On prognostic estimates of radiation risk in medicine and radiation protection
On prognostic estimates of radiation risk in medicine and radiation protection
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DOI：
10.1007/s00411-019-00794-1
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发表时间：
2019-04
影响因子：
1.7
通讯作者：
Alexander Ulanowski;J. Kaiser;U. Schneider;L. Walsh
中科院分区：
文献类型：
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作者：
Alexander Ulanowski;J. Kaiser;U. Schneider;L. Walsh
重新讨论了表示辐射暴露的累积有害影响的问题。所有常规使用和计算复杂的终生风险或时间综合风险都基于当前人口和健康统计数据，具有未知的未来长期趋势，并预测到遥远的未来。研究表明，常规使用的终生或时间积分归因风险(LAR，AR)的应用应限于1Gy以下的照射。更一般的量，如超额终身风险(ELR)和暴露诱导死亡风险(REID)，不受剂量限制，但在计算上甚至比LAR和AR更复杂，并依赖于人口和健康统计数据上未知的总辐射效应。适当评估高剂量(超过1 Gy射线)照射后特定结果的时间综合风险需要考虑其他辐射相关结果的竞争风险，由此产生的ELR估计具有本质上的非线性剂量响应。以目前人口和健康统计数据为基础的传统应用的时间综合风险所造成的限制是：(A)这些风险不太适合于对一般人口不容易代表的非典型暴露人群的风险估计；(B)由于人口特定疾病发病率未来长期趋势的发展存在很大不确定性，对于未来几十年的风险预测不是最佳的。这里考虑了基于生存机会减少的替代疾病特异量、基线和归属生存分数，并被证明在绕过这些限制的大部分方面非常有用。另一个主要的数值，被称为辐射导致的生存减少(RADS)，这里推荐用来代表累积的辐射风险，条件是存活到一定年龄。RADS在统计文献中历来被称为“累积风险”，它只基于辐射引起的危险，对相互竞争的风险不敏感。因此，RADS非常适合于紧急情况下的风险预测，以及估计在治疗性或干预性医疗照射后暴露的人或其他高度不典型的暴露人群，如宇航员的辐射风险。
The problem of expressing cumulative detrimental effect of radiation exposure is revisited. All conventionally used and computationally complex lifetime or time-integrated risks are based on current population and health statistical data, with unknown future secular trends, that are projected far into the future. It is shown that application of conventionally used lifetime or time-integrated attributable risks (LAR, AR) should be limited to exposures under 1 Gy. More general quantities, such as excess lifetime risk (ELR) and, to a lesser extent, risk of exposure-induced death (REID), are free of dose constraints, but are even more computationally complex than LAR and AR and rely on the unknown total radiation effect on demographic and health statistical data. Appropriate assessment of time-integrated risk of a specific outcome following high-dose (more than 1 Gy) exposure requires consideration of competing risks for other radiation-attributed outcomes and the resulting ELR estimate has an essentially non-linear dose response. Limitations caused by basing conventionally applied time-integrated risks on current population and health statistical data are that they are: (a) not well suited for risk estimates for atypical groups of exposed persons not readily represented by the general population; and (b) not optimal for risk projections decades into the future due to large uncertainties in developments of the future secular trends in the population-specific disease rates. Alternative disease-specific quantities, baseline and attributable survival fractions, based on reduction of survival chances are considered here and are shown to be very useful in circumventing most aspects of these limitations. Another main quantity, named as radiation-attributed decrease of survival (RADS), is recommended here to represent cumulative radiation risk conditional on survival until a certain age. RADS, historically known in statistical literature as “cumulative risk”, is only based on the radiation-attributed hazard and is insensitive to competing risks. Therefore, RADS is eminently suitable for risk projections in emergency situations and for estimating radiation risks for persons exposed after therapeutic or interventional medical applications of radiation or in other highly atypical groups of exposed persons, such as astronauts.