Diagnostic and Prognostic Value of TAT, PIC, TM, and t-PAIC in Malignant Tumor Patients With Venous Thrombosis

The clinical significance of TAT, PIC, TM, and t-PAIC in vascular events of BCR/ABL-negative myeloproliferative neoplasms

Predicting the likelihood vascular events in patients with BCR/ABL1-negative myeloproliferative neoplasms (MPN) is essential for the treatment of the disease. However, effective assessment methods are lacking. Thrombin-antithrombin complex (TAT), plasmin-α2- plasmininhibitor complex (PIC), thrombomodulin (TM), and tissue plasminogen activator-inhibitor complex (t-PAIC) are the new direct indicators for coagulation and fibrinolysis. The aim of this study was to investigate the changes of these four new indicators in thrombotic and hemorrhagic events in BCR/ABL1-negative MPN. The study cohort of 74 patients with BCR/ABL negative myeloproliferative disorders included essential thrombocythemia, polycythemia vera, and primary myelofibrosis (PMF). A panel of 4 biomarkers, including TAT, PIC, TM, and t-PAIC were determined using Sysmex HISCL5000 automated analyzers, whereas fibrin/fibrinogen degradation products (FDP), D-dimer and Antithrombin III (ATIII) were analyzed using Sysmex CS5100 coagulation analyzer. A total of 24 (32.4%) patients experienced thrombotic events and hemorrhagic events occurred in 8 patients (10.8%). Compared to patients without hemorrhagic-thrombotic events, patients with thrombotic events had higher fibrinogen (FIB) level, FDP level and lower ATIII activity, while patients with hemorrhagic events had lower white blood cell count and hemoglobin level, higher FDP level (P < 0.05). Patients with a JAK2V617F mutation were more likely to experience thrombotic events (P < 0.05). In addtion, patients with thrombotic events had higher TAT, PIC, TM, and t-PAIC levels than patients without hemorrhagic-thrombotic events (P < 0.05), whereas patients with hemorrhagic events had a lower median value in TAT and TM (no statistical difference, P > 0.05). Patients with higher TAT, TM and t-PAIC were more likely to experience thrombotic events (P < 0.05), and only TAT was positively correlated with thrombotic events (Spearman  r =0.287, P = 0.019). TAT, PIC, TM, and t-PAIC combined with ATIII and FDP have a certain value for predicting thrombosis in patients with BCR/ABL1-negative MPN. These 6 parameters are worth further exploration as predictive factors and prognostic markers for early thrombotic events.

Prognostic value of coagulation markers in patients with colorectal caner: A prospective study

Background and Aims

The occurrence, growth, and metastasis of colorectal cancer (CRC) are connected to the hypercoagulable state of blood (CRC). This study aimed to identify significant coagulation factors to predict metastasis and prognosis of CRC.

Methods

Thrombomodulin (TM), thrombin-antithrombin complex (TAT), α2-plasmininhibitor-plasmin complex (PIC), and tissue plasminogen activator-inhibitor complex (t-PAIC) were detected by chemiluminescence immunoassay using Sysmex HISCL5000 automated analyzers. The Sysmex CS 5100 automatic blood coagulation analyzer was used to detect d-dimer (DD), fibrin degradation product (FDP), prothrombin time (PT), thrombin time (TT), international normalized ratio (INR), fibrinogen (Fbg), and activated partial thromboplastin time (APTT). Area under the curve (AUC) and the receiver operating characteristic curve (ROC) were used to assess the diagnostic efficacy of markers. Kaplan-Meier analysis was used to calculate survival probabilities. Independent prognostic factors and the nomogram were developed using single-factor and multifactor cox regression analysis model.

Results

The following indicators (TM, TAT, PIC, t-PAIC, DD, FDP, PT, INR, APTT, and Fbg) were markedly higher in CRC patients than in healthy controls, and they were higher in the metastasis (M) group than in the nonmetastasis (NM) group. The combination "TAT + PIC + DD + FDP + Fbg" can distinguish M from NM with exceptional sensitivity and specificity. Patients with CRC who had high levels of TAT, PIC, DD, FDP, Fbg, TM, tPAIC, PT, and INR had significantly shorter survival.

Conclusion

The prognosis of CRC patients can be predicted by coagulation indicators. The independent predictive variables for overall survival were found to be TM and DD. To forecast CRC patient survival, a nomogram was created.

[Predictive value of four items of new thrombus markers combined with conventional coagulation tests for thrombosis in antiphospholipid syndrome]

OBJECTIVE

To explore the predictive value of four items of new thrombus markers combined with conventional coagulation tests for thrombosis in antiphospholipid syndrome.

