Hospices Civils de Lyon, Groupement Hospitalier de Gériatrie, Service Pharmacie, 40 Avenue de La Table de Pierre, 69340 Francheville, France.
Med Biol Eng Comput. 2013 Jun;51(6):657-64. doi: 10.1007/s11517-013-1035-8. Epub 2013 Jan 20.
Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.
老年人跌倒与重要的发病率、死亡率和高医疗保健成本有关。由于与跌倒风险相关的大量变量，确定有风险的患者是一项具有挑战性的任务。这项工作的目的是验证一种仅使用文献知识来检测高跌倒风险患者的工具。为了建立跌倒风险模型，我们使用了 30 篇文章（对应 160 项研究）。我们使用了来自两家老年医院的 288 名患者（88±7 岁）的回顾性病例对照队列和 106 名患者（89±6 岁）的前瞻性队列来验证我们模型的性能。我们确定了 26 个与跌倒风险增加相关的变量。这些变量分为疾病、药物和环境。三种相关评分的组合给出了一个总的跌倒评分。该模型在回顾性和前瞻性队列中的敏感性和特异性分别为 31.4%、81.6%、38.5%和 90%。该模型的性能与使用来自多个队列研究的数据对模型进行调整的已有预测工具观察到的结果相似。这项工作表明，文献知识可以与贝叶斯网络相结合。
