Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-25T23:41:55.529Z Has data issue: false hasContentIssue false

Subglottic suction frequency and adverse ventilator-associated events during critical illness

Published online by Cambridge University Press:  11 January 2021

Hatem O. Abdallah
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Melanie F. Weingart
Affiliation:
Department of Medicine, University of Colorado, Aurora, Colorado
Risa Fuller
Affiliation:
Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
David Pegues
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Rebecca Fitzpatrick
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Brendan J. Kelly*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
*
Author for correspondence: Brendan J. Kelly, E-mail: brendank@pennmedicine.upenn.edu

Abstract

Objective:

Tracheal intubation and mechanical ventilation provide essential support for patients with respiratory failure, but the course of mechanical ventilation may be complicated by adverse ventilator-associated events (VAEs), which may or may not be associated with infection. We sought to understand how the frequency of subglottic suction, an indicator of the quantity of sputum produced by ventilated patients, relates to the onset of all VAEs and infection-associated VAEs.

Design:

We performed a case-crossover study including 87 patients with VAEs, and we evaluated 848 days in the pre-VAE period at risk for a VAE.

Setting and participants:

Critically ill patients were recruited from the medical intensive care unit of an academic medical center.

Methods:

We used the number of as-needed subglottic suctioning events performed per calendar day to quantify sputum production, and we compared the immediate pre-VAE period to the preceding period. We used CDC surveillance definitions for VAE and to categorize whether events were infection associated or not.

Results:

Sputum quantity measured by subglottic suction frequency is greater in the period immediately prior to VAE than in the preceding period. However, it does not discriminate well between infection-associated VAEs and VAEs without associated infection.

Conclusions:

