Prevalence of Risk Factors for VTE In Hospitalized Medical and Surgical Patients. Data From the Comparison of Methods for Thromboembolic Risk Assessment with Clinical Perceptions and AwareneSS In Real Life Surgical and Medical Patients (COMPASS) Study
Bibliographic record
Abstract
Abstract Abstract 3337 Introduction: Risk assessment models (RAM) are helpful tools for the screening VTE risk in hospitalized patients. Most of the available RAMs have been constructed on a disease-based or surgery-based approach and include some of the most relevant risk factors for VTE. There is limited information on the impact and importance of individual and comorbidity related risk factors for VTE present during hospitalization on the global VTE risk. Incorporation of the most frequent VTE risk and bleeding risk factors related to comorbidities might improve the ability of RAM to detect real-life patients at risk VTE and to evaluate drawbacks for the application of thromboprophylaxis. Aim of the study: The primary aim of the COMPASS programme was to evaluate the prevalence of the all known VTE and bleeding risk factors reported in the literature in real-life surgical and medical hospitalized patients. Methods: A prospective multicenter cross-sectional observational study was conducted in 6 hospitals in Greece and 1 in France. All inpatients aged >40 years hospitalised for medical diseases and inpatients aged >18 years admitted due to a surgical procedure and hospitalisation for a period exceeding three days were included. Patients and their treating physicians were interviewed with standardised questionnaire including all VTE and bleeding risk factors described in literature (130 items) on the third day of hospitalisation. Patients not giving informed consent, or receiving anticoagulant treatment for any reason or hospitalised in order to undergo diagnostic investigation without any further therapeutic intervention were excluded. Results: A total of 806 patients were enrolled in the study (414 medical and 392 surgical). Most frequent causes of hospitalisation in medical patients were infection (42%), ischemic stroke (14%), cancer (13%), gastrointestinal disease (9%), pulmonary disease (4%), renal disease (3%) and rheumatologic disease (1,4%). Surgical patients were hospitalised for vascular disease (22%) cancer (19,4%) gastrointestinal disease (12,5%), infection (8%), orthopaedic surgery and trauma (14%) or minor surgery (7%). Analysis of the frequency of risk factors for VTE showed that active cancer, recent hospitalisation, venous insufficiency and total bed rest without bathroom privileges were frequent in both groups. Medical patients had significantly more frequently than surgical patients several important predisposing risk factors for VTE. Moreover, medical patient had more frequently than surgical ones bleeding risk factors. The data for the most frequent risk factors are summarised in Table 1. Conclusion: COMPASS is the first registry that provides key data on the prevalence of all known VTE and bleeding risk factors in real life medical and surgical patients hospitalised in two countries of European Union. The analysis of the data shows that in addition to risk stemin from the disease or surgical act both medical and surgical patients share common VTE risk factors. The careful analysis of the most frequent and relevant VTE risk factors will allow the derivation of a practical VTE and bleeding risk assessment model taken into account these factors. Disclosures: Chrysanthidis: Sanofi-Aventis: Employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".