From Harvard Medical School (E.A., S.S.M., S.S.C., M.T.B., M.B.W.); Department of Neurology (E.A., S.S.C., M.T.B., M.B.W.), Massachusetts General Hospital, Boston; Department of Neurology (E.A.), University of California, San Francisco; and Computer Science and Artificial Intelligence Laboratory (E.A., S.P.) and Department of Electrical Engineering and Computer Science (M.M.G., W.-H.W.), Massachusetts Institute of Technology, Cambridge.
Neurology. 2020 Aug 4;95(5):e563-e575. doi: 10.1212/WNL.0000000000009916. Epub 2020 Jul 13.
To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication.
We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life. Good outcome was defined as a Cerebral Performance Category (CPC) score of 1-2 and poor outcome as CPC of 3-5.
An improvement in specificity for poor outcome prediction of 4.2% would be sufficient to make continuous EEG monitoring cost-effective (baseline AANPP specificity = 83.9%). In sensitivity analysis, the effect of increased sensitivity on the cost-effectiveness of EEG depends on the utility () assigned to a poor outcome. For patients who regard surviving with a poor outcome (CPC 3-4) worse than death ( = -0.34), an increased sensitivity for poor outcome prediction of 13.8% would make AANPP + EEG monitoring cost-effective (baseline AANPP sensitivity = 76.3%). In the closed system, an improvement in sensitivity of 1.8% together with an improvement in specificity of 3% was sufficient to make AANPP + EEG monitoring cost-effective, assuming lifetime return of 50% (USD $70,687).
Incorporating continuous EEG monitoring into cardiac arrest prognostication is cost-effective if relatively small improvements in sensitivity and specificity are achieved.
确定脑电图监测在心脏骤停预后判断中的成本效益参数。
我们进行了成本效益分析，以根据美国神经病学学会实践参数（AANPP）决策算法（神经检查、体感诱发电位和神经元特异性烯醇化酶）来评估在标准心脏骤停预后判断中添加连续脑电图监测的成本效益，以获得每增加一个质量调整生命年（QALY）的成本。我们在一个封闭系统中进行了终生成本效益分析，该系统将收入返还给从心脏骤停中幸存且预后良好的支付方，并在剩余的生命年限内为卫生系统做出贡献。良好的预后定义为神经功能预后评分（CPC）为 1-2，不良预后为 CPC 为 3-5。
如果能够提高 4.2%的不良预后预测特异性，那么连续脑电图监测将具有成本效益（AANPP 基线特异性为 83.9%）。在敏感性分析中，脑电图对敏感性提高的成本效益的影响取决于不良预后的效用（）赋值。对于那些认为不良预后（CPC 3-4）比死亡（= -0.34）更糟糕的患者，不良预后预测敏感性提高 13.8%将使 AANPP+脑电图监测具有成本效益（AANPP 基线敏感性为 76.3%）。在封闭系统中，如果能够提高 1.8%的敏感性和 3%的特异性，同时假设终生回报为 50%（70687 美元），那么 AANPP+脑电图监测将具有成本效益。
如果能够实现敏感性和特异性的相对较小的提高，那么将连续脑电图监测纳入心脏骤停预后判断具有成本效益。
