Prospective multicenter external validation of the rib fracture frailty index.

IF 3.7 2区 医学 Q2 CRITICAL CARE MEDICINE
Jeff Choi, Joshua A Villarreal, Rachel Handelsman, Jacob Kirkorowciz, Ariel Knight, Arathi Kumar, Emily McNabb, Jon Perlstein, Ronald B Tesoriero, Elaina Y Tsui, Cheri White, Joseph D Forrester
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引用次数: 0

Abstract

Background: The Rib Fracture Frailty (RFF) Index is an internally validated machine learning-based risk assessment tool for adult patients with rib fractures that requires minimal provider entry. Existing frailty risk scores have yet to undergo head-to-head performance comparison with age, a widely used proxy for frailty in clinical practice. Our aim was to externally validate the RFF Index in a small-scale implementation feasibility study.

Methods: Prospective observational cohort study conducted across five ACS COT-verified trauma centers. Participants included ≥18-year-old adults presenting January 1, 2021, to December 31, 2021, with traumatic rib fractures. The primary outcome was a composite outcome score comprised of three clinical factors: hospitalization ≥5 days, discharge disposition, and inpatient mortality. Proportional odds logistic regression evaluated associations of age model or RFF Index score model with composite outcome scores. Models were compared using standard discrimination and calibration metrics. Secondary analysis delineated predictive performance among patients with lower (Injury Severity Score < 15) and higher Injury Severity Score ≥ 15) injury burden.

Results: Of 849 participants, 546 (64%) were male and median age was 62 years (interquartile range, 46-76 years). A one-point increase in RFF score was associated with 6% increased odds of higher composite outcome score (odds ratio [OR], 1.06; 95% confidence interval [95% CI], 1.04-1.08), while a 1-year increase in age did not show statistically significant association (OR, 1.10; 95% CI, 0.75-1.61). The RFF score had higher discrimination (OR, 0.09; 95% CI, 0.08-0.11 vs. OR, 0.06; 95% CI, 0.04-0.08; p = 0.04) and calibration performance compared with age, but on secondary analysis, higher predictive performance was limited to patients with lower injury burden. Both RFF Index and age had poor calibration for predicting patients discharged to home after hospitalization ≥5 days.

Conclusion: This prospective external validation study found RFF Index may be a better alternative to age for predicting adverse outcomes among patients with traumatic rib fractures and lower overall injury burden. Staged implementation studies in accordance with clinical prediction model implementation guidelines are required to evaluate the RFF Index's clinical efficacy and guide potential adoption.

Level of evidence: Prognostic and Epidemiological; Level II.

肋骨骨折脆弱指数的前瞻性多中心外部验证。
背景:肋骨骨折脆弱性(RFF)指数是一种内部验证的基于机器学习的风险评估工具,适用于成年肋骨骨折患者,需要最少的医疗服务提供者。现有的衰弱风险评分还没有经过与年龄的直接比较,年龄是临床实践中广泛使用的衰弱指标。我们的目标是在一项小规模实施可行性研究中对RFF指数进行外部验证。方法:前瞻性观察队列研究在五个ACS cot验证的创伤中心进行。参与者包括2021年1月1日至2021年12月31日出现外伤性肋骨骨折的≥18岁的成年人。主要转归是由三个临床因素组成的综合转归评分:住院≥5天、出院处置和住院死亡率。比例logistic回归评价年龄模型或RFF指数评分模型与综合结局评分的相关性。使用标准判别和校准指标对模型进行比较。二级分析描述了损伤负担较低(损伤严重程度评分< 15)和较高(损伤严重程度评分≥15)的患者的预测性能。结果:在849名参与者中,546名(64%)为男性,中位年龄为62岁(四分位数范围为46-76岁)。RFF评分每增加1分,获得较高综合结局评分的几率增加6%(优势比[OR], 1.06;95%可信区间[95% CI], 1.04-1.08),而年龄增加1年无统计学意义的关联(OR, 1.10;95% ci, 0.75-1.61)。RFF评分具有较高的歧视(OR, 0.09;95% CI为0.08-0.11,OR为0.06;95% ci, 0.04-0.08;P = 0.04)和校准性能与年龄相比,但在二次分析中,较高的预测性能仅限于损伤负担较低的患者。RFF指数和年龄对于预测住院≥5天出院患者的校准效果较差。结论:这项前瞻性外部验证研究发现,RFF指数可能是预测外伤性肋骨骨折患者不良结局的更好替代年龄,并且总体损伤负担较低。需要根据临床预测模型实施指南进行分阶段实施研究,以评估RFF指数的临床疗效并指导潜在的采用。证据水平:预后;II级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
11.80%
发文量
637
审稿时长
2.7 months
期刊介绍: The Journal of Trauma and Acute Care Surgery® is designed to provide the scientific basis to optimize care of the severely injured and critically ill surgical patient. Thus, the Journal has a high priority for basic and translation research to fulfill this objectives. Additionally, the Journal is enthusiastic to publish randomized prospective clinical studies to establish care predicated on a mechanistic foundation. Finally, the Journal is seeking systematic reviews, guidelines and algorithms that incorporate the best evidence available.
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