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首页> 外文期刊>JMIR Medical Informatics >Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology
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Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology

机译:快速顺序器官失效评估和全身炎症反应综合征参数解开流行和二分法:败血症病理生理学支持的观察数据驱动方法

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Background Considering morbidity, mortality, and annual treatment costs, the dramatic rise in the incidence of sepsis and septic shock among intensive care unit (ICU) admissions in US hospitals is an increasing concern. Recent changes in the sepsis definition (sepsis-3), based on the quick Sequential Organ Failure Assessment (qSOFA), have motivated the international medical informatics research community to investigate score recalculation and information retrieval, and to study the intersection between sepsis-3 and the previous definition (sepsis-2) based on systemic inflammatory response syndrome (SIRS) parameters. Objective The objective of this study was three-fold. First, we aimed to unpack the most prevalent criterion for sepsis (for both sepsis-3 and sepsis-2 predictors). Second, we intended to determine the most prevalent sepsis scenario in the ICU among 4 possible scenarios for qSOFA and 11 possible scenarios for SIRS. Third, we investigated the multicollinearity or dichotomy among qSOFA and SIRS predictors. Methods This observational study was conducted according to the most recent update of Medical Information Mart for Intensive Care (MIMIC-III, Version 1.4), the critical care database developed by MIT. The qSOFA (sepsis-3) and SIRS (sepsis-2) parameters were analyzed for patients admitted to critical care units from 2001 to 2012 in Beth Israel Deaconess Medical Center (Boston, MA, USA) to determine the prevalence and underlying relation between these parameters among patients undergoing sepsis screening. We adopted a multiblind Delphi method to seek a rationale for decisions in several stages of the research design regarding handling missing data and outlier values, statistical imputations and biases, and generalizability of the study. Results Altered mental status in the Glasgow Coma Scale (59.28%, 38,854/65,545 observations) was the most prevalent sepsis-3 (qSOFA) criterion and the white blood cell count (53.12%, 17,163/32,311 observations) was the most prevalent sepsis-2 (SIRS) criterion confronted in the ICU. In addition, the two-factored sepsis criterion of high respiratory rate (≥22 breaths/minute) and altered mental status (28.19%, among four possible qSOFA scenarios besides no sepsis) was the most prevalent sepsis-3 (qSOFA) scenario, and the three-factored sepsis criterion of tachypnea, high heart rate, and high white blood cell count (12.32%, among 11 possible scenarios besides no sepsis) was the most prevalent sepsis-2 (SIRS) scenario in the ICU. Moreover, the absolute Pearson correlation coefficients were not significant, thereby nullifying the likelihood of any linear correlation among the critical parameters and assuring the lack of multicollinearity between the parameters. Although this further bolsters evidence for their dichotomy, the absence of multicollinearity cannot guarantee that two random variables are statistically independent. Conclusions Quantifying the prevalence of the qSOFA criteria of sepsis-3 in comparison with the SIRS criteria of sepsis-2, and understanding the underlying dichotomy among these parameters provides significant inferences for sepsis treatment initiatives in the ICU and informing hospital resource allocation. These data-driven results further offer design implications for multiparameter intelligent sepsis prediction in the ICU.
机译:背景技术考虑发病率,死亡率和年度治疗成本,脓毒症发病率的显着增加和美国医院的重症监护股(ICU)录取的抗化性休克是一个越来越多的关注。基于快速顺序器官失败评估(QSOFA)的败血症定义(SEPSIS-3)的最新变化已经激励了国际医学信息学研究界调查了分数重新计算和信息检索,并研究了SEPSIS-3之间的交汇处基于全身炎症反应综合征(SIRS)参数的前一种定义(SEPSIS-2)。目的本研究的目的是三倍。首先,我们旨在解压缩败血症最普遍的标准(对于SEPSIS-3和SEPSIS-2预测器)。其次,我们打算在4个可能的QSOFA和11个可能的SIR场景中确定ICU中最普遍的败血症情景。第三,我们调查了QSOFA和SIRS预测因子之间的多色性或二分法。方法采用该观察研究,根据医疗信息MART为重症监护(MIMIC-III,1.4版),由麻省理工学院开发的关键护理数据库进行进行。分析了QSOFA(SEPSIS-3)和SIRS(SEPSIS-2)参数,用于录取2001年至2012年在贝特以色列专业医疗中心(波士顿,MA,USA)中达到关键护理单位的患者,以确定这些之间的患病率和潜在的关系患有败血症筛查的患者的参数。我们采用了多卷发德尔福方法,以寻求关于处理缺失数据和异常值,统计避难所和偏见的研究设计的几个阶段的决策的理由,以及研究的概括性。结果格拉斯哥昏迷的精神状态改变(59.28%,38,854 / 65,55,545观察)是最普遍的败血症-3(QSOFA)标准和白细胞计数(53.12%,17,163 / 32,311观察)是最普遍的脓毒症 - 2(SIRS)标准面临ICU。此外,高呼吸速率(≥22呼吸/分钟)和精神状态改变的双因子脓毒症标准(除败血症之外的四种可能的QSOFA情景中,28.19%)是最普遍的败血症-3(QSOFA)情景, Tachypnea,高心率和高白细胞计数的三个代表性脓毒症标准(12.32%,除了败血症之外的11个可能的情况中)是ICU中最普遍的败血区-2(SIRS)情景。此外,绝对的Pearson相关系数不显着,从而抑制了临界参数之间任何线性相关性的可能性,并确保参数之间的多色性。虽然这一进一步的止胆点证据了他们的二分法,但没有多元形性不能保证两个随机变量在统计上独立。结论与SEPSIS-2的SIRS标准相比,定量SEPSIS-3 QSOFA标准的患病率,并了解这些参数中的潜在的二分法,为ICU中的败血症治疗举措提供了显着推论,并告知医院资源分配。这些数据驱动的结果进一步为ICU中的Muliparameter智能败血症预测提供了设计意义。

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