Improved patient-ventilator asynchrony (PVA) identification using waveform analysis by critical care physicians (CCPs) may improve patient outcomes. This study aimed to assess the ability of CCPs to identify different types of PVAs using waveform analysis as well as factors related to this ability.
METHODS:
We surveyed 12 university-affiliated medical ICUs (MICUs) in Tunisia. CCPs practicing in these MICUs were asked to visually identify 4 clinical cases, each corresponding to a different PVA. We collected the following characteristics regarding CCPs: scientific grade, years of experience, prior training in mechanical ventilation, prior exposure to waveform analysis, and the characteristics of the MICUs in which they practice. Respondents were categorized into 2 groups based on their ability to correctly identify PVAs (defined as the correct identification of at least 3 of the 4 PVA cases). Univariate analysis was performed to identify factors related to the correct identification of PVA.
RESULTS:
Among 136 included CCPs, 72 (52.9%) responded to the present survey. The respondents comprised 59 (81.9%) residents, and 13 (18.1%) senior physicians. Further, 50 (69.4%) respondents had attended prior training in mechanical ventilation. Moreover, 21 (29.2%) of the respondents could correctly identify PVAs. Double-triggering was the most frequently identified PVA type, 43 (59.7%), followed by auto-triggering, 36 (50%); premature cycling, 28 (38.9%); and ineffective efforts, 25 (34.7%). Univariate analysis indicated that senior physicians had a better ability to correctly identify PVAs than residents (7 [53.8%] vs 14 [23.7%], P = .044).
CONCLUSIONS:
The present study revealed a significant deficiency in the accurate visual identification of PVAs among CCPs in the MICUs. When compared to residents, senior physicians exhibited a notably superior aptitude for correctly recognizing PVAs.
BeitlerJR, SandsSA, LoringSH, OwensRL, MalhotraA, SpraggRG, et al. Quantifying unintended exposure to high tidal volumes from breath-stacking dyssynchrony in ARDS: the BREATHE criteria. Intensive Care Med, 2016; 42(9):1427–1436.
6.
YonisH, GobertF, TapponnierR, GuérinC. Reverse-triggering in a patient with ARDS. Intensive Care Med, 2015; 41(9):1711–1712.
7.
BlanchL, VillagraA, SalesB, MontanyaJ, LucangeloU, LujánM, et al. Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med, 2015; 41(4):633–641.
8.
GilstrapD, MacIntyreN. Patient-ventilator interactions. Implications for clinical management. Am J Respir Crit Care Med, 2013; 188(9):1058–1068.
BandeiraM, AlmeidaA, MeloL, de MouraPH, Ribeiro SilvaEO, SilvaJ, et al. Accuracy of algorithms and visual inspection for detection of trigger asynchrony in critical patients: a systematic review. Crit Care Res Pract, 2021; 2021:e6942497.
14.
GeorgopoulosD, PrinianakisG, KondiliE. Bedside waveforms interpretation as a tool to identify patient-ventilator asynchronies. Intensive Care Med, 2006; 32(1):34–47.
15.
LonghiniF, ColomboD, PisaniL, IdoneF, ChunP, DoorduinJ, et al. Efficacy of ventilator waveform observation for detection of patient-ventilator asynchrony during NIV: a multi-center study. ERJ Open Res, 2017; 3(4):00075–02017.
16.
NilsestuenJO, HargettKD. Using ventilator graphics to identify patient-ventilator asynchrony. Respir Care, 2005; 50(2):202–234.discussion 232–234.
17.
AkoumianakiE, MaggioreSM, ValenzaF, BellaniG, JubranA, LoringSH, et al. PLUG Working Group (Acute Respiratory Failure Section of the European Society of Intensive Care Medicine). The application of esophageal pressure measurement in patients with respiratory failure. Am J Respir Crit Care Med, 2014; 189(5):520–531.
18.
RamirezII, ArellanoDH, AdasmeRS, LanderosJM, SalinasFA, VargasAG, et al. Ability of ICU health care professionals to identify patient-ventilator asynchrony using waveform analysis. Respir Care, 2017; 62(2):144–149.
19.
SharmaA, Minh DucNT, Luu Lam ThangT, NamNH, NgSJ, AbbasKS, et al. A consensus-based checklist for reporting of survey studies (CROSS). J Gen Intern Med, 2021; 36(10):3179–3187.
TurnbullJM. What is … normative versus criterion-referenced assessment. Med Teach, 1989; 11(2):145–150.
22.
