Petit Clemence, Bezemer Rick, Atallah Louis
Department of Electrical Engineering, Technical University of Eindhoven, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands.
Patient Care and Measurements Department, Philips Research Eindhoven, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.
J Clin Monit Comput. 2018 Jun;32(3):391-402. doi: 10.1007/s10877-017-0054-7. Epub 2017 Aug 21.
Most deaths occurring due to a surgical intervention happen postoperatively rather than during surgery. The current standard of care in many hospitals cannot fully cope with detecting and addressing post-surgical deterioration in time. For millions of patients, this deterioration is left unnoticed, leading to increased mortality and morbidity. Postoperative deterioration detection currently relies on general scores that are not fully able to cater for the complex post-operative physiology of surgical patients. In the last decade however, advanced risk and warning scoring techniques have started to show encouraging results in terms of using the large amount of data available peri-operatively to improve postoperative deterioration detection. Relevant literature has been carefully surveyed to provide a summary of the most promising approaches as well as how they have been deployed in the perioperative domain. This work also aims to highlight the opportunities that lie in personalizing the models developed for patient deterioration for these particular post-surgical patients and make the output more actionable. The integration of pre- and intra-operative data, e.g. comorbidities, vitals, lab data, and information about the procedure performed, in post-operative early warning algorithms would lead to more contextualized, personalized, and adaptive patient modelling. This, combined with careful integration in the clinical workflow, would result in improved clinical decision support and better post-surgical care outcomes.
大多数因外科手术干预导致的死亡发生在术后而非手术过程中。许多医院目前的护理标准无法充分及时地发现和应对术后病情恶化。对数以百万计的患者来说，这种病情恶化未被察觉，导致死亡率和发病率上升。目前术后病情恶化的检测依赖于通用评分，而这些评分无法完全适应外科手术患者复杂的术后生理状况。然而在过去十年中，先进的风险和预警评分技术开始在利用围手术期可用的大量数据改善术后病情恶化检测方面显示出令人鼓舞的结果。我们仔细调研了相关文献，以总结最有前景的方法以及它们在围手术期领域的应用方式。这项工作还旨在突出为这些特定外科手术患者的病情恶化个性化定制模型并使输出结果更具可操作性所带来的机遇。将术前和术中数据，如合并症、生命体征、实验室数据以及所施行手术的信息，整合到术后早期预警算法中，将产生更具情境化、个性化和适应性的患者模型。这与在临床工作流程中的精心整合相结合，将改善临床决策支持并带来更好的术后护理效果。
