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Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: A multi-institutional case-control study
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Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study Jing Cao , Shuya Li , Yufen Ma , Zhen Li , Ge Liu , Ying Liu , Jing Jiao , Chen Zhu , Baoyun Song , Jingfen Jin , Yilan Liu , Xianxiu Wen , Shouzhen Cheng , Xia Wan , Xinjuan Wu PII: DOI: Reference:
S0020-7489(20)30313-8 https://doi.org/10.1016/j.ijnurstu.2020.103825 NS 103825
To appear in:
International Journal of Nursing Studies
Received date: Revised date: Accepted date:
8 April 2020 16 July 2020 29 October 2020
Please cite this article as: Jing Cao , Shuya Li , Yufen Ma , Zhen Li , Ge Liu , Ying Liu , Jing Jiao , Chen Zhu , Baoyun Song , Jingfen Jin , Yilan Liu , Xianxiu Wen , Shouzhen Cheng , Xia Wan , Xinjuan Wu , Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study, International Journal of Nursing Studies (2020), doi: https://doi.org/10.1016/j.ijnurstu.2020.103825
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Risk factors associated with deep venous thrombosisin patients with different bed-rest durations: a multi-institutional case–control study Jing Caoa, Shuya Li (Co-Author)a, Yufen Maa, Zhen Lia, Ge Liua, Ying Liua, Jing Jiaoa, Chen Zhua, Baoyun Songb, Jingfen Jinc, Yilan Liud, Xianxiu Wene, Shouzhen Chengf, Xia Wang, Xinjuan Wu(Corresponding Author)a* a
Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China b Department of Nursing, Henan Provincial People’s Hospital, Zhengzhou, Henan, China c Department of Nursing, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China d Department of Nursing, Wuhan Union Hospital, Wuhan, Hubei, China e Department of Nursing, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China f Department of Nursing, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China g Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Beijing, China *Corresponding author: Xinjuan WU, RN, MSN Department of Nursing, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Ph:(010) +86 01069155820 Email: [email protected] First author: Jing CAO, RN, MSN Department of Nursing, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Email:[email protected] Co-Author: Shuya LI, RN, MSN Department of Emergency, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Email: [email protected]
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Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study Background: Deep vein thrombosis represents a threat to public health and a heavy economic burden to society, and often occurs as a complication or cause of death in bedridden patients. How to prevent deep vein thrombosis is a general concern in clinical practice. However, it remains uncertain whether the risk factors for deep vein thrombosis would be affected by different bed-rest durations. Solving this issue will be invaluable for the provision of more rational medical care to prevent deep vein thrombosis. Objective: To explore whether risk factors for deep vein thrombosis are affected by bed-rest durations and to identify different risk factors in groups with different bed-rest durations. Design: A retrospective multicenter case–control study. Settings and participants: This multicenter study was conducted in wards with high rates of bed rest in 25 general hospitals in China. Participants were bedridden patients from these wards. Methods: Bedridden patients were identified from the research database of bedridden patients’ major immobility complications. These data were collected from prospective descriptive studies by a standardized web-based online case report form. Cases were defined as bedridden patients who suffered deep vein thrombosis during hospitalization (n=186). Each case was matched with three controls, bedridden patients who did not suffer deep vein thrombosis in the same center with the same bed-rest duration (n=558). Descriptive statistics, univariate analysis, and multivariate conditional logistic regression models were employed. Results: Among 23,985 patients, the overall incidence of deep vein thrombosis during hospitalization was 1.0%. Multivariate analysis showed that for patients with bed-rest duration of 4 weeks or less, older age (odds ratio [OR] =1.027, 95% confidence interval [CI] 1.013–1.041) and being in a surgical department (OR=2.527, 95% CI 1.541–4.144) were significantly associated with increased risk of deep vein thrombosis. Female sex (OR=4.270, 95% CI 1.227– 14.862), smoking (OR=10.860, 95% CI 2.130–55.370), and special treatment (OR=3.455, 95% CI 1.006–11.869) were independent factors predicting deep vein thrombosis for patients with bed-rest durations from 5 to 8 weeks. For those with bed-rest durations from 9 to 13 weeks, Charlson Comorbidity Index (OR=1.612, 95% CI 1.090–2.385) was the only independent risk factor for deep vein thrombosis. Conclusions: Risk factors for deep vein thrombosis varied among patients with different bed-rest durations. This finding is helpful for nurses to increase their awareness of prevention of deep vein thrombosis in patients with different bed-rest durations, and lays a more solid foundation for clinical decision making. Keywords: Deep vein thrombosis, Lower limb, Risk factor, Bed rest, Case–control study
2
What is already known about the topic? Bed rest is one of the main risk factors that leads to the occurrence of deep vein thrombosis. The longer the patient stayed in bed, the greater the possibility that deep vein thrombosis would develop. There is no clear evidence to identify whether risk factors for deep vein thrombosis would be affected by different bed-rest durations. What this paper adds It is interesting that the risk factors for deep vein thrombosis were different among bedridden patients when we took bed-rest duration into consideration. For patients with bed-rest durations of 4 weeks or less, older age and being in a surgical department were significantly associated with increased risk of deep vein thrombosis. Female sex, smoking, and special treatment were independent factors predicting deep vein thrombosis for patients with bed-rest durations from 5 to 8 weeks. For those with bed-rest durations from 9 to 13 weeks, Charlson Comorbidity Index was the only independent risk factor for deep vein thrombosis.
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1. Introduction For inpatients, to be bedridden is a normal situation. Bed rest is not only useful in the management of patients unable to mobilize (Brower, 2009) but is also used as a treatment of acute and chronic injury and illness, specifically in patients with myocardial infarction or psychiatric diseases, and after orthopedic surgery (Kortebein et al., 2008). One study found that older patients were on bed rest for 23% of 3500 patient-days studied (Brown, Friedkin, & Inouye, 2004). With the aging of the global population and the increasing incidence of chronic diseases, being bedridden is becoming increasingly common (J. Li et al., 2019). Deep vein thrombosis is one of the major complications suffered by bedridden patients. The mechanism factor predisposing to deep vein thrombosis is reported to be an induced state of hypercoagulability or venous stasis (Wells, Forgie, & Rodger, 2014). While hypercoagulability may result from both congenital and acquired risk factors, in most situations, stasis’s contribution to the risk of deep vein thrombosis results from acquired conditions such as bed rest imposed by medical conditions (Topp, Ditmyer, King, Doherty, & Hornyak, 2002; Zhu, Martinez, & Emmerich, 2009). Among young males, merely 1 week of bed rest strongly reduces muscle mass, strength, and physical performance (Dirks et al., 2016), which interfere with the function of the musculature in pumping the blood upstream though the veins. Deep vein thrombosis is considered an important cause of death, resulting in a danger to public health and considerable economic burden (Beckman, Hooper, Critchley, & Ortel, 2010; Kapoor, Mehta, Patel, & Golwala, 2016; Spandorfer & Galanis, 2015; Xing, Li, Long, & Xiang, 2018). Existing research shows evidence that bed rest is an important risk factor for deep vein thrombosis (Spandorfer & Galanis, 2015). Our previous study showed that appropriate measures can reduce the incidence of deep vein thrombosis among bedridden inpatients (Wu et al., 2018). However, for inpatients with diverse bed-rest durations, such measures did not always have a beneficial effect. Therefore, we speculated that the influential factors in deep vein thrombosis may not be the same among inpatients with diverse bed-rest durations. Given the few relevant studies on this issue, we designed and performed this study based on our hypothesis. 2. Objective The purpose of this study was to explore whether influential factors for deep vein thrombosis were affected by different durations of bed rest and to identify precisely the risk factors in groups with diverse bed-rest durations. The results of this study should help to promote more sound and effective nursing measures to prevent deep vein thrombosis in bedridden patients. 3. Methods This study was part of a nationwide project that aimed to explore and establish a standardized nursing intervention model for major immobility complications among bedridden inpatients. The project was a multicenter study carried out in 25 hospitals in six different geographic regions of China (Beijing, Henan, Zhejiang, Guangdong, Hubei, and Wuhan) from June 2015 to June 2018. 3.1. Study design This was a multicenter, retrospective, matched case–control study. The study was approved by the ethical committee of Peking Union Medical College Hospital (S-700). All data were anonymous or were kept confidential. 3.2. Settings 4
This study was conducted in wards with high proportions of bedridden patients in 25 general hospitals in six different geographic regions of China (Beijing, Henan, Zhejiang, Hubei, Sichuan, and Guangdong). The reasons for being bedridden included disease-related immobility, being under sedation after surgery, and bed-rest requirements for disease management (Liu et al., 2019). In this study, wards in which bedridden patients accounted for more than 20% of the total number of patients were selected (Jiao et al., 2019). Based on the enrollment standard, in our study the top three selected wards were the orthopedics ward (32.24%), neurosurgery ward (15.79%), and neurology ward (13.28%). 3.3. Participants The study population was identified from the research database of bedridden patients’ major immobility complications (pressure injuries, deep vein thrombosis, pneumonia, and urinary tract infections). This database was established through prospective descriptive studies funded by the National Health Commission of the People's Republic of China. A bedridden patient was defined as an adult patient (>18 years old) with all the basic physiological needs carried out in bed except for active or passive bedside standing/wheelchair use for examination or treatment (Liu et al., 2019). Patients were recruited when they had to stay in bed for at least the first 24 hours after admission to the current ward. Bed-rest duration started from the day of recruitment until the end of bed rest with a maximum observation period of 90 days. In this study, patients received risk assessment and preventive interventions. On considering the regional differences and patient’s condition, each center could tailor these interventions to the specific circumstances of the ward. Optional interventions for deep vein thrombosis include physical interventions (such as graduated compression stockings, intermittent pneumatic compression, or venous foot pump) and pharmacologic interventions (such as low molecular weight heparin, vitamin K antagonists, or factor Xa inhibitor). We extracted data of 23,985 bedridden patients during the period November 2015 to June 2016 from the database. Patients were categorized based on whether they were diagnosed with deep vein thrombosis during hospitalization. Cases were defined as bedridden patients with deep vein thrombosis while controls were defined as bedridden patients without deep vein thrombosis. To avoid bias, 199 patients with deep vein thrombosis before recruitment and 46 patients without confirmed results of Doppler ultrasound or venography examination were excluded. Stratification matching was used to match controls with cases. Firstly, the medical center was used as a matching factor to screen all control cases from the same medical centers because different medical centers have a considerable influence on the treatment of deep vein thrombosis. Bed-rest duration (in weeks) was then taken as the second matching factor, and each control was selected randomly by the random number table method for each deep vein thrombosis case from the dataset. Each case patient was matched with three control patients. However, seven patients had no matched controls. Finally, 744 patients were included in this study (186 of 438 cases and 558 of 23,547 controls) (Fig. 1).Patients’ bed-rest duration was divided into three groups: 4 weeks or less, 5–8 weeks, and 9–13 weeks. 3.4. Instrument A standardized web-based online case report form was used by trained investigators to collect data. The case report form included hospital characteristics (such as level of hospital and type of ward), patients’ demographic characteristics (such as age and sex), clinical characteristics (such as diagnosis, bed-rest duration, and Charlson Comorbidity Index), presentation, and risk factors for 5
major immobility complications. The calculation of Charlson Comorbidity Index was based on the 19 conditions of each patient (Charlson, Pompei, Ales, & MacKenzie, 1987). Diagnosis and procedure codes from the International Classification of Diseases, 10th Edition (ICD-10) were used. Data about patients’ underlying diseases and nursing measures were also collected from the hospitals’ medical records. 3.5. Data collection A strictly standardized procedure of data collection was adopted to ensure the quality of the research database of bedridden patients’ major immobility complications, the detailed description of which has been published previously (J. Li et al., 2019; Liu et al., 2019). A total of 787 registered nurses from 25 general hospitals were recruited as data collectors. Before data collection, nurses were trained to evaluate patients and fill in the online case report form; meanwhile, a training video and handbook were also provided to ensure the quality of the data-collection process. Head nurses of the selected wards took charge of checking the data every day, with a coordinator in each hospital checking compliance to the study protocol. Moreover, through e-mails or teleconferences the project steering group provided feedback every week on errors made during data collection. The evaluation of patients and demographic data were collected on the day of recruitment. Diagnosis of deep vein thrombosis and other related variables were collected daily during the bed-rest period in hospital. Upon completion of construction of the database of bedridden patients’ major immobility complications in 2016, five researchers of this study took charge of extracting data from the database. Between August and December 2018, the relevant data for this study were extracted and confirmed through double-checking. 3.6. Data analysis The descriptive statistics were presented as means (95% confidence interval [CI]) and medians (range) for continuous variables, and frequencies (percentage) for categorical variables. χ2 tests and Fisher’s exact tests were used to compare categorical variables, while Mann–Whitney tests were used to compare continuous variables. Multivariate conditional logistic regression models were constructed to select risk factors for deep vein thrombosis in each different bed-rest duration group. In the regression models, pooled data included variables significantly associated with the development of deep vein thrombosis in univariate analyses. P values of less than 0.05 were considered statistically significant. The data analysis was performed using SAS 9.4 for Windows (SAS, Cary, NC, USA).
