老年综合评估结合医联体分级诊疗在高龄糖尿病中的应用

胥娟; 胡心仪; 殷泉忠; 顾一航

doi:10.16766/j.cnki.issn.1674-4152.002943

老年综合评估结合医联体分级诊疗在高龄糖尿病中的应用

doi: 10.16766/j.cnki.issn.1674-4152.002943

1.
南通大学附属江阴医院内分泌科, 江苏江阴 214400
2.
南通大学研究生院, 江苏南通 226000
3.
南通大学附属江阴医院全科医学科, 江苏江阴 214400

基金项目:

徐州医科大学附属医院发展基金资助项目 XYFY2020006

江苏大学临床医学科技发展基金研究项目 JLY2021074

江苏省卫健委临床适宜技术推广应用单位项目 LSD2022007

江苏省卫健委临床技术应用研究单位项目 LD2022005

详细信息

通讯作者:
顾一航，E-mail: guyihang88@163.com

中图分类号: R587.1 R197.1
计量
- 文章访问数: 147
- HTML全文浏览量: 50
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2022-10-11
- 网络出版日期: 2023-05-31

Application of comprehensive geriatric assessment combined with medical association grading diagnosis and treatment in senile diabetes patients

1.
Department of Endocrinology, Jiangyin Hospital, Nantong University, Jiangyin, Jiangsu 214400, China

摘要

摘要: 目的研究老年综合评估技术（CGA）结合医联体分级诊疗在筛查高龄糖尿病危险因素和对其综合干预的效果。方法选取2018年1月—2022年8月在江阴市人民医院医疗集团就诊的624例老年患者，按是否高龄分别调查糖尿病发病率。对319例高龄老人使用CGA分析非糖尿病和糖尿病组的各项指标差异，对各影响因素采用二元logistic回归分析。选取40例高龄糖尿病患者，使用随机数字表法分成研究组和对照组各20例，研究组采用医联体分级诊疗模式管理，对照组采用传统模式管理，3个月后比较2组患者的各项指标。结果高龄人群的糖尿病患病率(37.6%, 120/319)高于非高龄者(29.8%，91/305;χ²=4.218, P=0.040）；高龄糖尿病组与非糖尿病组有23项指标差异有统计学意义（P＜0.05）；高龄糖尿病的影响因素有文化程度（OR=0.322）、吸烟史（OR=4.398）、糖尿病家族史（OR=7.714）、糖化血红蛋白（OR=16.609）、BMI（OR=1.200）、共病指数（OR=1.240）、用药数量（OR=1.259）、认知功能缺陷（OR=7.594）、睡眠障碍（OR=4.101）、抑郁（OR=4.745）、明显衰弱（OR=8.200）和便秘（OR=4.003）。高龄糖尿病患者经医联体分级诊疗模式对可控因素有效干预3个月后，各项血糖指标及不良反应发生率均显著降低（P＜0.05）。结论 CGA结合医联体分级诊疗模式可以更有效地管理高龄糖尿病。
- 老年综合评估 /
- 医联体分级诊疗 /
- 高龄患者 /
- 糖尿病
Abstract: Objective To study the application value of comprehensive geriatric assessment (CGA) combined with medical association grading diagnosis and treatment in screening the risk factors and comprehensive intervention effect of senile diabetes patients. Methods A total of 624 elderly patients who received medical treatment in Jiangyin People's Hospital Medical Group from January 2018 to August 2022 were selected, the incidence of diabetes was investigated according to whether they were of advanced age. CGA was used to analyze the differences of various indicators between non-diabetes and diabetes groups in 319 elderly people, and binary logistic regression analysis was used to analyze the influencing factors. Then, 40 elderly patients with diabetes were selected and randomly divided into study group and control group, with 20 patients in each group. The study group was managed using a hierarchical diagnosis and treatment model in a medical consortium, while the control group was managed using traditional methods. After 3 months, various indicators of the two groups of patients were compared. Results The prevalence of diabetes in the elderly (37.6%, 120/319) was significantly higher than that in the non-elderly (29.8%, 91/305; χ²=4.218, P=0.040). The elderly diabetes group and the non-diabetes group had statistically significant differences in 23 indicators (P < 0.05). The degree of education (OR=0.322), smoking history (OR=4.398), family history of diabetes (OR=7.714), HbA1c (OR=16.609), BMI (OR=1.200), comorbidity index (OR=1.240), number of drugs taken (OR=1.259), cognitive dysfunction (OR=7.594), sleep disorder (OR=4.101), depression (OR=4.745), apparent weakness (OR=8.200), and constipation (OR=4.003) were the risk factors for senile diabetes. After 3 months of effective intervention on controllable factors, the various blood glucose indicators and the incidence of adverse reactions were all significantly reduced in the senile diabetes patients (P < 0.05). Conclusion CGA combined with medical association-general practice grading diagnosis and treatment mode can manage senile diabetes more effectively.
- Comprehensive geriatric assessment /
- Medical association grading diagnosis and treatment /
- Senility /
- Diabetes

