Artificial neural network-based prediction of prolonged length of stay and need for post-acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention

Abstract

Background: Prolonged length of stay (LOS) and post-acute care after percutaneous coronary intervention (PCI) is common and costly. Risk models for predicting prolonged LOS and post-acute care have limited accuracy. Our goal was to develop and validate models using artificial neural networks (ANN) to predict prolonged LOS > 7days and need for post-acute care after PCI.

Methods: We defined prolonged LOS as ≥7 days and post-acute care as patients discharged to: extended care, transitional care unit, rehabilitation, other acute care hospital, nursing home or hospice care. Data from 22 675 patients who presented with ACS and underwent PCI was shuffled and split into a derivation set (75% of dataset) and a validation dataset (25% of dataset). Calibration plots were used to examine the overall predictive performance of the MLP by plotting observed and expected risk deciles and fitting a lowess smoother to the data. Classification accuracy was assessed by a receiver-operating characteristic (ROC) and area under the ROC curve (AUC).

Results: Our MLP-based model predicted prolonged LOS with an accuracy of 90.87% and 88.36% in training and test sets, respectively. The post-acute care model had an accuracy of 90.22% and 86.31% in training and test sets, respectively. This accuracy was achieved with quick convergence. Predicted probabilities from the MLP models showed good (prolonged LOS) to excellent calibration (post-acute care).

Conclusions: Our ANN-based models accurately predicted LOS and need for post-acute care. Larger studies for replicability and longitudinal studies for evidence of impact are needed to establish these models in current PCI practice.

Keywords: acute coronary syndrome; artifical intelligence; legnth of stay; percutaneous coronary intervention; post acute care.