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Introduction and Evaluation of a Novel Clinical Decision Support Tool to Improve Extubation Decision-Making in the ICU
CCCF Academy. Zheng K. 11/12/19; 283351; EP90
Ms. Katina Zheng
Ms. Katina Zheng
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ePoster
Topic: Other

Katina Zheng1, Aimee J. Sarti2, Christophe L. Herry3, Stephanie Sutherland2, Irene Watpool2, Rebecca Porteous4, Michael Hickey2, Caitlin Anstee4, Anna Fazekas4,5, Andrew JE Seely2,5,6, Canadian Critical Care Trials Group
 
1Faculty of Medicine, University of Ottawa, Ottawa, Canada
2Department of Critical Care Medicine, The Ottawa Hospital, Ottawa, Canada
3Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
4The Ottawa Hospital Research Institute, Ottawa, Canada
5Division of Thoracic Surgery, The Ottawa Hospital, Ottawa, Canada
6University of Ottawa, Ottawa, Canada


Introduction:
Timely and successful extubation is critical to care in the intensive care unit yet remains unsuccessful in ~15% of patients1,2. A variety of methods to assess extubation readiness have been developed, including the spontaneous breathing trial (SBT)3, the Rapid Shallow Breathing Index (RSBI)4, respiratory rate variability (RRV)5,6, cough strength and more4,7,8; however, no standardized clinical decision support tool has ever been implemented to assist in extubation readiness assessment. 
 
Objectives: 
In this observational Phase I study, we aimed to evaluate feasibility of implementation of a clinical decision support tool, Extubation AdvisorTM(EA), which combines an RRV-derived model to predict the risk of extubation failure, RSBI, clinical impression of extubation failure risk, and a standardized extubation readiness checklist.
 
Methods:
All patients underwent capnography waveform capture (Excel Bedmaster), with calculation of RRV. Patients were included if they were being considered for extubation and assessed with an SBT. RTs entered salient SBT information including a checklist, and their perception of extubation failure risk into the EA on a tablet. RTs were consented for email surveys and interviews on data entry. RT interviews were audio-recorded, transcribed verbatim and analyzed by a team of researchers with expertise in qualitative research. Themes emerged iteratively from the coded data. MDs and RTs were sent EA report 72 hours post extubation and asked to evaluate it.
 
Results:
117 patients were enrolled June 2017-October 2018, and 153 SBTs were included. 9 SBTs were excluded for protocol violations (i.e. one-way extubations, direct to tracheostomy), and 43 for inadequate waveform or clinical data. Of the remaining patients, 80 underwent extubation, of which 71 (89%) had complete EA reports. Of 9 unsuccessful reports, 6 had missing information and 3 had software issues. In total, 68 (85%) extubations were successful and 12 (15%) extubations failed. Based on the RRV-predictive model, the incidence of extubation failure in below-average risk patients was 11% compared to 21% in above-average risk patients. Response rates to surveys were low overall (21% from 245 data entry questionnaires and 33% from 48 EA report questionnaires). On average, 75% of RT respondents reported excellent or very good data entry process, clarity, time to complete and completeness, but rated workflow integration lower (56%). On average, 73% of MDs and RTs reported excellent or above average on clarity, accuracy, and completeness, but rated potential impact and usefulness lower (56%). Several themes emerged from RT interviews (n=15) including: EA was found to be clear and easy to use, with few technological issues, and interestingly we observed RT concerns that the EA may be threatening to their job security.
 
Conclusions:
In this mixed-methods evaluation of the first implementation of a waveform-derived predictive model within a bedside tool, the EA was found to be feasibly implemented, and perceived by most RTs and MDs to be a clear, efficient, accurate and complete tool with the potential to aid in extubation decision-making, although lower scores were found for RT workflow integration and perception of utility and impact. Although the tool was designed to empower RT expression of extubation risk, this proved threatening to some. This study helps refine EA design and with planning of future interventional studies.


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1. Epstein SK, Ciubotaru RL, Wong JB. Effect of failed extubation on the outcome of mechanical ventilation. Chest. 1997;112(1):186-192. doi:10.1378/chest.112.1.186
2. La DE, The FOR. A comparison of four methods of weaning patients from mechanical ventilation. 1995;332(6). doi:10.3171/2011.4.JNS102137
3. Zeggwagh AA, Abouqal R, Madani N, Zekraoui A, Kerkeb O. Weaning from mechanical ventilation: A model for extubation. Intensive Care Med. 1999;25(10):1077-1083. doi:10.1007/s001340051015
4. Yang K, Tobin M. A Prospective Study of Indexes Predicting the Outcome of Trials of Weaning from Mechanical Ventilation. N Engl J Med. 1991;324(21):1445-1450. doi:10.1056/NEJM199105233242101
5. M.-Y. B, S.-S. H, H.-W. Y, B.I.-T. K, Y.-T. L, J.-H. W. Breathing pattern variability: A weaning predictor in postoperative patients recovering from systemic inflammatory response syndrome. Intensive Care Med. 2004;30(2):241-247. doi:http://dx.doi.org/10.1007/s00134-003-2073-8
6. Wysocki M, Cracco C, Teixeira A, et al. Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation. Crit Care Med. 2006;34(8):2076-2083. doi:10.1097/01.CCM.0000227175.83575.E9
7. Meade M, Guyatt G, Cook D, et al. Predicting Success in Weaning From Mechanical Ventilation. Chest. 2001;120(6):400S-424S. doi:10.1378/chest.120.6_suppl.400S
8. Frutos-Vivar F, Ferguson ND, Esteban A, et al. Risk factors for extubation failure in patients following a successful spontaneous breathing trial. Chest. 2006;130(6):1664-1671. doi:10.1378/chest.130.6.1664

ePoster
Topic: Other

Katina Zheng1, Aimee J. Sarti2, Christophe L. Herry3, Stephanie Sutherland2, Irene Watpool2, Rebecca Porteous4, Michael Hickey2, Caitlin Anstee4, Anna Fazekas4,5, Andrew JE Seely2,5,6, Canadian Critical Care Trials Group
 
