Application value of prediction model established based on limb muscle strength status combined with clinical data for occurrence of deep vein thrombosis in acute stage of stroke

SHEN Xiaofang; JIN Jin; ZHANG Lan; YANG Lingli; XU Ji

doi:10.7619/jcmp.20221968

Journal of Clinical Medicine in Practice > 2023 > 27(8): 113-117. > DOI: 10.7619/jcmp.20221968

SHEN Xiaofang, JIN Jin, ZHANG Lan, YANG Lingli, XU Ji. Application value of prediction model established based on limb muscle strength status combined with clinical data for occurrence of deep vein thrombosis in acute stage of stroke[J]. Journal of Clinical Medicine in Practice, 2023, 27(8): 113-117. DOI: 10.7619/jcmp.20221968

Citation:

PDF (1473 KB)

Application value of prediction model established based on limb muscle strength status combined with clinical data for occurrence of deep vein thrombosis in acute stage of stroke

Department of Neurology, Suzhou Ninth People′s Hospital of Jiangsu Province, Suzhou, Jiangsu, 215200

More Information

Received Date: June 25, 2022
Revised Date: September 15, 2022
Available Online: May 10, 2023

Graphical Abstract

Abstract

Abstract

Objective
To explore the application value of the prediction model established based on limb muscle strength combined with clinical data in the occurrence of deep vein thrombosis (DVT) in acute stage of stroke.
Methods
Clinical data of 697 stroke patients were retrospectively analyzed, and they were randomly divided into modeling group (n=488) and validation group (n=209) according to a ratio of 7∶3, and the data of the two groups were analyzed for differences. The modeling group was divided into DVT group and non-DVT group according to whether patients had DVT in the acute stage. Multiple Logistic regression analysis was used to screen out the influencing factors of DVT in acute stage of stroke, and R software was used to obtain the prediction model expressed in line graph. Receiver operating characteristic (ROC) curve was used to evaluate the model differentiation. The calibration degree of the model was evaluated by Bootstrap method (self-sampling method); the decision curve was used to evaluate the clinical effectiveness of the model.
Results
There was no significant difference in clinical data between the modeling group and the verification group (P>0.05). In the modeling group, 77 of 488 stroke patients developed DVT during acute hospitalization (DVT group), with an incidence of 15.78% (77/488). Logistic regression analysis showed that age, diabetes mellitus, dyslipidemia, Padua score, D-dimer and limb muscle strength were all influencing factors of DVT in acute stage of stroke (P < 0.05). The area under the curve of DVT risk in the modeling group was 0.890 (95%CI, 0.866 to 0.923), and was 0.851 (95%CI, 0.781 to 0.911) in the verification group, which indicated that the model was well differentiated; the average absolute error of deviation calibration curve of the modeling group and the verification group was 0.012 and 0.015, respectively, which indicated that the calibration degree of the prediction model was high. The threshold probability value in the decision curve was set at 33%, and the net clinical benefit for the modeling group and the validation group was 62% and 64%, respectively, which indicated that the prediction model was effective in clinic.
Conclusion
The prediction model established based on limb muscle strength status, age, diabetes and dyslipidemia, Padua score and D-dimer index has certain value in predicting the risk of DVT in acute stage of stroke.
- stroke,
- acute phase,
- limb muscle strength,
- deep vein thrombosis,
- predictive model

FullText(HTML)

References (16)

