Key result
Derived ICU-VTE prediction model achieves ~0.84 AUC, outperforming Caprini, Padua, and existing ICU-VTE scores.
Why the study?
ICU patients have a high incidence of VTE and guidelines recommend assessing risk, but no validated tool exists specifically for ICU patients.
Does the ICU-VTE prediction model improve predictive accuracy for VTE in ICU patients compared to existing risk assessment tools?
Cohort (n=566)
Does the ICU-VTE prediction model improve predictive accuracy for VTE in ICU patients compared to existing risk assessment tools?
p-value: p=<0.001
A newly derived ICU-VTE prediction model demonstrated higher predictive accuracy for VTE in ICU patients compared to existing risk assessment tools.
May improve ICU VTE stratification; hypothesis-generating, requiring external validation before practice change.
Background ICU patients have a high incidence of VTE. The American College of Chest Physicians antithrombotic practice guidelines recommend assessing the risk of VTE in all ICU patients. Although several VTE risk assessment tools exist to evaluate the risk factors among hospitalized patients, there is no validated tool specifically for assessing the risk of VTE in ICU patients. Methods A retrospective corhort study was conducted between June 2018 and October 2022. We obtained data from the electronic medical records of patients with a variety of diagnoses admitted to a mixed ICU. Multivariable logistic regression analysis was used to evaluate the independent risk factors of VTE. Receiver operating characteristic (ROC) curves were used to analyse the predictive accuracy of different tools. Results A total of 566 patients were included, and VTE occurred in 89 patients (15.7%), 62.9% was asymptomatic VTE. A prediction model (the ICU-VTE prediction model) was derived from the independent risk factors identified using multivariate analysis. The ICU-VTE prediction model included eight independent risk factors: history of VTE (3 points), immobilization ≥4 days (3 points), multiple trauma (3 points), age ≥70 years (2 points), platelet count >250 × 10 3 /μL (2 points), central venous catheterization (1 point), invasive mechanical ventilation (1 point), and respiratory failure or heart failure (1 point). Patients with a score of 0–4 points had a low (1.81%) risk of VTE. Patients were at intermediate risk, scoring 5–6 points, and the overall incidence of VTE in the intermediate-risk category was 17.1% (odds ratio [OR], 11.1; 95% confidence interval [CI], 4.2-29.4). Those with a score ≥7 points had a high (44.1%) risk of VTE (OR, 42.6; 95% CI, 16.4-110.3). The area under the curve (AUC) of the ICU-VTE prediction model was 0.838, and the differences in the AUCs were statistically significant between the ICU-VTE prediction model and the other three tools (ICU-VTE score, Z = 3.723, P < 0.001; Caprini risk assessment model, Z = 6.212, P < 0.001; Padua prediction score, Z = 7.120, P < 0.001). Conclusions We identified eight independent risk factors for acquired VTE among hospitalized patients in the ICU, deriving a new ICU-VTE risk assessment model. The model aims to predict asymptomatic VTE in ICU patients. The new model has higher predictive accuracy than the current tools. A prospective study is required for external validation of the tool and risk stratification in ICU patients.
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Zhang et al. (2024) conducted a cohort in Venous Thromboembolism (VTE) in ICU patients (n=566). ICU-VTE prediction model vs. ICU-VTE score, Caprini risk assessment model, and Padua prediction score was evaluated on Predictive accuracy (Area Under the Curve) for VTE (p=<0.001). The derived ICU-VTE prediction model achieved an AUC of 0.838 for predicting VTE, significantly outperforming the ICU-VTE score, Caprini, and Padua risk assessment models (all P<0.001).
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