开发和验证机器学习驱动的风险指数,以预测患者在转诊,评估和等待肾脏移植的过程中退出。

IF 1.9 4区 医学 Q2 SURGERY
Solaf Al Awadhi, Enshuo Hsu, Thomas B. H. Potter, Ioannis A. Kakadiaris, David A. Axelrod, Faith Parsons, Andrea M. Meinders, Victoria Cassell, Catherine Pulicken, Zulqarnain Javed, Paula K. Shireman, Stefano Casarin, A. L. Jonathan Gelfond, Amy D. Waterman
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引用次数: 0

摘要

背景:肾移植是治疗肾衰竭的最佳方法;然而,在获取方面的差距仍然存在。我们开发并验证了风险指数,以预测在国家登记处未捕获的寻求移植过程关键阶段的早期辍学。方法:我们纳入2016年6月至2023年11月在休斯顿卫理公会医院转诊的肾移植患者。我们从电子健康记录和公开的人口普查数据中收集了人口统计学、临床、患者和背景层面的社会经济变量。我们使用机器学习(ML)模型来预测退学风险较高的患者的特征:(1)转诊时(开始评估之前),(2)评估过程中(等待名单之前),(3)等待名单期间(接受移植之前)。采用AUROC对模型性能进行评价。结果:在4133名转诊患者中,46%没有参加第一次移植评估。在2414名医学上符合移植条件并开始评估的患者中,54%没有进入候补名单。在2457名等待名单的患者中,31%的人在等待名单上变得不活跃。高风险患者一直是年龄较大、肥胖和社会经济上处于不利地位的患者,并存在阶段特异性差异:社会因素——如单身、失业、受教育程度较低、生活在高度贫困地区——以及非裔美国人种族在转诊时占主导地位(AUROC 0.79);临床合并症和非裔美国人和西班牙裔在评估中都很突出(AUROC 0.71);西班牙裔、吸烟和数字排斥是等候名单的主要驱动因素(AUROC 0.76)。结论:ML模型在转诊、评估和等待列表中有效识别辍学风险,能够早期识别有风险的患者。有针对性的干预措施可以减少差异,提高评估的完成度,增加移植的可及性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing and Validating Machine Learning-Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant

Background

Transplant is the optimal treatment for kidney failure; however, disparities in access persist. We developed and validated risk indices to predict early dropout at key stages of the transplant-seeking process not captured in national registries.

Methods

We included patients referred for kidney transplant at Houston Methodist Hospital between June 2016, and November 2023. We collected demographic, clinical, patient- and contextual-level socioeconomic variables from electronic health records and publicly available census data. We used machine learning (ML) models to predict the characteristics of patients at higher risk of dropping out: (1) at referral (before starting evaluation), (2) in the process of evaluation (before waitlisting), and (3) during waitlisting (before receiving a transplant). Model performance was evaluated using AUROC.

Results

Of 4133 referred patients, 46% did not attend their first transplant evaluation visit. Of 2414 patients who were medically eligible for transplant and started evaluation, 54% did not become waitlisted. Of 2457 waitlisted patients, 31% became inactive on the waitlist. Higher risk patients were consistently older, obese, and socioeconomically disadvantaged, with stage-specific differences: social factors—such as being single, unemployed, less educated, and living in high-deprivation areas—and African American race dominated at referral (AUROC 0.79); clinical comorbidities and both African American and Hispanic ethnicity were prominent at evaluation (AUROC 0.71); and Hispanic ethnicity, smoking, and digital exclusion were key drivers at waitlisting (AUROC 0.76).

Conclusion

ML models effectively identified dropout risk at referral, evaluation, and waitlisting, enabling early identification of at-risk patients. Targeted interventions could reduce disparities, improve evaluation completion, and increase transplant access.

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来源期刊
Clinical Transplantation
Clinical Transplantation 医学-外科
CiteScore
3.70
自引率
4.80%
发文量
286
审稿时长
2 months
期刊介绍: Clinical Transplantation: The Journal of Clinical and Translational Research aims to serve as a channel of rapid communication for all those involved in the care of patients who require, or have had, organ or tissue transplants, including: kidney, intestine, liver, pancreas, islets, heart, heart valves, lung, bone marrow, cornea, skin, bone, and cartilage, viable or stored. Published monthly, Clinical Transplantation’s scope is focused on the complete spectrum of present transplant therapies, as well as also those that are experimental or may become possible in future. Topics include: Immunology and immunosuppression; Patient preparation; Social, ethical, and psychological issues; Complications, short- and long-term results; Artificial organs; Donation and preservation of organ and tissue; Translational studies; Advances in tissue typing; Updates on transplant pathology;. Clinical and translational studies are particularly welcome, as well as focused reviews. Full-length papers and short communications are invited. Clinical reviews are encouraged, as well as seminal papers in basic science which might lead to immediate clinical application. Prominence is regularly given to the results of cooperative surveys conducted by the organ and tissue transplant registries. Clinical Transplantation: The Journal of Clinical and Translational Research is essential reading for clinicians and researchers in the diverse field of transplantation: surgeons; clinical immunologists; cryobiologists; hematologists; gastroenterologists; hepatologists; pulmonologists; nephrologists; cardiologists; and endocrinologists. It will also be of interest to sociologists, psychologists, research workers, and to all health professionals whose combined efforts will improve the prognosis of transplant recipients.
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