METHODS

A total of 121 antiphospholipid syndrome (APS) patients who hospitalized at Peking University People's Hospital from March 2022 to January 2023 were selected and divided into thrombus group (50 cases) and nonthrombus group (71 cases) according to whether thrombosis occurred. The differences of laboratory characteristics including antiphospholipid antibodies were compared between the thrombotic and non-thrombotic groups. Chemiluminescent immunoassay was used to detect thrombomodulin (TM), thrombin-antithrombin complex (TAT), Plasmin-α2 plasmin inhibitor complex (PIC), and tissue plasminogen activator inhibitor complex (t-PAIC) in plasma from venous. The independent risk factors of thrombosis in patients with APS were determined using binary Logistic regression. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the efficacy of each index on the prediction of thrombosis.

RESULTS

Compared with the patients without thrombosis, the patients with thrombosis were older [49 (32, 64) years vs. 36 (32, 39) years, P < 0.05]. The percentages of male, smoking, hypertension, and global antiphospholipid syndrome score (GAPSS)≥10 in the patients with thrombosis were significantly higher than those in the patients without thrombosis (P < 0.05). The positive rates of anticardiolipin antibody (aCL) and lupus anticoagulant (LA) in the thrombotic group were significantly higher than those in the non-thrombotic group (P < 0.05), and the levels of prothrombin time, activated partial thromboplastin time, fibrinogen, fibrin degradation product in the thrombotic group were significantly higher than those in the non-thrombotic group (P < 0.05).Among the thrombosis group, venous thrombosis accounted for 19 (38.00%), including deep vein thrombosis (16, 84.21%) and pulmonary embolism accounted (5, 26.32%); Arterial thrombosis accounted for 35 (70.00%), including myocardial infarction (6, 17.14%) cerebral infarction (30, 85.71%). The patients in the thrombotic group had significantly greater TM levels than those in the non-thrombotic group (P < 0.05).There were no significant dif-ferences between the two groups in TAT (Z=-1.420, P=0.156), PIC (Z=-0.064, P=0.949), and t-PAIC (Z=-1.487, P=0.137). Univariate and binary Logistic regression analysis of relevant variables showed that advanced age [OR=1.126, P=0.002], elevated TM [OR=1.325, P=0.048], prolonged prothrombin time (PT) [OR=4.127, P=0.008] were independent risk factors for thrombosis in the patients with APS. ROC curve analysis of the above three independent risk factors showed that the combined detection of age, PT and TM had the highest Yoden index (0.727) and sensitivity (83.0%), with a specificity of 89.7%.

CONCLUSION

TAT, PIC, TM, and t-PAIC may reflect thrombus formation from the coagulation system, fibrinolysis system, and endothelial system. The combined of age TM and PT is superior to the application of a single marker, which has diagnostic value for the early identification of APS thrombosis.

Trimester-specific reference intervals of hemostasis biomarkers for healthy pregnancy

Physiological changes in hemostasis during pregnancy have been reported by several authors. This study aimed at establishing reference intervals for the hemostasis biomarkers thrombin-antithrombin complex (TAT), α2-plasmininhibitor-plasmin complex (PIC), thrombomodulin (TM) and tissue plasminogen activator-inhibitor complex (tPAI-C), in healthy pregnancies. After excluding outliers, a total of 496 healthy pregnant women (128 first-trimester, 142 second-trimester, 107 third-trimester and 119 pre-labor) and 103 healthy nonpregnant women were enrolled from Shenzhen Bao'an Women's and Children's Hospital. Hemostasis biomarkers, TAT, PIC, TM and tPAI-C, were measured by using a quantitative chemiluminescence enzyme immunoassay performed on HISCL automated analysers. The median and reference intervals (the 2.5th and 97.5th percentiles) were calculated to establish trimester-specific reference intervals for healthy pregnant women. The reference intervals for TAT, PIC, TM and tPAI-C in the first trimester were 0.7-7.6 1 µg/L, 0.2-0.9 mg/L, 2.8-11.0 TU/ml, and 1.2-6.5 1 µg/L, respectively. The reference intervals in the second trimester were 1.7-12.0 1 µg/L, 0.2-1.0 mg/L, 3.7-11.6 TU/ml, and 2.8-8.8 1 µg/L, respectively. The reference intervals in the third trimester were 2.7-16.1 1 µg/L, 0.1-1.4 mg/L, 2.9-12.9 TU/ml, and 1.9-8.0 1 µg/L, respectively. At pre-labor, the reference intervals were 4.8-32.9 1 µg/L, 0.2-1.9 mg/L, 4.2-12.6 TU/ml, and 2.8-15.4 1 µg/L, respectively. Gestational reference intervals for TAT, PIC, TM and tPAI-C in healthy pregnancies are provided, but only for TAT with increasing concentrations throughout pregnancy, the reference intervals for non-pregnant were not applicable.

