Skip to main content

Advertisement

SpringerLink
Log in

Utility of predictive tools for risk stratification of elderly individuals with all-cause acute respiratory infection

  • Original Paper
  • Published:
Infection Aims and scope Submit manuscript

Abstract

Purpose

A number of scoring tools have been developed to predict illness severity and patient outcome for proven pneumonia, however, less is known about the utility of clinical prediction scores for all-cause acute respiratory infection (ARI), especially in elderly subjects who are at increased risk of poor outcomes.

Methods

We retrospectively analyzed risk factors and outcomes of individuals ≥ 60 years of age presenting to the emergency department with a clinical diagnosis of ARI.

Results

Of 276 individuals in the study, 40 had proven viral infection and 52 proven bacterial infection, but 184 patients with clinically adjudicated ARI (67%) remained without a proven microbial etiology despite extensive clinical (and expanded research) workup. Patients who were older, had multiple comorbidities, or who had proven bacterial infection were more likely to require hospital and ICU admission. We identified a novel model based on 11 demographic and clinical variables that were significant risk factors for ICU admission or mortality in elderly subjects with all-cause ARI. As comparators, a modified PORT score was found to correlate more closely with all-cause ARI severity than a modified CURB-65 score (r, 0.54, 0.39). Interestingly, modified Jackson symptom scores were found to inversely correlate with severity (r, − 0.34) but show potential for differentiating viral and bacterial etiologies.

Conclusions

Modified PORT, CURB-65, Jackson symptom scores, and a novel ARI scoring tool presented herein all offer predictive ability for all-cause ARI in elderly subjects. Such broadly applicable scoring metrics have the potential to assist in treatment and triage decisions at the point of care.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Garten R, et al. Update: influenza activity in the United States during the 2017–18 season and composition of the 2018–19 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2018;67:634–42.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Barnes SR, et al. Mortality estimates among adult patients with severe acute respiratory infections from two sentinel hospitals in southern Arizona, United States, 2010–2014. BMC Infect Dis. 2018;18:78.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Jain S, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. N Engl J Med. 2015;373:415–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Thompson WW, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289:179–86.

    Article  PubMed  Google Scholar 

  5. Garibaldi RA. Epidemiology of community-acquired respiratory tract infections in adults. Incidence, etiology, and impact. Am J Med. 1985;78:32–7.

    Article  CAS  PubMed  Google Scholar 

  6. Mullooly JP, et al. Influenza- and RSV-associated hospitalizations among adults. Vaccine. 2007;25:846–55.

    Article  PubMed  Google Scholar 

  7. Thompson WW, et al. Influenza-associated hospitalizations in the United States. JAMA. 2004;292:1333–40.

    Article  CAS  PubMed  Google Scholar 

  8. Janssens JP, Krause KH. Pneumonia in the very old. Lancet Infect Dis. 2004;4:112–24.

    Article  PubMed  Google Scholar 

  9. Fine MJ, et al. Processes and outcomes of care for patients with community-acquired pneumonia: results from the Pneumonia Patient Outcomes Research Team (PORT) cohort study. Arch Intern Med. 1999;159:970–80.

    Article  CAS  PubMed  Google Scholar 

  10. Shibli F, et al. Etiology of community-acquired pneumonia in hospitalized patients in northern Israel. Isr Med Assoc J. 2010;12:477–82.

    PubMed  Google Scholar 

  11. Metlay JP, Fine MJ. Testing strategies in the initial management of patients with community-acquired pneumonia. Ann Intern Med. 2003;138:109–18.

    Article  PubMed  Google Scholar 

  12. Falsey AR, Murata Y, Walsh EE. Impact of rapid diagnosis on management of adults hospitalized with influenza. Arch Intern Med. 2007;167:354–60.

    Article  PubMed  Google Scholar 

  13. Raz R, et al. A predictive model for the management of community-acquired pneumonia. Infection. 2003;31:3–8.

    Article  CAS  PubMed  Google Scholar 

  14. Neill AM, et al. Community acquired pneumonia: aetiology and usefulness of severity criteria on admission. Thorax. 1996;51:1010–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Farr BM, Sloman AJ, Fisch MJ. Predicting death in patients hospitalized for community-acquired pneumonia. Ann Intern Med. 1991;115:428–36.

    Article  CAS  PubMed  Google Scholar 

  16. British Thoracic Society Research Committee. Community-acquired pneumonia in adults in British hospitals in 1982–1983: a survey of aetiology, mortality, prognostic factors and outcome. The British Thoracic Society and the Public Health Laboratory Service. Q J Med, 1987. 62: p. 195–220.

