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Przybilla J, Ahnert P, Bogatsch H, et al. Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia. J Clin Med. 2020;9(2)doi: 10.3390/jcm9020393.
Przybilla, J., Ahnert, P., Bogatsch, H., Bloos, F., Brunkhorst, F. M., SepNet Critical Care Trials Group, Progress Study Group, Bauer, M., Loeffler, M., Witzenrath, M., Suttorp, N., & Scholz, M. (2020). Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia. Journal of clinical medicine, 9(2), . https://doi.org/10.3390/jcm9020393
Przybilla, Jens, et al. "Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia." Journal of clinical medicine vol. 9,2 (2020). doi: https://doi.org/10.3390/jcm9020393
Przybilla J, Ahnert P, Bogatsch H, Bloos F, Brunkhorst FM, SepNet Critical Care Trials Group, Progress Study Group, Bauer M, Loeffler M, Witzenrath M, Suttorp N, Scholz M. Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia. J Clin Med. 2020 Feb 05;9(2). doi: 10.3390/jcm9020393. PMID: 32121038; PMCID: PMC7074475.
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J Clin Med. 2020 Feb 05;9(2). doi: 10.3390/jcm9020393.

Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia.

Journal of clinical medicine

Jens Przybilla, Peter Ahnert, Holger Bogatsch, Frank Bloos, Frank M Brunkhorst, SepNet Critical Care Trials Group, Progress Study Group, Michael Bauer, Markus Loeffler, Martin Witzenrath, Norbert Suttorp, Markus Scholz

Affiliations

  1. Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.
  2. German Center for Lung Research (DZL), Aulweg 130, 35392 Gießen, Germany.
  3. Clinical Trial Centre Leipzig, Universität Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.
  4. Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
  5. Center for Sepsis Control & Care (CSCC), Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany.
  6. Center for Clinical Studies, Jena University Hospital, Salvador-Allende-Platz 27, 07747 Jena, Germany.
  7. SepNet Critical Care Trials Group c/o Sepsis-Stiftung, Carl-Zeiß-Str. 12, 07743 Jena, Germany.
  8. Department of Infectious Diseases and Respiratory Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany.
  9. Division of Pulmonary Inflammation, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany.

PMID: 32121038 PMCID: PMC7074475 DOI: 10.3390/jcm9020393

Abstract

Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient's risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.

Keywords: SOFA score; community-acquired pneumonia; continuous-time Markov model; medical decision making; prognosis; sepsis; stochastic model

References

  1. JAMA. 1998 May 13;279(18):1452-7 - PubMed
  2. Am J Respir Crit Care Med. 2006 Dec 1;174(11):1249-56 - PubMed
  3. Eur Respir J. 2010 Oct;36(4):826-33 - PubMed
  4. Stat Methods Med Res. 2017 Jun;26(3):1350-1372 - PubMed
  5. Thorax. 2003 May;58(5):377-82 - PubMed
  6. Theor Biol Med Model. 2006 Feb 15;3:8 - PubMed
  7. Thorax. 2015 Jun;70(6):551-8 - PubMed
  8. Thorax. 2009 Dec;64(12):1062-9 - PubMed
  9. Chest. 2006 Apr;129(4):968-78 - PubMed
  10. BMC Bioinformatics. 2011 Mar 17;12:77 - PubMed
  11. Clin Infect Dis. 2008 Aug 1;47(3):375-84 - PubMed
  12. Biom J. 2005 Dec;47(6):834-46 - PubMed
  13. Pneumologie. 2016 Mar;70(3):151-200 - PubMed
  14. JAMA. 2012 Jun 13;307(22):2390-9 - PubMed
  15. JAMA. 2001 Oct 10;286(14):1754-8 - PubMed
  16. Crit Care. 2019 Apr 4;23(1):110 - PubMed
  17. Biometrics. 1986 Dec;42(4):855-65 - PubMed
  18. N Engl J Med. 1997 Jan 23;336(4):243-50 - PubMed
  19. Clin Infect Dis. 2007 Mar 1;44 Suppl 2:S27-72 - PubMed
  20. Clin Microbiol Infect. 2013 Dec;19(12):1174-80 - PubMed
  21. Health Serv Res. 1976 Winter;11(4):442-51 - PubMed
  22. N Engl J Med. 2008 Jan 10;358(2):125-39 - PubMed
  23. Int J Health Care Qual Assur. 2017 Mar 13;30(2):187-202 - PubMed
  24. PLoS One. 2016 May 19;11(5):e0156047 - PubMed
  25. Chest. 2020 Jan;157(1):34-41 - PubMed
  26. BMC Pulm Med. 2016 Jul 28;16(1):108 - PubMed
  27. Clin Infect Dis. 1998 Jul;27(1):185-90 - PubMed
  28. Intensive Care Med. 1996 Jul;22(7):707-10 - PubMed
  29. Shock. 2005 Jul;24(1):74-84 - PubMed
  30. JAMA Intern Med. 2016 Sep 1;176(9):1266-76 - PubMed
  31. Thorax. 2008 Aug;63(8):665-6 - PubMed
  32. Chest. 2008 Nov;134(5):955-962 - PubMed

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