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
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
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DOI：
10.1111/eci.13406
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发表时间：
2020-11-29
影响因子：
5.5
通讯作者：
Amin, Amit P.
中科院分区：
文献类型：
--
作者：
Kulkarni, Hemant;Thangam, Manoj;Amin, Amit P.
背景：经皮冠状动脉介入治疗（PCI）后住院时间延长（LOS）和急性期后护理是常见且昂贵的。用于预测长期LOS和急性后护理的风险模型的准确性有限。我们的目标是开发和验证使用人工神经网络（ANN）的模型来预测PCI术后延长的LOS bbb70天和急性后护理需求。方法将延长的LOS定义为>= 7天，急性后护理定义为患者出院后：延长护理、过渡护理单位、康复、其他急性护理医院、养老院或临终关怀。来自22 675名ACS患者并接受PCI的数据被洗牌并分成衍生集（75%的数据集）和验证集（25%的数据集）。校准图用于通过绘制观察到的和预期的风险十分位数并拟合数据的低平滑度来检查MLP的整体预测性能。通过受试者工作特征（ROC）和ROC曲线下面积（AUC）评估分类准确性。结果基于mlp的模型在训练集和测试集上预测延长LOS的准确率分别为90.87%和88.36%。急性后护理模型在训练集和测试集上的准确率分别为90.22%和86.31%。该精度是通过快速收敛实现的。MLP模型的预测概率显示良好（延长的LOS）到优秀的校准（急性后护理）。结论基于人工神经网络的模型准确预测了LOS和急性后护理需求。为了在目前的PCI实践中建立这些模型，需要对可重复性进行更大规模的研究，并对影响证据进行纵向研究。
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.