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Fig. 1. Flowchart of the study population.
4. Results Among 23,985 patients, 239 (1.0%) suffered deep vein thrombosis during hospitalization, and 186 patients, who were diagnosed with deep vein thrombosis by Doppler ultrasound or venography, were matched successfully to controls. All thrombosis located in the lower limbs and the locations of deep vein thrombosis are presented in Table 1. The number of bed-rest days for each patient at the time of diagnosis are presented in Fig. 2. Table 1 Locations of deep vein thrombosis in the case patients (N=186)a Left lower extremity
Right lower extremity
Total
Distribution of DVT Calf veins
82 (44.1%)
62 (33.3%)
144 (77.4%)
Popliteal vein
14 (7.5%)
18 (9.7%)
32 (17.2%)
Posterior tibial vein
26 (14.0%)
15 (8.1%)
41 (22.0%)
Peroneal vein
12 (6.5%)
8 (4.3%)
20 (10.8%)
Iliac vein
4 (2.2%)
3 (1.6%)
7 (3.8%)
Superficial femoral vein
19 (10.2%)
11 (5.9%)
30 (16.1%)
Common femoral vein
18 (9.7%)
15 (8.1%)
33 (17.7%)
Other places
14 (7.5%)
11 (5.9%)
25 (13.4%)
a
Abbreviation: DVT, deep vein thrombosis.
7
25
DVT cases
20
15
10
5
0 2
4
6
8
10
12
14
16
18
20
23
25
29
31
36
40
47
57
90
The number of bed rest days Fig. 2. The number of bed-rest days for each case of deep vein thrombosis at the time of diagnosis. Patients’ characteristics are shown in Table 2. The mean age of patients in the case group was 63.99 ± 16.45 years (ranging from 19 to 92 years), while the mean age of patients in the control group was 57.37 ± 16.58 years (range 18–105 years). The mean bed-rest duration was 20.55 ± 17.82 days (range 2–106 days) for the case group and 20.70 ± 18.28 days (range 2–119 days) for the control group. In total, 430 males (57.8%) and 314 females (42.2%) were included in this study. Table 2 Characteristics of the study populationa,b Patients with DVT
Patients without DVT
(cases, n = 186)
(controls, n = 558)
Total
Bed rest time ≤4 weeks
152 (81.72%)
456 (81.72%)
608 (81.7%)
5–8 weeks
26 (13.98%)
78 (13.98%)
104 (14.0%)
≥9 weeks
8 (4.30%)
24 (4.30%)
32 (4.3%)
65 (53.75, 77.25)
58 (47.00, 69.00)
Age (years)
P
0.001
0.999
-4.939
Gender Male
93 (50.00%)
337 (60.39%)
430 (57.8%)
Female
93 (50.00%)
221 (39.61%)
314 (42.2%)
Education level Illiterate
31 (16.67%)
64 (11.47%)
95 (12.8%)
Primary school
47 (25.27%)
143 (25.63%)
190 (25.5%)
Junior middle school
41 (22.04%)
161 (28.85%)
202 (27.2%)
Senior/Secondary school
34 (18.28%)
113 (20.25%)
147 (19.8%)
8
χ2
<0.001
6.179
0.013
7.645
0.177
Junior college
16 (8.60%)
43 (7.71%)
59 (7.9%)
Bachelor or above
17 (9.14%)
34 (6.09%)
51 (6.9%)
Smoking
10.041
Non-smoker
138 (74.19%)
393 (70.43%)
531 (71.4%)
Ex-smoker
27 (14.52%)
130 (23.30%)
157 (21.1%)
Smoker
21 (11.29%)
35 (6.27%)
56 (7.5%)
2
BMI (kg/m ) Underweight (<18.5)
10 (5.38%)
38 (6.81%)
Normal weight (18.5–23.9)
91 (48.92%)
268 (48.03%)
359 (48.3%)
Overweight (24.0–27.9)
53 (28.49%)
151 (27.06%)
204 (27.4%)
Obesity(>28.0)
32 (17.20%)
101 (18.10%)
133 (17.9%)
182 (97.85%)
557 (99.82%)
Yes
4 (2.15%)
1 (0.18%)
185 (99.46%)
558 (100.00%)
Yes
1 (0.54%)
0 (0.00%)
0.888
8.121
0.015
-1.732
0.083
739 (99.3%) 5 (0.7%)
Family history of DVT No
0.635 48 (6.5%)
History of DVT No
0.007
743 (99.9%) 1 (0.1%)
a
Abbreviations: BMI, body mass index; DVT, deep vein thrombosis.
b
Percentages are shown as group percentages.
Disease data and hospital characteristics associated with deep vein thrombosis are shown in Table 3. In the univariate analysis, the following variables were significantly (P <0.05) associated with deep vein thrombosis: age, sex, smoking, history of deep vein thrombosis, history of fracture/osteopathy, history of anticoagulant drugs, special treatment, admission to a surgical department, and Charlson Comorbidity Index score. Table 3 Disease data and hospital characteristics for the study population a,b Patients with DVT
Patients without DVT
Total
χ2/Z
P
212 (28.5%)
3.518
0.061
62 (8.3%)
17.103
＜0.001
(cases, n = 186)
(controls, n = 558)
Cardio-cerebrovascular disease
43 (23.12%)
169 (30.29%)
Fracture/osteopathy
29 (15.59%)
33 (5.91%)
Injury in trunk and extremities
25 (13.44%)
93 (16.67%)
118 (15.9%)
1.088
0.297
Soft tissue injury
22 (11.83%)
49 (8.78%)
71 (9.5%)
1.500
0.221
16 (8.60%)
69 (12.37%)
85 (11.4%)
1.952
0.162
Tumor
14 (7.53%)
40 (7.17%)
54 (7.3%)
0.027
0.870
Digestive system diseases
11 (5.91%)
45 (8.06%)
56 (7.5%)
0.927
0.336
Respiratory diseases
7 (3.76%)
18 (3.23%)
25 (3.4%)
0.124
0.725
Urinary diseases
5 (2.69%)
6 (1.08%)
11 (1.5%)
2.491
0.114
14 (7.53%)
36 (6.45%)
50 (6.7%)
0.257
0.612
4.518
0.034
Admitting diagnosis
Other diseases
Undetermined
c
d
Anticoagulant drugs No
123 (66.13%)
414 (74.19%)
537 (72.2%)
Yes
63 (33.87%)
144 (25.81%)
207 (27.8%)
9
Coagulant/Hemostatic drugs No
170 (91.40%)
493 (88.35%)
663 (89.1%)
Yes
16 (8.60%)
65 (11.65%)
81 (10.9%)
No
175 (94.09%)
510 (91.40%)
685 (92.1%)
Yes
11 (5.91%)
48 (8.60%)
59 (7.9%)
Vasoconstrictor
Mobility Completely lost
25 (13.44%)
50 (8.96%)
75 (10.1%)
Seriously limited
81 (43.55%)
245 (43.91%)
326 (43.9%)
Partially limited
66 (35.48%)
205 (36.74%)
271 (36.5%)
13 (6.99%)
57 (10.22%)
70 (9.4%)
No deficiency Perception ability Completely lost
13 (6.99%)
38 (6.81%)
51 (6.9%)
Seriously limited
23 (12.37%)
81 (14.52%)
104 (14.0%)
39 (20.97%)
120 (21.51%)
159 (21.4%)
110 (59.14%)
318 (56.99%)
428 (57.7%)
Partially limited No deficiency Special treatment
e
No
153 (82.26%)
504 (90.32%)
657 (88.3%)
Yes
33 (17.74%)
54 (9.68%)
87 (11.7%)
Restraints No Yes Puncture and catheterization
155 (83.33%)
471 (84.41%)
626 (84.1%)
31 (16.67%)
87 (15.59%)
118 (15.9%)
f
No
152 (81.72%)
424 (75.99%)
576 (77.4%)
Yes
34 (18.28%)
134 (24.01%)
168 (22.6%)
Critical care unit No
155 (83.33%)
490 (87.81%)
645 (86.7%)
Yes
31 (16.67%)
68 (12.19%)
99 (13.3%)
Surgical departments No
49 (26.34%)
209 (37.46%)
258 (34.7%)
Yes
137 (73.66%)
349 (62.54%)
486 (65.3%)
Hospital type Secondary hospital Tertiary hospital
18 (9.68%)
54 (9.68%)
72 (9.7%)
168 (90.32%)
504 (90.32%)
672 (90.3%)
3.86±0.175
3.26±0.098
3.41±2.352
CCI a b
1.335
0.279
1.381
0.275
4.391
0.222
0.598
0.897
8.786
0.005
0.121
0.729
2.624
0.128
2.427
0.134
7.603
0.006
0.001
0.999
–3.144
0.002
Abbreviations: DVT, deep vein thrombosis; CCI, Charlson Comorbidity Index.
