Relationship Between red blood cell distribution width and Preoperative deep venous thrombosis of the lower extremity in elderly patients with hip fracture: A Propensity-Score Matched and Conditional Logistic Regression Analysis

doi:10.21203/rs.3.rs-2115987/v1

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Relationship Between red blood cell distribution width and Preoperative deep venous thrombosis of the lower extremity in elderly patients with hip fracture: A Propensity-Score Matched and Conditional Logistic Regression Analysis

https://doi.org/10.21203/rs.3.rs-2115987/v1

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Background: Deep vein thrombosis (DVT) was a severe and common complication that occurred in the patients with hip fractures, and the red blood cell distribution width (RDW) had been confirmed to be a risk factor of preoperative DVT. However, this relationship remains yet clear, and thus this study aimed to elucidate the relationship between RDW and preoperative DVT in the elderly patients with hip fractures.

Methods: We retrospectively collected the medical data of elderly patients (≥60 years) with hip fractures who were admitted to our hospital from October 2017 to December 2020. We used the receiver operating characteristic (ROC) curve to determine the optimal cut-off value of RDW, based on which, all patients were divided into the high or low RDW group. A propensity score matching (PSM) in a 1:1 ratio with the caliper value set as 0.02 was performed to balance variables and match cases with controls. Finally, the other potential confounding factors were controlled for by using conditional logistic regression analysis to identify the association between the RDW and preoperative DVT.

Results: A total of 2,520 patients were enrolled, the optimal cut-off value of RDW was 14.23%, and 1641 patients had a lower value, while 879 had a lower value. After PSM, 747 pairs constituted the study group. The standardized mean deviations (SMD) of all covariates were less than 0.10 (range, -0.06 to 0.04), indicating the groups were well matched. The conditional logistic regression analysis showed high RDW was associated with 1.83-fold increased risk (adjusted RR, 1.83; 95% CI, 1.28 to 2.62) of preoperative DVT after adjusting the covariates. In parallel, intertrochanteric fracture, high BMI, and longer time to surgery were identified as independent risk factors for preoperative DVT.

Conclusion: High RDW level, together with other risk factors, was identified to be independently associated with preoperative DVT in elderly patients with hip fractures, thus facilitating the individualized risk assessment, risk stratification and accordingly development of targeted preventive interventions.

red blood cell distribution width

deep vein thrombosis

hip fracture

elderly patients

Deep vein thrombosis (DVT) was one of the most prevalent complications of orthopedic surgeries, representing 8% to 19% based on study populations, surgery pattern, and study designs[1-6]. As was reported that, DVT was associated with an up to 8.1% all-cause mortality rate within 30 days of surgeries [7-9]. In addition, the post-thrombotic syndrome and recurrent thrombosis generally affected the patient quality of life, and even caused death [10]. Thus, early screening and prompt diagnosis of DVT remained the primary method for prevention and intervention during management of DVT.

D-dimer was widely used in practice for DVT diagnosis because of its high sensitivity, but elderly patients when the traditional threshold (0.5 mg/L) was used [11]. The doppler ultrasound (DUS) had both high sensitivity and specificity [12], nevertheless, it was not appropriate to be a tool to routinely screen DVT in patients planning to undergo urgent orthopedic surgery due to low cost-effectiveness and waste of time [13-15]. The red blood cell distribution width (RDW), reflecting the degree of heterogeneity of erythrocyte volume, demonstrated to be the novel and promising potential in predicting or screening DVT in a variety of contexts [16-20], especially in emergent surgery settings where it exhibited a significantly improved diagnostic performance [21]. Furthermore, a higher level of RDW demonstrated to be significantly and independently associated with the higher risk and the greater severity of DVT [18]. Specific at elderly hip fractures, the RDW level proved to be significantly increased because of the oxidative stress and inflammatory response, which was speculated to be directly related to thrombus formation [22,23]. Thus, elucidation of the relationship between RDW and the incident preoperative DVT may provide certain clinical significance in perioperative management of hip fractures in the elderly, but relevant studies are lacking.

In this study, we aimed first, to study the epidemiologic characteristics of preoperative DVT in patients with hip fractures, and second, to elucidate the relationship between the RDW and preoperative DVT.

Inclusion and exclusion criteria

The elderly patients who were admitted to the 3rd Hospital of Hebei Medical University due to hip fractures from October 2017 to December 2020 were included in the retrospective cohort study. Participants were enrolled in this study if they met the following criteria, all included patients aged ≥ 60 years and experienced both blood routine examination and preoperative DUS. Patients were excluded if they had the venous thrombus embolism (VTE) within 3 months, long-term anticoagulation treatment, multiple fractures, high-energy injuries, malignancy, pulmonary embolism, significant delay to admission or prolonged limb immobility, incomplete data, acute infection, acute heart failure, etc. A flow diagram of the patient inclusion process was shown in Figure 1.

