PDF(Pubmed)
Abstract：
UNASSIGNED: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare.
UNASSIGNED: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN.
UNASSIGNED: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model\'s contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA.
UNASSIGNED: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase.
UNASSIGNED: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.
UNASSIGNED: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN.
UNASSIGNED: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model\'s contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA.
UNASSIGNED: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase.
UNASSIGNED: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.
摘要：
■急性肾损伤（AKI）是临床恶化和肾毒性的标志。虽然有许多研究提供了早期检测AKI的预测模型，使用基于分布式研究网络（DRN）的时间序列数据预测AKI发生的研究很少见。
■在这项研究中，我们旨在通过将基于可解释长短期记忆(LSTM)的模型应用于使用DRN的肾毒性药物的患者的基于医院电子健康记录(EHR)的时间序列数据来检测AKI的早期发生.
■我们使用DRN对6家医院的数据进行了多机构回顾性队列研究。对于每个机构，使用5种用于AKI的药物构建了基于患者的数据集，并使用可解释的多变量LSTM（IMV-LSTM）模型进行训练。这项研究使用倾向评分匹配来减轻人口统计学和临床特征的差异。此外,证明了每个机构和药物的AKI预测模型贡献变量的时间注意力值，使用单向方差分析确认了病例和对照数据之间非常重要的特征分布差异。
■这项研究分析了8643例和31,012例有和没有AKI的患者，分别,6家医院在分析AKI发作的分布时，万古霉素显示起病较早（中位数12，IQR5-25天），与其他药物相比，阿昔洛韦最慢（中位数23，IQR10-41天）。我们用于AKI预测的时间深度学习模型对大多数药物表现良好。阿昔洛韦在每种药物的受试者工作特征曲线评分下的平均面积最高（0.94），其次是对乙酰氨基酚（0.93），万古霉素（0.92），萘普生（0.90），和塞来昔布（0.89）。根据AKI预测模型中变量的时间注意力值，已证实的淋巴细胞和钙万古霉素的关注度最高，而淋巴细胞，白蛋白,血红蛋白会随着时间的推移而减少，尿液pH值和凝血酶原时间有增加的趋势。
■可以通过基于EHR的DRN应用基于时间序列数据的IMV-LSTM来实现对AKI爆发的早期监测。这种方法可以帮助识别风险因素，并在AKI发生前开出引起肾毒性的药物时，早期发现药物不良反应。
■在这项研究中，我们旨在通过将基于可解释长短期记忆(LSTM)的模型应用于使用DRN的肾毒性药物的患者的基于医院电子健康记录(EHR)的时间序列数据来检测AKI的早期发生.
■我们使用DRN对6家医院的数据进行了多机构回顾性队列研究。对于每个机构，使用5种用于AKI的药物构建了基于患者的数据集，并使用可解释的多变量LSTM（IMV-LSTM）模型进行训练。这项研究使用倾向评分匹配来减轻人口统计学和临床特征的差异。此外,证明了每个机构和药物的AKI预测模型贡献变量的时间注意力值，使用单向方差分析确认了病例和对照数据之间非常重要的特征分布差异。
■这项研究分析了8643例和31,012例有和没有AKI的患者，分别,6家医院在分析AKI发作的分布时，万古霉素显示起病较早（中位数12，IQR5-25天），与其他药物相比，阿昔洛韦最慢（中位数23，IQR10-41天）。我们用于AKI预测的时间深度学习模型对大多数药物表现良好。阿昔洛韦在每种药物的受试者工作特征曲线评分下的平均面积最高（0.94），其次是对乙酰氨基酚（0.93），万古霉素（0.92），萘普生（0.90），和塞来昔布（0.89）。根据AKI预测模型中变量的时间注意力值，已证实的淋巴细胞和钙万古霉素的关注度最高，而淋巴细胞，白蛋白,血红蛋白会随着时间的推移而减少，尿液pH值和凝血酶原时间有增加的趋势。
■可以通过基于EHR的DRN应用基于时间序列数据的IMV-LSTM来实现对AKI爆发的早期监测。这种方法可以帮助识别风险因素，并在AKI发生前开出引起肾毒性的药物时，早期发现药物不良反应。
