PDF(Pubmed)
Abstract：
BACKGROUND: Infusion failure may have severe consequences for patients receiving critical, short-half-life infusions. Continued interruptions to infusions can lead to subtherapeutic therapy.
OBJECTIVE: This study aims to identify and rank determinants of the longevity of continuous infusions administered through syringe drivers, using nonlinear predictive models. Additionally, this study aims to evaluate key factors influencing infusion longevity and develop and test a model for predicting the likelihood of achieving successful infusion longevity.
METHODS: Data were extracted from the event logs of smart pumps containing information on care profiles, medication types and concentrations, occlusion alarm settings, and the final infusion cessation cause. These data were then used to fit 5 nonlinear models and evaluate the best explanatory model.
RESULTS: Random forest was the best-fit predictor, with an F1-score of 80.42, compared to 5 other models (mean F1-score 75.06; range 67.48-79.63). When applied to infusion data in an individual syringe driver data set, the predictor model found that the final medication concentration and medication type were of less significance to infusion longevity compared to the rate and care unit. For low-rate infusions, rates ranging from 2 to 2.8 mL/hr performed best for achieving a balance between infusion longevity and fluid load per infusion, with an occlusion versus no-occlusion ratio of 0.553. Rates between 0.8 and 1.2 mL/hr exhibited the poorest performance with a ratio of 1.604. Higher rates, up to 4 mL/hr, performed better in terms of occlusion versus no-occlusion ratios.
CONCLUSIONS: This study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study\'s outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.
OBJECTIVE: This study aims to identify and rank determinants of the longevity of continuous infusions administered through syringe drivers, using nonlinear predictive models. Additionally, this study aims to evaluate key factors influencing infusion longevity and develop and test a model for predicting the likelihood of achieving successful infusion longevity.
METHODS: Data were extracted from the event logs of smart pumps containing information on care profiles, medication types and concentrations, occlusion alarm settings, and the final infusion cessation cause. These data were then used to fit 5 nonlinear models and evaluate the best explanatory model.
RESULTS: Random forest was the best-fit predictor, with an F1-score of 80.42, compared to 5 other models (mean F1-score 75.06; range 67.48-79.63). When applied to infusion data in an individual syringe driver data set, the predictor model found that the final medication concentration and medication type were of less significance to infusion longevity compared to the rate and care unit. For low-rate infusions, rates ranging from 2 to 2.8 mL/hr performed best for achieving a balance between infusion longevity and fluid load per infusion, with an occlusion versus no-occlusion ratio of 0.553. Rates between 0.8 and 1.2 mL/hr exhibited the poorest performance with a ratio of 1.604. Higher rates, up to 4 mL/hr, performed better in terms of occlusion versus no-occlusion ratios.
CONCLUSIONS: This study provides clinicians with insights into the specific types of infusion that warrant more intense observation or proactive management of intravenous access; additionally, it can offer valuable information regarding the average duration of uninterrupted infusions that can be expected in these care areas. Optimizing rate settings to improve infusion longevity for continuous infusions, achieved through compounding to create customized concentrations for individual patients, may be possible in light of the study\'s outcomes. The study also highlights the potential of machine learning nonlinear models in predicting outcomes and life spans of specific therapies delivered via medical devices.
摘要：
背景：输注失败可能会对接受危重治疗的患者造成严重后果，半衰期短的输液。持续中断输注可导致亚治疗性治疗。
目的：本研究旨在确定和排名通过注射器驱动器连续输注的寿命的决定因素，使用非线性预测模型。此外,本研究旨在评估影响输注寿命的关键因素,并开发和测试一个预测输注寿命成功可能性的模型.
方法：从包含护理资料信息的智能泵事件日志中提取数据，药物类型和浓度，闭塞报警设置，以及最终停止输液的原因。然后将这些数据用于拟合5个非线性模型并评估最佳解释模型。
结果：随机森林是最佳拟合预测因子，与其他5个模型相比，F1得分为80.42（平均F1得分为75.06；范围为67.48-79.63）。当应用于单个注射器驱动器数据集中的输液数据时，预测模型发现,与速率和护理单位相比,最终药物浓度和药物类型对输注寿命的影响较小.对于低速输液，2至2.8mL/hr的速率对于实现每次输注的输注寿命和液体负荷之间的平衡表现最佳。闭塞与无闭塞的比率为0.553。0.8和1.2mL/hr之间的速率表现出最差的性能,比率为1.604。更高的利率，高达4毫升/小时，在闭塞与无闭塞比率方面表现更好。
结论：这项研究为临床医生提供了对特定类型输液的见解，这些输液需要进行更深入的观察或积极的静脉通路管理；此外，它可以提供有关在这些护理区域中可以预期的不间断输液的平均持续时间的有价值的信息.优化速率设置，以提高连续输注的输注寿命，通过复合实现，为个体患者创建定制浓度，根据研究结果，这可能是可能的。该研究还强调了机器学习非线性模型在预测通过医疗设备提供的特定疗法的结果和寿命方面的潜力。
目的：本研究旨在确定和排名通过注射器驱动器连续输注的寿命的决定因素，使用非线性预测模型。此外,本研究旨在评估影响输注寿命的关键因素,并开发和测试一个预测输注寿命成功可能性的模型.
方法：从包含护理资料信息的智能泵事件日志中提取数据，药物类型和浓度，闭塞报警设置，以及最终停止输液的原因。然后将这些数据用于拟合5个非线性模型并评估最佳解释模型。
结果：随机森林是最佳拟合预测因子，与其他5个模型相比，F1得分为80.42（平均F1得分为75.06；范围为67.48-79.63）。当应用于单个注射器驱动器数据集中的输液数据时，预测模型发现,与速率和护理单位相比,最终药物浓度和药物类型对输注寿命的影响较小.对于低速输液，2至2.8mL/hr的速率对于实现每次输注的输注寿命和液体负荷之间的平衡表现最佳。闭塞与无闭塞的比率为0.553。0.8和1.2mL/hr之间的速率表现出最差的性能,比率为1.604。更高的利率，高达4毫升/小时，在闭塞与无闭塞比率方面表现更好。
结论：这项研究为临床医生提供了对特定类型输液的见解，这些输液需要进行更深入的观察或积极的静脉通路管理；此外，它可以提供有关在这些护理区域中可以预期的不间断输液的平均持续时间的有价值的信息.优化速率设置，以提高连续输注的输注寿命，通过复合实现，为个体患者创建定制浓度，根据研究结果，这可能是可能的。该研究还强调了机器学习非线性模型在预测通过医疗设备提供的特定疗法的结果和寿命方面的潜力。
