The Association between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm

Critical Care Medicine

Volumn 46, Issue 2, 2018, Pages E151-E157

  Sottile, Peter D ^a   Albers, David ^b   Higgins, Carrie ^a   McKeehan, Jeffery ^a   Moss, Marc M ^a  

^a UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^b University of Columbia   (United States)

Author keywords

Machine learning; neuromuscular blockade; respiratory distress syndrome, adult; ventilator induced lung injury; ventilators, mechanical

Indexed keywords

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL VENTILATION; BREATHING; DEEP SEDATION; DETECTION ALGORITHM; FEMALE; HEART FAILURE; HOSPITALIZATION; HUMAN; HYPOXEMIA; INTENSIVE CARE UNIT; LEARNING ALGORITHM; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL INTENSIVE CARE UNIT; MEDICAL RECORD; NEUROMUSCULAR BLOCKING; OBSERVATIONAL STUDY; POSITIVE END EXPIRATORY PRESSURE VENTILATION; PROSPECTIVE STUDY; RESPIRATORY DISTRESS; SEDATION; TIDAL VOLUME; VENTILATOR INDUCED LUNG INJURY; ADULT RESPIRATORY DISTRESS SYNDROME; DEVICE FAILURE; MECHANICAL VENTILATOR; MIDDLE AGED;

ALGORITHMS; DEEP SEDATION; EQUIPMENT FAILURE; FEMALE; HUMANS; MALE; MIDDLE AGED; NEUROMUSCULAR BLOCKADE; PROSPECTIVE STUDIES; RESPIRATORY DISTRESS SYNDROME, ADULT; TIDAL VOLUME; VENTILATORS, MECHANICAL;

EID: 85056350802     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0000000000002849     Document Type: Article

References (29)

1. Webb HH, Tierney DF: Experimental pulmonary edema due to intermittent positive pressure ventilation with high inflation pressures. Protection by positive end-expiratory pressure. Am Rev Respir Dis 1974;110:556-565
2. Acute Respiratory Distress Syndrome Network, Brower RG, Matthay MA, et al: Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med 2000;342:1301-1308
3. Futier E, Constantin J-M, Paugam-Burtz C, et al: A trial of intraoperative low-tidal-volume ventilation in abdominal surgery. N Engl J Med 2013;369:428-437
4. Gilstrap D, MacIntyre N: Patient-ventilator interactions. Implications for clinical management. Am J Respir Crit Care Med 2013;188:1058-1068
5. Mulqueeny Q, Ceriana P, Carlucci A, et al: Automatic detection of ineffective triggering and double triggering during mechanical ventilation. Intensive Care Med 2007;33:2014-2018
6. Blanch L, Sales B, Montanya J, et al: Validation of the Better Care® system to detect ineffective efforts during expiration in mechanically ventilated patients: A pilot study. Intensive Care Med 2012;38:772-780
7. Blanch L, Villagra A, Sales B, et al: Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med 2015;41:633-641
8. Pohlman MC, McCallister KE, Schweickert WD, et al: Excessive tidal volume from breath stacking during lung-protective ventilation for acute lung injury. Crit Care Med 2008;36:3019-3023
9. de Wit M, Pedram S, Best AM, et al: Observational study of patientventilator asynchrony and relationship to sedation level. J Crit Care 2009;24:74-80
10. Kress JP, Pohlman AS, O'Connor MF, et al: Daily interruption of sedative infusions in critically ill patients undergoing mechanical ventilation. N Engl J Med 2000;342:1471-1477
11. Wheeler AP, Bernard GR: Acute lung injury and the acute respiratory distress syndrome: A clinical review. Lancet 2007;369:1553-1564
12. Michel V, Gramfort A, Varoquaux G, et al: A supervised clustering approach for fMRI-based inference of brain states. Pattern Recognit 2012;45:2041-2049
13. Yi Q-X, Huang J-F, Wang F-M, et al: Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environ Sci Technol 2007;41:6770-6775
14. Chen C-W, Lin W-C, Hsu C-H, et al: Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: Feasibility of using a computer algorithm. Crit Care Med 2008;36:455-461
15. Beitler JR, Sands SA, Loring SH, et al: Quantifying unintended exposure to high tidal volumes from breath stacking dyssynchrony in ARDS: The BREATHE criteria. Intensive Care Med 2016;42:1427-1436
16. Thille AW, Rodriguez P, Cabello B, et al: Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med 2006;32:1515-1522
17. Chanques G, Kress JP, Pohlman A, et al: Impact of ventilator adjustment and sedation-analgesia practices on severe asynchrony in patients ventilated in assist-control mode. Crit Care Med 2013;41:2177-2187
18. Raykar VC, Yu S, Zhao LH, et al: Learning from crowds. J Mach Learn Res 2010;11:1297-1322
19. Zhu T, Dunkley N, Behar J, et al: Fusing continuous-valued medical labels using a Bayesian Model. Ann Biomed Eng 2015;43:2892-2902
20. Pelosi P, Bottino N, Chiumello D, et al: Sigh in supine and prone position during acute respiratory distress syndrome. Am J Respir Crit Care Med 2003;167:521-527
21. Mauri T, Eronia N, Abbruzzese C, et al: Effects of sigh on regional lung strain and ventilation heterogeneity in acute respiratory failure patients undergoing assisted mechanical ventilation. Crit Care Med 2015;43:1823-1831
22. Spieth PM, Carvalho AR, Pelosi P, et al: Variable tidal volumes improve lung protective ventilation strategies in experimental lung injury. Am J Respir Crit Care Med 2009;179:684-693
23. Lasko TA, Denny JC, Levy MA: Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLoS One 2013;8:e66341
24. Hripcsak G, Albers DJ: Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013;20:117-121
25. Hripcsak G, Albers DJ, Perotte A: Parameterizing time in electronic health record studies. J Am Med Inform Assoc 2015;22:794-804
26. Levine ME, Albers DJ, Hripcsak G: Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data. AMIA Annu Symp Proc 2016;2016:779-788
27. Hripcsak G, Albers DJ: Correlating electronic health record concepts with healthcare process events. J Am Med Inform Assoc 2013;20:e311-e318
28. Albers DJ, Elhadad N, Tabak E, et al: Dynamical phenotyping: Using temporal analysis of clinically collected physiologic data to stratify populations. PLoS One 2014;9:e96443
29. Claassen J, Perotte A, Albers D, et al: Nonconvulsive seizures after subarachnoid hemorrhage: Multimodal detection and outcomes. Ann Neurol 2013;74:53-64