Evaluating of Existing VTE Risk Scales in Glioma Patients.

INTRODUCTION: Postoperative venous thromboembolism (VTE) is a frequently occurring complication among glioma patients. Several risk assessment models (RAMs), including the Caprini RAM, the IMPROVE Risk Score, the IMPROVED VTE Risk Score, and the Padua Prediction Score, have not been validated within the glioma patient population. The purpose of this study was to assess the predictive accuracy of established VTE risk scales in patients with glioma. MATERIALS AND METHODS: A single-center, retrospective, observational cohort study was conducted on 265 glioma patients who underwent surgery at the Almazov Medical and Research Centre between 2021 and 2022. VTE detection followed the current clinical guidelines. Threshold values for the Caprini, IMPROVE VTE, IMPROVEDD, and Padua scales were determined using ROC analysis methods, with cumulative weighting for sensitivity and specificity in predicting VTE development. The areas under the ROC curves (AUC) were calculated, and comparisons were made using the DeLong test. RESULTS: The area under the curve for the Caprini risk assessment model was 80.41, while the IMPROVEDD VTE risk score was 75.38, the Padua prediction score was 76.9, and the IMPROVE risk score was 72.58. No significant differences were observed in the AUC values for any of the scales. The positive predictive values of all four scales were low, with values of 50 (28-72) for Caprini, 48 (28-69) for IMPROVEDD VTE, 50 (30-70) for Padua, and 64 (35-87) for IMPROVE RAM. No significant differences were found in terms of PPV, NPV, positive likelihood ratio, and negative likelihood ratio among the analyzed scales. CONCLUSIONS: The Caprini Risk Assessment Model, the IMPROVE Risk Score, the IMPROVED VTE Risk Score, and the Padua Prediction Score exhibit acceptable specificity and sensitivity for glioma patients. However, their low positive predictive ability, coupled with the complexity of interpretation, limits their utility in neurosurgical practice.

Reference intervals and biological variation in parameters of the thrombin generation test in healthy individuals.

INTRODUCTION: Establish the referenceintervals (RIs) and analyze biological variability (BV) to introduce the thrombin generation test (TGT) into clinical practice. METHODS: To determine the RIs parameters of TGT, we analyzed platelet-poor plasma (PPP) (n = 123), rich (PRP) (n = 76), and microparticle-mediated TGT (MP-TGT) (n = 32) in healthy participants. For the BV study, we collected samples from five participants over 5 weeks. A nested analysis of variance (ANOVA) was performed to evaluate the BV results. RESULTS: The between-individual variation (CVG ), within-individual variation (CVI ), analytical variation (CVA ) for TGT on PPP for all parameters were from 5.5% to 17.3%, 5.4% to 17.7%, and 2.6% to 5.3%, respectively. For PRP, the CVG , CVI , and CVA were ranged from 3.0% to 23.7%, 8.4% to 23.0%, and 4.1% to 6.9%, respectively. The index of individuality (II) ranged from 0.3 to 3.1 for PPP and from 0.3 to 4.5 for PRP. The reference change value (RCV) for PPP was from 19.8% to 50.1%, while for PRP, it was 27.2% to 66.5%. We recommend using the RIs for the parameters ETP (nM/min): 1101.6-2332.1 and Peak (nM): 163.5-381.3 for PPP and ETP (nM/min): 1088.5-2634.9; Peak (nM): 72.6-210.7 for PRP. The resulting MP-TGT are highly dependent on age require a larger sample. CONCLUSION: For TGT on PPP and PRP the RIs developed on our population for Peak and ETP parameters can be used. Time parameters: Lagtime and ttPeak, min with II < 0.6, require monitoring over time with RCV calculation.