Abstract
Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.
Highlights
Acute respiratory failure (ARF) is a common pathophysiological result of pulmonary complications [pneumonia, neurogenic pulmonary edema, and acute respiratory distress syndrome (ARDS)] in moderate or severe traumatic brain injury (M-STBI), worsening outcomes, and extending intensive care unit (ICU) and hospital stays and increasing the cost of hospital care [1,2,3,4,5,6,7].Machine Learning Predicted Respiratory Failureaccurately predicting ARF risk may help to identify cases requiring intensive airway management
There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes
The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05]
Summary
Acute respiratory failure (ARF) is a common pathophysiological result of pulmonary complications [pneumonia, neurogenic pulmonary edema, and acute respiratory distress syndrome (ARDS)] in moderate or severe traumatic brain injury (M-STBI), worsening outcomes, and extending intensive care unit (ICU) and hospital stays and increasing the cost of hospital care [1,2,3,4,5,6,7]. Accurately predicting ARF risk may help to identify cases requiring intensive airway management. This would help to allocate resources efficiently and improve morbidity reduction by appropriately monitoring patients at risk. There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF
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