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.
We retrospectively analyzed risk factors and outcomes of individuals ≥ 60 years of age presenting to the emergency department with a clinical diagnosis of ARI.
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.
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 to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
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.
Jain S, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. N Engl J Med. 2015;373:415–27.
Thompson WW, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289:179–86.
Garibaldi RA. Epidemiology of community-acquired respiratory tract infections in adults. Incidence, etiology, and impact. Am J Med. 1985;78:32–7.
Mullooly JP, et al. Influenza- and RSV-associated hospitalizations among adults. Vaccine. 2007;25:846–55.
Thompson WW, et al. Influenza-associated hospitalizations in the United States. JAMA. 2004;292:1333–40.
Janssens JP, Krause KH. Pneumonia in the very old. Lancet Infect Dis. 2004;4:112–24.
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.
Shibli F, et al. Etiology of community-acquired pneumonia in hospitalized patients in northern Israel. Isr Med Assoc J. 2010;12:477–82.
Metlay JP, Fine MJ. Testing strategies in the initial management of patients with community-acquired pneumonia. Ann Intern Med. 2003;138:109–18.
Falsey AR, Murata Y, Walsh EE. Impact of rapid diagnosis on management of adults hospitalized with influenza. Arch Intern Med. 2007;167:354–60.
Raz R, et al. A predictive model for the management of community-acquired pneumonia. Infection. 2003;31:3–8.
Neill AM, et al. Community acquired pneumonia: aetiology and usefulness of severity criteria on admission. Thorax. 1996;51:1010–6.
Farr BM, Sloman AJ, Fisch MJ. Predicting death in patients hospitalized for community-acquired pneumonia. Ann Intern Med. 1991;115:428–36.
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.
Fine MJ, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336:243–50.
Lim WS, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377–82.
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.
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.
Tsalik EL, et al. An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med. 2014;6:111.
Tsalik EL, et al. Host gene expression classifiers diagnose acute respiratory illness etiology. Sci Transl Med. 2016;8:322ra11.
Glickman SW, et al. Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad Emerg Med. 2010;17:383–90.
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.
Tsalik EL, et al. Discriminative value of inflammatory biomarkers for suspected sepsis. J Emerg Med. 2012;43:97–106.
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.
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.
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.
Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B (Methodol). 1996;58:267–88.
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
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.
Rainer TH, et al. Diagnostic utility of CRP to neopterin ratio in patients with acute respiratory tract infections. J Infect. 2009;58:123–30.
Gilbert DN. Procalcitonin as a biomarker in respiratory tract infection. Clin Infect Dis. 2011;52:S346-50.
Memar MY, et al. Procalcitonin: the marker of pediatric bacterial infection. Biomed Pharmacother. 2017;96:936–43.
Schuetz P, et al. Procalcitonin to initiate or discontinue antibiotics in acute respiratory tract infections. Cochrane Database Syst Rev. 2017;10:CD007498.
Christ-Crain M, et al. Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397]. Crit Care. 2006;10:R96.
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.
Vincent JL. The clinical challenge of sepsis identification and monitoring. PLoS Med. 2016;13:e1002022.
Meehan TP, et al. Quality of care, process, and outcomes in elderly patients with pneumonia. JAMA. 1997;278:2080–4.
Lee N, et al. Outcomes of adults hospitalised with severe influenza. Thorax. 2010;65:510–5.
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)].
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.
About this article
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
- Respiratory infection
- Viral infection