An abbreviated Caprini model for VTE risk assessment in trauma.
Max D HazeltineErin M ScottJon David DorfmanPublished in: Journal of thrombosis and thrombolysis (2021)
The Caprini risk assessment model (RAM) is widely used to assess risk of venous thromboembolism (VTE). However, it is cumbersome with 31 variables and poses challenges with inter-rater reliability. This study aimed to determine if an abbreviated model could perform similarly in VTE risk assessment. We performed a retrospective review of trauma patients ≥ 18 years old and admitted for over 24 h at a Level I trauma center from January 1, 2018, to December 31, 2018. Demographic and clinical data were analyzed to generate Caprini scores. Using a p-value cutoff of < 0.05, the individual components of the original Caprini RAM most highly associated with VTE were identified and used to calculate an abbreviated Caprini score. Logistic regression assessed odds of inpatient VTE with the original or abbreviated Caprini RAMs. Receiver operating characteristic curves and c-statistics were generated to assess discriminatory ability. The study sample included 1279 patients. Ten risk factors were included in the abbreviated model (recent major surgery, length of surgery > 2 h, transfusion, restricted mobility > 72 h, central venous catheter, current major surgery, age, history of VTE, hip or leg fracture, and serious trauma). Compared to the original, the abbreviated model had a similar odds ratio (1.17 vs 1.07, both p-values < 0.001), c-statistic (0.747 vs 0.753), sensitivity (0.73 vs 0.76) and specificity (0.62 vs 0.61). An abbreviated Caprini RAM performs similarly to the original, may streamline workflow and allow for automation in electronic health records, potentially enhancing its use in resource limited settings.
Keyphrases
- venous thromboembolism
- risk assessment
- electronic health record
- trauma patients
- direct oral anticoagulants
- minimally invasive
- risk factors
- coronary artery bypass
- heavy metals
- human health
- newly diagnosed
- mental health
- palliative care
- end stage renal disease
- coronary artery disease
- deep learning
- surgical site infection
- big data
- sickle cell disease
- ultrasound guided
- atrial fibrillation
- adverse drug
- patient reported outcomes