PeñuelasO, MurielA, AbrairaV, Frutos-VivarF, ManceboJ, RaymondosK, et al. Inter-country variability over time in the mortality of mechanically ventilated patients. Intensive Care Med, 2020; 46(3):444-453.
2.
TobinMJ, JubranA, LaghiF. Patient-ventilator interaction. Am J Respir Crit Care Med, 2001; 163(5):1059-1063.
3.
LiuP, LyuS, Mireles-CabodevilaE, MillerAG, AlbuainainFA, Ibarra-EstradaM, et al. Survey of ventilator waveform interpretation among ICU professionals. Respir Care, 2024:11677.
4.
CorregerE, MuriasG, ChaconE, EstrugaA, SalesB, Lopez-AguilarJ, et al. Interpretación de las curvas del respirador en pacientes con insuficiencia respiratoria aguda. Med Intensiva, 2012; 36(4):294-306.
5.
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.
6.
Mireles-CabodevilaE, de AbreuMG. Classification and quantification of patient-ventilator interactions: we need consensus!. Respir Care, 2022; 67(5):620-623.
7.
Mireles-CabodevilaE, SiubaMT, ChatburnRL. A taxonomy for patient-ventilator interactions and a method to read ventilator waveforms. Respir Care, 2022; 67(1):129-148.
8.
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.
9.
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.
10.
PhamT, MontanyaJ, TeliasI, PirainoT, MagransR, CoudroyR, et al. BEARDS study investigators. Automated detection and quantification of reverse triggering effort under mechanical ventilation. Crit Care, 2021; 25(1):60.
11.
RamírezII, Gutiérrez-AriasR, DamianiLF, AdasmeRS, ArellanoDH, SalinasFA, et al. Specific training improves the detection and management of patient-ventilator asynchrony. Respir Care, 2024; 69(2):166-175.
12.
BlanchL, SalesB, MontanyaJ, LucangeloU, OscarGE, VillagraA, 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(5):772-780.
13.
Mellado ArtigasR, DamianiLF, PirainoT, PhamT, ChenL, RauseoM, et al. Reverse triggering dyssynchrony 24 h after initiation of mechanical ventilation. Anesthesiology, 2021; 134(5):760-769.
14.
de HaroC, Santos-PulpónV, TelíasI, Xifra-PorxasA, SubiràC, BatlleM, et al. BEARDS study investigators. Flow starvation during square flow–assisted ventilation detected by supervised deep learning techniques. Crit Care, 2024; 28(1):75.
15.
ThilleAW, RodriguezP, CabelloB, LelloucheF, BrochardL. Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med, 2006; 32(10):1515-1522.
16.
De WitM, MillerKB, GreenDA, OstmanHE, GenningsC, EpsteinSK. Ineffective triggering predicts increased duration of mechanical ventilation. Crit Care Med, 2009; 37(10):2740-2745.
17.
SchmidtM, BanzettRB, RauxM, Morélot-PanziniC, DangersL, SimilowskiT, et al. Unrecognized suffering in the ICU: addressing dyspnea in mechanically ventilated patients. Intensive Care Med, 2014; 40(1):1-10.
18.
AlbaicetaGM, BrochardL, Dos SantosCC, FernándezR, GeorgopoulosD, GirardT, et al. The central nervous system during lung injury and mechanical ventilation: a narrative review. Br J Anaesth, 2021; 127(4):648-659.
19.
RuéM, AndrinopoulouER, AlvaresD, ArmeroC, ForteA, BlanchL. Bayesian joint modeling of bivariate longitudinal and competing risks data: an application to study patient-ventilator asynchronies in critical care patients. Biom J, 2017; 59(6):1184-1203.
20.
MagransR, FerreiraF, SarlabousL, López-AguilarJ, GomàG, Fernandez-GonzaloS, et al. ASYNICU group. The effect of clusters of double triggering and ineffective efforts in critically ill patients. Crit Care Med, 2022; 50(7):E619-e629.
21.
de HaroC, López-AguilarJ, MagransR, MontanyaJ, Fernández-GonzaloS, TuronM, et al. Asynchronies in the Intensive Care Unit (ASYNICU) Group. Double cycling during mechanical ventilation: frequency, mechanisms, and physiologic implications. Crit Care Med, 2018; 46(9):1385-1392.
22.
DemouleA, DecaveleM, AntonelliM, CamporotaL, AbrougF, AdlerD, et al. Dyspnea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement. Intensive Care Med, 2024; 50(2):159-180.
23.
EsperanzaJA, SarlabousL, de HaroC, MagransR, Lopez-AguilarJ, BlanchL. Monitoring asynchrony during invasive mechanical ventilation. Respir Care, 2020; 65(6):847-869.
24.
SubiràC, de HaroC, MagransR, FernándezR, BlanchL. Minimizing asynchronies in mechanical ventilation: current and future trends. Respir Care, 2018; 63(4):464-478.
25.
de HaroC, OchagaviaA, López-AguilarJ, Fernandez-GonzaloS, Navarra-VenturaG, MagransR, et al. Asynchronies in the Intensive Care Unit (ASYNICU) Group. Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities. Intensive Care Med Exp, 2019; 7(Suppl 1):43.
26.
MulqueenyQ, RedmondSJ, TassauxD, VignauxL, JollietP, CerianaP, et al. Automated detection of asynchrony in patient-ventilator interaction. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. Annu Int Conf IEEE Eng Med Biol Soc, 2009:5324-5327.
27.
BakkesT, van DiepenA, De BieA, MontenijL, MojoliF, BouwmanA, et al. Automated detection and classification of patient–ventilator asynchrony by means of machine learning and simulated data. Comput Methods Programs Biomed, 2023; 230:107333.
