Delirium prediction in the ICU: designing a screening tool for preventive interventions.
Delirium prediction in the ICU: designing a screening tool for preventive interventions.
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ICU中谵妄的预测：设计预防干预的筛查工具。
DOI：
10.1093/jamiaopen/ooac048
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发表时间：
2022-07
期刊：
影响因子：
2.1
通讯作者：
Osmani, Venet
中科院分区：
文献类型：
--
作者：
Bhattacharyya, Anirban;Sheikhalishahi, Seyedmostafa;Torbic, Heather;Yeung, Wesley;Wang, Tiffany;Birst, Jennifer;Duggal, Abhijit;Celi, Leo Anthony;Osmani, Venet
关键词：
谵妄的发生是常见的，预防策略是资源密集型的。筛查工具可以优先考虑有风险的患者。使用机器学习，我们可以捕获对谵妄预测构成挑战的时间和治疗效果。我们的目标是开发一个谵妄预测模型，可以用作筛选工具。从eICU协作研究数据库（eICU-CRD）和重症监护医学信息集市第三版（MIMIC-III）数据库中，我们的研究纳入了具有一个或多个混乱评估方法-重症监护病房（CAM-ICU）值和重症监护病房（ICU）住院时间超过24小时的患者。我们使用21个定量临床参数验证了我们的模型，并使用不同的阈值和应用的解释技术在一系列观察和预测窗口中评估了性能。我们使用3种算法，即逻辑回归、随机森林和双向长短期记忆（BiLSTM），基于分层重复交叉验证来评估我们的模型。BiLSTM代表了基于递归神经网络的长短期记忆的演变，并通过向后输入保留了过去和未来的信息。模型性能使用受试者操作特征下的面积、精确度召回曲线下的面积、召回率、精确度（阳性预测值）和阴性预测值指标来衡量。我们分别对来自eICU-CRD和MIMIC-III数据库的16546例患者（47%女性）和6294例患者（44%女性）的结果进行了评价。 BiLSTM模型的性能最好，精确度和召回率从37.52%（95%置信区间[CI]，36.00%-39.05%）至17.45（95%CI，15.83%~ 19.08%）和86.1%（95%CI，82.49%~ 89.71%）~ 75.58%（95%CI，68.33%~ 82.83%）。在优化后，准确率和召回率分别从26.96%（95%CI，24.99%-28.94%）变为11.34%（95%CI，10.71%-11.98%）和93.73%（95%CI，93.1%-94.37%）变为92.57%（95%CI，88.19%-96.95%）。在MIMIC-III队列中获得了相当的结果。我们的模型使用更少的变量进行了与当代模型的比较。使用滑动窗口，修改阈值以增加召回率和特征排序的可解释性等技术，我们解决了现有模型的缺点。
Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool. From the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care version III (MIMIC-III) database, patients with one or more Confusion Assessment Method-Intensive Care Unit (CAM-ICU) values and intensive care unit (ICU) length of stay greater than 24 h were included in our study. We validated our model using 21 quantitative clinical parameters and assessed performance across a range of observation and prediction windows, using different thresholds and applied interpretation techniques. We evaluate our models based on stratified repeated cross-validation using 3 algorithms, namely Logistic Regression, Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM represents an evolution from recurrent neural network-based Long Short-Term Memory, and with a backward input, preserves information from both past and future. Model performance is measured using Area Under Receiver Operating Characteristic, Area Under Precision Recall Curve, Recall, Precision (Positive Predictive Value), and Negative Predictive Value metrics. We evaluated our results on 16 546 patients (47% female) and 6294 patients (44% female) from eICU-CRD and MIMIC-III databases, respectively. Performance was best in BiLSTM models where, precision and recall changed from 37.52% (95% confidence interval [CI], 36.00%–39.05%) to 17.45 (95% CI, 15.83%–19.08%) and 86.1% (95% CI, 82.49%–89.71%) to 75.58% (95% CI, 68.33%–82.83%), respectively as prediction window increased from 12 to 96 h. After optimizing for higher recall, precision and recall changed from 26.96% (95% CI, 24.99%–28.94%) to 11.34% (95% CI, 10.71%–11.98%) and 93.73% (95% CI, 93.1%–94.37%) to 92.57% (95% CI, 88.19%–96.95%), respectively. Comparable results were obtained in the MIMIC-III cohort. Our model performed comparably to contemporary models using fewer variables. Using techniques like sliding windows, modification of threshold to augment recall and feature ranking for interpretability, we addressed shortcomings of current models.
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