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Applying a new weaning index in ICU older patients

Introduction

With the increase in life expectation, more admissions to hospital, use of mechanical ventilation (MV) and weaning trials in older patients have been observed.

Objective

To evaluate the variables associated with successful weaning from mechanical ventilation in older patients.

Methods

We evaluated a cohort from September 2004 to January 2008 with 479 patients. We excluded one patient aged under 18 years, 35 tracheostomized and 112 with neurologic diseases, resulting in 331 patients. Besides the conventional weaning indexes, we evaluated the performance of a new integrative weaning index (IWI). The study was approved by the Ethics Committee of Pedro Ernesto University Hospital (2206-CEP). The chances of successful weaning were investigated using relative risk and logistic regression. The Hosmer-Lemeshow goodness-of-fit test was used to calibrate and the C statistic was calculated to evaluate the association between predicted probabilities and observed proportions in the logistic regression model.

Results

Prevalence of successful weaning in the sample was 83.7%. There was no difference in mortality of older and nonolder patients (P = 0.16), in the days of mechanical ventilation (P = 0.22) and days of weaning (P = 0.55). In older patients, the IWI was the only variable associated with respiratory weaning in this population (P < 0.0001). See Tables 1 to 5.

Table 1 Etiology and population
Table 2 Analysis of the outcome variables by sample and by age
Table 3 Analysis of the respiratory variables according to the results by age
Table 4 Logistic regression to the success of weaning by age
Table 5 Estimated probability of success according to the logistic model by age group

Conclusion

The IWI was the main independent variable in weaning of the older patient population, and it can contribute to this critical moment.

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Correspondence to LM Azeredo.

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Azeredo, L., Nemer, S., Caldeira, J. et al. Applying a new weaning index in ICU older patients. Crit Care 15, P35 (2011). https://doi.org/10.1186/cc10183

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Keywords

  • Public Health
  • Logistic Regression
  • Regression Model
  • Relative Risk
  • Patient Population