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护理学报 ›› 2021, Vol. 28 ›› Issue (22): 12-17.doi: 10.16460/j.issn1008-9969.2021.22.012

• 研究生园地 • 上一篇    下一篇

2型糖尿病患者酮症酸中毒风险预测模型的构建与验证

李雪嘉1, 杨开庆2   

  1. 1.大理大学 护理学院,云南 大理 671000;
    2.大理州人民医院 工会,云南 大理 671000
  • 收稿日期:2021-06-25 出版日期:2021-11-25 发布日期:2021-12-13
  • 通讯作者: 杨开庆(1964-),女,云南剑川人,本科学历,主任护师,硕士研究生导师。E-mail:1275491867@qq.com
  • 作者简介:李雪嘉(1993-),女,四川泸州人,本科学历,硕士研究生在读,护师。

Establishment and Verification of Diabetic Ketoacidosis Risk Prediction Model for Type 2 Diabetic Patients

LI Xue-jia1, YANG Kai-qing2   

  1. 1. School of Nursing, Dali University, Dali 671000, China;
    2. Labor Union, Dali Bai Autonomous Prefecture People's Hospital,Dali 671000, China
  • Received:2021-06-25 Online:2021-11-25 Published:2021-12-13

摘要: 目的 通过2型糖尿病酮症酸中毒风险预测模型的构建与验证,早期识别2型糖尿病酮症酸中毒的高危人群,进行针对性的预防及控制,有效降低2型糖尿病酮症酸中毒的发生率。方法 选取大理州人民医院2019年1月—2020年12月住院治疗的2型糖尿病患者943例,采用Logistic回归分析筛选出独立危险因素并构建风险预测模型。选取2020年3月—2020年12月大理大学第一附属医院住院治疗的2型糖尿病患者393例对模型进行验证。结果 最终急性感染(OR=8.210)、应激事件(OR=129.267)、随机静脉血糖(OR=1.986)、糖化血红蛋白(OR=1.421)4个因素构建出风险预测模型。建模组受试者操作曲线下面积为0.994,最大约登指数为0.929,灵敏度为0.954,特异度为0.975,Hosmer-Lemeshow检验P=0.975。验证组受试者操作曲线下面积为0.901,最大约登指数为0.754,灵敏度为0.889,特异度为0.865。结论 急性感染、应激事件、随机静脉血糖、糖化血红蛋白是2型糖尿病酮症酸中毒发生的独立危险因素,据此构建的风险预测模型预测性能良好。

关键词: 2型糖尿病, 酮症酸中毒, 危险因素, 预测模型

Abstract: Objective To establish and verify the risk prediction model of diabetic ketoacidosis, to provide reference for the early identification, prevention and control of the disease. Methods A total of 943 patients with type 2 diabetes hospitalized in Dali People's Hospital from January 2019 to December 2020 were selected by convenience sampling method. Logistic regression analysis was used to screen out independent risk factors to establish a risk prediction model. A total of 393 patients with type 2 diabetes hospitalized in the First Affiliated Hospital of Dali University from March 2020 to December 2020 were selected to verify the model. Results Four factors of acute infection(OR=8.210, stress events(OR=129.267), random venous blood glucose(OR=1.986), and HbA1c(OR=1.421)were included to construct a risk prediction model. The area under the ROC curve of the modeling group was 0.994; the maximum value of Youden index 0.929; the sensitivity 0.954; the specificity 0.975, and Hosmer-Lemeshow test P=0.975. The test results of the validation group indicated that the area under the ROC curve was 0.901 and the maximum value of Youden index, the sensitivity and the specificity was 0.754, 0.889, and 0.865 respectively. Conclusion Acute infection, stress event, random venous blood glucose, and glycosylatedhemoglobin are independent risk factors for type 2 diabetic ketoacid, and the prediction model constructed in this study shows good prediction efficiency.

