School of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.
Surgical Treatment and Rehabilitation Service (STARS) Education and Research Alliance, The University of Queensland and Metro North Health, Brisbane, Queensland, Australia.
J Gerontol A Biol Sci Med Sci. 2023 Aug 27;78(9):1659-1668. doi: 10.1093/gerona/glad053.
To develop and validate the stratify-hip algorithm (multivariable prediction models to predict those at low, medium, and high risk across in-hospital death, 30-day death, and residence change after hip fracture).
Multivariable Fine-Gray and logistic regression of audit data linked to hospital records for older adults surgically treated for hip fracture in England/Wales 2011-14 (development n = 170 411) and 2015-16 (external validation, n = 90 102). Outcomes included time to in-hospital death, death at 30 days, and time to residence change. Predictors included age, sex, pre-fracture mobility, dementia, and pre-fracture residence (not for residence change). Model assumptions, performance, and sensitivity to missingness were assessed. Models were incorporated into the stratify-hip algorithm assigning patients to overall low (low risk across outcomes), medium (low death risk, medium/high risk of residence change), or high (high risk of in-hospital death, high/medium risk of 30-day death) risk.
For complete-case analysis, 6 780 of 141 158 patients (4.8%) died in-hospital, 8 693 of 149 258 patients (5.8%) died by 30 days, and 4 461 of 119 420 patients (3.7%) had residence change. Models demonstrated acceptable calibration (observed:expected ratio 0.90, 0.99, and 0.94), and discrimination (area under curve 73.1, 71.1, and 71.5; Brier score 5.7, 5.3, and 5.6) for in-hospital death, 30-day death, and residence change, respectively. Overall, 31%, 28%, and 41% of patients were assigned to overall low, medium, and high risk. External validation and missing data analyses elicited similar findings. The algorithm is available at https://stratifyhip.co.uk.
The current study developed and validated the stratify-hip algorithm as a new tool to risk stratify patients after hip fracture.
开发和验证 stratify-hip 算法（多变量预测模型，用于预测髋部骨折患者的院内死亡、30 天内死亡和骨折后居住地变化的低、中、高风险）。
使用英格兰/威尔士 2011-14 年（发展 n=170411）和 2015-16 年（外部验证，n=90102）接受髋关节手术治疗的老年患者的审计数据与医院记录相关联的多变量精细格雷和逻辑回归。结果包括院内死亡的时间、30 天内死亡的时间和居住地变化的时间。预测因素包括年龄、性别、骨折前活动能力、痴呆症和骨折前居住地（不包括居住地变化）。评估了模型假设、性能和对缺失值的敏感性。将模型纳入 stratify-hip 算法，将患者分为总体低（所有结局的低风险）、中（低死亡风险，中/高居住地变化风险）或高（院内死亡风险高，30 天内死亡风险高/中）风险。
对于完整病例分析，141158 例患者中有 6780 例（4.8%）在院内死亡，149258 例患者中有 8693 例（5.8%）在 30 天内死亡，119420 例患者中有 4461 例（3.7%）居住地发生变化。模型显示出可接受的校准（观察到的：预期比 0.90、0.99 和 0.94）和区分度（曲线下面积 73.1、71.1 和 71.5；Brier 评分 5.7、5.3 和 5.6）分别用于院内死亡、30 天内死亡和居住地变化。总体而言，31%、28%和 41%的患者被分配到总体低、中、高风险。外部验证和缺失数据分析得出了类似的结果。该算法可在 https://stratifyhip.co.uk 上获得。
本研究开发并验证了 stratify-hip 算法，作为一种新的工具，用于对髋部骨折患者进行风险分层。
