Background: The D-dimer-to-fibrinogen ratio (DFR) is a good indicator of thrombus activity in thrombotic diseases, but its clinical role in acute ischaemic stroke (AIS) patients with different etiologies has not been studied. We evaluated the diagnostic value of the DFR for different subtypes of AIS.
Methods: We conducted a single-center retrospective study of 269 patients with AIS who were referred to our stroke center within 4.5 h from Jan 2017 to Oct 2019. Coagulation data including DFRs were compared among the different stroke subtypes, and a separate retrospective validation sample was utilized to evaluate the prediction efficiency of the DFR for subtype diagnosis.
Results: A higher DFR was observed in patients with cardioembolism than in those with large artery atherosclerosis (LAA) (odds ratio (OR) per 0.1 increase of the DFR: 1.49 [1.01-2.18]) after we adjusted for vascular risk factors. The diagnostic value of the DFR for detecting cardioembolism (AUC = 0.722, 95 % CI = 0.623-0.820) exceeded that of isolated D-dimer or fibrinogen. The validation sample (n = 117) further supported the notion that a diagnosis of cardioembolism was more common in patients with a DFR > 0.11 (multivariable risk ratio = 3.11[1.33-7.31], P = 0.009).
Conclusion: High DFRs were associated with cardioembolism in patients with AIS. The utilization of DFR can be beneficial for distinguishing a cardiac embolic source from atherosclerotic stroke.
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http://dx.doi.org/10.1016/j.jocn.2024.05.007 | DOI Listing |
Infect Drug Resist
January 2025
Department of Thoracic Surgery, The Second People's Hospital of Liaocheng, Linqing, Shandong, 252600, People's Republic of China.
Objective: This study aimed to investigate the levels of coagulation parameters in elderly patients with severe pneumonia and analyse their correlation with disease severity and prognosis.
Methods: A retrospective study was conducted on 207 elderly patients (aged ≥60 years) with severe pneumonia admitted to our hospital between January 2022 and December 2023. Demographic data, clinical characteristics and coagulation parameters, including prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time and fibrinogen (FIB), were collected.
Front Med (Lausanne)
January 2025
Hepatobiliary Pancreatic Surgery Department, Huadu District People's Hospital of Guangzhou, Guangzhou, China.
Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.
Objective: To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.
BMC Musculoskelet Disord
January 2025
Department of Anesthesiology, General Hospital of Central Theater Command of PLA, Wuhan, China.
Objective: The aim of this study was to investigate the effect of SARS-CoV-2 Omicron BA. 5.2 (hereafter referred to as Omicron BA.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Background: Pancreatic cancer is one of the most malignant tumors with an inferior prognosis. This study aims to determine the prognostic significance of immune-inflammatory scores and coagulation indices in patients with metastatic pancreatic cancer(MPC) and develop a predictive nomogram.
Methods: This study retrospectively analyzed the clinical data of 384 patients with MPC who underwent intra-arterial infusion chemotherapy (IAIC).
Front Neurol
January 2025
Department of Neurosurgery, Tokyo Medical and Dental University, Bunkyo-ku, Japan.
Objective: Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to improve patient outcomes. In this study, we developed a machine learning model to predict the occurrence of neurological deterioration after mild TBI using information obtained on admission.
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