Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.

IF 10.5 1区医学 Q1 MEDICINE, GENERAL & INTERNAL

JAMA Network Open Pub Date : 2025-05-01 DOI:10.1001/jamanetworkopen.2025.8910

Zhen Ying, Yujuan Fan, Congling Chen, Yuchen Liu, Qi Tang, Zhiwei Chen, Qian Yang, Hongmei Yan, Liming Wu, Jiaping Lu, Zhiwen Liu, Jun Liu, Xiaoying Li, Ying Chen

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引用次数: 0

Abstract

Importance: Type 2 diabetes (T2D) is one of the most prevalent chronic diseases in the world. Insulin titration for glycemic control in T2D is crucial but limited by the lack of personalized and real-time tools.

Objective: To examine whether an artificial intelligence-based insulin clinical decision support system (iNCDSS) for glycemic control in hospitalized patients with T2D is noninferior to standard insulin therapy administered by senior physicians.

Design, setting, and participants: This multicenter, single-blind, parallel randomized clinical trial (RCT) was conducted between October 1, 2021, and September 8, 2022, in endocrinology wards of 3 medical centers. Eligible participants were adults (aged ≥18 years) with glycated hemoglobin levels between 7.0% and 11.0% who had received antidiabetic treatments in the previous 3 months.

Interventions: Participants were randomized in a 1:1 ratio to receive insulin dosage titration by iNCDSS or senior endocrinology physicians for 5 consecutive days.

Main outcomes and measures: The primary outcome was the proportion of time in the target glucose range (70-180 mg/dL) during the 5-day study period; the noninferiority margin was 6 percentage points. Secondary outcomes included other glycemic control measurements and adverse events.

Results: A total of 149 participants (mean [SD] age, 64.2 [12.0] years; 84 male [56.4%]) were enrolled and randomized to the iNCDSS group (n = 75) or physician group (n = 74). The mean (SD) target glucose range (primary outcome) was 76.4% (16.4%) in the iNCDSS group and 73.6% (16.8%) in the physician group, which achieved the prespecified noninferiority criterion (estimated treatment difference, 2.7%; 95% CI, -2.7% to 8.0%). There were no significant differences in adverse events between the 2 groups. Most physicians were satisfied with the iNCDSS for its clear, time-saving, effective, and safe clinical support.

Conclusions and relevance: In this RCT of an iNCDSS, the system demonstrated noninferiority to senior endocrinology physicians in insulin titration in an inpatient setting, indicating its potential as a favorable tool for insulin titration in patients with T2D.

Trial registration: ClinicalTrials.gov Identifier: NCT04642378.

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实时人工智能辅助胰岛素滴定系统用于2型糖尿病患者血糖控制：一项随机临床试验

重要性：2型糖尿病（T2D）是世界上最普遍的慢性疾病之一。胰岛素滴定对t2dm患者的血糖控制至关重要，但由于缺乏个性化和实时工具而受到限制。目的：探讨基于人工智能的胰岛素临床决策支持系统（iNCDSS）在T2D住院患者血糖控制中的应用是否优于高级医师的标准胰岛素治疗。设计、环境和参与者：这项多中心、单盲、平行随机临床试验（RCT）于2021年10月1日至2022年9月8日在3个医疗中心的内分泌科病房进行。符合条件的受试者为成人（年龄≥18岁），糖化血红蛋白水平在7.0% - 11.0%之间，且在过去3个月内接受过降糖治疗。干预措施：参与者按1:1的比例随机接受iNCDSS或高级内分泌科医生连续5天的胰岛素剂量滴定。主要结局和指标：主要结局是5天研究期间血糖维持在目标范围（70-180 mg/dL）的时间比例；非劣效性差为6个百分点。次要结局包括其他血糖控制指标和不良事件。结果：共有149名参与者(平均[SD]年龄64.2[12.0]岁；84名男性[56.4%]入组，随机分为iNCDSS组（n = 75）和内科医生组（n = 74）。iNCDSS组的平均（SD）目标血糖范围（主要结局）为76.4%(16.4%)，内科医生组为73.6%(16.8%)，达到预定的非劣效性标准(估计治疗差异为2.7%；95% CI, -2.7%至8.0%)。两组患者不良事件发生率无显著差异。大多数医生对iNCDSS的清晰、省时、有效和安全的临床支持表示满意。结论和相关性：在这项iNCDSS的随机对照试验中，该系统在住院患者的胰岛素滴定中对高级内分泌科医生表现出非劣效性，表明它有潜力成为t2dm患者胰岛素滴定的有利工具。试验注册：ClinicalTrials.gov标识符：NCT04642378。

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来源期刊

JAMA Network Open Medicine-General Medicine

CiteScore

16.00

自引率

2.90%

发文量

2126

审稿时长

16 weeks

期刊介绍： JAMA Network Open, a member of the esteemed JAMA Network, stands as an international, peer-reviewed, open-access general medical journal.The publication is dedicated to disseminating research across various health disciplines and countries, encompassing clinical care, innovation in health care, health policy, and global health. JAMA Network Open caters to clinicians, investigators, and policymakers, providing a platform for valuable insights and advancements in the medical field. As part of the JAMA Network, a consortium of peer-reviewed general medical and specialty publications, JAMA Network Open contributes to the collective knowledge and understanding within the medical community.