Percentages are shown as group percentages.
c
Other diseases include skin disease, mental disorder, central nervous system inflammatory disease, aplastic anemia, spinal
cord disease, Alzheimer’s disease, Parkinson’s disease, viral encephalitis, hydrocephalus, polyneuropathy, and contact with health care institutions for special operations. d
Undetermined refers to diseases of unknown origin.
e
Special treatment included the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint
apparatus. f
Puncture and catheterization involved femoral vein catheterization, femoral artery catheterization, internal jugular vein 10
catheterization, subclavian vein catheterization, peripherally inserted central catheter, or other types.
Multivariate conditional logistic regression analysis was performed in each group with different bed-rest duration, the results of which are shown in Table 4. For patients with bed-rest durations ≤4 weeks, older age (odds ratio [OR]=1.027, 95% CI 1.013–1.041; P<0.01), and admitted to a surgical department (OR=2.527, 95% CI 1.541–4.144; P<0.01) were significantly associated with increased risk of deep vein thrombosis. For patients with bed-rest durations of 5–8 weeks, female (OR=4.270, 95% CI 1.227–14.862; P=0.023), smoking (OR=10.860, 95% CI 2.130–55.370; P=0.004), and special treatment (including the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus) (OR=3.455, 95% CI 1.006–11.869; P=0.049) were found to be independent factors predicting deep vein thrombosis. For those with bed-rest durations of 9–13 weeks, Charlson Comorbidity Index (OR=1.612, 95% CI 1.090–2.385; P<0.017) was significantly associated with increased risk of deep vein thrombosis. Table 4 Multivariate logistic regression models for different categories of bed-rest durationa B
S.E.
Wald
P
OR
95% CI
0.027
0.007
14.727
0.000
1.027
1.013–1.041
0.927
0.252
13.506
0.000
2.527
1.541–4.144
-3.468
0.510
46.212
0.000
0.031
1.452
0.636
5.205
0.023
4.270
1.227–14.862
Smoker
2.385
0.831
8.235
0.004
10.860
2.130–55.370
Ex–smoker
0.154
0.802
0.037
0.848
1.166
0.242–5.616
1.240
0.630
3.876
0.049
3.455
1.006–11.869
-2.301
0.578
15.831
0.000
0.100
0.478
0.200
5.714
0.017
1.612
-3.644
1.267
8.274
0.004
0.026
Bed-rest duration ≤4 weeks Age (years) Surgical departments
Yes No
Constant Bed-rest duration 5–8 weeks Gender
Female Male
Smoking
Non–smoker Special treatmentb
Yes No
Constant Bed-rest duration 9–13 weeks CCI Constant
1.090–2.385
a
Abbreviations: S.E., standard error; OR, odds ratio; CI, confidence interval; CCI, Charlson Comorbidity Index.
b
Special treatment included the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts,
and restraint apparatus.
5. Discussion Bed rest is a common clinical condition that may lead to physiological changes in most organs, and bed rest of long duration can result in adverse complications including deep vein thrombosis. Clear understanding and timely detection of its risk factors is crucial to preventing and monitoring deep vein thrombosis. As far as we know, few studies have explored the relationships between deep vein thrombosis and clinical factors in patients with diverse durations of bed rest. To answer this question and close the research gap, we performed this multivariate matched case–control 11
study, the results of which revealed that risk factors for deep vein thrombosis varied in patients with different bed-rest durations, a finding that potentially provides considerable implications for clinical practice. The findings of this study could serve to ground clinical decisions in a transparently selected pool of studies and allow for a more efficient, evidence-based management of resources for bedridden patients worldwide. In addition, it could increase awareness among nurses regarding prevention of deep vein thrombosis in patients with different bed-rest durations. In our case-control study of bedridden patients at 25 hospitals, the incidence of patients developing deep vein thrombosis during hospitalization was 10.0 per 1000 admissions, which was much lower than reported in earlier studies. For example, a prospective study in India of 125 patients with lower limb trauma reported that the prevalence of deep vein thrombosis was 4.8% (Kapoor et al., 2016), while in a multicenter prospective cohort study in China comprising 862 hospital-based patients with acute stroke, the overall incidence of deep vein thrombosis within 2 weeks after acute stroke was 12.4% (Z. Li et al., 2015). The lower incidence in our study may have resulted from the diversity of diseases included. During the short bedridden periods (≤4 weeks), results showed that deep vein thrombosis was more likely to occur in patients admitted to a surgical department, while the probability of developing deep vein thrombosis increased with age. Regarding age, the result was consistent with the previous study by Spandorfer & Galanis (2015). The reason for this may be functional impairment in older adults (Engbers, Blom, Cushman, Rosendaal, & van Hylckama Vlieg, 2017) consisting of a combination of reduced mobility, decreased muscle tone, increased morbidity, and vascular puncture. These results suggest that we should pay more attention to older patients during the evaluation of patients at admission. Admission to a surgical department was a risk factor for deep vein thrombosis, especially in the orthopedic surgery department. Surgical patients were likely to spend more time in bed with physical immobility in the early days after surgery. Moreover, patients diagnosed with fracture/osteopathy or who underwent surgery in the orthopedic surgery department had a higher chance of developing deep vein thrombosis than other surgical patients. Previous studies reported that there was a significant correlation between the occurrence of fracture and the development of deep vein thrombosis (Smith, Parvizi, & Purtill, 2011; Wilson et al., 2002), consistent with our study. Relative immobility, vessel injury, impaired venous function, limb swelling, and activated coagulation pathways may be the reasons for this phenomenon. For this reason nurses in surgical departments, especially in orthopedic surgery, should pay close attention and take prompt action to prevent deep vein thrombosis in patients. During the longer bedridden periods (5–8 weeks), the independent risk factors for deep vein thrombosis were female sex, smoking, and special treatment. Female sex was significantly associated with deep vein thrombosis in multivariate analysis (P< 0.05), which had been confirmed in previous studies. In a Norwegian study, the incidence of all first events of venous thromboembolism was 1.43 per 1000 person-years, and was slightly higher in women than in men (Naess et al., 2007). In a German cohort study, women presented 1.6 times more often for evaluation of suspected deep vein thrombosis (Bauersachs et al., 2010). In general, women with higher sex hormone levels were at higher risk for thrombophilia, with existing research confirming that combined oral contraceptives were associated with an increased risk of venous thrombosis (Stegeman et al., 2013). Thus, screening for thrombophilia should be considered for female patients. Smoking was also implicated as an independent predictive factor for deep vein thrombosis, which was also consistent with previous studies. A meta-analysis found that, 12
compared with nonsmokers, smokers had a statistically insignificant OR of 1.15 for venous thromboembolism (95% CI, 0.92–1.44) (Ageno, Becattini, Brighton, Selby, & Kamphuisen, 2008). Later, a large case–control study found a positive association between smoking and venous thromboembolism, with an OR of 1.43 (95% CI, 1.28–1.60) (Pomp, Rosendaal, & Doggen, 2008). Smokers were found to obtain increased levels of factor VII, prothrombin, factor XI peptide, and factor X peptide (Pomp et al., 2008), which may result in an increased risk of developing deep vein thrombosis. Regarding special treatment, in our study this referred to the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus. These special treatments not only increased the time patients stayed in bed but also restrained their activities or movement in bed, which may result in blood stasis and consequent deep vein thrombosis (Golemi, Salazar Adum, Tafur, & Caprini, 2019). During the prolonged bedridden periods (9–13 weeks), the only independent risk factor for deep vein thrombosis was the Charlson Comorbidity Index. It is recognized that patients tend to have a worse prognosis if the index is high (Charlson et al., 1987). In previous studies, patients with severe conditions were more likely to develop complications such as pressure injury and hospital-acquired pneumonia (Jiao et al., 2019; Liu et al., 2019). Previous research also suggested that congestive heart or respiratory failure, and malignant conditions (especially in the lung, pancreas, colon and rectum, kidney, and prostate) constituted the main risk factors for venous thromboembolism (Goldhaber, 2010). Patients with a higher Charlson Comorbidity Index thus tend to have a worse physical condition. Under such circumstances, longer bed-rest duration and inter-environment disturbance may cause deep vein thrombosis. In univariate analysis, patients with deep vein thrombosis tended to have a history of deep vein thrombosis, fracture/osteopathy diagnosis, and use of anticoagulant drugs, and these differences were statistically significant. However, in multivariate analysis these three factors were deleted from the models. For history of deep vein thrombosis, there may be some bias attributable to the limited number of patients. For diagnosis of fracture/osteopathy, the effect of this variable may be obscured by the independent risk factor of being admitted to a surgical ward. Besides, fracture/osteopathy included many different types, such as upper limb fracture, lower limb fracture, cervical spinal fracture, and lumbar fracture, which may also have effects on the results of a multivariate analysis. Regarding the use of anticoagulant drugs, there may be two reasons, the first being the heterogeneity of the type, doses, and usage of anticoagulants. The second is that the use of anticoagulant drugs was both a preventive measure and a treatment of deep vein thrombosis, which could result in bias. In this study, we provided a novel perspective different from that of previous literature. Instead of regarding being bedridden as a risk factor for deep vein thrombosis, we considered it as an overall condition and identified different risk factors in groups with different bed-rest durations, which was the major strength of our study. Given the fact that being bedridden is becoming more common, we thought it valuable to discern the variation in deep vein thrombosis risk factors among patients with different bed-rest durations. Although the data were collected retrospectively, the collecting process was under strict quality control: the case report form was subjected to several reliability and validity checks, and all definitions were determined in advance, to ensure standardized data entry across all of the medical centers and reduce the likelihood of errors. In addition, by combining data from 25 hospitals, our study created a sample size large enough to 13
simultaneously assess multiple risk factors in a multivariable model. However, this study had some potential limitations. Firstly, the retrospective data were incomplete; for example, patients' D-dimer indexes were absent. Secondly, considering the current situation of different medical centers each exercising considerable influence on the treatment of deep vein thrombosis, in this study medical center and bed-rest duration were selected as matching factors rather than the classification of disease, which might have introduced some bias. Further analysis of the potential threat of the disease itself in the course of deep vein thrombosis development is needed in the future. Thirdly, because patients with more than one major immobility complication were excluded from the database, the incidence of deep vein thrombosis may have been underestimated. 6. Conclusions In this study, we matched cases with deep vein thrombosis and controls without deep vein thrombosis, and performed multivariate analyses separately according to bed-rest duration category, which allowed us to clearly and reliably assess the risk factors for deep vein thrombosis. Risk factors for deep vein thrombosis varied among the bed-rest duration categories of 4 weeks or less, 5–8 weeks, and 9–13 weeks. Older age and admission to a surgical department were significantly associated with increased risk of deep vein thrombosis for patients with bed-rest durations ≤4 weeks. Female sex, smoking, and special treatment (including the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus) were independent factors predicting deep vein thrombosis for patients with bed-rest durations of 5–8 weeks. Higher Charlson Comorbidity Index was associated with a higher risk of developing deep vein thrombosis for those with bed-rest durations of 9–13 weeks. After a patient is admitted to hospital, attention should be paid to the key risk factors for deep vein thrombosis and more appropriate clinical options should be chosen for individuals who spend different amounts of time in bed. Acknowledgments The authors wish to thank the patients recruited across the 25 hospitals involved in this study. Financial support and sponsorship This work was supported by the National Health and Family Planning Commission (Beijing, China; grant number 201502017). Author Contributions J C, SY L, Z L, YF M, G L, Y L, J J, C Z, BY S, JF J, YL L, XX W, SZ C, and XJ W collected the data, J C and SY L performed the analysis and wrote the manuscript. JC assisted with performing and planning experiments. XJ W oversaw the project and reviewed the manuscript. X W assisted with the experimental setup. All authors read and approved the final manuscript. Declarations of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. No prior presentation.
