Venous thromboembolism comprises deep-vein thrombosis, thrombus in transit, acute pulmonary embolism, and chronic thromboembolic pulmonaryhypertension (CTEPH). Pulmonary thromboemboli commonly resolve, with restoration of normal pulmonary hemodynamics. When they fail to resorb, permanent occlusion of the deep veins and/or CTEPH are the consequences. Apart from endogenous fibrinolysis, venous thrombi resolve by a process of mechanical fragmentation, through organization of the thromboembolus by invasion of endothelial cells, leukocytes, and fibroblasts leading to recanalization. Recent data utilizing various models have contributed to a better understanding of venous thrombosis and the resolution process that is directed at maintaining vascular patency. This review summarizes the plasmatic and cellular components of venous thrombus formation and resolution.
BACKGROUND: Critically ill patients appear to be at high risk of developing deep vein thrombosis (DVT) and pulmonary embolism during their stay in the intensive care unit (ICU). However, little is known about the clinical course of venous thromboembolism in the ICU setting. We therefore evaluated, through a systematic review of the literature, the available data on the impact of a diagnosis of DVT on hospital and ICU stay, duration of mechanical ventilation and mortality in critically ill patients. We also tried to determine whether currently adopted prophylactic measures need to be revised and improved in the ICU setting. MATERIALS AND METHODS: MEDLINE and EMBASE databases were searched up to week 4 of June 2012. Two reviewers selected studies and extracted data. Pooled results are reported as relative risks and weighted mean differences and are presented with 95% confidence intervals (CI). RESULTS: Seven studies for a total of 1,783 patients were included. A diagnosis of DVT was frequent in these patients with a mean rate of 12.7% (95% CI: 8.7-17.5%). DVT patients had longer ICU and hospital stays compared to those without DVT (7.28 days; 95% CI: 1.4-13.15; and 11.2 days; 95% CI: 3.82-18.63 days, respectively). The duration of mechanical ventilation was significantly increased in DVT patients (weighted mean difference: 4.85 days; 95% CI: 2.07-7.63). DVT patients had a marginally significant increase in the risk of hospital mortality (relative risk 1.31; 95% CI: 0.99-1.74; p=0.06), and a not statistically significant increase in the risk of ICU mortality (RR 1.64; 95% CI: 0.91-2.93; p=0.10). CONCLUSIONS: A diagnosis of DVT upon ICU admission appears to affect clinically important outcomes including duration of ICU and hospital stay and hospital mortality. Larger, prospective studies are warranted.
OBJECTIVE: To validate all diagnostic prediction models for ruling out pulmonary embolism that are easily applicable in primary care. DESIGN: Systematic review followed by independent external validation study to assess transportability of retrieved models to primary care medicine. SETTING: 300 general practices in the Netherlands. PARTICIPANTS: Individual patient dataset of 598 patients with suspected acute pulmonary embolism in primary care. MAIN OUTCOME MEASURES: Discriminative ability of all models retrieved by systematic literature search, assessed by calculation and comparison of C statistics. After stratification into groups with high and low probability of pulmonary embolism according to pre-specified model cut-offs combined with qualitative D-dimer test, sensitivity, specificity, efficiency (overall proportion of patients with low probability of pulmonary embolism), and failure rate (proportion of pulmonary embolism cases in group of patients with low probability) were calculated for all models. RESULTS: Ten published prediction models for the diagnosis of pulmonary embolism were found. Five of these models could be validated in the primary care dataset: the original Wells, modified Wells, simplified Wells, revised Geneva, and simplified revised Geneva models. Discriminative ability was comparable for all models (range of C statistic 0.75-0.80). Sensitivity ranged from 88% (simplified revised Geneva) to 96% (simplified Wells) and specificity from 48% (revised Geneva) to 53% (simplified revised Geneva). Efficiency of all models was between 43% and 48%. Differences were observed between failure rates, especially between the simplified Wells and the simplified revised Geneva models (failure rates 1.2% (95% confidence interval 0.2% to 3.3%) and 3.1% (1.4% to 5.9%), respectively; absolute difference -1.98% (-3.33% to -0.74%)). Irrespective of the diagnostic prediction model used, three patients were incorrectly classified as having low probability of pulmonary embolism; pulmonary embolism was diagnosed only after referral to secondary care. CONCLUSIONS: Five diagnostic pulmonary embolism prediction models that are easily applicable in primary care were validated in this setting. Whereas efficiency was comparable for all rules, the Wells rules gave the best performance in terms of lower failure rates.