Construction of Patient-level Prediction Model for In-hospital Mortality in Congenital Heart Disease Surgery: Regression and Machine Learning analysis

Abstract

Background: Prediction of in-hospital death is important for patient management as well as risk-adjusted evaluation of Congenital heart disease (CHD) surgery performance. Using a large database containing CHD surgery records of 12 years, we aim to establish patient-level in-hospital mortality prediction models.Methods: Patients with congenital heart disease who underwent surgery at Shanghai Children’s Medical Center from January 1, 2006, to December 31, 2017 were included in the study. Each procedure was assigned a complexity score based on Aristotle Score with modification. In-hospital mortalities for various surgery procedures were estimated. In-hospital death prediction models including a procedure complexity score and patient-level risk factors were constructed using logistic regression analysis and machine learning methods. The predictive values of the models were tested. Results: Among 24,684 patients underwent CHD operations, there were 595 (2.4%) in-hospital deaths. The results showed that AUC of the prediction model based on logistic regression is 0.864 (95% CI: 0.833-0.895, P <0.001), the sensitivity is 0.831 and the specificity is 0.786. The AUC of the Gradient boosting model is 0.884 (95 %% CI: 0.858-0.909, P <0.001), the sensitivity and specificity were 0.838 and 0.785 respectively. The feature importance analysis found that the variable (average score) that had the greatest impact on the model's prediction performance was operation score (95.6), and other variables (average scores) were Age (days) (95.5), Ultrasound MV （54.6）, Ultrasound atrial level （54.5）, Palliative operation (45.8), Operation history (38.8), Ultrasound TV2 (32.1), Urgent operation (30.8), Ultrasound ventricular level (30.5), and Spo2 ≤ 90% (30.3).Conclusions: Model constructed using machine learning method and logistic regression containing procedure complexity score and pre-operative patient-level factors had high accuracy in in-hospital mortality prediction. Operation score and age have the greatest impact on model prediction performance.

Machine Learning Model for Predicting Risk of In-Hospital Mortality after Surgery in Congenital Heart Disease Patients

“…Risk of death in CHD patients is associated with complexity of surgical procedures [10]. Accurate prediction of in-hospital death is important to facilitate clinical decisions-making for the performance of certain procedures and improve patient's outcome [11]. Several major risk stratification categories are currently available for the prediction of mortality and morbidity in children undergoing surgery for CHD-Risk Adjustment in Congenital Heart Surgery-1 (RACHS-1) [12], Aristotles Basic Complexity, and Aristotles Comprehensive Complexity [13], Society of Thoracic Surgery-European Association for Cardiothoracic Surgery (STS-EACTS) Congenital Heart Surgery (STAT) Mortality Categories [14].…”