Open AccessNovel mechanical ventilator weaning predictive model
Author(s) -
Chung WeiChan,
Sheu ChauChyun,
Hung JenYu,
Hsu TuanJung,
Yang SsuHan,
Tsai JongRung
Publication year - 2020
Publication title -
the kaohsiung journal of medical sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.439
H-Index - 36
eISSN - 2410-8650
pISSN - 1607-551X
DOI - 10.1002/kjm2.12269
Subject(s) - medicine , mechanical ventilation , weaning , glasgow coma scale , spontaneous breathing trial , retrospective cohort study , medical record , emergency medicine , intensive care unit , intensive care medicine , anesthesia , surgery
Mechanical ventilation (MV) is a common life support system in intensive care units. Accurate identification of patients who are capable of being extubated can shorten the MV duration and potentially reduce MV‐related complications. Therefore, prediction of patients who can successfully be weaned from the mechanical ventilator is an important issue. The electronic medical record system (EMRs) has been applied and developed in respiratory therapy in recent years. It can increase the quality of critical care. However, there is no perfect index available that can be used to determine successful MV weaning. Our purpose was to establish a novel model that can predict successful weaning from MV. Patients' information was collected from the Kaohsiung Medical University Hospital respiratory therapy EMRs. In this retrospective study, we collected basic information, classic weaning index, and respiratory parameters during spontaneous breathing trials of patients eligible for extubation. According to the results of extubation, patients were divided into successful extubation and extubation failure groups. This retrospective cohort study included 169 patients. Statistical analysis revealed successful extubation predictors, including sex; height; oxygen saturation; Glasgow Coma Scale; Acute Physiology and Chronic Health Evaluation II score; pulmonary disease history; and the first, 30th, 60th, and 90th minute respiratory parameters. We built a predictive model based on these predictors. The area under the curve of this model was 0.889. We established a model for predicting the successful extubation. This model was novel to combine with serial weaning parameters and thus can help intensivists to make extubation decisions easily.