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Clinical Prediction Tool to Identify Children at Risk for Pulmonary Embolism

1Faculty of Medicine Ramathibodi Hospital, Mahidol University, Pakkret, Nonthaburi, Thailand, 2Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangbon, Krung Thep, Thailand, 3Faculty of Medicine Chiang Mai University, Chiang Mai, Thailand, Chiang Mai, Chiang Mai, Thailand, 4Department of Pediatric, Faculty of Medicine Prince of Songkla University, Hat Yai District, Songkhla, Thailand, 5Srinagarind hospital, Department of Pediatric, Faculty of Medicine Khon Kaen University, Mueang Khon Kaen District, Khon Kaen, Thailand, 6Department of Pediatric, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Ratchathewi, Krung Thep, Thailand, 7Department of Pediatric, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

Abstract Number: OC 08.5

Meeting: ISTH 2022 Congress

Theme: Pediatrics » Thrombosis in Neonates and Children

Background: The diagnosis of pediatric pulmonary embolism (PE) is usually delayed due to non-specific signs and symptoms. The Wells score is highly effective among adult patients, but the application to the pediatric population has shown relatively lower sensitivity and specificity. The establishment of a new PE prediction tool for pediatric patients is warranted.

Aims: To establish a clinical prediction tool for pediatric PE

Methods: A multi-center retrospective study, approved by the Institutional Review Board, included children ≤18 years of age who underwent computed tomography pulmonary angiogram due to the suspicion of PE. The patients were diagnosed at four university hospitals from 2006 to 2022. The patients were divided into PE-positive, and PE-negative groups. The reported risk factors (Table 1) for venous thromboembolism (VTE) were compared between the two groups using a univariate logistic regression analysis. Those with a P-value < 0.10 were selected for further analysis by a multivariate model. A clinical prediction tool was created using the ROC curve obtained from the data of the significant risk factors (P < 0.05).

Results: A total of 89 patients had at least one clinical presentation of PE. Of those patients, 36 (40.4%) were grouped as PE-positive and 53 (59.6%) as PE-negative. Four risk factors, including congenital heart disease/pulmonary surgery, thrombophilia, history of previous VTE, and nephrotic syndrome, showed statistically significant differences between the two groups (Table 1). The ROC curve demonstrated a clinical prediction tool that yields an 83.3% sensitivity and a 64.2% specificity in the patients with suspected PE who had one of the four risk factors.

Conclusion(s): The study identified a clinical prediction tool for the suspicion of PE in this population. Pediatric patients who show clinical presentations of PE and have at least one of the significant risk factors should be further investigated for the diagnosis of PE.

Table 1

Univariate logistic regression analysis of the thirteen reported risk factors and Multivariate logistic regression analysis of the four risk factors with P-value < 0.10 form the univariate model

To cite this abstract in AMA style:

Tiratrakoonseree T, Charoenpichitnun S, Natesirinilkul R, Songthawee N, Komvilaisak P, Pongpicha P, Vaewpanich J, Sirachainan N. Clinical Prediction Tool to Identify Children at Risk for Pulmonary Embolism [abstract]. https://abstracts.isth.org/abstract/clinical-prediction-tool-to-identify-children-at-risk-for-pulmonary-embolism/. Accessed November 29, 2023.

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