Department of Nephrology, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Tongji University School of Medicine, Shanghai, China.
J Med Internet Res. 2024 Nov 25;26:e54206. doi: 10.2196/54206.
Chronic kidney disease (CKD) is a significant public health concern. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app-based nursing systems for decelerating CKD progression.
This study aimed to evaluate the long-term efficacy of the KidneyOnline intelligent care system in slowing the progression of nondialysis-dependent CKD.
In this retrospective study, the KidneyOnline app was used for patients with CKD in China who were registered between January 2017 and April 2023. Patients were divided into 2 groups: an intervention group using the app's nurse-led, patient-oriented management system and a conventional care group that did not use the app. Patients' uploaded health data were processed via deep learning optical character recognition, and the artificial intelligence (AI) system provided personalized health care plans and interventions. Conversely, the conventional care group received suggestions from nephrologists during regular visits without AI. Monitoring extended for an average duration of 2.1 (SD 1.4) years. The study's objective is to assess the app's effectiveness in preserving kidney function. The primary outcome was the estimated glomerular filtration rate slope over the follow-up period, and secondary outcomes included changes in albumin-to-creatinine ratio (ACR) and mean arterial pressure.
A total of 12,297 eligible patients were enrolled for the analysis. Among them, 808 patients were successfully matched using 1:1 propensity score matching, resulting in 404 (50%) patients in the KidneyOnline care system group and another 404 (50%) patients in the conventional care group. The estimated glomerular filtration rate slope in the KidneyOnline care group was significantly lower than that in the conventional care group (odds ratio -1.3, 95% CI -2.4 to -0.1 mL/min/1.73 m per year vs odds ratio -2.8, 95% CI -3.8 to -1.9 mL/min/1.73 m per year; P=.009). Subgroup analysis revealed that the effect of the KidneyOnline care group was more significant in male patients, patients older than 45 years, and patients with worse baseline kidney function, higher blood pressure, and heavier proteinuria. After 3 and 6 months, the mean arterial pressure in the KidneyOnline care group decreased to 85.6 (SD 9.2) and 83.6 (SD 10.5) mm Hg, respectively, compared to 94.9 (SD 10.6) and 95.2 (SD 11.6) mm Hg in the conventional care group (P<.001). The ACR in the KidneyOnline care group showed a more significant reduction after 3 and 6 months (736 vs 980 mg/g and 572 vs 840 mg/g; P=.07 and P=.03); however, there was no significant difference in ACR between the two groups at the end of the follow-up period (618 vs 639 mg/g; P=.90).
The utilization of KidneyOnline, an AI-based, nurse-led, patient-centered care system, may be beneficial in slowing the progression of nondialysis-dependent CKD.
慢性肾脏病（CKD）是一个重大的公共卫生问题。因此，寻找减缓 CKD 进展和改善患者预后的实用策略迫在眉睫。目前，有限的证据可以证实基于移动应用的护理系统在减缓 CKD 进展方面的疗效。
本研究旨在评估 KidneyOnline 智能护理系统在减缓非透析依赖型 CKD 进展方面的长期疗效。
这是一项回顾性研究，使用中国的 KidneyOnline 应用程序对 2017 年 1 月至 2023 年 4 月期间注册的 CKD 患者进行研究。患者被分为 2 组：使用应用程序的护士主导、以患者为中心的管理系统的干预组和未使用应用程序的常规护理组。患者上传的健康数据通过深度学习光学字符识别进行处理，人工智能（AI）系统提供个性化的医疗保健计划和干预措施。相反，常规护理组在定期就诊期间接受肾病专家的建议，而没有 AI 辅助。监测平均持续时间为 2.1（SD 1.4）年。本研究的目的是评估该应用程序在保护肾功能方面的有效性。主要结局是随访期间估计肾小球滤过率斜率的变化，次要结局包括白蛋白与肌酐比值（ACR）和平均动脉压的变化。
共纳入了 12297 名符合条件的患者进行分析。其中，808 名患者通过 1:1 倾向评分匹配成功匹配，结果在 KidneyOnline 护理系统组中有 404（50%）名患者，在常规护理组中有另外 404（50%）名患者。KidneyOnline 护理系统组的估计肾小球滤过率斜率明显低于常规护理组（比值比-1.3，95%置信区间-2.4 至-0.1 mL/min/1.73 m 每年；比值比-2.8，95%置信区间-3.8 至-1.9 mL/min/1.73 m 每年；P=.009）。亚组分析显示，KidneyOnline 护理系统组在男性患者、年龄大于 45 岁的患者和基线肾功能更差、血压更高、蛋白尿更重的患者中效果更为显著。在 3 个月和 6 个月时，KidneyOnline 护理组的平均动脉压分别降至 85.6（SD 9.2）和 83.6（SD 10.5）mmHg，而常规护理组分别降至 94.9（SD 10.6）和 95.2（SD 11.6）mmHg（P<.001）。KidneyOnline 护理组在 3 个月和 6 个月时 ACR 的降低更为显著（736 与 980 mg/g 和 572 与 840 mg/g；P=.07 和 P=.03）；然而，在随访结束时，两组之间的 ACR 没有显著差异（618 与 639 mg/g；P=.90）。
使用基于人工智能的、由护士主导的、以患者为中心的护理系统 KidneyOnline 可能有助于减缓非透析依赖型 CKD 的进展。
