Risk prediction of recurrent venous thrombosis; where are we now and what can we add?

Quantitative protein mass spectrometry for multiplex measurement of coagulation and fibrinolytic proteins towards clinical application: What, why and how?

Plasma proteins involved in coagulation and fibrinolysis are essential to hemostasis. Consequently, their circulating levels and functionality are critical in bleeding and thrombosis development. Well-established laboratory tests to assess these are available; however, said tests do not allow high multiplicity, require large volumes of plasma and are often costly. A novel technology to quantify plasma proteins is quantitative protein mass spectrometry (QPMS). Aided by stable isotope-labeled internal standards a large number of proteins can be quantified in one single analytical run requiring <30 μL of plasma. This provides an opportunity to improve insight in the etiology and prognosis of bleeding and thrombotic disorders, in which the balance between different proteins plays a crucial role. This manuscript aims to give an overview of the QPMS potential applications in thrombosis and hemostasis research (quantifying the 38 proteins assigned to coagulation and fibrinolysis by the KEGG database), but also to explore the potential and hurdles if designed for clinical practice. Advantages and limitations of QPMS are described and strategies for improved analysis are proposed, using as an example the test requirements for antithrombin. Application of this technology in the future could represent a step towards individualized patient care.

Prediction models for recurrence and bleeding in patients with venous thromboembolism: A systematic review and critical appraisal

INTRODUCTION

Prediction models for recurrence and bleeding are infrequently used when deciding on anticoagulant treatment duration after venous thromboembolism (VTE) due to concerns about performance and validity. Our aim was to critically appraise these models by systematically summarizing data from derivation and validation studies.

MATERIALS AND METHODS

MEDLINE and CENTRAL were searched until November 15th, 2019. Studies on prediction models for recurrence or bleeding after at least 3 months of anticoagulation in adult patients with VTE were included. The PROBAST, ROBINS-I and RoB2 tools were used to assess risk of bias and applicability.

RESULTS

Selection yielded 18 studies evaluating 8 models for recurrence (7 on development; 9 on validation; 1 update). Generally, models for recurrent VTE appeared to perform poorly to moderately in external validation studies (C-statistics 0.39-0.66, one 0.83). However, impact studies show that HERDOO2 and Vienna prediction model may identify patients with unprovoked VTE at low recurrence risk. Sixteen studies evaluating 14 models for anticoagulation-related bleeding were identified (7 on development; 9 on validation). Although some models seemed promising in development studies, their predictive performance was poor to moderate in external validation (C-statistics 0.52-0.71). All but 3 studies were considered at high risk of bias, mainly due to limitations in the statistical analysis.

CONCLUSIONS

Prognostic models for recurrence and anticoagulation-related bleeding risk often have important methodological limitations and insufficient predictive accuracy. These findings do not support their use in clinical practice to weigh risks of recurrence and bleeding when deciding on continuing anticoagulation after initial treatment of VTE.

Assessing the risk of venous thromboembolism in multiple myeloma

Patients with multiple myeloma (MM) have an increased risk of venous thromboembolism (VTE) compared to the general population. This risk is highest during the first year of diagnosis and subsequently decreases over time. Development of VTE in patients with MM is associated with inferior outcomes, with patients with VTE and MM having an increased risk of death compared to those with MM without VTE. Primary thromboprophylaxis has the potential to decrease risk of VTEksanfilippo@wustl.edu (K.M. Sanfilippo) in MM and improve outcomes. Current studies assessing thromboprophylaxis in MM excluded patients at high risk of VTE. A meta-analysis of trials of primary thromboprophylaxis in ambulatory cancer patients at high risk of VTE identified by use of a risk-prediction score found a reduction in risk of VTE with prophylaxis with no significant increase in risk of major bleeding. However, these trials contained relatively few patients with MM. Three clinical risk prediction scores are available to assess risk of VTE in MM: 1) the International Myeloma Working Group (IMWG)/National Comprehensive Cancer Network (NCCN); 2) the SAVED score; and 3) the IMPEDE VTE score. The latter two have recently been shown to outperform the IMWG/ NCCN score for predicting VTE in MM. Biomarkers have the potential to improve prediction of VTE in patients with MM. Future research should focus on the addition of biomarkers to available risk scores in MM to improve discrimination in this high-risk patient population.

Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP)

BACKGROUND

Recurrent venous thromboembolism (VTE) is common. Current guidelines suggest that patients with unprovoked VTE should continue anticoagulants unless they have a high bleeding risk, whereas all others can stop. Prediction models may refine this dichotomous distinction, but existing models apply only to patients with unprovoked first thrombosis. We aimed to develop a prediction model for all patients with first VTE, either provoked or unprovoked.

METHODS AND FINDINGS

Data were used from two population-based cohorts of patients with first VTE from the Netherlands (Multiple Environment and Genetic Assessment of Risk Factors for Venous Thrombosis [MEGA] follow-up study, performed from 1994 to 2009; model derivation; n = 3,750) and from Norway (Tromsø study, performed from 1999 to 2016; model validation; n = 663). Four versions of a VTE prediction model were developed: model A (clinical, laboratory, and genetic variables), model B (clinical variables and fewer laboratory markers), model C (clinical and genetic factors), and model D (clinical variables only). The outcome measure was recurrent VTE. To determine the discriminatory power, Harrell's C-statistic was calculated. A prognostic score was assessed for each patient. Kaplan-Meier plots for the observed recurrence risks were created in quintiles of the prognostic scores. For each patient, the 2-year predicted recurrence risk was calculated. Models C and D were validated in the Tromsø study. During 19,201 person-years of follow-up (median duration 5.7 years) in the MEGA study, 507 recurrences occurred. Model A had the highest predictive capability, with a C-statistic of 0.73 (95% CI 0.71-0.76). The discriminative performance was somewhat lower in the other models, with C-statistics of 0.72 for model B, 0.70 for model C, and 0.69 for model D. Internal validation showed a minimal degree of optimism bias. Models C and D were externally validated, with C-statistics of 0.64 (95% CI 0.62-0.66) and 0.65 (95% CI 0.63-0.66), respectively. According to model C, in 2,592 patients with provoked first events, 367 (15%) patients had a predicted 2-year risk of >10%, whereas in 1,082 patients whose first event was unprovoked, 484 (45%) had a predicted 2-year risk of <10%. A limitation of both cohorts is that laboratory measurements were missing in a substantial proportion of patients, which therefore were imputed.

CONCLUSIONS

The prediction model we propose applies to patients with provoked or unprovoked first VTE-except for patients with (a history of) cancer-allows refined risk stratification, and is easily usable. For optimal individualized treatment, a management study in which bleeding risks are also taken into account is necessary.

Predicting the risk of recurrent venous thrombosis: What the future might bring

An important clinical problem in the management of venous thrombosis is to determine whether a patient can safely cease anticoagulant therapy. In this Forum article, we summarize the predictive performance of several prediction models for recurrent thrombosis, as well as for bleeding while using anticoagulants. Patients with provoked first thrombosis (considered "low risk") are now denied long-term treatment, although a strong gradient in risk can be found in this group. We furthermore discuss the problem of an unclear definition of "(un)provoked" and show that this affects the yield of currently available prediction scores plus the limitations of a "one-size-fits-all" strategy. Better prediction tools are urgently needed. We propose a strategy for future studies for which the following should be considered: (a) reporting of absolute risks next to C-statistics, (b) model applicable to all patients, (c) no discontinuation of anticoagulation for measurement of predictors.

Risk prediction of recurrent venous thrombosis; where are we now and what can we add?

BACKGROUND

Several models are available to predict recurrent venous thrombosis (VT) in patients with unprovoked first events.

OBJECTIVES

To validate these prediction models externally.

METHODS

Within the MEGA follow-up study (n = 3750), we externally validated the Vienna and DASH score. These models were validated (a) by using the original study's criteria for patients with unprovoked VT and (b) by using our own criteria for unprovoked VT. In addition, absolute recurrence risks based on individual VT risk factors were calculated.

RESULTS

The recurrence rate was 5.2 (95% CI, 4.6-5.9) per 100 patient-years in those who had a first unprovoked VT according to our definition. For the Vienna model it was 3.4 per 100 patient-years and for DASH 3.8 per 100 patient-years. The C-statistic was 0.62 for Vienna and 0.65 for DASH. The C-statistic declined to 0.58 for both Vienna and DASH when we used our own definition of "unprovoked VT." Within the provoked group a strong gradient in risk was found dependent on the presence of traditional risk factors or biomarkers in a patient.

CONCLUSIONS

The ability to distinguish patients' recurrence risks is lower than proposed in the original prediction model studies and dependent on the definition that is used for an unprovoked first event. Furthermore, our results suggest that a more-refined risk estimation is possible, also in patients with a provoked first event, who are currently all classified as low risk.