Department of Orthopaedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, SA, Australia.
Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Arch Gerontol Geriatr. 2021 May-Jun;94:104368. doi: 10.1016/j.archger.2021.104368. Epub 2021 Feb 1.
This study aimed to determine the incidence, predictors of postoperative delirium and develop a post-surgery delirium risk scoring tool.
A total of 6672 hip fracture patients with documented assessment for delirium were analyzed from the Australia and New Zealand Hip Fracture Registry between June 2017 and December 2018.Thirty-six variables for the prediction of delirium using univariate and multivariate logistic regression were assessed. The models were assessed for diagnostic accuracy using C-statistic and calibration using Hosmer-Lemeshow goodness-of-fit test. A Delirium Risk Score was developed based on the regression coefficients.
Delirium developed in 2599/6672 (39.0%) hip fracture patients. Seven independent predictors of delirium were identified; age above 80 years (OR=1.6 CI 1.4-1.9; p=0.001), male (OR=1.3 CI 1.1-1.5; p=0.007), absent pre-operative cognitive assessment (OR=1.5 CI 1.3-1.9; p=0.001), impaired pre-operative cognitive state (OR=1.7 CI 1.3 -2.1; p=0.001), surgery delay (OR=1.7 CI 1.2-2.5; p=0.002) and mobilisation day 1 post-surgery (OR=1.9 CI 1.4-2.6; p=0.001). The C-statistics for the training and validation datasets were 0.74 and 0.75, respectively. Calibration was good (χ2=35.72 (9); p<0.001). The Delirium Risk Score for patients ranged from 0 to 42 in the validation data and when used alone as a risk predictor, had similar levels of diagnostic accuracy (C-statistic=0.742) indicating its potential for use as a stand-alone risk scoring tool.
We have designed and validated a delirium risk score for predicting delirium following surgery for a hip fracture using seven predicting factors. This could assist clinicians in identifying high risk patients requiring higher levels of observation and post-surgical care.
本研究旨在确定髋关节手术后谵妄的发生率、预测因素,并开发一种术后谵妄风险评分工具。
本研究分析了 2017 年 6 月至 2018 年 12 月期间澳大利亚和新西兰髋关节骨折登记处中 6672 例接受谵妄评估的髋部骨折患者。使用单变量和多变量逻辑回归评估了 36 个用于预测谵妄的变量。使用 C 统计量评估模型的诊断准确性,并使用 Hosmer-Lemeshow 拟合优度检验评估校准情况。根据回归系数开发了一个谵妄风险评分。
6672 例髋部骨折患者中有 2599 例(39.0%)发生了谵妄。确定了 7 个谵妄的独立预测因素;年龄>80 岁(OR=1.6,95%CI 1.4-1.9;p=0.001)、男性(OR=1.3,95%CI 1.1-1.5;p=0.007)、术前无认知评估(OR=1.5,95%CI 1.3-1.9;p=0.001)、术前认知状态受损(OR=1.7,95%CI 1.3-2.1;p=0.001)、手术延迟(OR=1.7,95%CI 1.2-2.5;p=0.002)和术后第 1 天开始活动(OR=1.9,95%CI 1.4-2.6;p=0.001)。训练和验证数据集的 C 统计量分别为 0.74 和 0.75。校准情况良好(χ2=35.72(9);p<0.001)。验证数据中,患者的谵妄风险评分范围为 0 至 42,单独用作风险预测指标时,具有相似的诊断准确性(C 统计量=0.742),表明其具有作为独立风险评分工具的潜力。
我们设计并验证了一种基于七个预测因素的髋部骨折手术后谵妄风险评分,可用于预测术后谵妄。这可能有助于临床医生识别需要更高水平观察和术后护理的高危患者。