AlqahtaniJS, AlAhmariMD, AlshamraniKH, AlshehriAM, AlthumayriMA, GhazwaniAA, et al. Patient-ventilator asynchrony in critical care settings: national outcomes of ventilator waveform analysis. Heart Lung, 2020; 49(5):630–636.
23.
ColomboD, CammarotaG, AlemaniM, CarenzoL, BarraFL, VaschettoR, et al. Efficacy of ventilator waveforms observation in detecting patient-ventilator asynchrony. Crit Care Med, 2011; 39(11):2452–2457.
24.
ChacónE, EstrugaA, MuriasG, SalesB, MontanyaJ, LucangeloU, et al. Nurses' detection of ineffective inspiratory efforts during mechanical ventilation. Am J Crit Care, 2012; 21(4):e89-93–e93.
25.
RamírezII, AdasmeRS, ArellanoDH, RochaARM, AndradeFMD, Núñez-SilveiraJ, et al. Identifying and managing patient-ventilator asynchrony: an international survey. Med Intensiva (Engl Ed), 2021; 45(3):138–146.
26.
De BackerD, CecconiM, ChewMS, HajjarL, MonnetX, Ospina-TascónGA, et al. A plea for personalization of the hemodynamic management of septic shock. Crit Care, 2022; 26(1):372.
HanidziarD, WestoverMB. Monitoring of sedation in mechanically ventilated patients using remote technology. Curr Opin Crit Care, 2022; 28(3):360–366.
29.
VentreKM. The inscrutable signatures of patient-ventilator asynchrony: all the light we cannot see. Minerva Anestesiol, 2021; 87(3):278–282.
30.
Mireles-CabodevilaE, SiubaMT, ChatburnRL. A taxonomy for patient-ventilator interactions and a method to read ventilator waveforms. Respir Care, 2022; 67(1):129–148.
31.
OtoB, AnnesiJ, FoleyRJ. Patient-ventilator dyssynchrony in the intensive care unit: a practical approach to diagnosis and management. Anaesth Intensive Care, 2021; 49(2):86–97.
32.
SassoonCS. Triggering of the ventilator in patient-ventilator interactions. Respir Care, 2011; 56(1):39–51.
33.
SeeKC, SahagunJ, CoveM, SumCL, GarciaB, ChancoD, et al. Managing patient-ventilator asynchrony with a twice-daily screening protocol: a retrospective cohort study. Aust Crit Care, 2021; 34(6):539–546.
34.
MojoliF, OrlandoA, BianchiIM, PuceR, ArisiE, SalveG, et al. Waveforms-guided cycling-off during pressure support ventilation improves both inspiratory and expiratory patient-ventilator synchronisation. Anaesth Crit Care Pain Med, 2022; 41(6):101153.
MauriT, YoshidaT, BellaniG, GoligherEC, CarteauxG, RittayamaiN, et al. PLeUral pressure working Group (PLUG—Acute Respiratory Failure section of the European Society of Intensive Care Medicine). Esophageal and transpulmonary pressure in the clinical setting: meaning, usefulness, and perspectives. Intensive Care Med, 2016; 42(9):1360–1373.
38.
AkoumianakiE, LyazidiA, ReyN, MatamisD, Perez-MartinezN, GiraudR, et al. Mechanical ventilation–induced reverse-triggered breaths: a frequently unrecognized form of neuromechanical coupling. Chest, 2013; 143(4):927–938.
39.
BellaniG, MauriT, CoppadoroA, GrasselliG, PatronitiN, SpadaroS, et al. Estimation of patient's inspiratory effort from the electrical activity of the diaphragm. Crit Care Med, 2013; 41(6):1483–1491.
40.
ParthasarathyS, JubranA, TobinMJ. Assessment of neural inspiratory time in ventilator-supported patients. Am J Respir Crit Care Med, 2000; 162(2 Pt 1):546–552.
41.
SinderbyC, LiuS, ColomboD, CamarottaG, SlutskyAS, NavalesiP, et al. An automated and standardized neural index to quantify patient-ventilator interaction. Crit Care, 2013; 17(5):R239.
42.
SilvaDO, de SouzaPN, de Araujo SousaML, MoraisCCA, FerreiraJC, HolandaMA, et al. Impact on the ability of health care professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study). Crit Care, 2023; 27(1):128.
43.
SuL, LanY, ChiY, CaiF, BaiZ, LiuX, et al. Establishment and application of a patient-ventilator asynchrony remote network platform for ICU mechanical ventilation: a retrospective study. J Clin Med, 2023; 12(4):1570.