Subglottic suction frequency may serve as a valuable marker of sputum quantity, and it is associated with risk of a VAE. However, our results require validation in a broader population of mechanically ventilated patients and intensive care settings.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Klompas, M. Complications of mechanical ventilation—the CDC’s new surveillance paradigm. N Engl J Med 2013;368:14721475.CrossRefGoogle Scholar
Magill, SS, Klompas, M, Balk, R, et al. Developing a new, national approach to surveillance for ventilator-associated events. Crit Care Med 2013;41:24672475.CrossRefGoogle ScholarPubMed
Magill, SS, Rhodes, B, Klompas, M. Improving ventilator-associated event surveillance in the national healthcare safety network and addressing knowledge gaps: update and review. Curr Opin Infect Dis 2014;27:394400.CrossRefGoogle ScholarPubMed
Kobayashi, H, Uchino, S, Takinami, M, Uezono, S. The impact of ventilator-associated events in critically ill subjects with prolonged mechanical ventilation. Respir Care 2017;62:13791386.CrossRefGoogle ScholarPubMed
Meagher, AD, Lind, M, Senekjian, L, et al. Ventilator-associated events, not ventilator-associated pneumonia, is associated with higher mortality in trauma patients. J Trauma Acute Care Surg 2019;87:307314.CrossRefGoogle Scholar
Klompas, M, Li, L, Menchaca, JT, Gruber, S, Centers for Disease Control and Prevention Epicenters Program. Ultra-short-course antibiotics for patients with suspected Ventilator-Associated pneumonia but minimal and stable ventilator settings. Clin Infect Dis 2017;64:870876.Google ScholarPubMed
Fan, Y, Gao, F, Wu, Y, Zhang, J, Zhu, M, Xiong, L. Does ventilator-associated event surveillance detect ventilator-associated pneumonia in intensive care units? A systematic review and meta-analysis. Crit Care Med 2016;20:338.Google ScholarPubMed
Klompas, M. Discordance between novel and traditional surveillance definitions for ventilator-associated pneumonia: insights and opportunities to improve patient care. Infect Control Hosp Epidemiol 2014;35:11961198.CrossRefGoogle ScholarPubMed
Ramirez-Estrada, S, Peña-Lopez, Y, Kalwaje Eshwara, V, Rello, J. Ventilator-associated events versus ventilator-associated respiratory infections-moving into a new paradigm or merging both concepts, instead? Ann Translat Med 2018;6:425.CrossRefGoogle ScholarPubMed
Stevens, JP, Silva, G, Gillis, J, et al. Automated surveillance for ventilator-associated events. Chest 2014;146:16121618.CrossRefGoogle ScholarPubMed
Klompas, M. Ventilator-associated events: what they are and what they are not. Respir Care 2019;64:953961.CrossRefGoogle ScholarPubMed
Cocoros, NM, Klompas, M. Ventilator-associated events and their prevention. Infect Dis Clin N Am 2016;30:887908.CrossRefGoogle ScholarPubMed
Lewis, SC, Li, L, Murphy, MV, Klompas, M, CDC Prevention Epicenters. Risk factors for ventilator-associated events: a case-control multivariable analysis. Crit Care Med 2014;42:18391848.CrossRefGoogle ScholarPubMed
Klompas, M. Potential strategies to prevent ventilator-associated events. Am J Respir Crit Care Med 2015;192:14201430.CrossRefGoogle ScholarPubMed
Klompas, M, Anderson, D, Trick, W, et al. The preventability of ventilator-associated events. The CDC Prevention Epicenters Wake Up and Breathe Collaborative. Am J Respir Crit Care Med 2015;191:292301.CrossRefGoogle ScholarPubMed
Rello, J, Ramírez-Estrada, S, Romero, A, et al. Factors associated with ventilator-associated events: an international multicenter prospective cohort study. Eur J Clin Microbiol Infect Dis 2019;38:16931699.CrossRefGoogle ScholarPubMed
Liu, J, Zhang, S, Chen, J, et al. Risk factors for ventilator-associated events: a prospective cohort study. Am J Infect Control 2019;47:744749.CrossRefGoogle ScholarPubMed
Vaewpanich, J, Akcan-Arikan, A, Coss-Bu, JA, Kennedy, CE, Starke, JR, Thammasitboon, S. Fluid overload and kidney injury score as a predictor for ventilator-associated events. Front Pediatr 2019;7:204.CrossRefGoogle ScholarPubMed
Damas, P, Frippiat, F, Ancion, A, et al. Prevention of ventilator-associated pneumonia and ventilator-associated conditions: a randomized controlled trial with subglottic secretion suctioning. Crit Care Med 2015;43:2230.CrossRefGoogle ScholarPubMed
Caroff, DA, Li, L, Muscedere, J, Klompas, M. Subglottic secretion drainage and objective outcomes: a systematic review and Meta-Analysis. Crit Care Med 2016;44:830840.CrossRefGoogle ScholarPubMed
Mao, Z, Gao, L, Wang, G, et al. Subglottic secretion suction for preventing ventilator-associated pneumonia: an updated meta-analysis and trial sequential analysis. Crit Care Med 2016;20:353.Google ScholarPubMed
Rothman, KJ, Greenland, S. Planning study size based on precision rather than power. Epidemiology. 2018;29:599603.CrossRefGoogle ScholarPubMed
R Core Team. R: A language and environment for statistical computing. https://www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing. Published online 2018. Accessed November 3, 2020.Google Scholar
Maclure, M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol 1991;133:144153.CrossRefGoogle ScholarPubMed
Maclure, M, Mittleman, MA. Should we use a case-crossover design? Ann Rev Pub Health 2000;21:193221.Google ScholarPubMed
Hosmer, DW Jr, Lemeshow, S, Applied Logistic Regression, Sturdivant RX.. New York: John Wiley & Sons; 2013: 313–376.CrossRefGoogle Scholar
Rice, K. Equivalence between conditional and random-effects likelihoods for pair-matched case-control studies. J Am Stat Assoc 2008;103:385396.CrossRefGoogle Scholar
Bürkner, P-C. Brms: An R package for Bayesian multilevel models using stan. J Stat Softw 2017;80:128.CrossRefGoogle Scholar
Carpenter, B, Gelman, A, Hoffman, M, et al. Stan: a probabilistic programming language. J Stat Softw 2017;76:132.CrossRefGoogle Scholar
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 1 edition. Boca Raton, FL: CRC Press/Taylor & Francis Group; 2016.Google Scholar
Gabry, J, Simpson, D, Vehtari, A, Betancourt, M, Gelman, A. Visualization in Bayesian workflow. J Royal Stat Soc A 2019;182:389402.Google Scholar
Gelman, A, Tuerlinckx, F. Type S error rates for classical and Bayesian single and multiple comparison procedures. Comput Stat 2000;15:373390.CrossRefGoogle Scholar
Supplementary material: File

Abdallah et al. Supplementary Materials

Abdallah et al. Supplementary Materials

Download Abdallah et al. Supplementary Materials(File)
File 97.8 KB