Neefjes Elisabeth C W, van der Vorst Maurice J D L, Verdegaal Bertha A T T, Beekman Aartjan T F, Berkhof Johannes, Verheul Henk M W
Department of Medical Oncology, VU University Medical Center/Cancer Center Amsterdam, Amsterdam, the Netherlands.
Department of Internal Medicine, Rijnstate Hospital, Arnhem, the Netherlands.
Cancer Med. 2017 Aug;6(8):1861-1870. doi: 10.1002/cam4.1106. Epub 2017 Jul 7.
Delirium deteriorates the quality of life in patients with cancer, but is frequently underdiagnosed and not adequately treated. In this study, we evaluated the occurrence of delirium and its risk factors in patients admitted to the hospital for treatment or palliative care in order to develop a prediction model to identify patients at high risk for delirium. In a period of 1.5 years, we evaluated the risk of developing delirium in 574 consecutively admitted patients with cancer to our academic oncology department with the Delirium Observation Screening Scale. Risk factors for delirium were extracted from the patient's chart. A delirium prediction algorithm was constructed using tree analysis, and validated with fivefold cross-validation. A total of 574 patients with cancer were acutely (42%) or electively (58%) admitted 1733 times. The incidence rate of delirium was 3.5 per 100 admittances. Tree analysis revealed that the predisposing factors of an unscheduled admittance and a metabolic imbalance accurately predicted the development of delirium. In this group the incidence rate of delirium was 33 per 100 patients (1:3). The AUC of the model was 0.81, and 0.65 after fivefold cross-validation. We identified that especially patients undergoing an unscheduled admittance with a metabolic imbalance do have a clinically relevant high risk to develop a delirium. Based on these factors, we propose to evaluate preventive treatment of these patients when admitted to the hospital in order to improve their quality of life.
谵妄会降低癌症患者的生活质量，但常常未得到充分诊断和治疗。在本研究中，我们评估了因治疗或姑息治疗而入院的患者中谵妄的发生情况及其风险因素，以便开发一个预测模型来识别谵妄高危患者。在1.5年的时间里，我们使用谵妄观察筛查量表评估了连续入住我院肿瘤内科的574例癌症患者发生谵妄的风险。从患者病历中提取谵妄的风险因素。使用树分析构建谵妄预测算法，并通过五重交叉验证进行验证。共有574例癌症患者被急诊（42%）或择期（58%）收治1733次。谵妄的发生率为每100次入院3.5例。树分析显示，非计划入院和代谢失衡的易感因素能准确预测谵妄的发生。在这组患者中，谵妄的发生率为每100例患者33例（1:3）。该模型的曲线下面积（AUC）为0.81，五重交叉验证后为0.65。我们发现，尤其是那些因代谢失衡而非计划入院的患者发生谵妄的临床风险确实很高。基于这些因素，我们建议在这些患者入院时评估预防性治疗措施，以提高他们的生活质量。
