Early detection of postoperative pneumonia is critical for timely interventions to prevent the onset of the complication. Until now, the predication of postoperative pneumonia has been challenging, and there is need for reliable and accurate predictive model for patients after liver transplantation. This study, based upon large volume of data and ML methods, has the following major novel findings: (1) The incidence of postoperative pneumonia was high in patients after OLT, and the occurrence was significantly associated with prolonged hospital stay and increased mortality after liver transplantation; (2) A total of 14 factors were identified to be significantly correlated with postoperative pneumonia after OLT, including INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of hospital stay, total fluid transfusion, and operation time; (3) The XGBoost model exhibited the best overall performance in predicting postoperative pneumonia among the developed ML models, with the value of AUC of 0.734, sensitivity of 52.6%, and specificity of 77.5%; (4) Multiple lines of evidence support that the XGBoost model holds promise for future clinical application in predicting postoperative pneumonia in patients after liver transplantation.
XGBoost model is recognized as an efficient and scalable tree boosting system(20), and it has performed well in the ML competitions, especially the simplicity in use and the accuracy in prediction(21, 22). In the present study, we developed a total six ML models, of these, XGBoost model had the best overall performance, with a specificity of 77.5% and a sensitivity of 52.6% in predicting postoperative pneumonia in OLT patients. Considering the high prevalence of multi-drug resistant bacteria in post-transplant patients induced by the excessive use of antibiotics(4), high specificity is especially necessary in clinical practice to avoid an unnecessary and overuse of antibiotics in low-risk patients. By contrast, all patients received peri-operative antibiotic therapy for 72 hr, and this has posed considerable challenge in predicting pneumonia at an early stage(23). Therefore, the novel XGBoost model as established in this study may assist clinicians in making optimal interventions and treatments, and eventually improve care for affected patients.
It has been reported that a number of risk factors, including age of recipient, liver dysfunction score, indication for OLT, perioperative transfusions especially the blood and fresh frozen plasma units, restrictive preoperative pulmonary testing pattern and INR measured prior OLT, are significantly associated with post-liver transplant pneumonia(3, 24, 25). However, these factors are limited for its underutilization of within-category information, causing a loss of information(26). For instance, patients above or below the optimal cut-point value had been equally considered in the risk-factor prediction, yet the risk of post-transplant pneumonia may vary considerably. As the risk-factor prediction is developed with neither combining all factors together nor weighting difference between different factors, it is not widely used in clinical practice. In addition, the traditional scores were given on the basis of the assumption that all misclassification errors have equal costs. In fact, this assumption is indefensible if apply in real-world applications(27). In this study, we trained the RFE model on 33 features which were statistically significant, of which 14 best features with the highest sensitivity score, including preoperative laboratory results of INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time; preoperative length of hospital stay, total fluid transfusion, and operation time. The high clinical relevance of these factors laid a solid foundation for the consequent ML process and made the conclusion more practical and clinically valuable. Moreover, we found the 14 features in ML model were all routinely recorded and widely used, and no factors need special instrument or equipment to obtain, indicating that our models are feasible and can be widely used in hospitals.
To date, ML models have shown outstanding performance in prediction of diseases and clinical conditions, for which these models can be helpful in decision-making about the use of interventions and medications(27). For example, ML models can generate an individualized probability for each patient. Additionally, implementation of sophisticated computer algorithms at the bedside has become a reality since the popularity of EPR systems and wide availability of structured patient data. In our study, the EPR systems included HIS, LIS, PACS, and Docare Anesthesia System, which allowed us to integrate medical data generated during admission, covering demographic data, daily documentation, laboratory and imaging results, anesthesia records and thorough record of medication, and treatment. In addition, we separated the patients 1000 times (70% train and 30% test) into 1000 different pairs of train and test sets and this could minimize accidental error and enhance the accuracy of the current ML models. This result showed that in predicting post-transplant pneumonia, we should not apply only one of the ML model.
In the study, we found that patients with hepatic malignancy, better hepatic function before surgery, and longer hospital stay before surgery were significantly assocated with lower risk of developing postoperative pneumonia. We postulated that this could be attributed to the better preoperative treatment and preparation, suggested that interventions should be implemented to improve the patients’ overall preoperative conditions. In consistence with previous reports(28, 29), we identified that a number of intraoperative factors, such as the longer operation and anesthesia time, excessive blood product transfusion, and fluid transfusion, were significantly related to postoperative pneumonia in patients following liver transplantation. By contrast, we found that there was an association between the use of telipressin and dopamine and decreased incidence of postoperative pneumonia in patients after liver transplantation. These findings are clinically important for the intraoperative anesthetic management and help improving the clinical outcomes.
The study may have several limitations. Firstly, the ML models are developed on the basis of a single-center cohort study, and future multi-center study will be needed for external validation. Secondly, this study is performed retrospectively, for which collection and entry bias, as well as possible residual confounding may occur. Thirdly, we were unable to incorporate the metrics of liver donors as training variables in our study, due to the lack of donor information in the EPR systems of our hospital.
Our study has successfully established six novel ML models to predict postoperative pneumonia among OLT patients. Of these, the XGboost model has demonstrated overall best performance, and therefore holds promise for future clinical application to predict post-transplant pneumonia in OLT patients. To the best of our knowledge, this is the first ML-based study to provide a novel ML algorithm for prediction of postoperative pneumonia in patients after liver transplantation.