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References Ageno, W., Becattini, C., Brighton, T., Selby, R., & Kamphuisen, P. W. (2008). Cardiovascular risk factors and venous thromboembolism: a meta-analysis. Circulation, 117(1), 93-102. doi:10.1161/circulationaha.107.709204 Bauersachs, R. M., Riess, H., Hach-Wunderle, V., Gerlach, H., Carnarius, H., Eberle, S., . . . Schellong, S. M. (2010). Impact of gender on the clinical presentation and diagnosis of deep-vein thrombosis. Thromb Haemost, 103(4), 710-717. doi:10.1160/th09-10-0705 Beckman, M. G., Hooper, W. C., Critchley, S. E., & Ortel, T. L. (2010). Venous thromboembolism: a public health concern. American Journal of Preventive Medicine,
38(4 Suppl), S495-501. doi:10.1016/j.amepre.2009.12.017 Brower, R. G. (2009). Consequences of bed rest. Crit Care Med, 37(10 Suppl), S422-428. doi:10.1097/CCM.0b013e3181b6e30a Brown, C. J., Friedkin, R. J., & Inouye, S. K. (2004). Prevalence and outcomes of low mobility in hospitalized older patients. Journal of the American Geriatrics Society, 52(8), 1263-1270. doi:10.1111/j.1532-5415.2004.52354.x Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
J Chronic Dis, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8 Dirks, M. L., Wall, B. T., van de Valk, B., Holloway, T. M., Holloway, G. P., Chabowski, A., . . . van Loon, L. J. (2016). One Week of Bed Rest Leads to Substantial Muscle Atrophy and Induces Whole-Body Insulin Resistance in the Absence of Skeletal Muscle Lipid Accumulation. Diabetes, 65(10), 2862-2875. doi:10.2337/db15-1661 15
Engbers, M. J., Blom, J. W., Cushman, M., Rosendaal, F. R., & van Hylckama Vlieg, A. (2017). Functional Impairment and Risk of Venous Thrombosis in Older Adults. Journal of the
American Geriatrics Society, 65(9), 2003-2008. doi:10.1111/jgs.14964 Goldhaber, S. Z. (2010). Risk factors for venous thromboembolism. Journal of the American
College of Cardiology, 56(1), 1-7. doi:10.1016/j.jacc.2010.01.057 Golemi, I., Salazar Adum, J. P., Tafur, A., & Caprini, J. (2019). Venous thromboembolism prophylaxis
using
the
Caprini
score.
Dis
Mon,
65(8),
249-298.
doi:10.1016/j.disamonth.2018.12.005 Jiao, J., Yang, X. Y., Li, Z., Zhao, Y. W., Cao, J., Li, F. F., . . . Sun, J. (2019). Incidence and Related Factors for Hospital-Acquired Pneumonia Among Older Bedridden Patients in China: A Hospital-Based Multicenter Registry Data Based Study. Front Public Health,
7, 221. doi:10.3389/fpubh.2019.00221 Kapoor, C. S., Mehta, A. K., Patel, K., & Golwala, P. P. (2016). Prevalence of deep vein thrombosis in patients with lower limb trauma. J Clin Orthop Trauma, 7(Suppl 2), 220-224. doi:10.1016/j.jcot.2016.07.003 Kortebein, P., Symons, T. B., Ferrando, A., Paddon-Jones, D., Ronsen, O., Protas, E., . . . Evans, W. J. (2008). Functional impact of 10 days of bed rest in healthy older adults. J
Gerontol A Biol Sci Med Sci, 63(10), 1076-1081. doi:10.1093/gerona/63.10.1076 Li, J., Wu, X., Li, Z., Zhou, X., Cao, J., Jia, Z., . . . Cheng, S. (2019). Nursing resources and major immobility complications among bedridden patients: A multicenter descriptive study in China. J Nurs Manag, 27(5), 930-938. doi:10.1111/jonm.12731 Li, Z., Liu, L., Wang, Y., Zhao, X., Wang, D. Z., Wang, C., . . . Wang, Y. (2015). Factors impact 16
the adherence rate of prophylaxis for deep venous thrombosis in acute ischaemic stroke patients: an analysis of the China National Stroke Registry. Neurological
Research, 37(5), 427-433. doi:10.1179/1743132815y.0000000035 Liu, Y., Wu, X., Ma, Y., Li, Z., Cao, J., Jiao, J., . . . Lin, F. (2019). The prevalence, incidence, and associated factors of pressure injuries among immobile inpatients: A multicentre, cross-sectional, exploratory descriptive study in China. Int Wound J, 16(2), 459-466. doi:10.1111/iwj.13054 Naess, I. A., Christiansen, S. C., Romundstad, P., Cannegieter, S. C., Rosendaal, F. R., & Hammerstrøm, J. (2007). Incidence and mortality of venous thrombosis: a population-based
study.
J
Thromb
Haemost,
5(4),
692-699.
doi:10.1111/j.1538-7836.2007.02450.x Pomp, E. R., Rosendaal, F. R., & Doggen, C. J. (2008). Smoking increases the risk of venous thrombosis and acts synergistically with oral contraceptive use. Am J Hematol, 83(2), 97-102. doi:10.1002/ajh.21059 Smith, E. B., Parvizi, J., & Purtill, J. J. (2011). Delayed surgery for patients with femur and hip fractures-risk of deep venous thrombosis. Journal of Trauma, 70(6), E113-116. doi:10.1097/TA.0b013e31821b8768 Spandorfer, J., & Galanis, T. (2015). In the Clinic. Deep venous thrombosis. Ann Intern Med,
162(9), Itc1. doi:10.7326/aitc201505050 Stegeman, B. H., de Bastos, M., Rosendaal, F. R., van Hylckama Vlieg, A., Helmerhorst, F. M., Stijnen, T., & Dekkers, O. M. (2013). Different combined oral contraceptives and the risk of venous thrombosis: systematic review and network meta-analysis. Bmj, 347, 17
f5298. doi:10.1136/bmj.f5298 Topp, R., Ditmyer, M., King, K., Doherty, K., & Hornyak, J., 3rd. (2002). The effect of bed rest and potential of prehabilitation on patients in the intensive care unit. AACN Clin Issues,
13(2), 263-276. doi:10.1097/00044067-200205000-00011 Wells, P. S., Forgie, M. A., & Rodger, M. A. (2014). Treatment of venous thromboembolism.
Jama, 311(7), 717-728. doi:10.1001/jama.2014.65 Wilson, D., Cooke, E. A., McNally, M. A., Wilson, H. K., Yeates, A., & Mollan, R. A. (2002). Altered venous function and deep venous thrombosis following proximal femoral fracture. Injury, 33(1), 33-39. doi:10.1016/s0020-1383(01)00137-1 WU, X., Cai, M., Cao, J., Jiao, J., Liu, G., Li, Z., . . . Li, F. (2018). The effects of standardized nursing on improvement of nursing quality among bedridden patients:a multi-centered study. Chinese Journal of Nursing, 53(06), 645-649. Xing, F., Li, L., Long, Y., & Xiang, Z. (2018). Admission prevalence of deep vein thrombosis in elderly Chinese patients with hip fracture and a new predictor based on risk factors for thrombosis
screening.
BMC
Musculoskelet
Disord,
19(1),
444.
doi:10.1186/s12891-018-2371-5 Zhu, T., Martinez, I., & Emmerich, J. (2009). Venous thromboembolism: risk factors for recurrence.
Arterioscler
Thromb
doi:10.1161/atvbaha.108.182428
18
Vasc
Biol,
29(3),
298-310.
Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study Jing Cao , Shuya Li , Yufen Ma , Zhen Li , Ge Liu , Ying Liu , Jing Jiao , Chen Zhu , Baoyun Song , Jingfen Jin , Yilan Liu , Xianxiu Wen , Shouzhen Cheng , Xia Wan , Xinjuan Wu PII: DOI: Reference:
S0020-7489(20)30313-8 https://doi.org/10.1016/j.ijnurstu.2020.103825 NS 103825
To appear in:
International Journal of Nursing Studies
Received date: Revised date: Accepted date:
8 April 2020 16 July 2020 29 October 2020
Please cite this article as: Jing Cao , Shuya Li , Yufen Ma , Zhen Li , Ge Liu , Ying Liu , Jing Jiao , Chen Zhu , Baoyun Song , Jingfen Jin , Yilan Liu , Xianxiu Wen , Shouzhen Cheng , Xia Wan , Xinjuan Wu , Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study, International Journal of Nursing Studies (2020), doi: https://doi.org/10.1016/j.ijnurstu.2020.103825
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Risk factors associated with deep venous thrombosisin patients with different bed-rest durations: a multi-institutional case–control study Jing Caoa, Shuya Li (Co-Author)a, Yufen Maa, Zhen Lia, Ge Liua, Ying Liua, Jing Jiaoa, Chen Zhua, Baoyun Songb, Jingfen Jinc, Yilan Liud, Xianxiu Wene, Shouzhen Chengf, Xia Wang, Xinjuan Wu(Corresponding Author)a* a
Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China b Department of Nursing, Henan Provincial People’s Hospital, Zhengzhou, Henan, China c Department of Nursing, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China d Department of Nursing, Wuhan Union Hospital, Wuhan, Hubei, China e Department of Nursing, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China f Department of Nursing, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China g Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Beijing, China *Corresponding author: Xinjuan WU, RN, MSN Department of Nursing, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Ph:(010) +86 01069155820 Email: [email protected] First author: Jing CAO, RN, MSN Department of Nursing, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Email:[email protected] Co-Author: Shuya LI, RN, MSN Department of Emergency, Chinese Academy of Medical Sciences – Peking Union Medical College, Peking Union Medical College Hospital.1 Shuaifuyuan, Dongcheng District, Beijing, China Email: [email protected]
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Risk factors associated with deep venous thrombosis in patients with different bed-rest durations: a multi-institutional case–control study Background: Deep vein thrombosis represents a threat to public health and a heavy economic burden to society, and often occurs as a complication or cause of death in bedridden patients. How to prevent deep vein thrombosis is a general concern in clinical practice. However, it remains uncertain whether the risk factors for deep vein thrombosis would be affected by different bed-rest durations. Solving this issue will be invaluable for the provision of more rational medical care to prevent deep vein thrombosis. Objective: To explore whether risk factors for deep vein thrombosis are affected by bed-rest durations and to identify different risk factors in groups with different bed-rest durations. Design: A retrospective multicenter case–control study. Settings and participants: This multicenter study was conducted in wards with high rates of bed rest in 25 general hospitals in China. Participants were bedridden patients from these wards. Methods: Bedridden patients were identified from the research database of bedridden patients’ major immobility complications. These data were collected from prospective descriptive studies by a standardized web-based online case report form. Cases were defined as bedridden patients who suffered deep vein thrombosis during hospitalization (n=186). Each case was matched with three controls, bedridden patients who did not suffer deep vein thrombosis in the same center with the same bed-rest duration (n=558). Descriptive statistics, univariate analysis, and multivariate conditional logistic regression models were employed. Results: Among 23,985 patients, the overall incidence of deep vein thrombosis during hospitalization was 1.0%. Multivariate analysis showed that for patients with bed-rest duration of 4 weeks or less, older age (odds ratio [OR] =1.027, 95% confidence interval [CI] 1.013–1.041) and being in a surgical department (OR=2.527, 95% CI 1.541–4.144) were significantly associated with increased risk of deep vein thrombosis. Female sex (OR=4.270, 95% CI 1.227– 14.862), smoking (OR=10.860, 95% CI 2.130–55.370), and special treatment (OR=3.455, 95% CI 1.006–11.869) were independent factors predicting deep vein thrombosis for patients with bed-rest durations from 5 to 8 weeks. For those with bed-rest durations from 9 to 13 weeks, Charlson Comorbidity Index (OR=1.612, 95% CI 1.090–2.385) was the only independent risk factor for deep vein thrombosis. Conclusions: Risk factors for deep vein thrombosis varied among patients with different bed-rest durations. This finding is helpful for nurses to increase their awareness of prevention of deep vein thrombosis in patients with different bed-rest durations, and lays a more solid foundation for clinical decision making. Keywords: Deep vein thrombosis, Lower limb, Risk factor, Bed rest, Case–control study
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What is already known about the topic? Bed rest is one of the main risk factors that leads to the occurrence of deep vein thrombosis. The longer the patient stayed in bed, the greater the possibility that deep vein thrombosis would develop. There is no clear evidence to identify whether risk factors for deep vein thrombosis would be affected by different bed-rest durations. What this paper adds It is interesting that the risk factors for deep vein thrombosis were different among bedridden patients when we took bed-rest duration into consideration. For patients with bed-rest durations of 4 weeks or less, older age and being in a surgical department were significantly associated with increased risk of deep vein thrombosis. Female sex, smoking, and special treatment were independent factors predicting deep vein thrombosis for patients with bed-rest durations from 5 to 8 weeks. For those with bed-rest durations from 9 to 13 weeks, Charlson Comorbidity Index was the only independent risk factor for deep vein thrombosis.
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1. Introduction For inpatients, to be bedridden is a normal situation. Bed rest is not only useful in the management of patients unable to mobilize (Brower, 2009) but is also used as a treatment of acute and chronic injury and illness, specifically in patients with myocardial infarction or psychiatric diseases, and after orthopedic surgery (Kortebein et al., 2008). One study found that older patients were on bed rest for 23% of 3500 patient-days studied (Brown, Friedkin, & Inouye, 2004). With the aging of the global population and the increasing incidence of chronic diseases, being bedridden is becoming increasingly common (J. Li et al., 2019). Deep vein thrombosis is one of the major complications suffered by bedridden patients. The mechanism factor predisposing to deep vein thrombosis is reported to be an induced state of hypercoagulability or venous stasis (Wells, Forgie, & Rodger, 2014). While hypercoagulability may result from both congenital and acquired risk factors, in most situations, stasis’s contribution to the risk of deep vein thrombosis results from acquired conditions such as bed rest imposed by medical conditions (Topp, Ditmyer, King, Doherty, & Hornyak, 2002; Zhu, Martinez, & Emmerich, 2009). Among young males, merely 1 week of bed rest strongly reduces muscle mass, strength, and physical performance (Dirks et al., 2016), which interfere with the function of the musculature in pumping the blood upstream though the veins. Deep vein thrombosis is considered an important cause of death, resulting in a danger to public health and considerable economic burden (Beckman, Hooper, Critchley, & Ortel, 2010; Kapoor, Mehta, Patel, & Golwala, 2016; Spandorfer & Galanis, 2015; Xing, Li, Long, & Xiang, 2018). Existing research shows evidence that bed rest is an important risk factor for deep vein thrombosis (Spandorfer & Galanis, 2015). Our previous study showed that appropriate measures can reduce the incidence of deep vein thrombosis among bedridden inpatients (Wu et al., 2018). However, for inpatients with diverse bed-rest durations, such measures did not always have a beneficial effect. Therefore, we speculated that the influential factors in deep vein thrombosis may not be the same among inpatients with diverse bed-rest durations. Given the few relevant studies on this issue, we designed and performed this study based on our hypothesis. 2. Objective The purpose of this study was to explore whether influential factors for deep vein thrombosis were affected by different durations of bed rest and to identify precisely the risk factors in groups with diverse bed-rest durations. The results of this study should help to promote more sound and effective nursing measures to prevent deep vein thrombosis in bedridden patients. 3. Methods This study was part of a nationwide project that aimed to explore and establish a standardized nursing intervention model for major immobility complications among bedridden inpatients. The project was a multicenter study carried out in 25 hospitals in six different geographic regions of China (Beijing, Henan, Zhejiang, Guangdong, Hubei, and Wuhan) from June 2015 to June 2018. 3.1. Study design This was a multicenter, retrospective, matched case–control study. The study was approved by the ethical committee of Peking Union Medical College Hospital (S-700). All data were anonymous or were kept confidential. 3.2. Settings 4
This study was conducted in wards with high proportions of bedridden patients in 25 general hospitals in six different geographic regions of China (Beijing, Henan, Zhejiang, Hubei, Sichuan, and Guangdong). The reasons for being bedridden included disease-related immobility, being under sedation after surgery, and bed-rest requirements for disease management (Liu et al., 2019). In this study, wards in which bedridden patients accounted for more than 20% of the total number of patients were selected (Jiao et al., 2019). Based on the enrollment standard, in our study the top three selected wards were the orthopedics ward (32.24%), neurosurgery ward (15.79%), and neurology ward (13.28%). 3.3. Participants The study population was identified from the research database of bedridden patients’ major immobility complications (pressure injuries, deep vein thrombosis, pneumonia, and urinary tract infections). This database was established through prospective descriptive studies funded by the National Health Commission of the People's Republic of China. A bedridden patient was defined as an adult patient (>18 years old) with all the basic physiological needs carried out in bed except for active or passive bedside standing/wheelchair use for examination or treatment (Liu et al., 2019). Patients were recruited when they had to stay in bed for at least the first 24 hours after admission to the current ward. Bed-rest duration started from the day of recruitment until the end of bed rest with a maximum observation period of 90 days. In this study, patients received risk assessment and preventive interventions. On considering the regional differences and patient’s condition, each center could tailor these interventions to the specific circumstances of the ward. Optional interventions for deep vein thrombosis include physical interventions (such as graduated compression stockings, intermittent pneumatic compression, or venous foot pump) and pharmacologic interventions (such as low molecular weight heparin, vitamin K antagonists, or factor Xa inhibitor). We extracted data of 23,985 bedridden patients during the period November 2015 to June 2016 from the database. Patients were categorized based on whether they were diagnosed with deep vein thrombosis during hospitalization. Cases were defined as bedridden patients with deep vein thrombosis while controls were defined as bedridden patients without deep vein thrombosis. To avoid bias, 199 patients with deep vein thrombosis before recruitment and 46 patients without confirmed results of Doppler ultrasound or venography examination were excluded. Stratification matching was used to match controls with cases. Firstly, the medical center was used as a matching factor to screen all control cases from the same medical centers because different medical centers have a considerable influence on the treatment of deep vein thrombosis. Bed-rest duration (in weeks) was then taken as the second matching factor, and each control was selected randomly by the random number table method for each deep vein thrombosis case from the dataset. Each case patient was matched with three control patients. However, seven patients had no matched controls. Finally, 744 patients were included in this study (186 of 438 cases and 558 of 23,547 controls) (Fig. 1).Patients’ bed-rest duration was divided into three groups: 4 weeks or less, 5–8 weeks, and 9–13 weeks. 3.4. Instrument A standardized web-based online case report form was used by trained investigators to collect data. The case report form included hospital characteristics (such as level of hospital and type of ward), patients’ demographic characteristics (such as age and sex), clinical characteristics (such as diagnosis, bed-rest duration, and Charlson Comorbidity Index), presentation, and risk factors for 5
major immobility complications. The calculation of Charlson Comorbidity Index was based on the 19 conditions of each patient (Charlson, Pompei, Ales, & MacKenzie, 1987). Diagnosis and procedure codes from the International Classification of Diseases, 10th Edition (ICD-10) were used. Data about patients’ underlying diseases and nursing measures were also collected from the hospitals’ medical records. 3.5. Data collection A strictly standardized procedure of data collection was adopted to ensure the quality of the research database of bedridden patients’ major immobility complications, the detailed description of which has been published previously (J. Li et al., 2019; Liu et al., 2019). A total of 787 registered nurses from 25 general hospitals were recruited as data collectors. Before data collection, nurses were trained to evaluate patients and fill in the online case report form; meanwhile, a training video and handbook were also provided to ensure the quality of the data-collection process. Head nurses of the selected wards took charge of checking the data every day, with a coordinator in each hospital checking compliance to the study protocol. Moreover, through e-mails or teleconferences the project steering group provided feedback every week on errors made during data collection. The evaluation of patients and demographic data were collected on the day of recruitment. Diagnosis of deep vein thrombosis and other related variables were collected daily during the bed-rest period in hospital. Upon completion of construction of the database of bedridden patients’ major immobility complications in 2016, five researchers of this study took charge of extracting data from the database. Between August and December 2018, the relevant data for this study were extracted and confirmed through double-checking. 3.6. Data analysis The descriptive statistics were presented as means (95% confidence interval [CI]) and medians (range) for continuous variables, and frequencies (percentage) for categorical variables. χ2 tests and Fisher’s exact tests were used to compare categorical variables, while Mann–Whitney tests were used to compare continuous variables. Multivariate conditional logistic regression models were constructed to select risk factors for deep vein thrombosis in each different bed-rest duration group. In the regression models, pooled data included variables significantly associated with the development of deep vein thrombosis in univariate analyses. P values of less than 0.05 were considered statistically significant. The data analysis was performed using SAS 9.4 for Windows (SAS, Cary, NC, USA).
