Diagnostic Approach for Venous Thromboembolism in Cancer Patients.
Hélène HelferYara SkaffFlorent HappeSadji DjennaouiJean ChidiacGeraldine PoenouMarc RighiniIsabelle MahePublished in: Cancers (2023)
Venous thromboembolic disease (VTE) is a common complication in cancer patients. The currently recommended VTE diagnostic approach involves a step-by-step algorithm, which is based on the assessment of clinical probability, D-dimer measurement, and/or diagnostic imaging. While this diagnostic strategy is well validated and efficient in the noncancer population, its use in cancer patients is less satisfactory. Cancer patients often present nonspecific VTE symptoms resulting in less discriminatory power of the proposed clinical prediction rules. Furthermore, D-dimer levels are often increased because of a hypercoagulable state associated with the tumor process. Consequently, the vast majority of patients require imaging tests. In order to improve VTE exclusion in cancer patients, several approaches have been developed. The first approach consists of ordering imaging tests to all patients, despite overexposing a population known to have mostly multiple comorbidities to radiations and contrast products. The second approach consists of new diagnostic algorithms based on clinical probability assessment with different D-dimer thresholds, e.g., the YEARS algorithm, which shows promise in improving the diagnosis of PE in cancer patients. The third approach uses an adjusted D-dimer threshold, to age, pretest probability, clinical criteria, or other criteria. These different diagnostic strategies have not been compared head-to-head. In conclusion, despite having several proposed diagnostic approaches to diagnose VTE in cancer patients, we still lack a dedicated diagnostic algorithm specific for this population.
Keyphrases
- venous thromboembolism
- machine learning
- direct oral anticoagulants
- end stage renal disease
- high resolution
- ejection fraction
- newly diagnosed
- chronic kidney disease
- computed tomography
- deep learning
- prognostic factors
- patient reported outcomes
- big data
- artificial intelligence
- peritoneal dialysis
- mass spectrometry
- magnetic resonance imaging
- optical coherence tomography