Relationship of thromboelastography and conventional clotting test values with severe bleeding in critically ill patients with coagulopathy: A prospective study

Reference interval for the C-ACT activated clotting time test in healthy dogs using a water bath and manual assessment of clot formation

AIMS

To establish a reference range for the canine C-ACT activated clotting time (ACT) test using a water bath and visual clot assessment technique.

METHODS

Healthy, privately owned dogs (n = 48) were prospectively recruited to the study. Blood samples were collected via direct jugular venipuncture for complete blood count, serum biochemistry, prothrombin time (PT) and activated partial thromboplastin time (aPTT). Five animals with major abnormalities or who became agitated during phlebotomy were excluded. For the 43 remaining animals, 2 mL of blood was then collected via the cephalic vein and added directly to a C-ACT tube that was shaken vigorously before being placed in a water bath at 37°C. Tubes were visually assessed for clot formation and C-ACT was recorded in seconds when the magnet within the tube lodged in the clot.

RESULTS

Forty-three animals were eligible for inclusion. The nonparametric reference interval (capturing the central 95% of the data) was 50-80 seconds, with a 90% CI for the lower limit of 50-55 seconds and a 90% CI for the upper limit of 75-80 seconds. The C-ACT ACT test had a positive correlation with aPTT (0.42, 95% CI = 0.13-0.64). There was no evidence of a correlation between C-ACT ACT and age, weight, PT, haematocrit, white blood cell count, platelet count or total protein.

CONCLUSIONS AND CLINICAL RELEVANCE

The results of this study suggest that the normal reference interval for ACT in dogs using C-ACT tubes in a 37°C water bath is 50-80 seconds. Care should be taken extrapolating the results of this study to the general population, as the smaller study design had less control for confounders than a larger study. However, when using the described analytical methods, C-ACT tube ACT test results > 80 seconds should be considered prolonged in dogs and should prompt further investigation.

ABBREVIATIONS

ACT: Activated clotting time; aPTT: Activated partial thromboplastin time; EDTA: Ethylenediaminetetraacetic acid; Hct: Haematocrit; PT: Prothrombin time.

Identification of Early Derangements of Coagulation, Hematological and Biochemical Profiles in Patients with Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a severe disease involving various pathological processes. We aimed to use rapid -thromboelastography (r-TEG) combined with conventional coagulation assays (CCAs) and other laboratory tests, to identify early derangements in coagulation, hematological, and biochemical profiles in patients with AP.

METHODS

We enrolled 177 patients diagnosed with AP and 121 controls. Blood samples were analyzed using r-TEG, CCAs, and hematological and biochemical tests within 2 h of patient admission. All testing parameters were compared between the patients and the controls. Pearson's correlation coefficient was used to determine the correlation between the parameters among the patients. Logistic regression analysis was performed to evaluate the effects of the variables (demographic, coagulation, hematological and biochemical) on AP.

RESULTS

Using r-TEG and CCAs, we observed differences in coagulation parameters between the patients with AP and the controls. The r-TEG results showed a pro-coagulant state and increased platelet activation in AP patients. Pearson's correlation analysis showed that inflammatory indicators were strongly correlated with coagulation/platelets in the pathological process of AP. Logistic regression analysis revealed that age, K, neutrophil (NEUT), triglyceride (TG) and blood amylase (AMY) were significantly associated with the development of AP.

CONCLUSION

Coagulation profile and platelet play essential roles in the pathogenesis of AP. Pro-coagulant state and increased platelet activation in patients with AP were demonstrated using r-TEG. The r-TEG parameter K, age, NEUT, TG, and AMY may be used as potential indicators of AP.

Thromboelastography parameters in diagnosing periprosthetic joint infection and predicting reimplantation timing

BACKGROUND

Coagulation-related biomarkers are drawing new attention in the diagnosis of periprosthetic joint infection (PJI). The thromboelastography (TEG) assay provides a comprehensive assessment of blood coagulation; therefore, it could be a promising test for PJI. This study aims to assess the value of TEG in diagnosing PJI and to determine the clinical significance of TEG in analysing reimplantation timing for second-stage revision.

METHODS

From October 2017 to September 2020, 62 patients who underwent revision arthroplasty were prospectively included. PJI was defined by the 2011 Musculoskeletal Infection Society criteria, in which 23 patients were diagnosed with PJI (Group A), and the remaining 39 patients were included as having aseptic loosening (Group B). In group A, 17 patients completed a two-stage revision in our centre. C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), D-dimer, and TEG parameters (clotting time, α-angle, MA [maximum amplitude], amplitude at 30 min, and thrombodynamic potential index) were measured preoperatively in all included patients. In addition, receiver operating characteristic curves were used to evaluate the diagnostic value of these biomarkers.

RESULTS

ESR (area under curve [AUC], 0.953; sensitivity, 81.82; specificity, 94.87) performed best for PJI diagnosis, followed by MA (AUC, 0.895; sensitivity, 82.61; specificity, 97.44) and CRP (AUC, 0.893; sensitivity, 82.61; specificity, 94.74). When these biomarkers were combined in pairs, the diagnostic value improved compared with any individual biomarker. The overall success rate of the two-stage revision was 100%. Furthermore, ESR and MA were valuable in determining the time of reimplantation, and their values all decreased below the cut-off values before reimplantation.

CONCLUSION

TEG could be a promising test in assisting PJI diagnosis, and a useful tool in judging the proper timing of reimplantation.

Comparison between thromboelastography and conventional coagulation assays in patients with deep vein thrombosis

BACKGROUND

Conventional coagulation assays (CCAs) are of limited value in the assessment of coagulation status in patients with deep vein thrombosis (DVT). We aimed to compare thromboelastography (TEG) and CCAs in identifying DVT and evaluating coagulation status in DVT patients.

METHODS

Sixty-six patients diagnosed with DVT and forty healthy controls were enrolled. Blood samples were collected within 4 h of patients' admission to hospital before any operation and tested by TEG and CCAs. TEG and CCA parameters were compared between DVT patients and controls. The ability of each parameter in identifying DVT was assessed. Pearson's correlation was used to determine the correlation between TEG and CCA parameters among the study population.

RESULTS

TEG showed significant differences between DVT patients and controls, indicating a hypercoagulable tendency in patients suffering from DVT. In contrast, no significant difference regarding CCAs was observed between the DVT and control group. Furthermore, TEG displayed a better capacity in identifying DVT than CCAs. In addition, Pearson's correlation analysis showed TEG and CCA parameters had few correlations.

CONCLUSION

TEG has advantages in identifying DVT and detecting hypercoagulability, and provides a better insight in evaluating coagulation status in patients with DVT than CCAs.

Hemostatic Strategies in Trauma

Bleeding is a leading cause of early death from trauma. Consequently, effective hemostasis can improve the odds of survival after severe traumatic injury. Understanding the pathophysiology of trauma-induced coagulopathy can provide insights into effective strategies to assess and halt hemorrhage. Both physical assessment and appropriate laboratory studies are important in the diagnosis and evaluation of coagulopathy to identify the most effective mechanical and pharmacological strategies to achieve hemostasis. This article uses a case study approach to explore evidence-based techniques to evaluate hemorrhage and strategies to promote hemostasis.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

BACKGROUND

Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.

METHODS

Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model.

RESULTS

A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value.

CONCLUSIONS

Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.