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晚期肾病新型多学科护理项目(PEAK)的临床结局

Clinical Outcomes of a Novel Multidisciplinary Care Program in Advanced Kidney Disease (PEAK).

作者信息

Shimonov Daniil, Tummalapalli Sri Lekha, Donahue Stephanie, Narayana Vidya, Wu Sylvia, Walters Lisa S, Billman Roberta, Desiderio Barbara, Pressman Sandra, Fielding Oliver, Sweeney Kariel, Cukor Daniel, Levine Daniel M, Parker Thomas S, Srivatana Vesh, Silberzweig Jeffrey, Liu Frank, Bohmart Andrew

机构信息

The Rogosin Institute, New York, New York, USA.

Division of Nephrology and Hypertension, Department of Medicine, Weill Cornell Medicine, New York, New York, USA.

出版信息

Kidney Int Rep. 2024 Jul 24;9(10):2904-2914. doi: 10.1016/j.ekir.2024.07.018. eCollection 2024 Oct.

Abstract

INTRODUCTION

Multidisciplinary care (MDC) for late-stage chronic kidney disease (CKD) has been associated with improved patient outcomes compared with traditional nephrology care; however, the optimal MDC model is unknown. In 2015, we implemented a novel MDC model for patients with late-stage CKD informed by the Chronic Care Model conceptual framework, including an expanded MDC team, care plan meetings, clinical risk prediction, and a patient dashboard.

METHODS

We conducted a single-center, retrospective cohort study of adults with late-stage CKD (estimated glomerular filtration rate [eGFR] < 30 ml/min per 1.73 m) enrolled from May 2015 to February 2020 in the Program for Education in Advanced Kidney Disease (PEAK). Our primary composite outcome was an optimal transition to end-stage kidney disease (ESKD) defined as starting in-center hemodialysis (ICHD) as an outpatient with an arteriovenous fistula (AVF) or graft (AVG), or receiving home dialysis, or a preemptive kidney transplant. Secondary outcomes included home dialysis initiation, preemptive transplantation, vascular access at dialysis initiation, and location of ICHD initiation. We used logistic regression to examine trends in outcomes. Results were stratified by race, ethnicity, and insurance payor, and compared with national and regional averages from the United States Renal Data System (USRDS) averaged from 2015 to 2019.

RESULTS

Among 489 patients in the PEAK program, 37 (8%) died prior to ESKD and 151 (31%) never progressed to ESKD. Of the 301 patients (62%) who progressed to ESKD, 175 (58%) achieved an optimal transition to ESKD, including 54 (18%) on peritoneal dialysis, 16 (5%) on home hemodialysis, and 36 (12%) to preemptive transplant. Of the 195 patients (65%) starting ICHD, 51% started with an AVF or AVG and 52% started as an outpatient. The likelihood of starting home dialysis increased by 1.34 times per year from 2015 to 2020 (95% confidence interval [CI]: 1.05-1.71,  = 0.018) in multivariable adjusted results. Optimal transitions to ESKD and home dialysis rates were higher than the national USRDS data (58% vs. 30%; 23% vs. 11%) across patient race, ethnicity, and payor.

CONCLUSION

Patients enrolled in a novel comprehensive MDC model coupled with risk prediction and health information technology were nearly twice as likely to achieve an optimal transition to ESKD and start dialysis at home, compared with national averages.

摘要

引言

与传统肾病护理相比,晚期慢性肾脏病(CKD)的多学科护理(MDC)与改善患者预后相关;然而,最佳的MDC模式尚不清楚。2015年,我们根据慢性护理模式概念框架为晚期CKD患者实施了一种新型MDC模式,包括扩大的MDC团队、护理计划会议、临床风险预测和患者仪表板。

方法

我们对2015年5月至2020年2月在晚期肾病教育项目(PEAK)中登记的晚期CKD成人患者(估计肾小球滤过率[eGFR]<30 ml/(min·1.73 m²))进行了一项单中心回顾性队列研究。我们的主要复合结局是最佳过渡到终末期肾病(ESKD),定义为作为门诊患者开始进行中心血液透析(ICHD),使用动静脉内瘘(AVF)或移植物(AVG),或接受家庭透析,或进行抢先肾移植。次要结局包括开始家庭透析、抢先移植、透析开始时的血管通路以及ICHD开始的地点。我们使用逻辑回归来研究结局的趋势。结果按种族、民族和保险支付方进行分层,并与2015年至2019年美国肾脏数据系统(USRDS)的全国和地区平均水平进行比较。

结果

在PEAK项目的489例患者中,37例(8%)在进展至ESKD之前死亡,151例(31%)从未进展至ESKD。在进展至ESKD的301例患者(62%)中,175例(58%)实现了向ESKD的最佳过渡,包括54例(18%)接受腹膜透析,16例(5%)接受家庭血液透析,36例(12%)进行抢先移植。在开始ICHD的195例患者(65%)中,51%以AVF或AVG开始,52%作为门诊患者开始。在多变量调整结果中,2015年至2020年开始家庭透析的可能性每年增加1.34倍(95%置信区间[CI]:1.05 - 1.71,P = 0.018)。在患者种族、民族和支付方中,向ESKD的最佳过渡和家庭透析率高于美国肾脏数据系统的全国数据(58%对30%;23%对11%)。

结论

与全国平均水平相比,参加新型综合MDC模式并结合风险预测和健康信息技术的患者实现向ESKD最佳过渡并在家中开始透析的可能性几乎高出一倍。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0167/11489444/65b0c99c06f5/ga1.jpg

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