6
Fig. 1. Flowchart of the study population.
4. Results Among 23,985 patients, 239 (1.0%) suffered deep vein thrombosis during hospitalization, and 186 patients, who were diagnosed with deep vein thrombosis by Doppler ultrasound or venography, were matched successfully to controls. All thrombosis located in the lower limbs and the locations of deep vein thrombosis are presented in Table 1. The number of bed-rest days for each patient at the time of diagnosis are presented in Fig. 2. Table 1 Locations of deep vein thrombosis in the case patients (N=186)a Left lower extremity
Right lower extremity
Total
Distribution of DVT Calf veins
82 (44.1%)
62 (33.3%)
144 (77.4%)
Popliteal vein
14 (7.5%)
18 (9.7%)
32 (17.2%)
Posterior tibial vein
26 (14.0%)
15 (8.1%)
41 (22.0%)
Peroneal vein
12 (6.5%)
8 (4.3%)
20 (10.8%)
Iliac vein
4 (2.2%)
3 (1.6%)
7 (3.8%)
Superficial femoral vein
19 (10.2%)
11 (5.9%)
30 (16.1%)
Common femoral vein
18 (9.7%)
15 (8.1%)
33 (17.7%)
Other places
14 (7.5%)
11 (5.9%)
25 (13.4%)
a
Abbreviation: DVT, deep vein thrombosis.
7
25
DVT cases
20
15
10
5
0 2
4
6
8
10
12
14
16
18
20
23
25
29
31
36
40
47
57
90
The number of bed rest days Fig. 2. The number of bed-rest days for each case of deep vein thrombosis at the time of diagnosis. Patients’ characteristics are shown in Table 2. The mean age of patients in the case group was 63.99 ± 16.45 years (ranging from 19 to 92 years), while the mean age of patients in the control group was 57.37 ± 16.58 years (range 18–105 years). The mean bed-rest duration was 20.55 ± 17.82 days (range 2–106 days) for the case group and 20.70 ± 18.28 days (range 2–119 days) for the control group. In total, 430 males (57.8%) and 314 females (42.2%) were included in this study. Table 2 Characteristics of the study populationa,b Patients with DVT
Patients without DVT
(cases, n = 186)
(controls, n = 558)
Total
Bed rest time ≤4 weeks
152 (81.72%)
456 (81.72%)
608 (81.7%)
5–8 weeks
26 (13.98%)
78 (13.98%)
104 (14.0%)
≥9 weeks
8 (4.30%)
24 (4.30%)
32 (4.3%)
65 (53.75, 77.25)
58 (47.00, 69.00)
Age (years)
P
0.001
0.999
-4.939
Gender Male
93 (50.00%)
337 (60.39%)
430 (57.8%)
Female
93 (50.00%)
221 (39.61%)
314 (42.2%)
Education level Illiterate
31 (16.67%)
64 (11.47%)
95 (12.8%)
Primary school
47 (25.27%)
143 (25.63%)
190 (25.5%)
Junior middle school
41 (22.04%)
161 (28.85%)
202 (27.2%)
Senior/Secondary school
34 (18.28%)
113 (20.25%)
147 (19.8%)
8
χ2
<0.001
6.179
0.013
7.645
0.177
Junior college
16 (8.60%)
43 (7.71%)
59 (7.9%)
Bachelor or above
17 (9.14%)
34 (6.09%)
51 (6.9%)
Smoking
10.041
Non-smoker
138 (74.19%)
393 (70.43%)
531 (71.4%)
Ex-smoker
27 (14.52%)
130 (23.30%)
157 (21.1%)
Smoker
21 (11.29%)
35 (6.27%)
56 (7.5%)
2
BMI (kg/m ) Underweight (<18.5)
10 (5.38%)
38 (6.81%)
Normal weight (18.5–23.9)
91 (48.92%)
268 (48.03%)
359 (48.3%)
Overweight (24.0–27.9)
53 (28.49%)
151 (27.06%)
204 (27.4%)
Obesity(>28.0)
32 (17.20%)
101 (18.10%)
133 (17.9%)
182 (97.85%)
557 (99.82%)
Yes
4 (2.15%)
1 (0.18%)
185 (99.46%)
558 (100.00%)
Yes
1 (0.54%)
0 (0.00%)
0.888
8.121
0.015
-1.732
0.083
739 (99.3%) 5 (0.7%)
Family history of DVT No
0.635 48 (6.5%)
History of DVT No
0.007
743 (99.9%) 1 (0.1%)
a
Abbreviations: BMI, body mass index; DVT, deep vein thrombosis.
b
Percentages are shown as group percentages.
Disease data and hospital characteristics associated with deep vein thrombosis are shown in Table 3. In the univariate analysis, the following variables were significantly (P <0.05) associated with deep vein thrombosis: age, sex, smoking, history of deep vein thrombosis, history of fracture/osteopathy, history of anticoagulant drugs, special treatment, admission to a surgical department, and Charlson Comorbidity Index score. Table 3 Disease data and hospital characteristics for the study population a,b Patients with DVT
Patients without DVT
Total
χ2/Z
P
212 (28.5%)
3.518
0.061
62 (8.3%)
17.103
＜0.001
(cases, n = 186)
(controls, n = 558)
Cardio-cerebrovascular disease
43 (23.12%)
169 (30.29%)
Fracture/osteopathy
29 (15.59%)
33 (5.91%)
Injury in trunk and extremities
25 (13.44%)
93 (16.67%)
118 (15.9%)
1.088
0.297
Soft tissue injury
22 (11.83%)
49 (8.78%)
71 (9.5%)
1.500
0.221
16 (8.60%)
69 (12.37%)
85 (11.4%)
1.952
0.162
Tumor
14 (7.53%)
40 (7.17%)
54 (7.3%)
0.027
0.870
Digestive system diseases
11 (5.91%)
45 (8.06%)
56 (7.5%)
0.927
0.336
Respiratory diseases
7 (3.76%)
18 (3.23%)
25 (3.4%)
0.124
0.725
Urinary diseases
5 (2.69%)
6 (1.08%)
11 (1.5%)
2.491
0.114
14 (7.53%)
36 (6.45%)
50 (6.7%)
0.257
0.612
4.518
0.034
Admitting diagnosis
Other diseases
Undetermined
c
d
Anticoagulant drugs No
123 (66.13%)
414 (74.19%)
537 (72.2%)
Yes
63 (33.87%)
144 (25.81%)
207 (27.8%)
9
Coagulant/Hemostatic drugs No
170 (91.40%)
493 (88.35%)
663 (89.1%)
Yes
16 (8.60%)
65 (11.65%)
81 (10.9%)
No
175 (94.09%)
510 (91.40%)
685 (92.1%)
Yes
11 (5.91%)
48 (8.60%)
59 (7.9%)
Vasoconstrictor
Mobility Completely lost
25 (13.44%)
50 (8.96%)
75 (10.1%)
Seriously limited
81 (43.55%)
245 (43.91%)
326 (43.9%)
Partially limited
66 (35.48%)
205 (36.74%)
271 (36.5%)
13 (6.99%)
57 (10.22%)
70 (9.4%)
No deficiency Perception ability Completely lost
13 (6.99%)
38 (6.81%)
51 (6.9%)
Seriously limited
23 (12.37%)
81 (14.52%)
104 (14.0%)
39 (20.97%)
120 (21.51%)
159 (21.4%)
110 (59.14%)
318 (56.99%)
428 (57.7%)
Partially limited No deficiency Special treatment
e
No
153 (82.26%)
504 (90.32%)
657 (88.3%)
Yes
33 (17.74%)
54 (9.68%)
87 (11.7%)
Restraints No Yes Puncture and catheterization
155 (83.33%)
471 (84.41%)
626 (84.1%)
31 (16.67%)
87 (15.59%)
118 (15.9%)
f
No
152 (81.72%)
424 (75.99%)
576 (77.4%)
Yes
34 (18.28%)
134 (24.01%)
168 (22.6%)
Critical care unit No
155 (83.33%)
490 (87.81%)
645 (86.7%)
Yes
31 (16.67%)
68 (12.19%)
99 (13.3%)
Surgical departments No
49 (26.34%)
209 (37.46%)
258 (34.7%)
Yes
137 (73.66%)
349 (62.54%)
486 (65.3%)
Hospital type Secondary hospital Tertiary hospital
18 (9.68%)
54 (9.68%)
72 (9.7%)
168 (90.32%)
504 (90.32%)
672 (90.3%)
3.86±0.175
3.26±0.098
3.41±2.352
CCI a b
1.335
0.279
1.381
0.275
4.391
0.222
0.598
0.897
8.786
0.005
0.121
0.729
2.624
0.128
2.427
0.134
7.603
0.006
0.001
0.999
–3.144
0.002
Abbreviations: DVT, deep vein thrombosis; CCI, Charlson Comorbidity Index.