HTML全文

表 1 高龄糖尿病影响因素的单因素分析

Table 1. Univariate analysis of influencing factors of elderly diabetes

变量	类别	非糖尿病组(n=199)	糖尿病组(n=120)	统计量	P值	变量	类别	非糖尿病组(n=199)	糖尿病组(n=120)	统计量	P值
文化程度[例(%)]	小学及以下	103(51.8)	78(65.0)	5.347^b	0.021	焦虑[例(%)]	无	134(67.3)	55(45.8)	14.336^b	<0.001
	初中及以上	96(48.2)	42(35.0)				有	65(32.7)	65(54.2)
婚姻状况[例(%)]	丧偶	26(13.1)	28(23.3)	5.613^b	0.018	营养风险[例(%)]	无	141(70.9)	67(55.8)	7.444^b	0.006
	已婚	173(86.9)	92(76.7)				有	58(29.1)	53(44.2)
吸烟史[例(%)]	无	160(80.4)	79(65.8)	8.456^b	0.004	衰弱[例(%)]	无衰弱	120(60.3)	43(35.8)^a	17.951^b	< 0.001
	有	39(19.6)	41(34.2)				衰弱前期	28(14.1)	28(23.3)^a
糖尿病家族史[例(%)]	无	180(90.5)	52(43.3)	83.793^b	< 0.001		明显衰弱	51(25.6)	49(40.8)^a
	有	19(9.5)	68(56.7)			社会参与功能[例(%)]	中重度降低(0分)	65(32.7)	47(39.2)	7.022^b	0.030
BMI		23.02±3.34	24.38±2.82	-3.710^c	< 0.001		轻度降低(1分)	61(30.7)	46(38.3)
CCI		5.10±1.60	7.44±2.99	-9.092^c	< 0.001		完好(2分)	73(36.7)	27(22.5)a
服用药物数量		4.26±2.64	6.73±2.86	-7.842^c	< 0.001	睡眠障碍[例(%)]	无	90(45.2)	36(30.0)	7.262^b	0.007
日常生活能力[例(%)]	完全依赖(0分)	29(14.6)	47(39.2)	31.177^b	< 0.001		有	109(54.8)	84(70.0)
	协助(1分)	60(30.2)	39(32.5)^a			便秘	无	114(57.3)	34(28.3)	25.232^b	< 0.001
	自理(2分)	110(55.3)	34(28.3)^a				有	85(42.7)	86(71.7)
视力障碍[例(%)]	无	112(56.3)	41(34.2)	14.669^b	< 0.001	糖化血红蛋白(x±s，%)		5.89±0.38	7.63±1.81	-13.110^c	< 0.001
	有	87(43.7)	79(65.8)			空腹血糖(x±s，mmol/L)		5.23±1.13	7.39±2.72	-9.880^c	< 0.001
认知功能缺陷[例(%)]	无	180(90.5)	95(79.2)	8.018^b	0.005	总胆固醇(x±s，mmol/L)		4.18±1.18	4.63±1.52	-2.914^c	0.004
	有	19(9.5)	25(20.8)			高密度脂蛋白(x±s，mmol/L)		1.34±0.46	1.16±0.36	3.609^c	< 0.001
抑郁[例(%)]	无	125(62.8)	35(29.2)	33.901^b	< 0.001	低密度脂蛋白(x±s，mmol/L)		2.78±0.86	3.31±1.04	-4.854^c	< 0.001
	有	74(37.2)	85(70.8)			甘油三酯(x±s，mmol/L)		1.33±0.76	1.56±0.93	-2.342^c	0.020
注：与非糖尿病组比较，^aP<0.05；^b为χ²值，^c为t值。

下载: 导出CSV

表 2 高龄糖尿病影响因素的二元logistic回归分析赋值情况

Table 2. Assignment situation of binary logistic regression analysis of influencing factors of elderly diabetes

变量	赋值方法
文化程度	小学及以下=0，初中及以上=1
婚姻状况	丧偶=0，已婚=1
吸烟史、糖尿病家族史、视力障碍、认知功能缺陷、抑郁、焦虑、营养风险、睡眠障碍、便秘	无=0，有=1
日常生活能力	完全依赖=0，协助=1，自理=2
社会参与功能	中重度降低=0，轻度降低=1，完好=2
衰弱	无衰弱=0，衰弱前期=1，明显衰弱=2
BMI、CCI、服用药物数量、糖化血红蛋白、空腹血糖、总胆固醇、高密度脂蛋白、低密度脂蛋白、甘油三酯	以实际值赋值

