Exploring phenotype-based ventilator parameter optimization to mitigate postoperative pulmonary complications: a retrospective observational cohort study
Surgery Today(2023)
Abstract
Purpose To identify tidal volume (V T ) and positive end-expiratory pressure (PEEP) associated with the lowest incidence and severity of postoperative pulmonary complications (PPCs) for each phenotype based on preoperative characteristics. Methods The subjects of this retrospective observational cohort study were 34,910 adults who underwent surgery, using general anesthesia with mechanical ventilation. Initially, the least absolute shrinkage and selection operator regression was employed to select relevant preoperative characteristics. Then, the classification and regression tree (CART) was built to identify phenotypes. Finally, we computed the area under the receiver operating characteristic curves from logistic regressions to identify V T and PEEP associated with the lowest incidence and severity of PPCs for each phenotype. Results CARTs classified seven phenotypes for each outcome. A probability of the development of PPCs ranged from the lowest (3.51%) to the highest (68.57%), whereas the probability of the development of the highest level of PPC severity ranged from 3.3% to 91.0%. Across all phenotypes, the V T and PEEP associated with the most desirable outcomes were within a small range of V T 7–8 ml/kg predicted body weight with PEEP of between 6 and 8 cmH 2 O. Conclusions The ranges of optimal V T and PEEP were small, regardless of the phenotypes, which had a wide range of risk profiles.
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Key words
Postoperative pulmonary complications,General anesthesia,Individualized care,Phenotypes,Lung-protective ventilation,Machine learning algorithms
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