Percentages are shown as group percentages.
c
Other diseases include skin disease, mental disorder, central nervous system inflammatory disease, aplastic anemia, spinal
cord disease, Alzheimer’s disease, Parkinson’s disease, viral encephalitis, hydrocephalus, polyneuropathy, and contact with health care institutions for special operations. d
Undetermined refers to diseases of unknown origin.
e
Special treatment included the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint
apparatus. f
Puncture and catheterization involved femoral vein catheterization, femoral artery catheterization, internal jugular vein 10
catheterization, subclavian vein catheterization, peripherally inserted central catheter, or other types.
Multivariate conditional logistic regression analysis was performed in each group with different bed-rest duration, the results of which are shown in Table 4. For patients with bed-rest durations ≤4 weeks, older age (odds ratio [OR]=1.027, 95% CI 1.013–1.041; P<0.01), and admitted to a surgical department (OR=2.527, 95% CI 1.541–4.144; P<0.01) were significantly associated with increased risk of deep vein thrombosis. For patients with bed-rest durations of 5–8 weeks, female (OR=4.270, 95% CI 1.227–14.862; P=0.023), smoking (OR=10.860, 95% CI 2.130–55.370; P=0.004), and special treatment (including the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus) (OR=3.455, 95% CI 1.006–11.869; P=0.049) were found to be independent factors predicting deep vein thrombosis. For those with bed-rest durations of 9–13 weeks, Charlson Comorbidity Index (OR=1.612, 95% CI 1.090–2.385; P<0.017) was significantly associated with increased risk of deep vein thrombosis. Table 4 Multivariate logistic regression models for different categories of bed-rest durationa B
S.E.
Wald
P
OR
95% CI
0.027
0.007
14.727
0.000
1.027
1.013–1.041
0.927
0.252
13.506
0.000
2.527
1.541–4.144
-3.468
0.510
46.212
0.000
0.031
1.452
0.636
5.205
0.023
4.270
1.227–14.862
Smoker
2.385
0.831
8.235
0.004
10.860
2.130–55.370
Ex–smoker
0.154
0.802
0.037
0.848
1.166
0.242–5.616
1.240
0.630
3.876
0.049
3.455
1.006–11.869
-2.301
0.578
15.831
0.000
0.100
0.478
0.200
5.714
0.017
1.612
-3.644
1.267
8.274
0.004
0.026
Bed-rest duration ≤4 weeks Age (years) Surgical departments
Yes No
Constant Bed-rest duration 5–8 weeks Gender
Female Male
Smoking
Non–smoker Special treatmentb
Yes No
Constant Bed-rest duration 9–13 weeks CCI Constant
1.090–2.385
a
Abbreviations: S.E., standard error; OR, odds ratio; CI, confidence interval; CCI, Charlson Comorbidity Index.
b
Special treatment included the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts,
and restraint apparatus.
5. Discussion Bed rest is a common clinical condition that may lead to physiological changes in most organs, and bed rest of long duration can result in adverse complications including deep vein thrombosis. Clear understanding and timely detection of its risk factors is crucial to preventing and monitoring deep vein thrombosis. As far as we know, few studies have explored the relationships between deep vein thrombosis and clinical factors in patients with diverse durations of bed rest. To answer this question and close the research gap, we performed this multivariate matched case–control 11
study, the results of which revealed that risk factors for deep vein thrombosis varied in patients with different bed-rest durations, a finding that potentially provides considerable implications for clinical practice. The findings of this study could serve to ground clinical decisions in a transparently selected pool of studies and allow for a more efficient, evidence-based management of resources for bedridden patients worldwide. In addition, it could increase awareness among nurses regarding prevention of deep vein thrombosis in patients with different bed-rest durations. In our case-control study of bedridden patients at 25 hospitals, the incidence of patients developing deep vein thrombosis during hospitalization was 10.0 per 1000 admissions, which was much lower than reported in earlier studies. For example, a prospective study in India of 125 patients with lower limb trauma reported that the prevalence of deep vein thrombosis was 4.8% (Kapoor et al., 2016), while in a multicenter prospective cohort study in China comprising 862 hospital-based patients with acute stroke, the overall incidence of deep vein thrombosis within 2 weeks after acute stroke was 12.4% (Z. Li et al., 2015). The lower incidence in our study may have resulted from the diversity of diseases included. During the short bedridden periods (≤4 weeks), results showed that deep vein thrombosis was more likely to occur in patients admitted to a surgical department, while the probability of developing deep vein thrombosis increased with age. Regarding age, the result was consistent with the previous study by Spandorfer & Galanis (2015). The reason for this may be functional impairment in older adults (Engbers, Blom, Cushman, Rosendaal, & van Hylckama Vlieg, 2017) consisting of a combination of reduced mobility, decreased muscle tone, increased morbidity, and vascular puncture. These results suggest that we should pay more attention to older patients during the evaluation of patients at admission. Admission to a surgical department was a risk factor for deep vein thrombosis, especially in the orthopedic surgery department. Surgical patients were likely to spend more time in bed with physical immobility in the early days after surgery. Moreover, patients diagnosed with fracture/osteopathy or who underwent surgery in the orthopedic surgery department had a higher chance of developing deep vein thrombosis than other surgical patients. Previous studies reported that there was a significant correlation between the occurrence of fracture and the development of deep vein thrombosis (Smith, Parvizi, & Purtill, 2011; Wilson et al., 2002), consistent with our study. Relative immobility, vessel injury, impaired venous function, limb swelling, and activated coagulation pathways may be the reasons for this phenomenon. For this reason nurses in surgical departments, especially in orthopedic surgery, should pay close attention and take prompt action to prevent deep vein thrombosis in patients. During the longer bedridden periods (5–8 weeks), the independent risk factors for deep vein thrombosis were female sex, smoking, and special treatment. Female sex was significantly associated with deep vein thrombosis in multivariate analysis (P< 0.05), which had been confirmed in previous studies. In a Norwegian study, the incidence of all first events of venous thromboembolism was 1.43 per 1000 person-years, and was slightly higher in women than in men (Naess et al., 2007). In a German cohort study, women presented 1.6 times more often for evaluation of suspected deep vein thrombosis (Bauersachs et al., 2010). In general, women with higher sex hormone levels were at higher risk for thrombophilia, with existing research confirming that combined oral contraceptives were associated with an increased risk of venous thrombosis (Stegeman et al., 2013). Thus, screening for thrombophilia should be considered for female patients. Smoking was also implicated as an independent predictive factor for deep vein thrombosis, which was also consistent with previous studies. A meta-analysis found that, 12
compared with nonsmokers, smokers had a statistically insignificant OR of 1.15 for venous thromboembolism (95% CI, 0.92–1.44) (Ageno, Becattini, Brighton, Selby, & Kamphuisen, 2008). Later, a large case–control study found a positive association between smoking and venous thromboembolism, with an OR of 1.43 (95% CI, 1.28–1.60) (Pomp, Rosendaal, & Doggen, 2008). Smokers were found to obtain increased levels of factor VII, prothrombin, factor XI peptide, and factor X peptide (Pomp et al., 2008), which may result in an increased risk of developing deep vein thrombosis. Regarding special treatment, in our study this referred to the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus. These special treatments not only increased the time patients stayed in bed but also restrained their activities or movement in bed, which may result in blood stasis and consequent deep vein thrombosis (Golemi, Salazar Adum, Tafur, & Caprini, 2019). During the prolonged bedridden periods (9–13 weeks), the only independent risk factor for deep vein thrombosis was the Charlson Comorbidity Index. It is recognized that patients tend to have a worse prognosis if the index is high (Charlson et al., 1987). In previous studies, patients with severe conditions were more likely to develop complications such as pressure injury and hospital-acquired pneumonia (Jiao et al., 2019; Liu et al., 2019). Previous research also suggested that congestive heart or respiratory failure, and malignant conditions (especially in the lung, pancreas, colon and rectum, kidney, and prostate) constituted the main risk factors for venous thromboembolism (Goldhaber, 2010). Patients with a higher Charlson Comorbidity Index thus tend to have a worse physical condition. Under such circumstances, longer bed-rest duration and inter-environment disturbance may cause deep vein thrombosis. In univariate analysis, patients with deep vein thrombosis tended to have a history of deep vein thrombosis, fracture/osteopathy diagnosis, and use of anticoagulant drugs, and these differences were statistically significant. However, in multivariate analysis these three factors were deleted from the models. For history of deep vein thrombosis, there may be some bias attributable to the limited number of patients. For diagnosis of fracture/osteopathy, the effect of this variable may be obscured by the independent risk factor of being admitted to a surgical ward. Besides, fracture/osteopathy included many different types, such as upper limb fracture, lower limb fracture, cervical spinal fracture, and lumbar fracture, which may also have effects on the results of a multivariate analysis. Regarding the use of anticoagulant drugs, there may be two reasons, the first being the heterogeneity of the type, doses, and usage of anticoagulants. The second is that the use of anticoagulant drugs was both a preventive measure and a treatment of deep vein thrombosis, which could result in bias. In this study, we provided a novel perspective different from that of previous literature. Instead of regarding being bedridden as a risk factor for deep vein thrombosis, we considered it as an overall condition and identified different risk factors in groups with different bed-rest durations, which was the major strength of our study. Given the fact that being bedridden is becoming more common, we thought it valuable to discern the variation in deep vein thrombosis risk factors among patients with different bed-rest durations. Although the data were collected retrospectively, the collecting process was under strict quality control: the case report form was subjected to several reliability and validity checks, and all definitions were determined in advance, to ensure standardized data entry across all of the medical centers and reduce the likelihood of errors. In addition, by combining data from 25 hospitals, our study created a sample size large enough to 13
simultaneously assess multiple risk factors in a multivariable model. However, this study had some potential limitations. Firstly, the retrospective data were incomplete; for example, patients' D-dimer indexes were absent. Secondly, considering the current situation of different medical centers each exercising considerable influence on the treatment of deep vein thrombosis, in this study medical center and bed-rest duration were selected as matching factors rather than the classification of disease, which might have introduced some bias. Further analysis of the potential threat of the disease itself in the course of deep vein thrombosis development is needed in the future. Thirdly, because patients with more than one major immobility complication were excluded from the database, the incidence of deep vein thrombosis may have been underestimated. 6. Conclusions In this study, we matched cases with deep vein thrombosis and controls without deep vein thrombosis, and performed multivariate analyses separately according to bed-rest duration category, which allowed us to clearly and reliably assess the risk factors for deep vein thrombosis. Risk factors for deep vein thrombosis varied among the bed-rest duration categories of 4 weeks or less, 5–8 weeks, and 9–13 weeks. Older age and admission to a surgical department were significantly associated with increased risk of deep vein thrombosis for patients with bed-rest durations ≤4 weeks. Female sex, smoking, and special treatment (including the use of gypsum, braces, traction, splints, hardboard beds, thoracoabdominal belts, and restraint apparatus) were independent factors predicting deep vein thrombosis for patients with bed-rest durations of 5–8 weeks. Higher Charlson Comorbidity Index was associated with a higher risk of developing deep vein thrombosis for those with bed-rest durations of 9–13 weeks. After a patient is admitted to hospital, attention should be paid to the key risk factors for deep vein thrombosis and more appropriate clinical options should be chosen for individuals who spend different amounts of time in bed. Acknowledgments The authors wish to thank the patients recruited across the 25 hospitals involved in this study. Financial support and sponsorship This work was supported by the National Health and Family Planning Commission (Beijing, China; grant number 201502017). Author Contributions J C, SY L, Z L, YF M, G L, Y L, J J, C Z, BY S, JF J, YL L, XX W, SZ C, and XJ W collected the data, J C and SY L performed the analysis and wrote the manuscript. JC assisted with performing and planning experiments. XJ W oversaw the project and reviewed the manuscript. X W assisted with the experimental setup. All authors read and approved the final manuscript. Declarations of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. No prior presentation.
