Prediction of deterioration in critically ill patients with heart failure based on vital signs monitoring

Shengyu Zhang1, Kang Yang2, Wenyu Ye2, Haoyu Jiang2, Xianliang He2, Lei Wang1, Yijing Li2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, 2Shenzhen Mindray Bio-Medical Electronics Co., Ltd.


Aims: Heart failure (HF) is a serious death-related disease. HF patients can deteriorate rapidly, i.e., acute onset, and lead to severe clinical events, even death. It requires complex treatments to handle HF acute onset. There is a deficiency of time if doctors act after HF acute onset. Thus, the early detection of HF acute onset is of great help to reduce mortality. This study aims to develop a real-time machine learning model for HF acute onset prediction based on bedside vital signs monitoring. Methods: A group of 2273 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The start time of applying ventilator to patients was regarded as the HF acute onset, excluding scheduled operations of ventilator. Based on vital signs, we extracted a total number of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to assess the feature contribution for prediction in the model. Results: On the test set, the model predicted 96% of HF acute onset, 60% of which were identified more than 40 min in advance, resulting in an area under the receiver operating characteristic curve of 0.94. Conclusion: Current results suggest the generated model can help predict patients with acute onset of HF. The model prediction provide more timely notifications for doctors to achieve better patients treatment.