References

[1]	WRITING GROUP MEMBERS, MOZAFFARIAN D, BENJAMIN E J, et al. Executive summary: heart disease and stroke statistics: 2016 update: a report from the American heart association[J]. Circulation, 2016, 133(4): 447-454. doi: 10.1161/CIR.0000000000000366
[2]	POWERS W J, RABINSTEIN A A, ACKERSON T, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American heart association/American stroke association[J]. Stroke, 2019, 50(12): e344-e418.
[3]	DIZON M A M, DE LEON J M. Effectiveness of initiating deep vein thrombosis prophylaxis in patients with stroke: an integrative review[J]. J Neurosci Nurs, 2018, 50(5): 308-312. doi: 10.1097/JNN.0000000000000385
[4]	中华医学会神经病学分会, 中华医学会神经病学分会脑血管病学组. 中国各类主要脑血管病诊断要点2019[J]. 中华神经科杂志, 2019, 52(9): 710-715.
[5]	LI Z B, WANG M R, ZHANG L W, et al. Neuronavigation-guided corticospinal tract mapping in brainstem tumor surgery: better preservation of motor function[J]. World Neurosurg, 2018, 116: e291-e297. doi: 10.1016/j.wneu.2018.04.189
[6]	袁光华. 超声诊断基础与临床检查规范[M]. 北京: 科学技术文献出版社, 2005: 243-252.
[7]	WANG H J, ROSENDAAL F R, CUSHMAN M, et al. Procoagulant factor levels and risk of venous thrombosis in the elderly[J]. J Thromb Haemost, 2021, 19(1): 186-193. doi: 10.1111/jth.15127
[8]	KARASU A, ŠRÁMEK A, ROSENDAAL F R, et al. Aging of the venous valves as a new risk factor for venous thrombosis in the elderly: the BATAVIA study[J]. J Thromb Haemost, 2018, 16(1): 96-103. doi: 10.1111/jth.13880
[9]	CHUNG W S, LIN C L, KAO C H. Diabetes increases the risk of deep-vein thrombosis and pulmonary embolism. A population-based cohort study[J]. Thromb Haemost, 2015, 114(4): 812-818.
[10]	GREGSON J, KAPTOGE S, BOLTON T, et al. Cardiovascular risk factors associated with venous thromboembolism[J]. JAMA Cardiol, 2019, 4(2): 163. doi: 10.1001/jamacardio.2018.4537
[11]	ARPAIA G G, CALEFFI A, MARANO G, et al. Padua prediction score and IMPROVE score do predict in-hospital mortality in Internal Medicine patients[J]. Intern Emerg Med, 2020, 15(6): 997-1003. doi: 10.1007/s11739-019-02264-4
[12]	内科住院患者静脉血栓栓塞症预防中国专家建议写作组, 中华医学会呼吸病学分会, 中华医学会老年医学分会, 等. 内科住院患者静脉血栓栓塞症预防中国专家建议(2015)[J]. 中华结核和呼吸杂志, 2015, 38(7): 484-491. doi: 10.3760/cma.j.issn.1001-0939.2015.07.002
[13]	曹闻亚, 常红. Padua评分联合血清D-二聚体早期预测急性期缺血性脑卒中病人深静脉血栓形成的分析[J]. 护理研究, 2019, 33(16): 2841-2844. doi: 10.12102/j.issn.1009-6493.2019.16.025
[14]	王悦曦, 解佳妮, 王晓溶, 等. 肌肉链理论在脑卒中康复治疗中的研究进展[J]. 实用临床医药杂志, 2020, 24(21): 128-132. doi: 10.7619/jcmp.202021036
[15]	柯绪芬, 张晗. 脑梗死患者卒中后抑郁风险预测列线图模型的建立与验证[J]. 实用心脑肺血管病杂志, 2021, 29(8): 34-40.
[16]	荆莉, 沈延艳. 急性脑梗死患者机械取栓后颅内出血的风险列线图预测模型[J]. 中国医师杂志, 2021, 23(3): 366-369, 374.

