Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.

IF 2.5 2区 医学 Q1 ORTHOPEDICS
Hidde Dijkstra, Cathleen S Parsons, Hanne-Eva VAN Bremen, Hanna C Willems, Anne A H De Hond, Barbara C Van Munster, Job N Doornberg, Jacobien H F Oosterhoff
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引用次数: 0

Abstract

Background and purpose:  Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such as prognosis and decisions concerning whether to operate or not on frail, life-limited patients. We aimed to develop machine learning (ML)-driven prediction models for short- and long-term mortality in a large cohort of patients with hip fractures.

Methods:  In this national registry-based retrospective cohort study, patients aged ≥ 70 years registered in the nationwide Dutch Hip Fracture Audit from 2018-2023 were included. Predictive variables were selected based on the literature and/or clinical relevance. 6 ML algorithms, including logistic regression, were trained with internal cross-validation and evaluated on discrimination (c-statistic), sensitivity, specificity, calibration, and interpretability.

Results:  74,396 patients (median age 84, IQR 78-89; 68% female) were analyzed. Most patients lived at home (69%) and high malnutrition risk was seen in 10%. 18% had dementia. Mortality rates were 9.1% (30-day), 15% (90-day), and 26% (1-year). Logistic regression performed comparably to other algorithms, but was chosen as the preferred algorithm due to its superior interpretability (c-statistic: 30-day 0.82, 90-day 0.81, 1-year 0.80).

Conclusion:  We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. This information may inform SDM.

基于机器学习的老年髋部骨折患者共同决策的短期和长期死亡率预测:74,396例荷兰髋部骨折审计算法
背景和目的:老年人髋部骨折的治疗相关共同决策(SDM)是复杂的,因为需要平衡患者的特定因素,如生活目标、虚弱和手术风险。它包括诸如预后和决定是否对身体虚弱、生命有限的病人进行手术等考虑。我们旨在开发机器学习(ML)驱动的预测模型,预测髋部骨折患者的短期和长期死亡率。方法:在这项以全国登记为基础的回顾性队列研究中,纳入了2018-2023年荷兰髋部骨折审计中登记的年龄≥70岁的患者。根据文献和/或临床相关性选择预测变量。包括逻辑回归在内的6种ML算法进行了内部交叉验证训练,并对判别(c-statistic)、灵敏度、特异性、校准和可解释性进行了评估。结果:74,396例患者(中位年龄84岁,IQR 78-89;68%为女性)。大多数患者(69%)住在家中,10%的患者存在高营养不良风险。18%的人患有痴呆症。死亡率分别为9.1%(30天)、15%(90天)和26%(1年)。逻辑回归与其他算法的表现相当,但由于其优越的可解释性,被选为首选算法(c统计量:30天0.82,90天0.81,1年0.80)。结论:我们开发并验证了ML算法,包括逻辑回归,用于预测表现良好的老年髋部骨折患者的死亡率。这些信息可能会通知SDM。
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来源期刊
Acta Orthopaedica
Acta Orthopaedica 医学-整形外科
CiteScore
6.40
自引率
8.10%
发文量
105
审稿时长
4-8 weeks
期刊介绍: Acta Orthopaedica (previously Acta Orthopaedica Scandinavica) presents original articles of basic research interest, as well as clinical studies in the field of orthopedics and related sub disciplines. Ever since the journal was founded in 1930, by a group of Scandinavian orthopedic surgeons, the journal has been published for an international audience. Acta Orthopaedica is owned by the Nordic Orthopaedic Federation and is the official publication of this federation.
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