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Ventilator-Associated Complications in the Mechanically Ventilated Veteran

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Ventilator-Associated Complications in the Mechanically Ventilated Veteran University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2013 Ventilator-associated Complications In The Mechanically Ventilated Veteran Joan Grano University of Central Florida Part of the Nursing Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Grano, Joan, "Ventilator-associated Complications In The Mechanically Ventilated Veteran" (2013). Electronic Theses and Dissertations, 2004-2019. 2841. https://stars.library.ucf.edu/etd/2841 VENTILATOR-ASSOCIATED COMPLICATIONS IN THE MECHANICALLY VENTILATED VETERAN by JOAN T. GRANO ASN, St Petersburg Junior College, 1981 AA, St. Petersburg Junior College, 1982 BSN, University of South Florida, 1984 MSN, University of South Florida, 1986 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Nursing at the University of Central Florida Orlando, Florida Summer Term 2013 Major Professor: Mary Lou Sole © 2013 Joan T. Grano ii ABSTRACT Surveillance of ventilator-associated pneumonia (VAP) has been the common outcome measurement used for internal and external benchmarking for mechanically ventilated patients; and although not a clinical definition, it is commonly used as an outcome measurement for research studies. Criteria in the VAP definition include both subjective and objective components, leading to questions of validity. In addition, recent legislation has mandated the public reporting of healthcare-associated infections, including VAP, in many states. Infectious disease experts have recently recommended monitoring a new outcome, ventilator-associated events (VAE), that contain specific objective criteria. The Centers for Disease Prevention and Control (CDC) have refined this definition and released a new VAE protocol and algorithm, replacing the VAP surveillance definition, as a result. The VAE protocol assesses for ventilator- associated conditions (VAC). The primary aims of this study were to determine the incidence of VAC; and to assess four predictors for VAC, including two VAP prevention strategies (use of the subglottic secretion drainage endotracheal-tube [SSD-ETT]), and daily sedation vacation); and two patient-related factors (alcohol withdrawal during mechanical ventilation, and history of COPD). In addition, the incidence for VAE, using a new national algorithm was determined. Using a retrospective study design, electronic medical records of 280 veterans were reviewed to identify cases of VAC using the VAE algorithm. The setting was two intensive care units (ICU) at a large Veterans Administration Healthcare System (VAHCS) from October 2009 to September 2011. In addition to demographic information, variables were collected to determine if cases met event criteria (VAC, infection-related ventilator-associated complication iii [IVAC], and possible or probable VAP). Incidence rates were calculated for VAC and IVAC. Comparative data between those with and without VAC were assessed with independent sample T-test or non-parametric equivalents. The study sample was predominantly male (97.1%), Caucasian (92.1%), non-Hispanic (90.7%); with a mean (SD) age of 67.2 (10.4) years. Twenty patients met the VAC definition resulting in a VAC incidence of 7.38 per 1000 ventilator days. There were no statistically significant differences in demographics or disease characteristics found between the two groups (patients with VAC and patients without VAC). Using logistic regression, the impact of the four predictors for VAC was assessed. None of the four explanatory variables were predictive of the occurrence of VAC. Secondary outcomes (e.g. mechanical ventilation days, ICU days, hospital days, and mortality) of veterans with VAC were compared to veterans without VAC. Results indicated that the VAC group was associated with a significantly longer duration of ICU stay, longer mechanical ventilation period, more likely to have a tracheostomy, and had a higher mortality during hospitalization. Expanding mechanical ventilation quality performance measures to include VAE/VAC provides a better representation of infectious and non-infectious ventilator-associated problems, and provides more accurate morbidity and mortality in this high-risk ICU population. Further research is necessary to explore patient characteristics and prevention strategies that impact the development of all VAC. iv To the ones closest to my heart, Eric, Rachel, and Ashley; and To my Chair, Mary Lou Sole, who has given me the passion and confidence to pursue my research. v ACKNOWLEDGMENTS