Resumo

ANON, J.M. et al. Prolonged mechanical ventilation probability model. Med. Intensiva [online]. 2012, vol.36, n.7, pp.488-495. ISSN 0210-5691.  https://dx.doi.org/10.1016/j.medin.2012.01.003.

Objective: To design a probability model for prolonged mechanical ventilation (PMV) using variables obtained during the first 24hours of the start of MV. Design: An observational, prospective, multicenter cohort study. Scope: Thirteen Spanish medical-surgical intensive care units. Patients: Adult patients requiring mechanical ventilation for more than 24hours. Interventions: None. Study variables: APACHE II, SOFA, demographic data, clinical data, reason for mechanical ventilation, comorbidity, and functional condition. A multivariate risk model was constructed. The model contemplated a dependent variable with three possible conditions: 1. Early mortality; 2. Early extubation; and 3. PMV. Results: Of the 1661 included patients, 67.9% (n=1127) were men. Age: 62.1±16.2 years. APACHE II: 20.3±7.5. Total SOFA: 8.4±3.5. The APACHE II and SOFA scores were higher in patients ventilated for 7 or more days (p=0.04 and p=0.0001, respectively). Noninvasive ventilation failure was related to PMV (p=0.005). A multivariate model for the three above exposed outcomes was generated. The overall accuracy of the model in the training and validation sample was 0.763 (95%IC: 0.729-0.804) and 0.751 (95%IC: 0.672-0.816), respectively. The likelihood ratios (LRs) for early extubation, involving a cutoff point of 0.65, in the training sample were LR (+): 2.37 (95%CI: 1.77-3.19) and LR (-): 0.47 (95%CI: 0.41-0.55). The LRs for the early mortality model, for a cutoff point of 0.73, in the training sample, were LR (+): 2.64 (95%CI: 2.01-3.4) and LR (-): 0.39 (95%CI: 0.30-0.51). Conclusions: The proposed model could be a helpful tool in decision making. However, because of its moderate accuracy, it should be considered as a first approach, and the results should be corroborated by further studies involving larger samples and the use of standardized criteria.

Palavras-chave : Mechanical Ventilation; Prediction; Critical Care.

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