Haemostasis and Inflammatory Parameters as Potential Diagnostic Biomarkers for VTE in Trauma-Immobilized Patients

Acute respiratory distress syndrome (ARDS): from mechanistic insights to therapeutic strategies

Acute respiratory distress syndrome (ARDS) is a clinical syndrome of acute hypoxic respiratory failure caused by diffuse lung inflammation and edema. ARDS can be precipitated by intrapulmonary factors or extrapulmonary factors, which can lead to severe hypoxemia. Patients suffering from ARDS have high mortality rates, including a 28-day mortality rate of 34.8% and an overall in-hospital mortality rate of 40.0%. The pathophysiology of ARDS is complex and involves the activation and dysregulation of multiple overlapping and interacting pathways of systemic inflammation and coagulation, including the respiratory system, circulatory system, and immune system. In general, the treatment of inflammatory injuries is a coordinated process that involves the downregulation of proinflammatory pathways and the upregulation of anti-inflammatory pathways. Given the complexity of the underlying disease, treatment needs to be tailored to the problem. Hence, we discuss the pathogenesis and treatment methods of affected organs, including 2019 coronavirus disease (COVID-19)-related pneumonia, drowning, trauma, blood transfusion, severe acute pancreatitis, and sepsis. This review is intended to provide a new perspective concerning ARDS and offer novel insight into future therapeutic interventions.

Integrating biomarkers for hemostatic disorders into computational models of blood clot formation: A systematic review

In the pursuit of personalized medicine, there is a growing demand for computational models with parameters that are easily obtainable to accelerate the development of potential solutions. Blood tests, owing to their affordability, accessibility, and routine use in healthcare, offer valuable biomarkers for assessing hemostatic balance in thrombotic and bleeding disorders. Incorporating these biomarkers into computational models of blood coagulation is crucial for creating patient-specific models, which allow for the analysis of the influence of these biomarkers on clot formation. This systematic review aims to examine how clinically relevant biomarkers are integrated into computational models of blood clot formation, thereby advancing discussions on integration methodologies, identifying current gaps, and recommending future research directions. A systematic review was conducted following the PRISMA protocol, focusing on ten clinically significant biomarkers associated with hemostatic disorders: D-dimer, fibrinogen, Von Willebrand factor, factor Ⅷ, P-selectin, prothrombin time (PT), activated partial thromboplastin time (APTT), antithrombin Ⅲ, protein C, and protein S. By utilizing this set of biomarkers, this review underscores their integration into computational models and emphasizes their integration in the context of venous thromboembolism and hemophilia. Eligibility criteria included mathematical models of thrombin generation, blood clotting, or fibrin formation under flow, incorporating at least one of these biomarkers. A total of 53 articles were included in this review. Results indicate that commonly used biomarkers such as D-dimer, PT, and APTT are rarely and superficially integrated into computational blood coagulation models. Additionally, the kinetic parameters governing the dynamics of blood clot formation demonstrated significant variability across studies, with discrepancies of up to 1, 000-fold. This review highlights a critical gap in the availability of computational models based on phenomenological or first-principles approaches that effectively incorporate affordable and routinely used clinical test results for predicting blood coagulation. This hinders the development of practical tools for clinical application, as current mathematical models often fail to consider precise, patient-specific values. This limitation is especially pronounced in patients with conditions such as hemophilia, protein C and S deficiencies, or antithrombin deficiency. Addressing these challenges by developing patient-specific models that account for kinetic variability is crucial for advancing personalized medicine in the field of hemostasis.

Diagnostic value of fibrinogen in lower extremity deep vein thrombosis caused by rib fracture: A retrospective study

Objectives: To investigate the diagnostic value of fibrinogen (FIB) in patients with rib fractures complicated by lower extremity deep venous thrombosis (DVT).Methods: Analyzing data from 493 patients at Shijiazhuang Third Hospital, FIB levels at 24, 48, and 72 h post-injury were compared between DVT and non-DVT groups.Results: DVT group had elevated FIB levels at all times (p < .001). FIB at 24 h showed highest AUC, particularly in patients with BMI <28.Conclusion: In conclusion, measuring FIB at 24 h post-injury enhances DVT detection in rib fracture patients, with potential BMI-related variations.

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Objective

This study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.

Methods

We conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).

Results

Logistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.

Conclusion

This study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

Establishment and validation of a nomogram predicting the risk of deep vein thrombosis before total knee arthroplasty

PURPOSE

This study aimed to analyze the independent risk factors contributing to preoperative DVT in TKA and constructed a predictive nomogram to accurately evaluate its occurrence based on these factors.

METHODS

The study encompassed 496 patients who underwent total knee arthroplasty at our hospital between June 2022 and June 2023. The dataset was randomly divided into a training set (n = 348) and a validation set (n = 148) in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were used to screen the predictors of preoperative DVT occurrence in TKA and construct a nomogram. The performance of the predictive models was evaluated using the concordance index (C-index), calibration curves, and the receiver operating characteristic (ROC) curves. Decision curve analysis was used to analyze the clinical applicability of nomogram.

RESULTS

A total of 496 patients who underwent TKA were included in this study, of which 28 patients were examined for lower extremity DVT preoperatively. Platelet crit, Platelet distribution width, Procalcitonin, prothrombin time, and D-dimer were predictors of preoperative occurrence of lower extremity DVT in the nomograms of the TKA patients. In addition, the areas under the curve of the ROC of the training and validation sets were 0.935 (95%CI: 0.880-0.990) and 0.854 (95%CI: 0.697-1.000), and the C-indices of the two sets were 0.919 (95%CI: 0.860-0.978) and 0.900 (95%CI: 0.791-1.009). The nomogram demonstrated precise risk prediction of preoperative DVT occurrence in TKA as confirmed by the calibration curve and decision curve analysis.

CONCLUSIONS

This Nomogram demonstrates great differentiation, calibration and clinical validity. By assessing individual risk, clinicians can promptly detect the onset of DVT, facilitating additional life monitoring and necessary medical interventions to prevent the progression of DVT effectively.

Analysis of Risk Factors for Postoperative Deep Vein Thrombosis in Traumatic Spinal Fracture Complicated with Spinal Cord Injury

The purpose of this study is to investigate the risk factors for postoperative deep vein thrombosis (DVT) in patients with traumatic spinal fractures complicated with Spinal Cord Injury(SCI). We conducted a retrospective analysis of 110 patients with traumatic spinal fractures and SCI admitted to our hospital from March 2021 to April 2024. DVT was diagnosed using ultrasound. Patient history, general data, surgical data, laboratory tests, and thromboelastogram (TEG) results were collected. The patients were divided into a DVT group and a non-DVT group according to the results of ultrasound one week after surgery. The risk factors and diagnostic value were analyzed using binary logistic regression and receiver operating characteristic (ROC) curves in both univariate and multivariate analyses. Multivariate and ROC analysis results showed that D-dimer, lower extremity, duration of bedrest, and MA values of TEG were independent risk factors for DVT in SCI, with D-dimer having the highest diagnostic value (AUC = 0.883). The AUC values for lower extremity, duration of bedrest, and MA were 0.731, 0.750, and 0.625. In conclusion, Postoperative D-dimer > 5.065 mg/l, lower extremity < 3, duration of bedrest, and MA value of TEG are independent risk factors for postoperative DVT in SCI patients, D-dimer having the highest diagnostic value. When the above risk factors occur, clinicians need to be vigilant and take appropriate prevention and treatment measures.