Evaluation of ventilator associated events in critically ill patients with invasive mechanical ventilation: A prospective cohort study at a resource limited setting in Northern India

Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

Teaching Bundle Care Approach to Intensive Care Nurses to Prevent Ventilator Associated Pneumonia with a Stratified Validated Module

BACKGROUND

Module-based teaching of ventilator-associated pneumonia (VAP) with a systematic, stratified approach is expected to have a promising role in teaching. We hypothesized it to improve the skill and knowledge of intensive care unit nurses.

OBJECTIVES

The aim of this study was to determine the efficacy of the teaching module with debriefing sessions on the VAP bundle care approach, and the secondary objective was to estimate the improvement in individual components of the VAP bundle.

METHODS

A total of 200 paramedical staff were exposed to a teaching module consisting of pretest didactic lectures, debriefing sessions, reflection by participants, and feedback. Posttest analysis was done to assess knowledge. Skill assessment was done with directly observed procedural skills (DOPS) assessment, and feedback was taken from participants. Follow-up was done at 6 months to assess decay in knowledge and skills.

RESULTS

Preworkshop and postworkshop DOPS scores were analyzed using the Mann-Whitney U test. Subgroup analysis was performed using the paired t test. Median pretest and posttest scores were 6 (interquartile range, 4-8) and 13 (interquartile range, 11-15), respectively (P < .001). Comparing DOPS scores before and after exposure to the teaching module, the number of subjects with scores that were below expectations was 24 and 4; meeting expectations was 94 and 24; borderline expectations were 36 and 104; and above expectations was 46 and 58, respectively, between the groups (P < .001). A decay in knowledge and skills was noted in the follow-up.

CONCLUSION

A validated teaching module with debriefing sessions is useful in training bundle care approaches to intensive care unit nurses.

Relationship between ventilator bundle compliance and the occurrence of ventilator-associated events: a prospective cohort study

Background

Instead of ventilator-associated pneumonia (VAP), the modern definition of ventilator-associated events (VAEs) has been introduced to identify infectious and noninfectious respiratory complications. Some studies revealed that compliance to the ventilator bundle is associated with decreased occurrence of VAP, but little is known about its association with the decrease of VAEs occurrence.

Methods

A prospective cohort research design was used. Data were collected over eight months from May 2019 to February 2020 in five general intensive care units. The researchers assessed the compliance to ventilator care bundle using the Institute for Healthcare Improvement ventilation bundle checklist. Mechanically ventilated patients were prospectively assessed for the occurrence of VAEs using a pre-validated calculator from the Centers for Disease Control and Prevention. All are non-invasive tools and no intervention was done by the authors.

Results

A total of 141 mechanically ventilated patients completed the study. The odds ratio of having VAEs in patients who received ventilator bundle was -1.19 (95% CI, -2.01 to -0.38), a statistically significant effect, Wald χ2(1) = 8.18, p = 0.004.

Conclusion/ implications for practice

Ventilator bundle compliance was associated with a reduced risk for VAEs occurrence. Nurses should comply with the ventilator bundle because it is associated with decreased VAEs occurrence.