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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Development of a model-based clinical sepsis biomarker for critically ill patients
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Development of a model-based clinical sepsis biomarker for critically ill patients

机译：为重症患者开发基于模型的临床败血症生物标志物

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Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48h. Insulin sensitivity (S I) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S I is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss≥2) are created for both S I and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S I cut-off value of 0.00013L/mU/min. Multivariate clinical biomarker combining S I, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.
机译：脓毒症在重症监护病房（ICU）中经常发生，并且是入院，死亡率和费用的主要原因。治疗指南建议尽早干预，但是阳性血液培养结果可能需要长达48h。胰岛素敏感性（S I）随病情恶化而降低，因此可用于辅助诊断。一些血糖控制协议能够实时准确识别胰岛素敏感性。从36名败血症患者的血糖控制数据中计算出基于小时模型的胰岛素敏感性S I值。将这些患者的每小时S I与每小时脓毒症得分（ss）进行比较（ss = 0-4用于增加严重程度）。还开发了一种多元临床生物标记物，以最大程度地区分不同SS组。针对S I和多元临床生物标志物，创建了严重脓毒症（ss≥2）的接收者操作员特征（ROC）曲线。胰岛素敏感性作为严重脓毒症诊断的败血症生物标志物，在SI截止值为0.00013L /时可达到50％的敏感性，76％的特异性，4.8％的阳性预测值（PPV）和98.3％的阴性预测值（NPV）。 mU /分钟将S I，温度，心率，呼吸频率，血压及其各自的小时变化率结合在一起的多变量临床生物标记物可实现73％的敏感性，80％的特异性，8.4％的PPV和99.2％的NPV。因此，多元临床生物标志物为严重的败血症提供了有效的实时阴性预测诊断。对该生物标记物和败血症评分的患者间和患者内统计分布的检查显示了改善阳性预测值的潜在途径。

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