    Article  Google Scholar 

  17. Fine MJ, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336:243–50.

    Article  CAS  PubMed  Google Scholar 

  18. Lim WS, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Buising KL, et al. Identifying severe community-acquired pneumonia in the emergency department: a simple clinical prediction tool. Emerg Med Australas. 2007;19:418–26.

    PubMed  Google Scholar 

  20. Williams E, et al. CORB is the best pneumonia severity score for elderly hospitalised patients with suspected pneumonia. Intern Med J. 2014;44:613–5.

    Article  CAS  PubMed  Google Scholar 

  21. Tsalik EL, et al. An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med. 2014;6:111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tsalik EL, et al. Host gene expression classifiers diagnose acute respiratory illness etiology. Sci Transl Med. 2016;8:322ra11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Glickman SW, et al. Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad Emerg Med. 2010;17:383–90.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tsalik EL, et al. Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis. J Clin Microbiol. 2010;48:26–33.

    Article  CAS  PubMed  Google Scholar 

  25. Tsalik EL, et al. Discriminative value of inflammatory biomarkers for suspected sepsis. J Emerg Med. 2012;43:97–106.

    Article  PubMed  Google Scholar 

  26. Bone RC, et al., Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest, 1992. 101: p. 1644–55.

    Article  CAS  PubMed  Google Scholar 

  27. Jackson GG, et al. Transmission of the common cold to volunteers under controlled conditions. I. The common cold as a clinical entity. AMA Arch Intern Med. 1958;101:267–78.

    Article  CAS  PubMed  Google Scholar 

  28. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing 2018. https://www.R-project.org. Accessed 1 Mar 2018.

  29. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol). 1996;58:267–88.

    Google Scholar 

  30. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010. https://doi.org/10.18637/jss.v033.i01

    Article  PubMed  PubMed Central  Google Scholar 

  31. Taylor JA, et al. Development of a symptom score for clinical studies to identify children with a documented viral upper respiratory tract infection. Pediatr Res. 2010;68:252–7.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Rainer TH, et al. Diagnostic utility of CRP to neopterin ratio in patients with acute respiratory tract infections. J Infect. 2009;58:123–30.

    Article  PubMed  Google Scholar 

  33. Gilbert DN. Procalcitonin as a biomarker in respiratory tract infection. Clin Infect Dis. 2011;52:S346-50.

    Article  CAS  PubMed  Google Scholar 

  34. Memar MY, et al. Procalcitonin: the marker of pediatric bacterial infection. Biomed Pharmacother. 2017;96:936–43.

    Article  CAS  PubMed  Google Scholar 

  35. Schuetz P, et al. Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev. 2017;10:CD007498.

    PubMed  Google Scholar 

  36. Christ-Crain M, et al. Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397]. Crit Care. 2006;10:R96.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wallihan RG, et al. Molecular distance to health transcriptional score and disease severity in children hospitalized with community-acquired pneumonia. Front Cell Infect Microbiol. 2018;8:382.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Vincent JL. The clinical challenge of sepsis identification and monitoring. PLoS Med. 2016;13:e1002022.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Meehan TP, et al. Quality of care, process, and outcomes in elderly patients with pneumonia. JAMA. 1997;278:2080–4.

    Article  CAS  PubMed  Google Scholar 

  40. Lee N, et al. Outcomes of adults hospitalised with severe influenza. Thorax. 2010;65:510–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

ASB received funding from the Eugene A. Stead Foundation and the Infectious Diseases Society of America. JS is funded through an NIAID-sponsored T32 Transplant ID Training Grant. MTM received support through the National Institute of Allergy and Infectious Diseases (NIAID), the Veterans Health Administration, the Claude D. Pepper Center, and the PRIME consortium [National Institute of Aging (NIA)].

Author information

Authors and Affiliations

  1. Duke University School of Medicine, Durham, NC, USA

    Allison S. Bloom

  2. Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University, Durham, NC, USA

    Sunil Suchindran & Micah T. McClain

  3. Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA

    Julie Steinbrink & Micah T. McClain

  4. Durham Veteran’s Affairs Medical Center, Durham, NC, USA

    Micah T. McClain

Authors
  1. Allison S. Bloom
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunil Suchindran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julie Steinbrink
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Micah T. McClain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Allison S. Bloom or Micah T. McClain.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest related to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 13 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bloom, A.S., Suchindran, S., Steinbrink, J. et al. Utility of predictive tools for risk stratification of elderly individuals with all-cause acute respiratory infection. Infection 47, 617–627 (2019). https://doi.org/10.1007/s15010-019-01299-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s15010-019-01299-1

Keywords

Search

Navigation