Thank you to: • Each member of my dissertation committee (Dr. Steven Talbert, Dr. Maureen Covelli, and Dr. A. Scott McGuirt), for sharing their expertise, challenging me, and encouraging me to overcome every obstacle. • Each faculty member at the University of Central Florida, College of Nursing; that has shared their knowledge and provided me the tools to learn. • Each member of the Infection Control Team at Bay Pines Veterans Healthcare System (BPVAHCS), for their support and encouragement during my education and research. • My family, who has given me loyalty, love, and confidence in all my goals. Employment: • Employed as an Infection Control Preventionist/Analyst, Infection Control Section, Quality Systems; Department of Veterans Affairs, Bay Pines VA Healthcare System, Bay Pines, FL. • This material is based upon work supported by the Department of Veterans Affairs, Veterans Health Administration and Bay Pines VA Healthcare System. • The contents of this paper do not represent the views of the Department of Veterans Affairs or the United States Government. vi TABLE OF CONTENTS LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1: INTRODUCTION 1 Study Purpose and Aims 2 Conceptual Model: Pathogenesis of VAE 3 Impact of Prevention Strategies and Patient Factors on VAC 4 Ventilator-Associated Complications 6 References 7 CHAPTER 2: OUTCOME MEASUREMENTS FOR COMPLICATIONS OF MECHANICAL VENTILATION 11 Summary Abstract 11 Background: VAP Surveillance 11 Process to Improve Outcome Measurements 13 New VAE Surveillance Definition 17 Summary 19 References 19 CHAPTER 3: FINDINGS 24 Abstract 24 vii Introduction and Background 25 Methods 27 Setting and Sample 27 Measures 29 Demographic and Medical History 29 Procedures 31 Demographic, Medical, and Hospitalization History 31 VAE Algorithm Assessment for Occurrence of VAC 31 Assessment for Predictor Occurrence 31 Study Endpoints 33 Data Analysis 34 Results 34 Sample 34 Outcome Data 37 Discussion 38 Implications for Practice and Research 45 References 46 CHAPTER 4: PREVENTION STRATEGIES IMPACTED BY VAE SURVEILLANCE 51 Summary Abstract 51 viii Mechanical Ventilation 51 Mechanical Ventilation and VAE 52 IHI Ventilator Bundle and Sedation 58 Subglottic Secretion Drainage-Endotracheal Tube (SSD-ETT) 60 Implications 61 References 63 APPENDIX A: DATA COLLECTION TOOLS 68 APPENDIX B: BAY PINES VAHCS IRB APPROVAL 73 APPENDIX C: UNIVERSITY OF CENTRAL FLORIDA IRB APPROVAL 78 APPENDIX D: CITI TRAINING 80 ix LIST OF FIGURES Figure 1: Conceptual Model of VAE ............................................................................................ 4 Figure 2: VAE Surveillance Algorithm; from: http://www.cdc.gov/nhsn/acute-care-hospital/vae/ .................................................................................................................................................. 18 Figure 3: VAE Surveillance Algorithm; from: http://www.cdc.gov/nhsn/acute-care-hospital/vae/ .................................................................................................................................................. 30 Figure 4: VAE Surveillance Algorithm; from: http://www.cdc.gov/nhsn/acute-care-hospital/vae/ .................................................................................................................................................. 53 x LIST OF TABLES Table 1: Comparison of Assessment Criteria for VAP, Modified VAP, s-VAP, and Ventilator- Associated Complication ............................................................................................................ 15 Table 2: Sample Size for Logistic Regression with VAC Event Rate p1 at X=1, VAC Event Rate p2 at X=0 .................................................................................................................................... 28 Table 3: Sample Demographics and Characteristics of Patients With and Without VAC ............ 35 Table 4: Logistic Regression (LR) Model Results ...................................................................... 37 Table 5: Secondary Outcomes for Patients With and Without VAC ............................................ 38 Table 6: Types of Mechanical Ventilation .................................................................................. 40 Table 7: Types of Mechanical Ventilation .................................................................................. 54 xi CHAPTER 1: INTRODUCTION Surveillance of healthcare-associated infections (HAI) by the infection preventionist (IP) remains a cornerstone of every acute care Infection Control Program.1,2 The IP must accurately and timely identify HAIs in the intensive care
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