Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK.
Department of Orthopaedic surgery, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
Lancet Rheumatol. 2024 Sep;6(9):e607-e614. doi: 10.1016/S2665-9913(24)00149-8. Epub 2024 Jul 31.
Despite a rising rate of serious medical complications after shoulder replacement surgery, there are no prediction models in widespread use to guide surgeons in identifying patients at high risk and to provide patients with personalised risk estimates to support shared decision making. Our aim was to develop and externally validate a prediction model for serious adverse events within 90 days of primary shoulder replacement surgery.
Linked data from the National Joint Registry, National Health Service Hospital Episode Statistics Admitted Patient Care of England, and Civil Registration Mortality databases and Danish Shoulder Arthroplasty Registry and National Patient Register were used for our modelling study. Patients aged 18-100 years who had a primary shoulder replacement between April 1, 2012, and Oct 2, 2020, in England, and April 1, 2012, and Oct 2, 2018, in Denmark, were included. We developed a multivariable logistic regression model using the English dataset to predict the risk of 90-day serious adverse events, which were defined as medical complications requiring admission to hospital and all-cause death. We undertook internal validation using bootstrapping, and internal-external cross-validation across different geographical regions of England. The English model was externally validated on the Danish dataset.
Data for 40 631 patients undergoing primary shoulder replacement (mean age 72·5 years [SD 9·9]; 28 709 [70·7%] women and 11 922 [29·3%] men) were used for model development, of whom 2270 (5·6%) had a 90-day serious adverse event. On internal validation, the model had a C-statistic of 0·717 (95% CI 0·707-0·728) and was well calibrated. Internal-external cross-validation showed consistent model performance across all regions in England. Upon external validation on the Danish dataset (n=6653; mean age 70·5 years [SD 10·3]; 4503 [67·7%] women and 2150 [32·3%] men), the model had a C-statistic of 0·750 (95% CI 0·723-0·776). Decision curve analysis showed clinical utility, with net benefit across all risk thresholds.
This externally validated prediction model uses commonly available clinical variables to accurately predict the risk of serious medical complications after primary shoulder replacement surgery. The model is generalisable and applicable to most patients in need of a shoulder replacement. Its use offers support to clinicians and could inform and empower patients in the shared decision-making process.
National Institute for Health and Care Research and the Department of Orthopaedic Surgery, Herlev and Gentofte Hospital, Denmark.
尽管肩关节置换术后严重医疗并发症的发生率不断上升，但目前尚无广泛使用的预测模型来指导外科医生识别高风险患者，并为患者提供个性化的风险估计值以支持共同决策。我们的目的是开发并外部验证一种用于预测原发性肩关节置换术后 90 天内严重不良事件的预测模型。
本研究使用了来自英国国家关节登记处、英国国家卫生服务医院发病统计数据入院患者治疗数据、英国民事登记死亡率数据库和丹麦肩关节置换登记处及国家患者登记处的链接数据。纳入了年龄在 18-100 岁之间、于 2012 年 4 月 1 日至 2020 年 10 月 2 日在英格兰、2012 年 4 月 1 日至 2018 年 10 月 2 日在丹麦进行原发性肩关节置换的患者。我们使用英国数据集开发了一个多变量逻辑回归模型，以预测 90 天内严重不良事件的风险，这些不良事件定义为需要住院治疗的医疗并发症和全因死亡。我们通过自举法进行内部验证，并在英格兰不同地理区域进行内部-外部交叉验证。在丹麦数据集上进行了外部验证。
共纳入 40631 例接受原发性肩关节置换的患者（平均年龄 72.5 岁[9.9 岁]；28709[70.7%]为女性，11922[29.3%]为男性）用于模型开发，其中 2270 例（5.6%）发生 90 天内严重不良事件。内部验证时，该模型的 C 统计量为 0.717（95%CI 0.707-0.728），校准良好。内部-外部交叉验证显示，该模型在英格兰所有地区的表现一致。在丹麦数据集（n=6653；平均年龄 70.5 岁[10.3 岁]；4503[67.7%]为女性，2150[32.3%]为男性）上进行外部验证时，该模型的 C 统计量为 0.750（95%CI 0.723-0.776）。决策曲线分析表明该模型具有临床实用性，在所有风险阈值下均具有净获益。
该经过外部验证的预测模型使用了常用的临床变量，能够准确预测原发性肩关节置换术后严重医疗并发症的风险。该模型具有通用性，适用于大多数需要肩关节置换的患者。它的使用为临床医生提供了支持，并可以为患者在共同决策过程中提供信息和赋权。
英国国家卫生与保健研究院和丹麦 Herlev 及 Gentofte 医院骨科。
