Literature DB >> 16523330

The combination of the load/force balance and the frequency/tidal volume can predict weaning outcome.

Theodoros Vassilakopoulos1, Christina Routsi, Christina Sotiropoulou, Charis Bitsakou, Ioannis Stanopoulos, Charis Roussos, Spyros Zakynthinos.   

Abstract

OBJECTIVE: To examine whether an appropriately designed combination of an index of ventilatory endurance and the frequency divided by tidal volume ratio (f/VT) provides prognostic information for weaning outcome not offered by any index alone. DESIGN AND
SETTING: Prospective study in a multidisciplinary intensive care unit, university hospital. PATIENTS: 124 consecutive mechanically ventilated patients.
INTERVENTIONS: We designed an index of ventilatory endurance (load/force balance) calculated as the mean inspiratory airway pressure (PI) during controlled mechanical ventilation/maximum inspiratory pressure (MIP) [PI equals the triplicate of mean airway pressure (Paw) displayed by the ventilator] and tested its capacity in predicting weaning outcome at 48 h along with f/VT and many other indices in 75 consecutive mechanically ventilated patients ready to wean. A stepwise discriminant function analysis was used to test the performance of appropriately designed index combination. Threshold values of indices and their combination were prospectively validated in another group of 45 consecutive patients.
RESULTS: Stepwise discriminant analysis showed that PI/MIP and f/VT were the only indices that remained in the model with the function D=7.628xmean Paw/MIP+0.0158xf/VT-2.374. The cutoff point of D=0.5 had 94% sensitivity, 67% specificity, and 87% correct classifications. Prospective validation demonstrated similar results. The simplified discriminant function D=15xmean Paw/MIP+0.003xf/VT-5 and the cut-off point of D=1.0 had 89% sensitivity, 67% specificity, and 85% correct classifications.
CONCLUSIONS: The combination of mean Paw/MIP and f/VT in a simplified discriminant function is useful in predicting weaning outcome.

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Year:  2006        PMID: 16523330     DOI: 10.1007/s00134-006-0104-y

Source DB:  PubMed          Journal:  Intensive Care Med        ISSN: 0342-4642            Impact factor:   17.440


  22 in total

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5.  Weaning prediction: esophageal pressure monitoring complements readiness testing.

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7.  Effect on the duration of mechanical ventilation of identifying patients capable of breathing spontaneously.

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Authors:  A Jubran; M J Tobin
Journal:  Am J Respir Crit Care Med       Date:  1997-03       Impact factor: 21.405

9.  Inspiratory pressure/maximal inspiratory pressure ratio: a predictive index of weaning outcome.

Authors:  K L Yang
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  7 in total

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Authors:  Leopoldo N Segal; Erwin Oei; Beno W Oppenheimer; Roberta M Goldring; Rami T Bustami; Salvatore Ruggiero; Kenneth I Berger; Stanley B Fiel
Journal:  Intensive Care Med       Date:  2009-11-28       Impact factor: 17.440

2.  Extubation outcome after a successful spontaneous breathing trial: A multicenter validation of a 3-factor prediction model.

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3.  Inspiratory muscle strength training in infants with congenital heart disease and prolonged mechanical ventilation: a case report.

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4.  Effect of training on inspiratory load compensation in weaned and unweaned mechanically ventilated ICU patients.

Authors:  Barbara Kellerman Smith; Andrea Gabrielli; Paul W Davenport; A Daniel Martin
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Review 5.  Year in review in Intensive Care Medicine, 2006. II. Infections and sepsis, haemodynamics, elderly, invasive and noninvasive mechanical ventilation, weaning, ARDS.

Authors:  Peter Andrews; Elie Azoulay; Massimo Antonelli; Laurent Brochard; Christian Brun-Buisson; Daniel De Backer; Geoffrey Dobb; Jean-Yves Fagon; Herwig Gerlach; Johan Groeneveld; Duncan Macrae; Jordi Mancebo; Philipp Metnitz; Stefano Nava; Jerôme Pugin; Michael Pinsky; Peter Radermacher; Christian Richard
Journal:  Intensive Care Med       Date:  2007-01-13       Impact factor: 17.440

Review 6.  Clinical review: liberation from mechanical ventilation.

Authors:  Mohamad F El-Khatib; Pierre Bou-Khalil
Journal:  Crit Care       Date:  2008-08-06       Impact factor: 9.097

7.  A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases.

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Journal:  Front Med (Lausanne)       Date:  2022-01-18
  7 in total

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