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A decision support model to predict the presence of an acute infiltrate on chest radiograph

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European Journal of Clinical Microbiology & Infectious Diseases Aims and scope Submit manuscript

Abstract

A chest infiltrate is needed to make a diagnosis of community-acquired pneumonia, but chest X-rays might be time consuming, entail radiation exposure, and demand resources that are not always available. We sought to derive a model to predict whether a patient will have an infiltrate on chest X-ray (CXR). This prospective observational study included patients visiting the Emergency Department of Beilinson Hospital in the years 2003–2004 (derivation cohort) and 2010–2011 (validation cohort), who had undergone a CXR, and were suspected of having a respiratory infection. We excluded all patients with possible healthcare associated infections. A logistic regression model was derived and applied to the validation cohort. A total of 1,555 patients met inclusion criteria: 993 in the derivation cohort and 562 in the validation cohort with 287 (29%) and 226 (40%) having an infiltrate, respectively. The derivation model area-under-the curve (AUC) was 0.79 (95% CI 0.76–0.82). We categorized the patients into three groups—presence or absence of infiltrate, or undetermined. In the validation cohort, 70 (12%) patients were classified as ‘no infiltrate’; 3 (4%) of them had an infiltrate, 367 (65%) were classified as ‘infiltrate’; 190 (52%) of them had an infiltrate on CXR, and 125 (46%) were classified as ‘undetermined’; 33 (26%) of them with an infiltrate on CXR. Using this prediction model for the evaluation of patients with suspected respiratory infection in an ED setting may help avoid over 10% of CXRs.

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Funding

This work was supported by the Rabin Medical Center Research Authority by the “young researcher grant”, and by the Israel National Institute for Health Policy Research Grant.

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Authors and Affiliations

  1. Department of Medicine E, Rabin Medical Center, Beilinson Hospital, Petach-Tikva, Israel

    O. Zusman, L. Farbman, M. Elbaz, V. Daitch, N. Eliakim-Raz, T. Babich & L. Leibovici

  2. Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    O. Zusman, M. Cohen, N. Eliakim-Raz, T. Babich, L. Leibovici & D. Yahav

  3. Department of Radiology, Rabin Medical Center, Beilinson Hospital, Petach-Tikva, Israel

    M. Cohen

  4. Infectious Diseases Unit, Rambam Medical Center, Haifa, Israel

    M. Paul

  5. The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel

    M. Paul

  6. Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petach-Tikva, Israel

    D. Yahav

Authors
  1. O. Zusman
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  2. L. Farbman
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  3. M. Elbaz
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  4. V. Daitch
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  5. M. Cohen
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  6. N. Eliakim-Raz
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  7. T. Babich
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  8. M. Paul
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  9. L. Leibovici
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  10. D. Yahav
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Corresponding author

Correspondence to O. Zusman.

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All authors state they have no conflicts to disclose.

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Ethical approval was provided by the Rabin Medical Center institutional review board. Informed consent was waived by the institutional review board.

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Data availability: Data is not available due to restrictions in institutional policy.

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Zusman, O., Farbman, L., Elbaz, M. et al. A decision support model to predict the presence of an acute infiltrate on chest radiograph. Eur J Clin Microbiol Infect Dis 37, 227–232 (2018). https://doi.org/10.1007/s10096-017-3119-0

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  • DOI: https://doi.org/10.1007/s10096-017-3119-0

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