Monitoring ventilator weaning--predictors of success

Low mechanical ventilation times and reintubation rates associated with a specific weaning protocol in an intensive care unit setting: a retrospective study

OBJECTIVES

A number of complications exist with invasive mechanical ventilation and with the use of and withdrawal from prolonged ventilator support. The use of protocols that enable the systematic identification of patients eligible for an interruption in mechanical ventilation can significantly reduce the number of complications. This study describes the application of a weaning protocol and its results.

METHODS

Patients who required invasive mechanical ventilation for more than 24 hours were included and assessed daily to identify individuals who were ready to begin the weaning process.

RESULTS

We studied 252 patients with a median mechanical ventilation time of 3.7 days (interquartile range of 1 to 23 days), a rapid shallow breathing index value of 48 (median), a maximum inspiratory pressure of 40 cmH(2)0, and a maximum expiratory pressure of 40 cm H(2)0 (median). Of these 252 patients, 32 (12.7%) had to be reintubated, which represented weaning failure. Noninvasive ventilation was used postextubation in 170 (73%) patients, and 15% of these patients were reintubated, which also represented weaning failure. The mortality rate of the 252 patients studied was 8.73% (22), and there was no significant difference in the age, gender, mechanical ventilation time, and maximum inspiratory pressure between the survivors and nonsurvivors.

CONCLUSIONS

The use of a specific weaning protocol resulted in a lower mechanical ventilation time and an acceptable reintubation rate. This protocol can be used as a comparative index in hospitals to improve the weaning system, its monitoring and the informative reporting of patient outcomes and may represent a future tool and source of quality markers for patient care.

Intelligent decision support systems for mechanical ventilation

OBJECTIVE

An overview of different methodologies used in various intelligent decision support systems (IDSSs) for mechanical ventilation is provided. The applications of the techniques are compared in view of today's intensive care unit (ICU) requirements.

METHODS

Information available in the literature is utilized to provide a methodological review of different systems.

RESULTS

Comparisons are made of different systems developed for specific ventilation modes as well as those intended for use in wider applications. The inputs and the optimized parameters of different systems are discussed and rule-based systems are compared to model-based techniques. The knowledge-based systems used for closed-loop control of weaning from mechanical ventilation are also described. Finally, in view of increasing trend towards automation of mechanical ventilation, the potential utility of intelligent advisory systems for this purpose is discussed.

CONCLUSIONS

IDSSs for mechanical ventilation can be quite helpful to clinicians in today's ICU settings. To be useful, such systems should be designed to be effective, safe, and easy to use at patient's bedside. In particular, these systems must be capable of noise removal, artifact detection and effective validation of data. Systems that can also be adapted for closed-loop control/weaning of patients at the discretion of the clinician, may have a higher potential for use in the future.