Exposure

A published research suggested that the correlation between RDW and DVT remained, even RDW within its normal range [13], thus, we considered it more appropriate to group by using the cut-off value instead of the reference range.

Outcome (Preoperative DVT)

The diagnosis of DVT was performed according to the Robinov group's criteria, if any two or more of the following conditions had exited, DVT was confirmed [17]: filling defect, incompletely compressible vein, intraluminal thrombus, and poor phasic vibration with respiratory movements.

Covariables

A total of 32 variables from four aspects were included in the analysis. The demographics-related variables included sex, age, body mass index (BMI), hypertension, diabetes, heart disease, tumors, cigarette smoking, place of residence, history of operation. Surgery-related variables included time to surgery. The fracture-related variables included fracture type (femoral neck or intertrochanteric). The laboratory-related variables included total protein (TP), albumin (ALB), globulin (GLOB), alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), alkaline phosphatase (ALP), lactic dehydrogenase (LDH), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (LDLC), very low density lipoprotein (VLDL), albumin to globulin ratio (A/G), white blood cell (WBC), lymphocyte (LYM), monocyte count (MON), hemoglobin (HGB), hematocrit (HCT), red blood cell (RBC), mean corpuscular volume (MCV), mean hemoglobin concentration (MCHC), neutrophil count (NEUT), blood platelet (PLT), mean platelet volume (MPV), plateletcrit (PCT), platelet distribution width (PDW), prothrombin time (PT), prothrombin activity (PTA), partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), anti-thrombase III (AT III ), D-Dimer, and fibrinogen degradation product (FDP). And if a patient had the laboratory test more than once before the operation, the initial test was used for our analysis. The preoperative DUS result was obtained from the closest date to surgery.

Statistical analysis

Based on the distribution status, measurement data were expressed as mean ± standard deviation (SD) or the median and interquartile interval (IQR). The Shapiro-Wilk normality test was used to evaluate the normality. The student’s t test or Whitney U-test was used to evaluate the between-group difference. Categorical variables were expressed as frequency and percentage. And Statistical comparisons between groups were performed using Chi-square tests or Fisher's exact test.

The receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off value for age, BMI, time to surgery, and RDW. ROC curves were evaluated using the area under the curve (AUC), where AUC score of 0.50 indicates a random predictor. Thereafter, the original cohort was stratified into a high RDW group and a low RDW group by the cut-off value of RDW.

Propensity score matching (PSM) was used to nullify the potential confounding effects of interference factors from between-group distribution differences by matching each patient with high RDW and those with low RDW (in 1:1 match). Using the nearest-neighbor-matching method, we matched patients based on the 32 covariates with calipers of 0.02. The standardized mean differences (SMD) were used to assess covariate balance, with ≤0.1 indicating balance.

After matching, conditional logistic regression analysis was used to adjust the potential confounding factors and determine the relationship between RDW and DVT. In order to indicate the magnitude of association, we calculated the relative ratio (RR) value and 95% confidence interval (95% CI). Statistical analysis was performed by using SPSS 22.0 software (IBM, Armonk, NY) with the R software plug‐in (The R Foundation, Version 1.4.1), and Graph Pad Prism 9.0 software was used for mapping, differences were considered significant at P ˂0.05.

Finally, a total of 3761 patients with hip fractures were collected in the current study, and 2520 patients met the inclusion criteria. The ROC analysis showed the optimum cut-off value for RDW, age, time to surgery and BMI were 14.23%, 80.5 years (based on the clinical situation, we used 81 years), 7.5 days, and 22.04 kg/m², respectively (Table 1 and Figure 1). And 1641 patients had RDW value lower than 14.23%, and 879 had above 14.23%. After PSM, 747 pairs of patients constituted the study group (Figure 2).Before PSM, femoral neck fracture (51.8 vs 42.3), intertrochanteric fracture (48.2 vs 57.7),
diabetes (26.4 vs 20.7), tumor (2.3 vs 4.0), rural (50.6 vs 57.7), time to surgery (16.8 vs 22.2), smoking (5.9 vs 4.0), age (31.7 vs 38.6), ALB (84.0 vs 91.0), GLOB (13.2 vs 15.6), WBC (35.3 vs 35), NEU (56.4 vs 49.1), LYM (0.6 vs 0.5), MON (60.9 vs 53.0), RBC (0.1 vs 0.3), HGB (26.3 vs 19.6), HCT (29.5 vs 18.7), PLT (4.0 vs 8.4), MPV (3.7 vs 3.0), PDW (10.4 vs 9.8), PTA (1.5 vs 0.2), TT (6.2 vs2.4), FIB (41.4 vs 36.2), AT III (16.9 vs 22.9), D-D (19.1 vs 73.3), FDP (86.3 vs 77.6) differed between the high RDW group and the low RDW group. The preoperative DVT incidence rate was 12.6% (Table 2).

