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Outcome prediction in critical care: the Mortality Probability Models.

机译:重症监护中的结果预测:死亡率概率模型。

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PURPOSE OF REVIEW: The comparison of morbidity, mortality, and length-of-stay outcomes in patients receiving critical care requires adjustment based on their presenting illness. These adjustments are made with severity-of-illness models. These models must be periodically updated to reflect current medical practices. This article will review the history of the Mortality Probability Model (MPM), discuss why and how it was recently updated, and outline examples of MPM use. RECENT FINDINGS: All severity-of-illness models have limitations, especially if a unit's patient population becomes highly specialized. In these situations, customized models may provide better accuracy. The MPMs include those calculated at admission (MPM0) and additional models at 24, 48, and 72 h (MPM 24, MPM 48, and MPM 72). The model is now in its third iteration (MPM 0-III). Length of stay (LOS) and subgroup models have also been developed. SUMMARY: Understanding appropriate application of models such as MPM is important as transparency in healthcare drives demand for severity-adjusted outcomes data.
机译:审查目的:比较接受重症监护的患者的发病率,死亡率和住院时间长短,需要根据其所患疾病进行调整。这些调整是使用疾病严重程度模型进行的。这些模型必须定期更新以反映当前的医疗实践。本文将回顾死亡率概率模型(MPM)的历史,讨论为什么以及最近如何对其进行更新,并概述了MPM使用的示例。最近的发现:所有疾病的严重程度模型都有其局限性,特别是在单位的患者群体变得高度专业化的情况下。在这些情况下,定制模型可能会提供更好的准确性。 MPM包括在入场时计算的那些(MPM0)和在24、48和72小时的其他模型(MPM 24,MPM 48和MPM 72)。该模型现在处于第三次迭代(MPM 0-III)。还开发了住院时间(LOS)和亚组模型。简介:了解诸如MPM之类的模型的适当应用非常重要,因为医疗保健的透明度推动了对严重性调整后的结果数据的需求。

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