下载: 导出CSV

表 3 高龄糖尿病影响因素的二元logistic回归分析

Table 3. Binary logistic regression analysis of influencing factors of elderly diabetes

变量	B	SE	Waldχ²	P值	OR(95%CI)
文化程度(初中及以上)	-1.132	0.493	5.279	0.022	0.322(0.123~0.847)
吸烟史(有)	1.481	0.535	7.666	0.006	4.398(1.541~12.547)
糖尿病家族史(有)	2.043	0.619	10.886	0.001	7.714(2.292~25.962)
BMI	0.183	0.081	5.113	0.024	1.200(1.025~1.406)
糖化血红蛋白	2.810	0.500	31.569	0.000	16.609(6.232~44.262)
共病指数	0.215	0.108	3.994	0.046	1.240(1.004~1.531)
服用药物数量	0.231	0.101	5.230	0.022	1.259(1.034~1.534)
认知功能缺陷(有)	2.027	0.756	7.199	0.007	7.594(1.727~33.394)
睡眠障碍(有)	1.411	0.528	7.138	0.008	4.101(1.456~11.550)
抑郁(有)	1.557	0.518	9.021	0.003	4.745(1.718~13.107)
明显衰弱	2.104	0.617	11.617	0.001	8.200(2.445~27.500)
便秘(有)	1.387	0.534	6.738	0.009	4.003(1.405~11.408)

下载: 导出CSV

表 4 医联体分级诊疗对各影响因素干预效果前后对比

Table 4. Comparison of intervention effects of medical association grading diagnosis and treatment on various influencing factors before and after intervention

组别	例数	BMI(x±s)		CCI(x±s)		睡眠障碍[例(%)]		抑郁[例(%)]		便秘[例(%)]
组别	例数	干预前	干预后	干预前	干预后	干预前	干预后	干预前	干预后	干预前	干预后
对照组	20	24.91±2.47	24.13±2.65	6.60±2.92	6.32±2.14	14(70.0)	11(55.0)	14(70.0)	7(35.0)^b	16(80.0)	12(60.0)
研究组	20	24.78±3.11	23.02±2.14^b	6.70±2.62	5.04±2.32^b	14(70.0)	4(20.0)^b	13(65.0)	4(20.0)^b	14(70.0)	7(35.0)^b
统计量		0.140^a	1.457^a	-0.114^a	1.813^a	0.000^c	5.226^c	0.114^c	1.128^c	0.533^c	2.506^c
P值		0.889	0.153	0.910	0.0775	1.000	0.022	0.736	0.288	0.465	0.113
注：与同组干预前比较，^bP＜0.05；^a为t值，^c为χ²值。

下载: 导出CSV

表 5 2组高龄糖尿病患者治疗前后糖代谢水平比较(x±s)

Table 5. Comparison of glucose metabolism level between the two groups before and after treatment (x±s)

组别	例数	空腹血糖(mmol/L)		餐后2 h血糖(mmol/L)		糖化血红蛋白(%)
组别	例数	治疗前	治疗后	治疗前	治疗后	治疗前	治疗后
对照组	20	7.41±2.17	6.78±2.26	13.17±2.77	10.83±2.07^a	7.11±1.22	6.25±1.04^a
研究组	20	7.31±2.47	5.41±1.92^a	13.58±2.84	9.03±1.82^a	7.28±1.47	5.87±1.27^a
t值		0.145	2.066	0.462	2.921	-0.399	1.035
P值		0.886	0.045	0.646	0.006	0.692	0.307
注：与同组治疗前比较，^aP＜0.05。

下载: 导出CSV

表 6 2组高龄糖尿病患者不良反应发生情况比较[例(%)]

Table 6. Comparison of adverse reactions between the two groups of elderly diabetic patients

组别	例数	低血糖反应	胃肠道反应	肝功酶指标升高	总发生
对照组	20	3(15.0)	5(25.0)	3(15.0)	11(55.0)
研究组	20	1(5.0)	2(10.0)	1(5.0)	4(20.0)
χ²值		1.111	0.693	1.111	5.226
P值		0.292	0.405	0.292	0.022

下载: 导出CSV

参考文献(16)

