Background & aim: Today, Wells criteria are most often used to assess the risk of venous thromboembolism in the clinic, but there are other methods to assess this risk, one of which is the Caprini criteria. Considering the importance of the role and position of nursing in assessing the risk of venous thromboembolism, and the need to use different criteria to predict the likelihood of venous thromboembolism, and due to the lack of studies on the clinical comparison of these criteria, the present study aimed to investigate and compare the two criteria of Wells and Caprini in the incidence of venous thromboembolism.
Methods: This study was a retrospective cross-sectional descriptive study that was conducted on 420 patients hospitalized in affiliated to the Isfahan University of Medical Sciences. Convenience sampling was conducted in such a way that, based on the inclusion criteria, the sampling continued until the desired sample size was reached. Inclusion criteria included a definitive diagnosis of venous thromboembolism by a physician based on history, physical examination, and two gold standards of embolism protocol CT scan and Doppler ultrasound, and age over 18 years. Data collection was done using Information forms designed by the researcher according to the variables information forms. The researcher, referring to the patient's file, calculated and measured the two criteria of Wells and Caprini of the patient in the Intensive Care Unit and finally examined the sensitivity and specificity of these two criteria in predicting the incidence of venous thromboembolism during the hospitalization of patients in order to achieve a more accurate criterion for determining and predicting venous thromboembolism in patients. Data analysis was performed using the t-test and chi-square test. Quantitative data were presented as mean standard deviation, and qualitative data were presented as frequency and percentage. To evaluate and compare the best sensitivity and specificity, the relevant formulas and ROC curves were used. McNemar’s test was applied to compare sensitivity and specificity.
Results: A total of 420 patient records were reviewed, of which 247 patients had venous thromboembolism and 173 patients did not. The samples were divided into two groups based on disease status, and the variables of interest were extracted and compared between the two groups. Based on the independent t-test, there was no significant difference between the two groups in terms of mean age, mean Body Mass Index (BMI), and mean number of days hospitalized before ICU admission. However, there were significant differences in other demographic variables such as gender (P=0.035), reason for hospitalization (P=0.002), and mobility status (P<0.001). There was no statistically significant difference in the sensitivity and specificity of the Wells and Caprini tools for predicting VTE occurrence. That is, based on the statistical analysis, the difference in the diagnostic accuracy between these two tools was so small that cannot be considered a true or reliable difference.
Conclusion: Comparing these tools can help address numerous challenges in clinical practice, such as selecting the appropriate model for a specific type of patient or situation. In other words, the comparison can reveal which subgroups (e.g., internal medicine, surgical, or emergency patients) each model performs better for. Another challenge may be the predictive accuracy of the tool; one of the main concerns in predictive models is the ability to distinguish high-risk patients from low-risk ones. By comparing the two models, it is possible to determine which model has better sensitivity in the target population. Such a comparison can simplify clinical application, as models with numerous parameters or difficult-to-measure variables may have limited practical use. Both the Wells and the Caprini tools perform similarly in the diagnosis and prediction of venous thromboembolism, and no statistically significant difference in their diagnostic accuracy was observed. This issue can be used in clinical decision-making to select the appropriate tool, based on other criteria (such as ease of use, cost, or time required for assessment. |
