埃塞俄比亚西北部Debre Markos转诊医院II型慢性糖尿病并发症发生时间的多状态模型分析

Muluye Getie Ayaneh, Ashagrie Sharew Iyasu
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摘要

引言:糖尿病是一种以血糖水平升高为特征的慢性非传染性疾病。本研究的目的是共同模拟糖尿病患者在一系列临床状态下的转变,并评估每种状态与不同患者特征之间的关系。方法:对524名年龄在18岁或以上的II型糖尿病患者进行了一项基于医院的回顾性研究,这些患者在2005年1月1日至2017年12月31日期间接受了药物治疗。考虑了具有不同假设的多状态模型,以探讨不同预后因素对II型糖尿病患者过渡强度的影响。结果:在7.4年的中位随访时间内(四分位数范围=4.01),54.8%的糖尿病患者出现微血管或大血管并发症,10.5%的患者同时出现微血管和大血管并发症。16.66%的糖尿病患者死亡。通过使用似然比检验来评估马尔可夫假设表明,马尔可夫假设不仅仅适用于转换。患者从大血管状态到死亡状态的转变率受患者居住地(P=0.05)和诊断时年龄(P=0.01)的影响,基线血糖水平(P=0.03)和基线血肌酐水平(P=0.04)。结论:半马尔可夫模型与数据拟合良好,可作为分析糖尿病相关并发症或死亡时间的方便模型。
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Multistate Models for the Analysis of Time to Type II Chronic Diabetic Complications in Debre Markos Referral Hospital, Northwest Ethiopia
Introduction: Diabetes is a chronic, non-communicable disease characterized by elevated blood glucose levels. The purpose of this study was to jointly model the transition of diabetic patients in a series of clinical states and to assess the relationship between each state and different patient characteristics. Methods: A hospital-based retrospective study was conducted on 524 patients with type II diabetes, aged 18 years or older, who attended their medication between January 1, 2005, and December 31, 2017. Multistate models with different assumptions were considered to explore the effects of different prognostic factors on the transition intensity of type II diabetes mellitus patients. Results: During a median follow-up time of 7.4 years (Inter-Quartile Range=4.01), 54.8% of diabetic patients developed either microvascular or macrovascular complications, and 10.5% of them experienced both microand macrocomplications, and 16.66% of diabetes patients died. The assumption Markov was assessed by using the likelihood ratio test showed that Markov assumption was not held just for the transition. The transition rate of patients from the macrovascular state to the death state was affected by the residence of the patients (P=0.05) and age at diagnosis (p=0.01). The transition rates of patients with microvascular complications to death were significantly affected by baseline triglyceride level (P<0.001), age at first diagnosis (P=0.01), baseline glucose level (P=0.03, and baseline serum creatinine level (P=0.04). Conclusion: The semi-Markov model fitted the data well and could be used as a convenient model for the analysis of time to diabetes-related complications or death.
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26
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12 weeks
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