[1]	WANG Jingjing, WU Su, CAI Guilan, ZHEN Yong. Construction and application of a risk prediction model for lower limb deep vein thrombosis in patients with aneurysmal subarachnoid hemorrhage[J]. Journal of Clinical Medicine in Practice, 2024, 28(19): 95-99, 104. DOI: 10.7619/jcmp.20241629
[2]	ZHU Huayuan, ZHANG Yajuan, SUN Qian, HUANG Yan, YANG Bingyin. Establishment of a Nomogram prediction model for chronic constipation patients based on body position exercise training[J]. Journal of Clinical Medicine in Practice, 2024, 28(17): 109-113. DOI: 10.7619/jcmp.20241488
[3]	LIU Haijia, CHEN Ming, LI Yanzhen. Analysis in risk factors of postoperative recurrence of patients with refractory epilepsy and establishment of a risk prediction model[J]. Journal of Clinical Medicine in Practice, 2024, 28(8): 7-11. DOI: 10.7619/jcmp.20240080
[4]	ZHANG Jiangnan, LI Ronghua, ZHOU Hongmei, XU Minyi, CAI Liangyu. Establishment of a predictive model for the risk of deep vein thrombosis after orthopedic surgery in the lower extremities and its verification[J]. Journal of Clinical Medicine in Practice, 2023, 27(23): 73-78. DOI: 10.7619/jcmp.20231971
[5]	SONG Feng, FANG Canjun, HU Jing, CHEN Yi. Risk factors of preoperative deep venous thrombosis in young and middle-aged patients with hip fracture and construction of a Nomogram predictive model[J]. Journal of Clinical Medicine in Practice, 2023, 27(12): 32-37. DOI: 10.7619/jcmp.20230579
[6]	REN Yuanyuan, DING Fu, XU Zhihui, FU Rongjuan. Research status of fall risk prediction models for inpatients[J]. Journal of Clinical Medicine in Practice, 2023, 27(8): 144-148. DOI: 10.7619/jcmp.20230036
[7]	ZHANG Wei, LI Yang. Value of color Doppler ultrasound combined with coagulation indexes detection in predicting deep venous thrombosis of lower limbs after fracture[J]. Journal of Clinical Medicine in Practice, 2022, 26(24): 20-23. DOI: 10.7619/jcmp.20222678
[8]	SHAO Chao, GAO Wencan, ZHANG Jun, SHEN Bin, XU Jianwei, YIN Kai. Establishment of prediction model of deep venous thrombosis formation of lower extremity after general surgery[J]. Journal of Clinical Medicine in Practice, 2022, 26(22): 12-15, 19. DOI: 10.7619/jcmp.20221634
[9]	ZANG Peng, LI Jinxia, LI Ji, ZHANG Huizhi, GU Yan. Experimental study of Huangqi Guizhi Wuwu Decoctionin prevention and treatment of rats model with deep venous thrombosis[J]. Journal of Clinical Medicine in Practice, 2022, 26(13): 12-17. DOI: 10.7619/jcmp.20220385
[10]	DONG Yanping, WANG Jiemin, WANG Yujin, REN Yali, DONG Yucheng, YONG Yanjun, SU Xuandi, WANG Jixiang, SU Nan, WANG Fuli, XIA Duosheng. Construction and evaluation of Nomogram prediction model for postoperative recurrence of pterygium[J]. Journal of Clinical Medicine in Practice, 2022, 26(7): 52-56. DOI: 10.7619/jcmp.20214610

Cited By

Cited by

Periodical cited type(6)

1.	黄晨岚. 25羟维生素D、超敏c反应蛋白、估计肾小球滤过率与脑萎缩相关性的临床研究. 黑龙江医药. 2024(01): 44-47 .
2.	桂千，侯晓夏，徐勤荣，冯红选，吴冠会，卢艳丽，程庆璋. 脑微出血与非关键部位脑梗死患者认知功能的相关性. 实用临床医药杂志. 2023(13): 43-47+52 . 本站查看
3.	蹇秋枫，徐荣华，姚倩，周媛媛. 中国老年脑卒中患者认知障碍患病率和影响因素的Meta分析. 中国全科医学. 2023(32): 4070-4079+4088 .
4.	李梦洁，肖佳男，刘文娟，练芮含，张丽秀. 外周血1, 25-(OH)_2D_3、神经元特异性烯醇化酶、神经胶质纤维酸性蛋白在小儿复杂性惊厥中的表达意义. 中国儿童保健杂志. 2023(11): 1254-1259 .
5.	张丽. 脑梗死后认知功能损害与神经功能恢复及生活质量的相关性研究. 临床研究. 2022(08): 77-80 .
6.	雷霄明，薛孟周，夏彬，李晶，郭平平. 血清25-羟维生素D和脂联素与脑白质损伤患者认知功能的相关性研究. 实用临床医药杂志. 2022(19): 33-37 . 本站查看

Other cited types(1)

Get Citation

PDF

XML

Article views (188) PDF downloads (12) Cited by(7)

Turn off MathJax

Article Contents

Abstract

References

Application value of prediction model established based on limb muscle strength status combined with clinical data for occurrence of deep vein thrombosis in acute stage of stroke