[1]	United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights[Internet]. (2019-08-21)[2022-09-28]. https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html.
[2]	BORNSTEIN S R, RUBINO F, KHUNTI K, et al. Practical recommendations for the management of diabetes in patients with COVID-19[J]. Lancet Diabetes Endocrinol, 2020, 8(6): 546-550. doi: 10.1016/S2213-8587(20)30152-2
[3]	亓倩倩, 辛红菊, 周洲薇, 等. 老年综合征对老年2型糖尿病患者的躯体功能和跌倒风险的影响[J]. 中华全科医学, 2022, 20(3): 424-427, 502. doi: 10.16766/j.cnki.issn.1674-4152.002368 QI Q Q, XIN H J, ZHOU Z W, et al. Effect of senile syndrome on physical function and risk of falls in elderly patients with type 2 diabetes mellitus[J]. Chinese Journal of General Practice, 2022, 20(3): 424-427, 502. doi: 10.16766/j.cnki.issn.1674-4152.002368
[4]	张玉彩, 谢春, 香利强, 等. 紧密型医联体模式下全科医学科建设探索[J]. 中华全科医学, 2019, 17(4): 661-665. doi: 10.16766/j.cnki.issn.1674-4152.000761 ZHANG Y C, XIE C, XIANG L Q, et al. Exploration of the construction of general practice department under the mode of close medical association[J]. Chinese Journal of General Practice, 2019, 17(4): 661-665. doi: 10.16766/j.cnki.issn.1674-4152.000761
[5]	BEARD J R, OFFICER A M, CASSELS A K. The world report on ageing and health[J]. Gerontologist, 2016, 56(Suppl 2): S163-166. doi: 10.1093/geront/gnw037
[6]	SUETOMI R, OHTA Y, AKIYAMA M, et al. Adrenomedullin has a cytoprotective role against endoplasmic reticulum stress for pancreatic β-cells in autocrine and paracrine manners[J]. J Diabetes Investig, 2020, 11(4): 823-833. doi: 10.1111/jdi.13218
[7]	DANY F, DEWI R M, TJANDRARINI D H, et al. Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of indonesian basic health research 2018 among normoglycemic and prediabetic individuals[J]. BMC Public Health, 2020, 20: 1509. doi: 10.1186/s12889-020-09592-7
[8]	ZYWNO H, BZDEGA W, KOLAKOWSKI A, et al. The influence of coumestrol on sphingolipid signaling pathway and insulin resistance development in primary rat hepatocytes[J]. Biomolecules, 2021, 11(2): 268. doi: 10.3390/biom11020268
[9]	TOSUR M, VIAU-COLINDRES J, ASTUDILLO M, et al. Medication-induced hyperglycemia: pediatric perspective[J]. BMJ Open Diabetes Res Care, 2020, 8(1): e000801. DOI: 10.1136/bmjdrc-2019-000801.
[10]	GRAHEK I, SHENHAV A, MUSSLICK S, et al. Motivation and cognitive control in depression[J]. Neurosci Biobehav Rev, 2019, 102: 371-381. doi: 10.1016/j.neubiorev.2019.04.011
[11]	段卉妍, 黄文雅, 黄晓飞. 失眠与2型糖尿病相关性的研究进展[J]. 中国糖尿病杂志, 2022, 30(1): 70-72. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTL202201007.htm DUAN H Y, HUANG W Y, HUANG X F. Research progress on correlation between insomnia and type 2 diabetes mellitus[J]. Chinese Journal of Diabetes, 2022, 30(1): 70-72. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTL202201007.htm
[12]	LIU H T, GAO Y. Efficacy of short-term intensive treatment with insulin pump to improve islet β-cell function in newly diagnosed type 2 diabetes via inhibition of oxidative stress[J]. Exp Ther Med, 2019, 18(3): 2293-2298. http://pubmed.ncbi.nlm.nih.gov/31452715/
[13]	朱文华, 方力争, 臧国尧, 等. 医疗服务联合供给模式下的全科医学分级诊疗探索[J]. 中华医院管理杂志, 2018, 34(3): 181-184. ZHU W H, FANG L Z, ZANG G Y, et al. Exploration of graded diagnosis and treatment of general practice under the mode of combined medical service supply[J]. Chinese Journal of Hospital Administration, 2018, 34(3): 181-184.
[14]	FENG T Y, LI K, ZHENG P, et al. Weighted gene coexpression network analysis identified microRNA coexpression modules and related pathways in type 2 diabetes mellitus[J]. Oxid Med Cell Longev, 2019, 2019: 9567641. DOI: 10.1155/2019/9567641.
[15]	BERGMANS R S, RAPP A, KELLY K M, et al. Understanding the relationship between type 2 diabetes and depression: lessons from genetically informative study designs[J]. Diabet Med, 2021, 38(2): e14399. DOI: 10.1111/dme.14399.
[16]	WHITAKER K M, LUTSEY P L, OGILVIE R P, et al. Associations between polysomnography and actigraphy-based sleep indices and glycemic control among those with and without type 2 diabetes: the multi-wthnic study of atherosclerosis[J]. Sleep, 2018, 41(11): zsy172. DOI: 10.1001/jama.2016.19720.