Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.
Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning.
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DOI：
10.1093/jamiaopen/ooac080
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发表时间：
2022-12
期刊：
影响因子：
2.1
通讯作者：
Snyder, Michael P.
中科院分区：
文献类型：
--
作者：
Rangan, Ekanath Srihari;Pathinarupothi, Rahul Krishnan;Anand, Kanwaljeet J. S.;Snyder, Michael P.
对无创生命体征的心率(HR)、呼吸频率(RR)、外周血氧饱和度(SpO2)和体温(Temp)进行详尽的数据驱动计算，以独立考虑和所有可能的组合，以早期发现脓毒症。通过提取临床医生可解释的特征，我们在2630名患者的数据集上应用梯度增强决策树机器学习建立了240个模型。在地理上不同的数据集上执行了验证。相对于发病，根据16对监测间隔和预测时间对预测进行计时，并对结果进行排名。HR和TEMP的组合被发现是产生最大可预测性的最小特征集，受试者操作曲线下面积为0.94，灵敏度为0.85，特异度为0.90。HR和RR各自直接增强预测，而SpO2和TEMP只有与HR或RR结合时才有显著影响。在相对于标准方法的全身炎症反应综合征(SIRS)、国家早期预警评分(NEWS)和快速序贯器官衰竭评估(QSOFA)的基准中，VITAL-SEP优于所有三种方法。可以得出的结论是，使用重症监护病房的数据，即使是两个生命体征，也足以提前6小时预测脓毒症，其准确性有望与标准评分方法和文献中报道的其他脓毒症预测工具相媲美。VITAL-SEP可用于快速预测，特别是在资源有限的医院环境中，其中基于实验室的血液学或生化分析可能无法获得、不准确或导致临床过度延迟。前瞻性研究对于确定所提出的脓毒症预测模型的临床影响和评估其他结果，如死亡率和住院时间是至关重要的。
To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis. By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked. The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them. It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.
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影响因子：
3.9
作者：
Giacobbe DR;Signori A;Del Puente F;Mora S;Carmisciano L;Briano F;Vena A;Ball L;Robba C;Pelosi P;Giacomini M;Bassetti M
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