Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
Warwick Clinical Trial Unit, University of Warwick, Coventry, UK.
BMJ Open. 2019 Jul 23;9(7):e029813. doi: 10.1136/bmjopen-2019-029813.
To predict functional outcomes 6 months after ankle fracture in people aged ≥60 years using post-treatment and 6-week follow-up data to inform anticipated recovery, and identify people who may benefit from additional monitoring or rehabilitation.
Prognostic model development and internal validation.
24 National Health Service hospitals, UK.
Participants were the Ankle Injury Management clinical trial cohort (n=618) (ISRCTN04180738), aged 60-96 years, 459/618 (74%) female, treated surgically or conservatively for unstable ankle fracture. Predictors were injury and sociodemographic variables collected at baseline (acute hospital setting) and 6-week follow-up (clinic). Outcome measures were 6-month postinjury (primary) self-reported ankle function, using the Olerud and Molander Ankle Score (OMAS), and (secondary) Timed Up and Go (TUG) test by blinded assessor. Missing data were managed with single imputation. Multivariable linear regression models were built to predict OMAS or TUG, using baseline variables or baseline and 6-week follow-up variables. Models were internally validated using bootstrapping.
The OMAS baseline data model included: alcohol per week (units), postinjury EQ-5D-3L visual analogue scale (VAS), sex, preinjury walking distance and walking aid use, smoking status and perceived health status. The baseline/6-week data model included the same baseline variables, minus EQ-5D-3L VAS, plus five 6-week predictors: radiological malalignment, injured ankle dorsiflexion and plantarflexion range of motion, and 6-week OMAS and EQ-5D-3L. The models explained approximately 23% and 26% of the outcome variation, respectively. Similar baseline and baseline/6 week data models to predict TUG explained around 30% and 32% of the outcome variation, respectively.
Predictive accuracy of the prognostic models using commonly recorded clinical data to predict self-reported or objectively measured ankle function was relatively low and therefore unlikely to be beneficial for clinical practice and counselling of patients. Other potential predictors (eg, psychological factors such as catastrophising and fear avoidance) should be investigated to improve predictive accuracy.
ISRCTN04180738; Post-results.
利用治疗后和 6 周随访数据预测 60 岁及以上踝关节骨折患者 6 个月时的功能结局，以了解预期的恢复情况，并确定可能需要额外监测或康复的人群。
预后模型的建立和内部验证。
英国 24 家国民保健服务医院。
研究对象为踝关节损伤管理临床试验队列（n=618）（ISRCTN04180738），年龄 60-96 岁，618 例患者中 459 例（74%）为女性，采用手术或保守治疗不稳定的踝关节骨折。预测因素是基线（急性医院环境）和 6 周随访（诊所）时收集的损伤和社会人口统计学变量。结局测量是受伤后 6 个月（主要）自我报告的踝关节功能，使用 Olerud 和 Molander 踝关节评分（OMAS），以及（次要）由盲法评估者进行的计时起立行走测试（TUG）。缺失数据采用单值插补法处理。使用多元线性回归模型，根据基线变量或基线和 6 周随访变量预测 OMAS 或 TUG。使用 bootstrap 法对模型进行内部验证。
OMAS 基线数据模型包括：每周饮酒量（单位）、受伤后 EQ-5D-3L 视觉模拟量表（VAS）、性别、受伤前行走距离和助行器使用情况、吸烟状况和自我感知健康状况。基线/6 周数据模型包括相同的基线变量，减去 EQ-5D-3L VAS，加上 5 个 6 周预测变量：放射学对线不良、受伤踝关节背屈和跖屈活动范围以及 6 周 OMAS 和 EQ-5D-3L。这些模型分别解释了大约 23%和 26%的结果变异性。用于预测 TUG 的类似基线和基线/6 周数据模型分别解释了大约 30%和 32%的结果变异性。
使用常规记录的临床数据预测自我报告或客观测量的踝关节功能的预测模型的准确性相对较低，因此不太可能对临床实践和患者咨询有益。应研究其他潜在的预测因素（例如，心理因素，如灾难化和回避恐惧）以提高预测准确性。
ISRCTN04180738；Post-results。
