Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients

Abstract

BackgroundPost-operative heart transplantation patients often require admission to an intensive care unit (ICU). Early prediction of the ICU length of stay (ICU-LOS) of these patients is of great significance and can guide treatment while reducing the mortality rate among patients. However, conventional linear models have tended to perform worse than non-linear models.Materials and MethodsWe collected the clinical data of 365 patients from Wuhan Union Hospital who underwent heart transplantation surgery between April 2017 and August 2020. The patients were randomly divided into training data (N = 256) and test data (N = 109) groups. 84 clinical features were collected for each patient. Features were validated using the Least Absolute Shrinkage and Selection Operator (LASSO) regression’s fivefold cross-validation method. We obtained Shapley Additive explanations (SHAP) values by executing package “shap” to interpret model predictions. Four machine learning models and logistic regression algorithms were developed. The area under the receiver operating characteristic curve (AUC-ROC) was used to compare the prediction performance of different models. Finally, for the convenience of clinicians, an online web-server was established and can be freely accessed via the website https://wuhanunion.shinyapps.io/PredictICUStay/.ResultsIn this study, 365 consecutive patients undergoing heart transplantation surgery for moderate (NYHA grade 3) or severe (NYHA grade 4) heart failure were collected in Wuhan Union Hospital from 2017 to 2020. The median age of the recipient patients was 47.2 years, while the median age of the donors was 35.58 years. 330 (90.4%) of the donor patients were men, and the average surgery duration was 260.06 min. Among this cohort, 47 (12.9%) had renal complications, 25 (6.8%) had hepatic complications, 11 (3%) had undergone chest re-exploration and 19 (5.2%) had undergone extracorporeal membrane oxygenation (ECMO). The following six important clinical features were selected using LASSO regression, and according to the result of SHAP, the rank of importance was (1) the use of extracorporeal membrane oxygenation (ECMO); (2) donor age; (3) the use of an intra-aortic balloon pump (IABP); (4) length of surgery; (5) high creatinine (Cr); and (6) the use of continuous renal replacement therapy (CRRT). The eXtreme Gradient Boosting (XGBoost) algorithm presented significantly better predictive performance (AUC-ROC = 0.88) than other models [Accuracy: 0.87; sensitivity: 0.98; specificity: 0.51; positive predictive value (PPV): 0.86; negative predictive value (NPV): 0.93].ConclusionUsing the XGBoost classifier with heart transplantation patients can provide an accurate prediction of ICU-LOS, which will not only improve the accuracy of clinical decision-making but also contribute to the allocation and management of medical resources; it is also a real-world example of precision medicine in hospitals.

Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database

“…Yan et al found that DeepSurv was the most successful model in predicting the prognosis of chondrosarcoma compared with the RSF, CoxPH, and NMTLR models 11 . Deep learning can convert linear and nonlinear predictor variables into linear combinations to predict the prognosis of patients by using multistage neural networks and reduce the structural bias associated with missing follow‐up information, which can predict the survival probability of patients at any time point 27,29 . Therefore, when dealing with large samples and multivariate and nonlinear data, the DeepSurv model has obvious advantages in prediction compared with other models.…”

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

“…Only 15 manuscripts described model interpretability. Additive exPlanations (SHAP), which quantifies the contribution of each feature (variable) to the predicted outcome related to a specific instance (12,13). The rest of the papers used feature importance to explain the outcome of their ML models.…”

“…This could introduce systematic difference between training, testing, and validation datasets, thus confounding algorithm development (11). Algorithms applied to local datasets showed much higher performance upon validation, likely due to better data homogeneity (12,13).…”

“…This is in recognition that these subgroups inherently have low frequency, significant patient heterogeneity, and variable management practices. More recent algorithms addressed specific post HTx complications, such as renal dysfunction and ICU stay (12,14). These tools are key as they provide actionable knowledge that can guide multiorgan transplantation, pre-HTx rehabilitation, and perioperative practices to optimize outcomes (15).…”