Development and Validation of Multivariable Prediction Models for In-Hospital Death, 30-Day Death, and Change in Residence After Hip Fracture Surgery and the "Stratify-Hip" Algorithm.
Development and Validation of Multivariable Prediction Models for In-Hospital Death, 30-Day Death, and Change in Residence After Hip Fracture Surgery and the "Stratify-Hip" Algorithm.
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DOI：
10.1093/gerona/glad053
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发表时间：
2023-08-27
影响因子：
5.1
通讯作者：
Sheehan, Katie J.
中科院分区：
文献类型：
--
作者：
Goubar, Aicha;Martin, Finbarr C.;Sackley, Catherine;Foster, Nadine E.;Ayis, Salma;Gregson, Celia L.;Cameron, Ian D.;Walsh, Nicola E.;Sheehan, Katie J.
为了开发和验证分层算法（多变量预测模型，以预测在院内死亡，30天死亡，髋部骨折后处于低，中和高风险的模型）。
审计数据的多变量灰色和逻辑回归与老年人的医院记录有关，在英格兰/威尔士2011 - 14年接受髋关节骨折治疗（开发n = 170 411）和2015 - 16年（外部验证，n = 90 102）居住（不居住变更）评估了模型，对失误的敏感性。
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）分别为30天的死亡和住所变化，分别为31％，28％和41％的患者被分配给整体低，中等和高风险。
当前的研究开发并验证了分层算法，作为髋部骨折后风险分层患者的新工具。
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.
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影响因子：
2.9
作者：
Hayat, Shabina;Luben, Robert;Khaw, Kay-Tee;Wareham, Nicholas;Brayne, Carol
通讯作者：
Brayne, Carol
影响因子：
4.1
作者：
Fox MT;Persaud M;Maimets I;Brooks D;O'Brien K;Tregunno D
通讯作者：
Tregunno D
影响因子：
2
作者：
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通讯作者：
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影响因子：
3.7
作者：
通讯作者：
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影响因子：
7.2
作者：
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通讯作者：
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