Successful treatment of postoperative massive pulmonary embolism with paradoxal arterial embolism through extracorporeal life support and thrombolysis

Grapatsas, Konstantinos; Leivaditis, Vasileios; Tsilogianni, Zoi; Kotoulas, Sotirios; Kotoulas, Christophoros; Koletsis, Efstratios; Iliadis, Ilias; Spiliotopoulos, Konstantinos; Trakada, Georgia; Veletza, Lemonia; Kallianos, Anastasios; Tsiouda, Theodora; Kosmidis, Christoforos; Hohenforst-Schmidt, Wolfgang; Huang, Haidong; Haussmann, Rainer; Haussmann, Erich; Dahm, Manfred

doi:10.1016/j.rmcr.2017.10.014

Cited by 1 publication

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“…If an RAM provides inaccurate overestimation or underestimation of future events, it may lead to poor management of patient care and medical resources. On the other hand, if the model is highly predictable but difficult to apply, time-consuming, costly or less relevant, it will not be widely used 19 . Prevention of any disease is better than treatment.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a novel risk assessment model to estimate the probability of pulmonary embolism in postoperative patients

Wang

Ling

et al. 2021

Sci Rep

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Pulmonary embolism (PE) is a leading cause of mortality in postoperative patients. Numerous PE prevention clinical practice guidelines are available but not consistently implemented. This study aimed to develop and validate a novel risk assessment model to assess the risk of PE in postoperative patients. Patients who underwent Grade IV surgery between September 2012 and January 2020 (n = 26,536) at the Affiliated Dongyang Hospital of Wenzhou Medical University were enrolled in our study. PE was confirmed by an identified filling defect in the pulmonary artery system in CT pulmonary angiography. The PE incidence was evaluated before discharge. All preoperative data containing clinical and laboratory variables were extracted for each participant. A novel risk assessment model (RAM) for PE was developed with multivariate regression analysis. The discrimination ability of the RAM was evaluated by the area under the receiver operating characteristic curve, and model calibration was assessed by the Hosmer–Lemeshow statistic. We included 53 clinical and laboratory variables in this study. Among them, 296 postoperative patients developed PE before discharge, and the incidence rate was 1.04%. The distribution of variables between the training group and the validation group was balanced. After using multivariate stepwise regression, only variable age (OR 1.070 [1.054–1.087], P < 0.001), drinking (OR 0.477 [0.304–0.749], P = 0.001), malignant tumor (OR 2.552 [1.745–3.731], P < 0.001), anticoagulant (OR 3.719 [2.281–6.062], P < 0.001), lymphocyte percentage (OR 2.773 [2.342–3.285], P < 0.001), neutrophil percentage (OR 10.703 [8.337–13.739], P < 0.001), red blood cell (OR 1.872 [1.384–2.532], P < 0.001), total bilirubin (OR 1.038 [1.012–1.064], P < 0.001), direct bilirubin (OR 0.850 [0.779–0.928], P < 0.001), prothrombin time (OR 0.768 [0.636–0.926], P < 0.001) and fibrinogen (OR 0.772 [0.651–0.915], P < 0.001) were selected and significantly associated with PE. The final model included four variables: neutrophil percentage, age, malignant tumor and lymphocyte percentage. The AUC of the model was 0.949 (95% CI 0.932–0.966). The risk prediction model still showed good calibration, with reasonable agreement between the observed and predicted PE outcomes in the validation set (AUC 0.958). The information on sensitivity, specificity and predictive values according to cutoff points of the score in the training set suggested a threshold of 0.012 as the optimal cutoff value to define high-risk individuals. We developed a new approach to select hazard factors for PE in postoperative patients. This tool provided a consistent, accurate, and effective method for risk assessment. This finding may help decision-makers weigh the risk of PE and appropriately select PE prevention strategies.

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Section: Discussionmentioning

confidence: 99%