Modelling the impact of an influenza A/H1N1 pandemic on critical care demand from early pathogenicity data: the case for sentinel reporting
Modelling the impact of an influenza A/H1N1 pandemic on critical care demand from early pathogenicity data: the case for sentinel reporting
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DOI：
10.1111/j.1365-2044.2009.06070.x
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发表时间：
2009-09-01
期刊：
影响因子：
10.7
通讯作者：
Menon, D. K.
中科院分区：
文献类型：
--
作者：
Ercole, A.;Taylor, B. L.;Menon, D. K.
本研究估计了英格兰 H1N1 流感大流行的重症监护需求。研究了统计不确定性下不同入院率的影响。在大流行早期，流行病学参数的不确定性会导致各种可信的情景，预计需求从微不足道到压倒性不等。然而，即使输入假设发生很小的变化，重大事件场景的可能性也会越来越大。在任何病例入院之前，入院率的 95% 置信度导致重症监护床位占用率峰值的预测范围为重症监护床位总容量的 0% 至 37%，其中一半病例需要通气支持。如果入院率高于 0.25%，则将超出重症监护床位的可用量。此外，英格兰只有 10% 的重症监护病床位于儿科专科病房，但最佳估计表明，30% 需要重症监护的患者是儿童。儿科重症监护设施可能很快就会耗尽，因此建议年龄较大的儿童应在成人重症监护病房进行管理，以实现资源优化。至关重要的是，这项研究强调了哨点报告和实时建模来指导理性决策的必要性。
P>Projected critical care demand for pandemic influenza H1N1 in England was estimated in this study. The effect of varying hospital admission rates under statistical uncertainty was examined. Early in a pandemic, uncertainty in epidemiological parameters leads to a wide range of credible scenarios, with projected demand ranging from insignificant to overwhelming. However, even small changes to input assumptions make the major incident scenario increasingly likely. Before any cases are admitted to hospital, 95% confidence limit on admission rates led to a range in predicted peak critical care bed occupancy of between 0% and 37% of total critical care bed capacity, half of these cases requiring ventilatory support. For hospital admission rates above 0.25%, critical care bed availability would be exceeded. Further, only 10% of critical care beds in England are in specialist paediatric units, but best estimates suggest that 30% of patients requiring critical care will be children. Paediatric intensive care facilities are likely to be quickly exhausted and suggest that older children should be managed in adult critical care units to allow resource optimisation. Crucially this study highlights the need for sentinel reporting and real-time modelling to guide rational decision making.