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Type: Journal article
Title: Predicting in-hospital death during acute presentation with pulmonary embolism to facilitate early discharge and outpatient management
Author: Lau, J.
Chow, V.
Brown, A.
Kritharides, L.
Ng, A.
Citation: PLoS ONE, 2017; 12(7):e0179755-1-e0179755-13
Publisher: Public Library of Science (PLoS)
Issue Date: 2017
ISSN: 1932-6203
Statement of
Jerrett K. Lau, Vincent Chow, Alex Brown, Leonard Kritharides, Austin C. C. Ng
Abstract: Background: Pulmonary embolism continues to be a significant cause of death. The aim was to derive and validate a risk prediction model for in-hospital death after acute pulmonary embolism to identify low risk patients suitable for outpatient management. Methods: A confirmed acute pulmonary embolism database of 1,426 consecutive patients admitted to a tertiary-center (2000–2012) was analyzed, with odd and even years as derivation and validation cohorts respectively. Risk stratification for in-hospital death was performed using multivariable logistic-regression modelling. Models were compared using receiver-operating characteristic-curve and decision curve analyses. Results: In-hospital mortality was 3.6% in the derivation cohort (n = 693). Adding day-1 sodium and bicarbonate to simplified Pulmonary Embolism Severity Index (sPESI) significantly increased the C-statistic for predicting in-hospital death (0.71 to 0.86, P = 0.001). The validation cohort yielded similar results (n = 733, C-statistic 0.85). The new model was associated with a net reclassification improvement of 0.613, and an integrated discrimination improvement of 0.067. The new model also increased the C-statistic for predicting 30-day mortality compared to sPESI alone (0.74 to 0.83, P = 0.002). Decision curve analysis demonstrated superior clinical benefit with the use of the new model to guide admission for pulmonary embolism, resulting in 43 fewer admissions per 100 presentations based on a risk threshold for admission of 2%. Conclusions A risk model incorporating sodium, bicarbonate, and the sPESI provides accurate risk prediction of acute in-hospital mortality after pulmonary embolism. Our novel model identifies patients with pulmonary embolism who are at low risk and who may be suitable for outpatient management.
Keywords: Pulmonary Embolism
Description: Published: July 13, 2017
Rights: © 2017 Lau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0030097529
DOI: 10.1371/journal.pone.0179755
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