ABSTRACT

OBJECTIVE: To provide procedure-specific estimates of the risk of symptomatic venous thromboembolism (VTE) and major bleeding, in the absence of thromboprophylaxis, following gynecologic cancer surgery.

DATA SOURCES: We conducted comprehensive searches on Embase, MEDLINE, Web of Science, and Google Scholar for observational studies. We also reviewed reference lists of eligible studies and review articles. We performed separate searches for randomized trials addressing effects of thromboprophylaxis and conducted a web-based survey on thromboprophylaxis practice.

STUDY ELIGIBILITY CRITERIA: Observational studies enrolling ≥50 adult patients undergoing gynecologic cancer surgery procedures reporting absolute incidence for at least one of the following: symptomatic pulmonary embolism, symptomatic deep vein thrombosis, symptomatic VTE, bleeding requiring reintervention (including re-exploration and angioembolization), bleeding leading to transfusion or post-operative hemoglobin <70

STUDY APPRAISAL AND SYNTHESIS METHODS: Two reviewers independently assessed eligibility, performed data extraction, and evaluated risk of bias of eligible articles. We adjusted the reported estimates for thromboprophylaxis and length of follow-up and used the median value from studies to determine cumulative incidence at 4 weeks post-surgery stratified by patient VTE risk factors, and used the GRADE approach to rate evidence certainty.

RESULTS: We included 188 studies (398,167 patients) reporting on 37 gynecologic cancer surgery procedures. The evidence certainty was generally low to very low. Median symptomatic VTE risk (in the absence of prophylaxis) was <1%2.0% in 13 of 37 (35%). The risks of VTE varied from 0.1% in low VTE risk patients undergoing cervical conization to 33.5% in high VTE risk patients undergoing pelvic exenteration. Estimates of bleeding requiring reintervention varied from <0.1%<1%

CONCLUSIONS: VTE reduction with thromboprophylaxis likely outweighs increase in bleeding requiring reintervention in many gynecologic cancer procedures (e.g., open surgery for ovarian cancer and pelvic exenteration). In some procedures (e.g., laparoscopic total hysterectomy without lymphadenectomy), thromboembolism and bleeding risks are similar, and decisions depend on individual risk prediction and values and preferences regarding VTE and bleeding.

PMID:37827272 | DOI:10.1016/j.ajog.2023.10.006

07:11

PubMed articles on: Cancer & VTE/PE

Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals

Postgrad Med J. 2023 Oct 4:qgad095. doi: 10.1093/postmj/qgad095. Online ahead of print.

ABSTRACT

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.

PMID:37794609 | DOI:10.1093/postmj/qgad095

07:11

PubMed articles on: Cancer & VTE/PE

How we manage a high D-dimer

Haematologica. 2023 Oct 26. doi: 10.3324/haematol.2023.283966. Online ahead of print.