Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics
Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics
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DOI：
10.1371/journal.pone.0214904
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发表时间：
2019-04-04
期刊：
影响因子：
3.7
通讯作者：
Bihorac, Azra
中科院分区：
文献类型：
--
作者：
Adhikari, Lasith;Ozrazgat-Baslanti, Tezcan;Bihorac, Azra
研究背景急性肾损伤（阿基）是外科手术后常见的并发症，与发病率和死亡率的增加有关。大多数现有的围手术阿基风险预测模型的普遍性有限，并且没有充分利用术中生理时间序列数据。因此，有必要为智能，准确，强大的系统，以利用新的信息，因为它变得可用于预测发展术后阿基的风险。MethodsA回顾性单中心队列的2,911名成年人谁接受了手术在佛罗里达大学健康在2000年和2010年之间被用于本研究。机器学习和统计分析技术用于开发围手术期模型，以预测术后前三天、术后前七天和总体（术后住院期间）发生阿基的风险。通过纳入术中生理时间序列变量来检查风险预测的改善。我们提出的模型丰富了术前模型，该模型通过在随机森林分类器内通过机器学习堆叠方法整合术中统计特征来产生概率性阿基风险评分。模型的性能进行了评估，使用的受试者工作特征曲线（AUC），准确性，和净重新分类改进（NRI）下的面积。对于7天阿基结局，所提出的模型的AUC为0.86（准确度为0.78），而术前模型的AUC为0.84（准确度为0.76）。此外，通过整合术中特征，该算法能够从术前模型中重新分类40%的假阴性患者。每种结局的NRI是阿基在3天（8%），7天（7%）和总体（4%）。结论术后阿基预测通过动态结合术中数据的机器学习方法提高了灵敏度和特异性。
BackgroundAcute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.MethodsA retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).ResultsThe predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).ConclusionsPostoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.