Holmes L, Loughead K, Treasure T, Gallivan S
Cardiothoracic Unit, St George's Hospital, London, UK.
Lancet. 1994 Oct 29;344(8931):1200-2. doi: 10.1016/s0140-6736(94)90513-4.
In intensive care units, a predictive model that identified patients who are certain to die would spare suffering and free resources for more productive work. In a prospective study to determine factors which might predict the outcome of a protracted stay in intensive care units, information was collected for 162 patients who remained in intensive care longer than 48 hours after cardiac surgery. Of these patients, 21% presented as emergencies, 35% as urgent cases, and 44% as elective cases. They were drawn from 2256 adult patients operated upon during a 12-month period in three UK centres. 115 patients (71%) who were in intensive care for more than 48 hours survived to be discharged. The median duration of stay was 6 days (range 3-90 days) and the median duration of hospital stay was 21 days (7-111 days). An existing algorithm developed and calibrated to predict outcome for general patients in intensive care was applied to forecast outcomes. Contrary to expectations, the algorithm performed well for patients after cardiac surgery. In identifying deaths in intensive care and before hospital discharge, the specificities for death at various intervals after admission were all 97% or more. There is little scope for improving the algorithm's ability to forecast longer term outcome. Furthermore, if it were to be introduced to aid decisions about withdrawal of treatment, the potential saving in intensive care bed-days would be small--less than 3% overall.
在重症监护病房,一种能够识别必死患者的预测模型可以减轻痛苦,并为更有意义的工作释放资源。在一项前瞻性研究中,为了确定可能预测在重症监护病房长期住院结局的因素,收集了162例心脏手术后在重症监护病房停留超过48小时患者的信息。在这些患者中,21%为急诊病例,35%为紧急病例,44%为择期病例。他们来自英国三个中心在12个月期间接受手术的2256例成年患者。115例(71%)在重症监护病房停留超过48小时的患者存活并出院。中位住院时间为6天(范围3 - 90天),中位住院天数为21天(7 - 111天)。应用一种为预测重症监护病房普通患者结局而开发并校准的现有算法来预测结局。与预期相反,该算法对心脏手术后的患者表现良好。在识别重症监护病房死亡和出院前死亡方面,入院后不同时间段死亡的特异性均为97%或更高。提高该算法预测长期结局能力的空间很小。此外,如果引入该算法以辅助关于停止治疗的决策,重症监护病床日的潜在节省将很小——总体不到3%。