Early identification of lung cancer patients with venous thromboembolism: development and validation of a risk prediction model

INTRODUCTION

Venous thromboembolism(VTE) is a leading cause of death in patients with lung cancer. Furthermore, hospitalization of patients with advanced lung cancer for VTE treatment represents a major economic burden on the national public health resources. Therefore, we performed this prospective study to identify clinical biomarkers for the early identification of VTE in lung cancer patients.

METHODS

This prospective study enrolled 158 patients with confirmed lung cancer, including 27 who were diagnosed with VTE within six months of the follow-up after lung cancer diagnosis. Multivariate logistic regression analysis was used to evaluate the diagnostic performancese of all the relevant clinical features and laboratory indicators in identifying lung cancer patients with a higher risk of VTE. A novel risk prediction model was constructed consisting of five clinical variables with the best diagnostic performances and was validated using the receiver operation characteristic(ROC) curves. The diagnostic performances of the new risk prediction model was also compared with the Khorana risk score (KRS) and the Padua risk score (PRS).

RESULTS

The VTE group of lung cancer patients (n = 27) showed significantly higher serum levels of fibrin degradation products (FDP), D-dimer, thrombomodulin (TM), thrombin-antithrombin-complex (TAT), α2-plasmin inhibitor-plasmin Complex (PIC), and tissue plasminogen activator-plasminogen activator inhibitor complex (t-PAIC) compared to those in the non-VTE group (n = 131). ROC curve analyses showed that the diagnostic efficacy of the new VTE risk prediction model with TM ≥ 9.75 TU/ml, TAT ≥ 2.25ng/ml, t-PAIC ≥ 7.35ng/ml, history of VTE, and ECOG PS score ≥ 2 was superior than the KRS and the PRS in the early identification of lung cancer patients with a higher risk of VTE.

CONCLUSIONS

The new risk prediction model showed significantly high diagnostic efficacy in the early identification of lung cancer patients with a high risk of VTE. The diagnostic efficacy of the new risk prediction model was higher than the KRS and the PRS in this cohort of lung cancer patients.

Spontaneous intramuscular hemorrhage in cancer-associated dermatomyositis: a case and literature review

BACKGROUND

Spontaneous intramuscular hemorrhage (SIH) is a rare but life-threatening complication of dermatomyositis (DM). The pathogenetic mechanism and management of intramuscular hematoma in these patients remains unclear. Here we discuss a case of recurrent hemorrhage in a patient with cancer-associated DM, and review the relevant literature for timely diagnosis and treatment.

CASE PRESENTATION

A 53-year-old male patient presented with rashes, muscle weakness, and dysphagia and was diagnosed with DM. During treatment, he developed SIH of the arm and right psoas major muscle successively. MRI showed extensive edema of the right shoulder girdle muscle and muscle groups of the upper arm. During the second SIH, a CT scan showed new-onset hematoma formation in the right psoas major muscle. The detection of D-dimer, thrombin-antithrombin III complex (TAT), plasmin-α2-plasmininhibitor complex (PIC) and tissue plasminogen activator-inhibitor complex (t-PAIC) indicated predominant hyperfibrinolysis over thrombosis. Blood transfusion and supportive treatment were immediately performed, and the hematoma did not expand. However, his abdominal distension was not relieved after active treatment. Further electronic gastroscopy discovered gastric sinus ulcers, and histopathology of the biopsy confirmed signet-ring cell carcinoma.

CONCLUSIONS

Although patients with cancer-associated DM have an increased risk of thrombosis, prophylactic anticoagulation therapy needs deliberate consideration. It is important to monitor the coagulation parameters dynamically during anticoagulation therapy. Especially when the level of D-dimer is high, and it is uncertain whether the patient is in a state of thrombosis or hyperfibrinolysis, the detection of TAT, PIC, t-PAIC can help to determine whether to initiate anticoagulation therapy.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.