1Faculty of Medicine, University of Ottawa, Ottawa, Canada
2Department of Critical Care Medicine, The Ottawa Hospital, Ottawa, Canada
3Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Canada
4The Ottawa Hospital Research Institute, Ottawa, Canada
5Division of Thoracic Surgery, The Ottawa Hospital, Ottawa, Canada
6University of Ottawa, Ottawa, Canada


Introduction:
Timely and successful extubation is critical to care in the intensive care unit yet remains unsuccessful in ~15% of patients1,2. A variety of methods to assess extubation readiness have been developed, including the spontaneous breathing trial (SBT)3, the Rapid Shallow Breathing Index (RSBI)4, respiratory rate variability (RRV)5,6, cough strength and more4,7,8; however, no standardized clinical decision support tool has ever been implemented to assist in extubation readiness assessment. 
 
Objectives: 
In this observational Phase I study, we aimed to evaluate feasibility of implementation of a clinical decision support tool, Extubation AdvisorTM(EA), which combines an RRV-derived model to predict the risk of extubation failure, RSBI, clinical impression of extubation failure risk, and a standardized extubation readiness checklist.
 
Methods:
All patients underwent capnography waveform capture (Excel Bedmaster), with calculation of RRV. Patients were included if they were being considered for extubation and assessed with an SBT. RTs entered salient SBT information including a checklist, and their perception of extubation failure risk into the EA on a tablet. RTs were consented for email surveys and interviews on data entry. RT interviews were audio-recorded, transcribed verbatim and analyzed by a team of researchers with expertise in qualitative research. Themes emerged iteratively from the coded data. MDs and RTs were sent EA report 72 hours post extubation and asked to evaluate it.
 
Results:
117 patients were enrolled June 2017-October 2018, and 153 SBTs were included. 9 SBTs were excluded for protocol violations (i.e. one-way extubations, direct to tracheostomy), and 43 for inadequate waveform or clinical data. Of the remaining patients, 80 underwent extubation, of which 71 (89%) had complete EA reports. Of 9 unsuccessful reports, 6 had missing information and 3 had software issues. In total, 68 (85%) extubations were successful and 12 (15%) extubations failed. Based on the RRV-predictive model, the incidence of extubation failure in below-average risk patients was 11% compared to 21% in above-average risk patients. Response rates to surveys were low overall (21% from 245 data entry questionnaires and 33% from 48 EA report questionnaires). On average, 75% of RT respondents reported excellent or very good data entry process, clarity, time to complete and completeness, but rated workflow integration lower (56%). On average, 73% of MDs and RTs reported excellent or above average on clarity, accuracy, and completeness, but rated potential impact and usefulness lower (56%). Several themes emerged from RT interviews (n=15) including: EA was found to be clear and easy to use, with few technological issues, and interestingly we observed RT concerns that the EA may be threatening to their job security.
 
Conclusions:
In this mixed-methods evaluation of the first implementation of a waveform-derived predictive model within a bedside tool, the EA was found to be feasibly implemented, and perceived by most RTs and MDs to be a clear, efficient, accurate and complete tool with the potential to aid in extubation decision-making, although lower scores were found for RT workflow integration and perception of utility and impact. Although the tool was designed to empower RT expression of extubation risk, this proved threatening to some. This study helps refine EA design and with planning of future interventional studies.


Image Image

1. Epstein SK, Ciubotaru RL, Wong JB. Effect of failed extubation on the outcome of mechanical ventilation. Chest. 1997;112(1):186-192. doi:10.1378/chest.112.1.186
2. La DE, The FOR. A comparison of four methods of weaning patients from mechanical ventilation. 1995;332(6). doi:10.3171/2011.4.JNS102137
3. Zeggwagh AA, Abouqal R, Madani N, Zekraoui A, Kerkeb O. Weaning from mechanical ventilation: A model for extubation. Intensive Care Med. 1999;25(10):1077-1083. doi:10.1007/s001340051015
4. Yang K, Tobin M. A Prospective Study of Indexes Predicting the Outcome of Trials of Weaning from Mechanical Ventilation. N Engl J Med. 1991;324(21):1445-1450. doi:10.1056/NEJM199105233242101
5. M.-Y. B, S.-S. H, H.-W. Y, B.I.-T. K, Y.-T. L, J.-H. W. Breathing pattern variability: A weaning predictor in postoperative patients recovering from systemic inflammatory response syndrome. Intensive Care Med. 2004;30(2):241-247. doi:http://dx.doi.org/10.1007/s00134-003-2073-8
6. Wysocki M, Cracco C, Teixeira A, et al. Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation. Crit Care Med. 2006;34(8):2076-2083. doi:10.1097/01.CCM.0000227175.83575.E9
7. Meade M, Guyatt G, Cook D, et al. Predicting Success in Weaning From Mechanical Ventilation. Chest. 2001;120(6):400S-424S. doi:10.1378/chest.120.6_suppl.400S
8. Frutos-Vivar F, Ferguson ND, Esteban A, et al. Risk factors for extubation failure in patients following a successful spontaneous breathing trial. Chest. 2006;130(6):1664-1671. doi:10.1378/chest.130.6.1664

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