Predicting respiratory failure after pulmonary lobectomy using machine learning techniques

Abstract

BackgroundWhen pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures. MethodsThe National (Nationwide) Inpatient Sample for 2015 was used to establish our model. We identified 417 respiratory failure from a total of 4,062 patients who underwent pulmonary lobectomy. Risk factors for respiratory failure were identified, analyzed, and used in novel machine learning models to predict respiratory failure. ResultsFactors that contributed to increased odds of respiratory failure, such as preexisting chronic diseases, and intraoperative and postoperative events during hospitalization were identified. Two machine learning-based prediction models were generated and optimized by the knowledge accrued from the clinical course of postlobectomy patients. The first model, with high accuracy and specificity, is suited for performance evaluation, and the second model, with high sensitivity, is suited for clinical decision making. ConclusionWe identified risk factors for respiratory failure after lobectomy and introduced 2 machine learning-based techniques to predict respiratory failure for quality review and clinical decision-making settings. Such techniques can be used to not only provide targeted support but also standardize quality peer review measures.