ABSTRACT

OBJECTIVES: Our objectives were to estimate the incidence of venous thromboembolism (VTE) after robotic staging for endometrial cancer and to compare the incidence of VTE in patients who received a single dose of preoperative prophylaxis of enoxaparin with those who received extended postoperative prophylaxis.

METHODS: This study is a retrospective chart review of patients who underwent robot-assisted surgical staging for endometrial cancer. Patients were categorized into two groups: preoperative prophylaxis (PP), patients who received a single dose of enoxaparin preoperatively, and extended prophylaxis (EP), patients who received 28 days of enoxaparin postoperatively.

RESULTS: In total, 148 patients were included, with 117 patients in the PP group and 31 patients in the EP group. The overall incidence of VTE within 30 days postoperatively was 0.67%. No significant difference was found between the PP and the EP groups (0.9% and 0%, respectively; P = 1.00). Most patients in the cohort had endometrioid adenocarcinoma (78%) with low-grade disease (70%), although there were a greater number of patients in the PP group with uterine serous carcinoma compared with the EP group (17% vs 10%; P = 0.034). The PP group had higher estimated blood loss (106 vs 81 mL; P = 0.009) and longer operative times (178 vs 151 min; P = 0.028) compared with the EP group. Significantly more patients in the PP group underwent lymph node dissection compared with the EP group (32% vs 7%; P = 0.008).

CONCLUSIONS: The incidence of VTE following robot-assisted surgical staging for endometrial cancer in this study was 0.67%. No significant difference was found in VTE incidence between the PP group compared with the EP group. Mechanical prophylaxis plus a single dose of preoperative pharmacologic prophylaxis may suffice for low-risk patients following robotic surgical staging for endometrial cancer.

PMID:37788812 | DOI:10.14423/SMJ.0000000000001611

20:24

PubMed articles on: Cancer & VTE/PE

Obstructive Sleep Apnea and Venous Thromboembolism: Unraveling the Emerging Association

Cureus. 2023 Aug 30;15(8):e44367. doi: 10.7759/cureus.44367. eCollection 2023 Aug.

ABSTRACT

Oxidative stress has emerged as a significant contributor to skeletal muscle atrophy, influencing cellular processes that underlie muscle wasting. This review article delves into the intricate interplay between oxidative stress and muscle atrophy, shedding light on its mechanisms and implications. We begin by outlining the fundamental concepts of oxidative stress, delineating reactive oxygen species (ROS) and reactive nitrogen species (RNS), their sources, and the ensuing oxidative damage to cellular components. Subsequently, we delve into skeletal muscle atrophy, elucidating its diverse forms, molecular pathways, key signaling cascades, and the role of inflammation in exacerbating muscle wasting. Bridging these concepts, we explore the connections between oxidative stress and muscle atrophy, unveiling how oxidative stress impacts muscle protein synthesis and breakdown, perturbs cellular signaling pathways, and contributes to mitochondrial dysfunction. The review underscores the complexity of quantifying and interpreting oxidative stress markers, highlighting the challenges posed by the dynamic nature of oxidative stress and the presence of basal ROS levels. Addressing the specificity of oxidative stress markers, we emphasize the importance of selecting markers pertinent to muscle tissue and considering systemic influences. Standardization of experimental protocols emerges as a critical need to ensure consistency and reproducibility across studies. Looking ahead, we discuss the implications of oxidative stress in diverse scenarios, encompassing age-related muscle loss (sarcopenia), muscle wasting in chronic diseases like cancer cachexia, and disuse-induced muscle atrophy. Additionally, we delve into potential therapeutic strategies, including antioxidant supplementation, exercise, pharmacological interventions, nutritional approaches, and lifestyle modifications, as avenues to mitigate oxidative stress-driven muscle atrophy. The review concludes by outlining promising future directions in this field, calling for deeper exploration of specific oxidative stress markers, understanding the temporal dynamics of oxidative stress, validation through translational studies in humans, and the development of targeted therapeutic interventions. By advancing our understanding of the intricate relationship between oxidative stress and skeletal muscle atrophy, this review contributes to paving the way for innovative strategies to address muscle wasting and improve muscle health.

PMID:37779809 | PMC:PMC10540504 | DOI:10.7759/cureus.44367

20:24

PubMed articles on: Cancer & VTE/PE

D-Dimers Variability in the Perioperative Period of Breast Cancer Surgery Helps to Predict Cancer Relapse: A Single-Centre Prospective Study

Cancer Control. 2023 Jan-Dec;30:10732748231204713. doi: 10.1177/10732748231204713.

ABSTRACT

BACKGROUND: The importance of D-dimers (DD) assessment in the diagnostic algorithm of venous thromboembolic (VTE) disease is well known. Increase of DD concentration may be also associated with neoplastic disease. Many studies documented that high concentration of DD before solid tumour surgery indicates more advanced disease and poor life expectancy. The prognostic value of the DD concentration variability in the perioperative period, in women undergoing breast cancer surgery, has not been analysed so far. Thus, the aim of the present prospective study was to assess whether the trend of DD concentration changes in the perioperative period may predict cancer recurrence in women undergoing breast cancer surgery.

MATERIALS AND METHODS: 189 consecutive women with histopathological diagnosis of breast cancer (BC) referred for surgical treatment were included. DD concentration was measured twice in each patient: at the time of admission to hospital and at the time of discharge home. Enoxaparin in standard dose of 40 mg daily s. c. was used as primary VTE prophylaxis in all of the patients.

RESULTS: The recurrence of BC, within 1 year observation time, occurred in 13 patients (6.8%), in 11 (5.8%) patients with DD increase after surgery and only in 2 (1.1%) without an increase in DD, P = .0179. Increase in DD concentration after BC surgery was an independent positive predictor of disease relapse (OR 8.600, LCI 1.451, UCI 96.80, P = .0371) together with the lack of postoperative radiotherapy (OR 6.009, LCI 1.305, UCI 31.95, P = .0245), whereas the lack of postoperative chemotherapy predicted no BC relapse (OR .07355, LCI .0056, UCI .58, P = .0245).

CONCLUSIONS: Increase of DD in the early postoperative period may be considered as additional independent predictor of recurrence of BC within 1 year.

PMID:37791647 | PMC:PMC10552458 | DOI:10.1177/10732748231204713

20:24

PubMed articles on: Cancer & VTE/PE

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis

Eur J Haematol. 2023 Oct 4. doi: 10.1111/ejh.14110. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.

AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.

METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).

RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.

CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

PMID:37794526 | DOI:10.1111/ejh.14110

20:24

PubMed articles on: Cancer & VTE/PE

Retracted: Effect Evaluation of Bronchial Artery Embolization for Hemoptysis of Lung Cancer and Changes in Serum Tumor Markers and miR-34 Levels

Contrast Media Mol Imaging. 2023 Sep 27;2023:9839816. doi: 10.1155/2023/9839816. eCollection 2023.