1.Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wenqi FAN ; Chao DENG ; Ruoyao XU ; Zhenqi LIU ; Richard David LESLIE ; Zhiguang ZHOU ; Xia LI
Diabetes & Metabolism Journal 2025;49(2):235-251
Background:
Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM).
Methods:
We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently.
Results:
Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy.
Conclusion
AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.
3.Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wenqi FAN ; Chao DENG ; Ruoyao XU ; Zhenqi LIU ; Richard David LESLIE ; Zhiguang ZHOU ; Xia LI
Diabetes & Metabolism Journal 2025;49(2):235-251
Background:
Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM).
Methods:
We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently.
Results:
Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy.
Conclusion
AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.
5.Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wenqi FAN ; Chao DENG ; Ruoyao XU ; Zhenqi LIU ; Richard David LESLIE ; Zhiguang ZHOU ; Xia LI
Diabetes & Metabolism Journal 2025;49(2):235-251
Background:
Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM).
Methods:
We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently.
Results:
Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy.
Conclusion
AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.
7.Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Wenqi FAN ; Chao DENG ; Ruoyao XU ; Zhenqi LIU ; Richard David LESLIE ; Zhiguang ZHOU ; Xia LI
Diabetes & Metabolism Journal 2025;49(2):235-251
Background:
Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM).
Methods:
We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently.
Results:
Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy.
Conclusion
AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.
9.A machine learning-based trajectory predictive modeling method for manual acupuncture manipulation.
Jian KANG ; Li LI ; Shu WANG ; Xiaonong FAN ; Jie CHEN ; Jinniu LI ; Wenqi ZHANG ; Yuhe WEI ; Ziyi CHEN ; Jingqi YANG ; Jingwen YANG ; Chong SU
Chinese Acupuncture & Moxibustion 2025;45(9):1221-1232
OBJECTIVE:
To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction.
METHODS:
Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial.
RESULTS:
The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation.
CONCLUSION
The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans
;
Acupuncture Therapy/instrumentation*
;
Machine Learning
;
Adult
;
Male
;
Female
10.Efficacy and prognosis after radiotherapy in pediatric atypical teratoid / rhabdoid tumors
Wenfang TANG ; Wenqi FAN ; Yiyuan LI ; Renhua ZHOU ; Dongqing LU ; Qing ZHOU ; Mawei JIANG
Chinese Journal of Radiation Oncology 2024;33(6):511-517
Objective:To analyze the clinical efficacy and prognostic factors of comprehensive treatment for atypical teratoid / rhabdomyoma tumor (AT/RT).Methods:Clinical data of children diagnosed with AT/RT who underwent radiotherapy in Department of Oncology of Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine from November 2012 to September 2022 were retrospectively analyzed. Pearson Chi-square test or continuous-corrected Chi-square test or Fisher's exact probability method were used for inter-group comparison of categorical variables. Independent sample t-test or Wilcoxon rank-sum test were used for inter-group comparison of continuous variables. Kaplan-Meier method was used to calculate the 1-year and 2-year overall survival (OS) rate and progression free survival (PFS) rate. Univariate and multivariate Cox regression analyses were employed to determine relevant prognostic factors. Results:A total of 45 patients were included, with a male/female ratio of 1.65:1, including 27 children aged ≥3 years old. All patients received surgery and radiotherapy in which 39 patients received chemotherapy, 41 craniospinal irradiation (CSI), and 4 whole brain or focal radiation therapy. The median follow-up was 28 (13.5, 49) months. A total of 14 patients died after comprehensive treatment. The 1-year OS rate was 80.0% and the PFS rate was 71.1%. The 2-year OS rate was 75.5% and the PFS rate was 65.7%. Survival prognostic analysis showed negative imaging assessment after radiotherapy ( HR=0.087, 95% CI: 0.011-0.697, P=0.022) was a favorable factor for PFS. The primary tumor<4.8 cm ( HR=0.221, 95% CI: 0.052-0.935, P=0.040) and CSI ( HR=0.085, 95% CI: 0.011-0.651, P=0.018) were favorable factors for OS. In subgroup analysis, CSI also improved OS in children aged ≥3 years ( HR=0.014, 95% CI: 0-0.470, P=0.017), but there was no significant difference in PFS. In children without cerebrospinal fluid dissemination, negative radiographic results after radiotherapy ( HR=0.066, 95% CI: 0.009-0.481, P=0.007; HR=0.076, 95% CI: 0.008-0.695, P=0.024, respectively) and CSI (HR=0.105, 95% CI: 0.012-0.937, P=0.044; HR=0.054, 95% CI: 0.005-0.629, P=0.020, respectively) were favorable factors for PFS and OS in children, and the primary tumor<4.8 cm also suggested a longer OS ( HR=0.094, 95% CI: 0.013-0.690, P=0.020). Conclusions:Comprehensive treatment including radiotherapy improves clinical prognosis of children with AT/RT. Our study shows that negative imaging results after radiotherapy are associated with PFS improvement. The primary tumor<4.8 cm and CSI are favorable factors for OS. CSI is also a significantly positive prognostic factor in children aged ≥3 years and those without cerebrospinal fluid dissemination.

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