TABLE 1: The optimal cutoff value of continuity variables was determined by ROC analysis

Variables	Cut-off value	AUC	95% CI	P value
RDW	14.23	0.54	(0.51,0.58)	0.018*
Age (year)	80.50	0.52	(0.48,0.55)	0.384
Time to surgery (day)	7.50	0.69	(0.66,0.73)	<0.001*
BMI	22.04	0.59	(0.56,0.62)	<0.001*

Abbreviations: RDW, red blood cell distribution width; BMI, body mass index; AUC, the area under the ROC curve; 95% CI, 95% confidence interval.

*: P<0.05, it indicates statistically significant differences.

TABLE 2: Baseline characteristics of patients with hip fracture before and after PS matching

Variables	Before matching (N=2,520)				After matching (N=1,494)
	Low RDW (n=1,942)	High RDW (n=878)	SMD	P value	Low RDW (n=747)	High RDW (n=747)	SMD	P value
Fracture type (intertrochanteric)	791 (48.2)	507 (57.7)	0.192	<0.001*	419 (56.1)	412 (55.2)	-0.019	0.715
Hypertension	869 (53.0)	479 (54.5)	0.031	0.461	405 (54.2)	409 (54.8)	0.011	0.835
Diabetes	433 (26.4)	182 (20.7)	-0.140	0.002*	174 (23.3)	166 (22.2)	-0.026	0.622
Heart disease	540 (32.9)	287 (32.7)	-0.005	0.896	243 (32.5)	254 (34.0)	0.031	0.546
Tumor	38 (2.3)	35 (4.0)	0.085	0.017*	28 (3.7)	26 (3.5)	-0.014	0.782
Place of residence (urban)	811 (49.4)	372 (42.3)	-0.144	0.001*	327 (43.8)	330 (44.2)	0.008	0.876
Time to surgery (>7.5 days)	276 (16.8)	195 (22.2)	0.129	0.001*	162 (21.7)	161 (21.6)	-0.003	0.950
Smoking	97 (5.9)	35 (4.0)	-0.099	0.038*	36 (4.8)	33 (4.4)	-0.021	0.712
Gender (male)	539 (32.8)	274 (31.2)	-0.036	0.392	243 (32.5)	234 (31.3)	-0.026	0.617
Age (≥81 years)	520 (31.7)	339 (38.6)	0.141	0.001*	292 (39.1)	285 (38.2)	-0.019	0.710
BMI (≥22.04 kg/m²)	1054 (64.2)	546 (62.1)	0.044	0.294	447 (59.8)	470 (62.9)	-0.063	0.222
ALB (˂40 g/L)	1379 (84.0)	800 (91.0)	-0.244	<0.001*	675 (90.4)	675 (90.4)	0.000	1.000
GLOB (˂20 g/L)	217 (13.2)	137 (15.6)	-0.082	0.039*	107 (14.3)	119 (15.9)	-0.043	0.687
WBC (>9.5⨯10⁹/L)	580 (35.3)	308 (35.0)	-0.006	0.035*	277 (37.1)	275 (36.8)	-0.006	0.994
NEU (>6.3⨯10⁹/L)	926 (56.4)	432 (49.1)	-0.146	0.001*	404 (54.1)	394 (52.7)	-0.027	0.726
LYM (<1.1⨯10⁹/L)	859 (52.3)	407 (46.3)	-0.023	0.012*	376 (50.3)	368 (49.3)	<0.001	0.917
MON (>0.6⨯10⁹/L)	999 (60.9)	466 (53.0)	-0.157	0.001*	418 (56.0)	418 (56.0)	<0.001	1.000
RBC (< lower limit)	1135 (69.2)	680 (77.4)	0.038	<0.001*	574 (76.8)	570 (76.3)	0.886	0.886
HGB (˂ lower limit)	431 (26.3)	172 (19.6)	0.159	<0.001*	590 (79.0)	589 (78.8)	-0.003	0.710
HCT (˂ lower limit)	484 (29.5)	164 (18.7)	0.270	<0.001*	597 (79.9)	591 (79.1）	-0.020	0.854
PLT (> higher limit)	65 (4.0)	74 (8.4)	0.160	<0.001*	47 (6.3)	49 (6.6)	0.010	0.687
MPV (>11.0 f L)	61 (3.7)	26 (3.0)	-0.045	<0.001*	25 (3.3)	24 (3.2)	-0.008	0.982
PDW (>18.1 %)	22 (1.3)	27 (3.1)	0.100	<0.001*	16 (2.1)	16 (2.1)	<0.001	0.694
PT (˂ lower limit)	24 (1.5)	7 (0.8)	-0.076	0.148	7 (0.9)	6 (0.8)	-0.015	0.781
PTA (> higher limit)	24 (1.5)	2 (0.2)	-0.259	0.008*	6 (0.8)	2 (0.3)	-0.112	0.325
APTT (˂ lower limit)	527 (32.1)	3.1 (34.2)	-0.042	0.278	254 (34.0)	248 (33.2)	0.014	0.742
TT (>higher limit)	102 (6.2)	21 (2.4)	-0.250	<0.001*	25 (3.3)	20 (2.7)	-0.044	0.698
FIB (>higher limit)	679 (41.4)	318 (36.2)	0.114	0.026*	301 (40.3)	291 (39.0)	0.039	0.589
AT III (˂ lower limit)	2788 (16.9)	201 (22.9)	0.141	<0.001*	164 (22.0)	160 (21.4)	-0.013	0.932
D-D (>0.5 mg/L)	1327 (19.1)	644 (73.3)	0.172	<0.001*	578 (77.4)	583 (78.0)	-0.015	0.756
FDP (>5 mg/L)	1416 (86.3)	682 (77.6)	0.209	<0.001*	618 (82.7)	612 (81.9)	0.019	0.684