Key words: Type 2 diabetes mellitus, ketoacidosis, risk factor, prediction model

中图分类号: 

  • R473.58
[1] 谭晓娟,王慧,张瑞.新诊断合并糖尿病酮症酸中毒的1型糖尿病和2型糖尿病患者临床特征比较[J]. 中国医药,2020,15(2):235-238.DOI:10.3760/j.issn.1673-4777.2020.02.018.
[2] Vellanki P, Umpierrez GE.Diabetic Ketoacidosis: a Common Debut of Diabetes Among Africanamericans with Type 2 Diabetes[J]. Endocrine Practice, 2017, 23(8):971-978. DOI:10.4158/EP161679.RA.
[3] Sato Y, Morita K, Okada A, et al.Factors Affecting In-Hospital Mortality of Diabetic Ketoacidosis Patients: A Retrospective Cohort Study[J]. Diabetes Res Clin Pract,2021,171:108588. DOI:10.1016/j.diabr es.2020.108.
[4] 邱俊霖,苏会璇,陈文,等.糖尿病酮症酸中毒的流行病学调查[J].广西医科大学学报,2017,34(9):1394-1397. DOI:10.16190/j.cnki.45-1211/r.2017.09.041.
[5] Wang ZH, Kihl-Selstam E, Eriksson JW.Ketoacidosis Occurs in Both Type 1 and Type 2 Diabetes-a Population-based Study from Northern Sweden[J]. Diabet Med, 2008, 25(7):867-870. DOI:10.1111/j.1464-5491.2008.02461.x
[6] 戴庆,何军,张凡,等.1型糖尿病与2型糖尿病患者并发酮症酸中毒的临床特点分析[J].现代医药卫生,2017,33(22):3440-3441. DOI:10.3969/j.issn.1009-5519.2017.22.023.
[7] Seth P, Kaur H, Kaur M. Clinical Profile of Diabetic Ketoacidosis: A Prospective Study in a Tertiary Care Hospital [J]. J Clin Diagn Res, 2015,9(6):OC01-OC04. DOI:10.7860/JCDR/2015/8586.5995.
[8] Wu XY, She DM, Wang F, et al.Clinical Profiles, Outcomes and Risk Factors Among Type 2 Diabetic Inpatients with Diabetic Ketoacidosis and Hyperglycemic Hyperosmolar State: A Hospital-based Analysis Over a 6-Year Period[J]. BMC Endocr Disord, 2020, 20(1):182.DOI:10.1186/s12902-020-00659-5.
[9] Shahid W, Khan F, Makda A, et al.Diabetic Ketoacidosis: Clinical Characteristics and Precipitating Factors[J]. Cureus, 2020, 12(10):e10792-e10792. DOI:10.7759/cureus.10792.
[10] 陈曹杰,徐驰.糖尿病酮症酸中毒病死率的相关因素分析 [J]. 中华内分泌外科杂志, 2017, 11(6):467-470. DOI:10.3760/cma.j.issn.1674-6090.2017.06.007.
[11] Wolfsdorf J, Glaser N, Sperling MA, et al.Diabetic Ketoacidosis in Infants, Children, and Adolescents: A Consensus Statement from the American Diabetes Association[J]. Diabetes Care, 2006,29(5):1150-1159. DOI:10.2337/dc06-9909.
[12] 贾香连. 压力应激导致血糖代谢紊乱的中枢神经环路机制[D]. 深圳:中国科学院大学, 2019.DOI:10.27822/d.cnki.gszxj.2019.000006.
[13] Goldstein DE,Little RR,Lorenz RA, et al.Tests of Glycemia in Diabetes[J]. Diabetes Care, 2004, 27(7):1761-1773. DOI:10.2337/diacare.27.7.1761.
[14] 倪平,袁丽.糖尿病足溃疡复发的影响因素研究进展[J].护理学报,2017,24(9):35-38.DOI:10.16460/j.issn1008-9969.2017.09.035.
[15] Casadei G, Filippini M, Brognara L. Glycated Hemoglobin (HbA1c) as a Biomarker for Diabetic Foot Peripheral Neuropathy[J]. Diseases (Basel, Switzerland), 2021, 9(1):16.DOI:10.3390/diseases9010016.
[16] 顾经宇,唐伟,武晓泓,等. 糖尿病患者发生酮体蓄积的预测[J]. 江苏医药, 2011, 37(9):1053-1055. DOI:10.19460/j.cnki.0253-3685.2011.09.023.
[17] 周吉,顾涛,庄丽英,等.2型糖尿病患者血β-羟丁酸水平与空腹血糖及糖化血红蛋白的关系[J]. 糖尿病新世界,2016,19(11):66-67. DOI:10.16658/j.cnki.1672-4062.2016.11.066.
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