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Respiratory infection
Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia
  1. Jessica Quah1,
  2. Charlene Jin Yee Liew2,
  3. Lin Zou3,
  4. Xuan Han Koh4,
  5. Rayan Alsuwaigh1,
  6. Venkataraman Narayan5,
  7. Tian Yi Lu3,
  8. Clarence Ngoh3,
  9. Zhiyu Wang3,
  10. Juan Zhen Koh3,
  11. Christine Ang3,
  12. Zhiyan Fu3 and
  13. Han Leong Goh3
  1. 1Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore
  2. 2Department of Radiology, Changi General Hospital, Singapore
  3. 3Integrated Health Information Systems Pte Ltd, Singapore
  4. 4Health Services Research, Changi General Hospital, Singapore
  5. 5Data Management and Informatics, Changi General Hospital, Singapore
  1. Correspondence to Dr Jessica Quah; jessica.quah.l.s{at}singhealth.com.sg

Abstract

Background Chest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality.

Methods Deep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality.

Results 315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) μg/L vs 1.4 (5.9) μg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001).

Conclusion CXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

  • Imaging/CT MRI etc
  • Pneumonia
  • Respiratory Infection

Data availability statement

Data are available on reasonable request. Deidentified data are available from the corresponding author on reasonable request subjected to institutional approval.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjresp-2021-001045

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    Data availability statement

    Data are available on reasonable request. Deidentified data are available from the corresponding author on reasonable request subjected to institutional approval.

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    Footnotes

    • JQ and CJYL contributed equally.

    • Contributors All listed authors have substantial contributions to the conception or design of the work; or the acquisition, analysis or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content; and dinal approval of the version to be published; and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Disclaimer We grant BMJ Open Respiratory Research exclusive license for publication of this work.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.