Thrombomodulin (TM), thrombin-antithrombin complex (TAT), plasmin-α2-plasmininhibitor complex (PIC), and tissue plasminogen activator-inhibitor complex (t-PAIC) assessment of fibrinolytic activity in postpartum hemorrhage: a retrospective comparative cohort study

Background

Detecting the changes of coagulation function in the early stage of postpartum hemorrhage (PPH), which is the leading cause of maternal death, is the key to treatment this serious disease, however, there is less effective assessment methods. Thrombomodulin (TM), thrombin-antithrombin complex (TAT), plasmin-α2-plasmininhibitor complex (PIC), and tissue plasminogen activator-inhibitor complex (t-PAIC) are the new direct indicators for coagulation and fibrinolysis, and considered sensitive molecular markers of the fibrinolytic system changing. The aim of this study was to investigate the changes of these 4 new indicators in the early stage of PPH.

Methods

We retrospectively reviewed the new coagulate indicators, TM, TAT, PIC, and t-PAIC, obtained from PPH patients at Guangzhou Women and Children's Medical Center from January 2021 to July 2021. According to the amount of blood loss, the patients were divided into 3 groups: Mild group (blood loss <1,500 mL, n=17), Severe group (blood loss ≥1,500 mL, n=24); another 12 women who did not experience PPH were selected as the Normal group (n=12). The four indicators were measured at the time of PPH happened, or immediately when baby was born in the Normal group, and evaluated for the prediction of PPH.

Results

The t-PAIC level of the Severe group was significantly lower than the other 2 groups (Normal group vs. Mild group vs. Severe group: 13.9±2.0 vs. 9.4±1.8 vs. 7.5±0.9, P=0.020), and the PIC level was the highest (Normal group vs. Mild group vs. Severe group: 1.1±0.2 vs. 2.7±0.8 vs. 2.9±0.6, P=0.012). There was no significant difference in TM and TAT among the 3 groups. The odds ratio (OR) value of t-PAIC was 0.822, 95% confidence interval (CI): 0.697-0.970, P=0.020. The area under the curve (AUC) of PIC was 0.5, and t-PAIC was 0.775, the cut-off point was 6.295, the specificity was 75%, and the sensitivity was 75%.

Conclusions

With the increasing of blood loss, t-PAIC decreased gradually, and is associated with severe PPH. This indicator may be a new predictor of PPH, and should be used in the early period of PPH for treatment.

Clinical Efficacy of Soluble Thrombomodulin, Tissue Plasminogen Activator Inhibitor complex, Thrombin-Antithrombin complex,α2-Plasmininhibitor-Plasmin complex in Pediatric Sepsis

Objective

To investigated the clinical efficacy of Soluble thrombomodulin (sTM), tissue plasminogen activator inhibitor complex (t-PAI·C),thrombin-antithrombin complex (TAT),α2-plasmininhibitor-plasmin complex (PIC) in pediatric sepsis and pediatrics sepsis-induced coagulopathy (pSIC).

Methods

We prospectively collected patient data with sepsis diagnosed in the PICU of Shanghai Children's Medical Center from June 2019 to June 2021. sTM,t-PAI·C, TAT,PIC and classical coagulation laboratory tests (CCTs) were evaluated on the day of sepsis diagnosis.

Results

Fifty-nine children were enrolled, There were significant differences in t-PAI·C (P = 0.001), Plt (P < 0.001), PT (P < 0.001), INR (P < 0.001), aPTT (P < 0.001), and TT (P = 0.048) between the pSIC and non-pSIC groups, logistic regression analysis showed that Plt (P = 0.032) was an independent risk factor for pSIC. Logistic regression analysis showed that sTM (P = 0.007) and Plt (P = 0.016) were independent risk factors for the outcome in pediatrics sepsis following discharge. The AUC of sTM combined with Plt on the mortality outcome of children with sepsis at discharge was 0.889 (95%CI: 0.781,0.956). which was better than that for PRISM III (AUC, 0.723), pSOFA (AUC, 0.764), and blood Lac (AUC, 0.717) when sepsis was diagnosed in the PICU.

Conclusions

The t-PAI·C increased in children with pSIC. The prediction of sepsis outcome using sTM combined with Plt was better than with PRISM III, pSOFA, or Lac.Further research is still needed in the future to explore the clinical value of sTM, TAT, PIC, and t-PAI·C in diagnosis and outcome of pediatrics sepsis and pSIC.

Tissue-Type Plasminogen Activator-Inhibitor Complex as an Early Predictor of Septic Shock: A Retrospective, Single-Center Study

Background

Sepsis can progress to septic shock and death, and identifying biomarkers of this progression may permit timely intervention to prevent it. This study explored whether levels of tissue-type plasminogen activator-inhibitor complex (t-PAIC) in serum can predict septic shock early.