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References Ageno, W., Becattini, C., Brighton, T., Selby, R., & Kamphuisen, P. W. (2008). Cardiovascular risk factors and venous thromboembolism: a meta-analysis. Circulation, 117(1), 93-102. doi:10.1161/circulationaha.107.709204 Bauersachs, R. M., Riess, H., Hach-Wunderle, V., Gerlach, H., Carnarius, H., Eberle, S., . . . Schellong, S. M. (2010). Impact of gender on the clinical presentation and diagnosis of deep-vein thrombosis. Thromb Haemost, 103(4), 710-717. doi:10.1160/th09-10-0705 Beckman, M. G., Hooper, W. C., Critchley, S. E., & Ortel, T. L. (2010). Venous thromboembolism: a public health concern. American Journal of Preventive Medicine,
38(4 Suppl), S495-501. doi:10.1016/j.amepre.2009.12.017 Brower, R. G. (2009). Consequences of bed rest. Crit Care Med, 37(10 Suppl), S422-428. doi:10.1097/CCM.0b013e3181b6e30a Brown, C. J., Friedkin, R. J., & Inouye, S. K. (2004). Prevalence and outcomes of low mobility in hospitalized older patients. Journal of the American Geriatrics Society, 52(8), 1263-1270. doi:10.1111/j.1532-5415.2004.52354.x Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
J Chronic Dis, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8 Dirks, M. L., Wall, B. T., van de Valk, B., Holloway, T. M., Holloway, G. P., Chabowski, A., . . . van Loon, L. J. (2016). One Week of Bed Rest Leads to Substantial Muscle Atrophy and Induces Whole-Body Insulin Resistance in the Absence of Skeletal Muscle Lipid Accumulation. Diabetes, 65(10), 2862-2875. doi:10.2337/db15-1661 15
Engbers, M. J., Blom, J. W., Cushman, M., Rosendaal, F. R., & van Hylckama Vlieg, A. (2017). Functional Impairment and Risk of Venous Thrombosis in Older Adults. Journal of the
American Geriatrics Society, 65(9), 2003-2008. doi:10.1111/jgs.14964 Goldhaber, S. Z. (2010). Risk factors for venous thromboembolism. Journal of the American
College of Cardiology, 56(1), 1-7. doi:10.1016/j.jacc.2010.01.057 Golemi, I., Salazar Adum, J. P., Tafur, A., & Caprini, J. (2019). Venous thromboembolism prophylaxis
using
the
Caprini
score.
Dis
Mon,
65(8),
249-298.
doi:10.1016/j.disamonth.2018.12.005 Jiao, J., Yang, X. Y., Li, Z., Zhao, Y. W., Cao, J., Li, F. F., . . . Sun, J. (2019). Incidence and Related Factors for Hospital-Acquired Pneumonia Among Older Bedridden Patients in China: A Hospital-Based Multicenter Registry Data Based Study. Front Public Health,
7, 221. doi:10.3389/fpubh.2019.00221 Kapoor, C. S., Mehta, A. K., Patel, K., & Golwala, P. P. (2016). Prevalence of deep vein thrombosis in patients with lower limb trauma. J Clin Orthop Trauma, 7(Suppl 2), 220-224. doi:10.1016/j.jcot.2016.07.003 Kortebein, P., Symons, T. B., Ferrando, A., Paddon-Jones, D., Ronsen, O., Protas, E., . . . Evans, W. J. (2008). Functional impact of 10 days of bed rest in healthy older adults. J
Gerontol A Biol Sci Med Sci, 63(10), 1076-1081. doi:10.1093/gerona/63.10.1076 Li, J., Wu, X., Li, Z., Zhou, X., Cao, J., Jia, Z., . . . Cheng, S. (2019). Nursing resources and major immobility complications among bedridden patients: A multicenter descriptive study in China. J Nurs Manag, 27(5), 930-938. doi:10.1111/jonm.12731 Li, Z., Liu, L., Wang, Y., Zhao, X., Wang, D. Z., Wang, C., . . . Wang, Y. (2015). Factors impact 16
the adherence rate of prophylaxis for deep venous thrombosis in acute ischaemic stroke patients: an analysis of the China National Stroke Registry. Neurological
Research, 37(5), 427-433. doi:10.1179/1743132815y.0000000035 Liu, Y., Wu, X., Ma, Y., Li, Z., Cao, J., Jiao, J., . . . Lin, F. (2019). The prevalence, incidence, and associated factors of pressure injuries among immobile inpatients: A multicentre, cross-sectional, exploratory descriptive study in China. Int Wound J, 16(2), 459-466. doi:10.1111/iwj.13054 Naess, I. A., Christiansen, S. C., Romundstad, P., Cannegieter, S. C., Rosendaal, F. R., & Hammerstrøm, J. (2007). Incidence and mortality of venous thrombosis: a population-based
study.
J
Thromb
Haemost,
5(4),
692-699.
doi:10.1111/j.1538-7836.2007.02450.x Pomp, E. R., Rosendaal, F. R., & Doggen, C. J. (2008). Smoking increases the risk of venous thrombosis and acts synergistically with oral contraceptive use. Am J Hematol, 83(2), 97-102. doi:10.1002/ajh.21059 Smith, E. B., Parvizi, J., & Purtill, J. J. (2011). Delayed surgery for patients with femur and hip fractures-risk of deep venous thrombosis. Journal of Trauma, 70(6), E113-116. doi:10.1097/TA.0b013e31821b8768 Spandorfer, J., & Galanis, T. (2015). In the Clinic. Deep venous thrombosis. Ann Intern Med,
162(9), Itc1. doi:10.7326/aitc201505050 Stegeman, B. H., de Bastos, M., Rosendaal, F. R., van Hylckama Vlieg, A., Helmerhorst, F. M., Stijnen, T., & Dekkers, O. M. (2013). Different combined oral contraceptives and the risk of venous thrombosis: systematic review and network meta-analysis. Bmj, 347, 17
f5298. doi:10.1136/bmj.f5298 Topp, R., Ditmyer, M., King, K., Doherty, K., & Hornyak, J., 3rd. (2002). The effect of bed rest and potential of prehabilitation on patients in the intensive care unit. AACN Clin Issues,
13(2), 263-276. doi:10.1097/00044067-200205000-00011 Wells, P. S., Forgie, M. A., & Rodger, M. A. (2014). Treatment of venous thromboembolism.
Jama, 311(7), 717-728. doi:10.1001/jama.2014.65 Wilson, D., Cooke, E. A., McNally, M. A., Wilson, H. K., Yeates, A., & Mollan, R. A. (2002). Altered venous function and deep venous thrombosis following proximal femoral fracture. Injury, 33(1), 33-39. doi:10.1016/s0020-1383(01)00137-1 WU, X., Cai, M., Cao, J., Jiao, J., Liu, G., Li, Z., . . . Li, F. (2018). The effects of standardized nursing on improvement of nursing quality among bedridden patients:a multi-centered study. Chinese Journal of Nursing, 53(06), 645-649. Xing, F., Li, L., Long, Y., & Xiang, Z. (2018). Admission prevalence of deep vein thrombosis in elderly Chinese patients with hip fracture and a new predictor based on risk factors for thrombosis
screening.
BMC
Musculoskelet
Disord,
19(1),
444.
doi:10.1186/s12891-018-2371-5 Zhu, T., Martinez, I., & Emmerich, J. (2009). Venous thromboembolism: risk factors for recurrence.
Arterioscler
Thromb
doi:10.1161/atvbaha.108.182428
18
Vasc
Biol,
29(3),
298-310.