate logistic regression analyses were applied to identify predictors of SAP.Results: A total of 100 (10.7%) patients were diagnosed with SAP. The data showed that fibrinogen, FAR, and D-dimer, prothrombin time (PT), activated partial thromboplastin time (aPTT) were higher in patients with SAP, while albumin was much lower. Patients with SAP showed a significantly increased FAR when compared with non-SAP (P < 0.001). Patients were assigned to groups of high FAR (≥0.0977) and low FAR (<0.0977) based on the optimal cut-off value. Propensity score matching analysis further confirmed the association between FAR and SAP. After adjusting for confounding and risk factors, multivariate regression analysis showed that the high FAR (≥0.0977) was an independent variable predicting the occurrence of SAP (odds ratio =2.830, 95% CI = 1.654–4.840, P < 0.001). In addition, the FAR was higher in the severe pneumonia group when it was assessed by pneumonia severity index (P = 0.008).Conclusions: High FAR is an independent potential risk factor of SAP, which can help clinicians identify high-risk patients with SAP after AIS.

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Association Between Admission Blood Fibrinogen-To-Albumin Ratio and Clinical Outcomes After Acute Lacunar Stroke

Cited by 12 publications

References 36 publications

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

The Relationship Between the Fibrinogen to Albumin Ratio and Early Outcomes in Patients with Acute Pontine Infarction

High Fibrinogen to Albumin Ratio: A Novel Marker for Risk of Stroke-Associated Pneumonia?

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