Methods

We retrospectively analyzed 311 sepsis patients who had been admitted to the intensive care unit (ICU) at our tertiary care hospital between May 2018 and April 2021, and we divided them into those who progressed to septic shock (n = 203) or not (n = 108) based on sepsis-3 definition. After matching patients in the two groups based on propensity scoring, we screened for risk factors of septic shock using logistic regression. We assessed potential predictors of such shock based on the area under the receiver-operating characteristic curve (AUC), Kaplan-Meier survival curves, and correlation analysis.

Results

After propensity score matching to generate two equal groups of 108 patients, we found that serum t-PAIC was significantly higher in septic shock patients. Uni- and multivariate logistic regression identified t-PAIC as an independent risk factor for septic shock (OR 1.14, 95% CI 1.09–1.19, P < 0.001) and a biomarker that predicted it with an AUC up to 0.875 (95% CI, 0.829-0.920). Based on the optimal cut-off of t‐PAIC = 17.9 ng/mL, we found that patients at or above this threshold had significantly higher lactate levels and scores on the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA). Such patients also had significantly worse survival (HR 2.4, 95% CI 1.38–4.34, P = 0.004). Spearman's correlation coefficients were 0.66 between t-PAIC and lactate, and 0.52 between t-PAIC and SOFA.

Conclusions

Serum levels of t-PAIC may be an independent risk factor for septic shock, and they may correlate with the severity of such shock.

Coagulation and Fibrinolysis Biomarkers as Potential Indicators for the Diagnosis and Classification of Ovarian Hyperstimulation Syndrome

Background: Accurate diagnosis and classification of ovarian hyperstimulation syndrome (OHSS) is important for its management. We employed a new high-sensitivity chemiluminescence immunoassay to detect the thrombin-antithrombin complex (TAT), plasmin alpha2-plasmin inhibitor complex (PIC), soluble thrombomodulin (sTM), and tissue plasminogen activator-inhibitor complex (TPAI-C), and evaluated their diagnostic and classification performance for OHSS.

Methods: A total of 106 women were enrolled, including 51 patients with OHSS (25 mild or moderate OHSS, 26 severe OHSS), and 55 without OHSS (control group). TAT, PIC, sTM, and TPAI-C levels were measured using the Sysmex HISCL5000 automated analyzer.

Results: Compared to the control group, TAT, PIC, and TPAI-C levels were significantly higher (P < 0.001, P < 0.001, P < 0.001, respectively), whereas the sTM level was significantly lower (P < 0.001) in the patients with OHSS. The receiver operating characteristic was used to evaluate the diagnostic efficiency. For the diagnosis of OHSS, the area under the curves (AUCs) for TAT, PIC, sTM, and TPAI-C were 0.991, 0.973, 0.809, and 0.722, respectively. In particular, the sensitivity, specificity, positive predictive value, and negative predictive value for TAT and PIC were all above 90%. For the differential diagnosis of mild–moderate and severe OHSS, the AUCs for TAT, PIC, and TPAI-C were 0.736, 0.735, and 0.818, respectively. The cutoff values of TAT, PIC, and TPAI-C for the differential diagnosis of mild–moderate and severe OHSS were 11.5 ng/mL, 2.4 μg/mL, and 5.8 ng/mL, respectively. Based on these cutoff values, eight cases of mild–moderate OHSS exceeded the cutoff values, two of which developed to severe OHSS in the following days. However, of the remaining 17 cases of mild–moderate OHSS patients with negative biomarkers, none subsequently developed severe OHSS.

Conclusions: TAT, PIC, sTM, and TPAI-C can be used as sensitive biomarkers in the diagnosis of OHSS. Meanwhile, TAT, PIC, and TPAI-C also displayed remarkable potential in the classification of OHSS. In addition, the levels of TAT, PIC, and TPAI-C above the cutoff values in patients with mild–moderate OHSS might predict a high risk of developing severe OHSS.

Research progress on biomarkers of pulmonary embolism

OBJECTIVES

To present a review on the traditional and new biomarkers of pulmonary embolism (PE).

DATA SOURCE

A systematic search has been carried out using keywords as PE, biomarker, diagnosis and risk stratification.

RESULTS

The results of this work have been structured into three parts: first, conventional biomarkers for vascular, cardiac and inflammation, including static markers and dynamic markers for measuring the time course; next, a review of new biomarkers in recent years, such as RNAs and markers obtained through proteomics and mass spectrometry; finally, use of new detection methods to directly detect the activity of existing markers, such as the determination of coagulation factor II and plasmin activities based on the proteolytic activation of an engineered zymogen.

CONCLUSIONS

This work summarized the characteristics of current traditional biomarkers for clinical diagnosis and risk stratification of PE, as well as a series of newly discovered biomarkers obtained through various clinical experimental methods.