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Ability to identify patient-ventilator asynchronies in intensive care unit professionals: A multicenter cross-sectional analytical study Cover

Figures & Tables

Fig. 1.

Ventilatory asynchronies evaluated. A: Ineffective triggering; B: Double triggering; C: Auto–triggering; D: Insufficient flow; E: Excessive flow; F: Delayed cycling

Fig. 2.

Distribution of study participants by department in Colombia.

Table 1.

Characteristics of study participants.

VariableResult (n = 900)
Female, n (%)536 (59.56)
Age (years), median (IQR)32 (28 – 38)
Profession, n (%)
Nursing38 (4.22)
Physiotherapy478 (53.11)
Medicine185 (20.56)
Respiratory therapy199 (22.11)
Private institution, n (%)531 (59)
Postgraduate training, n (%)
Course80 (8.89)
Graduate299 (33.22)
PhD1 (0.11)
Training60 (6.67)
Specialty381 (42.33)
Master50 (5.56)
None29 (3.22)
Clinical postgraduate training, n (%)850 (94.44)
Work experience (year), median (IQR)6 (3 – 11)
Mixed ICU, n (%)426 (47.33)
Specific PVA training, n (%)286 (31.78)
Population served, n (%)
Adults811 (90.11)
Neonates49 (5.44)
Paediatrics40 (4.44)
Number of beds, median (IQR)13 (10 – 20)
Patients in MV per day, median (IQR)6 (4 – 9)
MV adjustment frequency, n (%)
1–2 times during the workday564 (62.67)
3–4 times during the workday185 (20.56)
5–6 times during the workday39 (4.33)
7–8 times during the workday11 (1.22)
9–10 times during the workday5 (0.56)
More than 10 times during the workday8 (0.89)
I do not make adjustments80 (8.89)
I do not feel confident making adjustments8 (0.89)
Frequency of waveform monitoring in MV, n (%)
1 time during a 6-hour shift184 (20.44)
1 time during a 12-hour shift26 (2.89)
2 times during a 6-hour shift85 (9.44)
2 times during a 12-hour shift72 (8)
3 times during a 12-hour shift68 (7.56)
Every 1 hour97 (10.78)
Every 2 hours169 (18.78)
Every 3 hours101 (11.22)
Do not waveform analysis MV98 (10.89)

[i] ICU: Intensive care unit, IQR: Interquartile range, MV: Mechanical ventilation, PVA: Patient-ventilator asynchronies, n: number of responses, %: percentage, PhD: Doctorate

Fig. 3.

Proportion of participants by number of correctly identified PVA.

Table 2.

Proportion of professionals who correctly identified each of the PVAs.

PVAProper identification, n (%)
Double triggering675 (75)
Late cycling206 (22,89)
Auto-triggering380 (42,22)
Insufficient flow296 (32,89)
Ineffective triggering261 (29)
Excessive flow278 (30,89)

[i] PVA: Patient-ventilator asynchronies, n: number of responses, %: percentage

Table 3.

Factors associated with proper identification of all PVAs (Simple logistic regression).

VariableOR (CI 95%)
Profession
Physiotherapy1
Medicine0.44 (0.15 – 1.28)
Respiratory therapy0.51 (0.19 – 1.36)
Nursing0.54 (0.07 – 4.07)
Work experience (year)0.99 (0.94 – 1.05)
Mixed ICU2.59 (1.10 – 4.79)*
Population served
Adult1
Paediatrics0 (0 - ∞)
Neonates3.18 (1.17 – 8.63)*
Frequency of waveform monitoring in MV, n (%)
1 time during a 6-hour shift1
1 time during a 12-hour shift0 (0 - ∞)
2 times during a 6-hour shift0.53 (0.11 – 2.55)
2 times during a 12-hour shift0.63 (0.13 – 3.03)
3 times during a 12-hour shift0.33 (0.04 – 2.68)
Every 1 hour0.95 (0.28 – 3.23)
Every 2 hours1.38 (0.53 – 3.59)
Every 3 hours1.39 (0.47 – 4.12)
Do not waveform analysis VM0 (0 - ∞)
Number of beds1.03 (0.99 – 1.06)
Patients in MV per day1.02 (0.96 – 1.09)
Postgraduate training, n (%)
Specialty1
Course0.46 (0.11 – 2.02)
Training0 (0 - ∞)
Graduate0.50 (0.22 – 1.14)
Master1.15 (0.33 – 4.03)
PhD0 (0 - ∞)
Specific PVA training, n (%)2.38 (1.18 – 4.76)*

[i] PVA: Patient-ventilator asynchronies. ICU: Intensive care unit, MV: Mechanical ventilation, OR: Odds ratio, n: number of responses, %: percentage, PhD: Doctorate, : Infinite

Table 4.

Factors associated with proper identification of all PVAs (multiple logistic regression).

VariableOR (CI 95%)
Mixed ICU2.59 (1.19 – 5.54)
Population served (Neonates)5.19 (1.77 – 15.20)
Specific PVA training, n (%)2.38 (1.16 – 4.76)

[i] ICU: Intensive care unit, PVA: Patient-ventilator asynchronies, n: number of responses, %: percentage, OR: Odds ratio

DOI: https://doi.org/10.2478/jccm-2025-0017 | Journal eISSN: 2393-1817 | Journal ISSN: 2393-1809
Language: English
Page range: 157 - 163
Submitted on: Nov 24, 2024
Accepted on: Mar 11, 2025
Published on: Apr 30, 2025
Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Andrés Mauricio Enríquez Popayán, Iván Ignacio Ramírez, Juan Felipe Zúñiga, Ruvistay Gutierrez-Arias, Mayda Alejandra Jiménez Pérez, Henry Mauricio Parada-Gereda, Luis Fernando Pardo Cocuy, Ana Lucia Rangel Colmenares, Nubia Castro Chaparro, Ana Pinza Ortega, Jorge Martínez Díaz, Johanna Hurtado Laverde, Joismer Alejandro Henao Cruz, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution 4.0 License.