Abbreviations: RDW, red blood cell distribution width; SMD, standardized mean deviations; BMI, body mass index; ALB, albumin; GLOB, globulin; WBC, white blood cell; NEU, neutrophil; LYM, lymphocyte; MON, monocyte count; RBC, red blood cell; HGB, hemoglobin; HCT, hematocrit; PLT, blood platelet; MPV, mean platelet volume; PDW, platelet distribution width; PT, prothrombin time; PTA, prothrombin activity; APTT, partial thromboplastin time; TT, thrombin time; FIB, fibrinogen; AT III, anti-thrombase III; D-D, D-Dimer; FDP, fibrinogen degradation product.

*: P<0.05, it indicates statistically significant differences.

After PSM, 747 pairs of patients were identified. The SMD of all included covariates which ranged from -0.063 to 0.039 were less than 0.10 and indicated that the groups were relatively well matched. There were no statistically significant differences in all covariates between the high RDW group and the low RDW group (P>0.05) (Table 2).

The result of conditional logistic regression analyses revealed no significant association of factors with preoperative DVT, except RDW (P=0.001; 95% CI, 1.28 to 2.62), fracture type (P=0.003; 95% CI, 0.20 to 0.72), BMI (P=0.002; 95% CI, 1.39 to 4.18), and time to surgery (P<0.001; 95% CI, 1.89 to 7.35) (Table 3). And both before and after PSM, there existed a correlative relationship between RDW and preoperative DVT in elderly patients with hip fractures. RDW was associated with 1.83-fold increased risk (adjusted RR, 1.83; 95% CI, 1.28 to 2.62) of preoperative DVT, and it showed the 1.28-fold increased risk in raw data (crude RR, 1.28; 95% CI, 1.15 to 1.43) and 0.78-fold increased risk in the unadjusted data (RR, 0.78; 95% CI, 0.70 to 0.88). The preoperative DVT incidence rate was 15.53% (Table 4).

TABLE 3: Risk for preoperative DVT

Variables	RR	95% CI	P value
RDW	1.83	1.28-2.62	0.001*
Fracture type (intertrochanteric)	2.65	1.40-5.04	0.003*
Hypertension	0.94	0.56-1.56	0.802
Diabetes	1.36	0.69-2.66	0.376
Heart disease	0.92	0.52-1.61	0.761
Tumor	1.19	0.30-4.63	0.807
Place of residence (urban)	1.70	0.97-2.98	0.064
Time to surgery (>7.5 days)	3.73	1.89-7.35	<0.001*
Smoking	3.11	0.81-11.92	0.099
Gender (male)	0.59	0.30-1.15	0.120
Age (≥81 years)	0.94	0.53-1.68	0.839
BMI (≥22.04 kg/m²)	2.41	1.39-4.18	0.002*
ALB (˂40 g/L)	1.38	0.45-4.25	0.573
GLOB (˂20 g/L)	0.76	0.39-1.46	0.401
WBC (>9.5⨯10⁹/L)	0.85	0.55-1.32	0.479
NEU (>6.3⨯10⁹/L)	0.99	0.66-1.48	0.954
LYM (˂1.1⨯10⁹/L)	0.98	0.58-1.66	0.947
MON (>0.6⨯10⁹/L)	1.05	0.73-1.50	0.798
RBC (˂ lower limit)	0.64	0.27-1.51	0.312
HGB (˂ lower limit)	0.92	0.40-2.10	0.845
HCT (˂ lower limit)	0.77	0.31-1.91	0.575
PLT (> higher limit)	1.19	0.71-2.00	0.507
MPV (>11.0 f L)	0.81	0.36-1.82	0.602
PDW (>18.1 %)	0.74	0.36-1.53	0.419
PT (˂ lower limit)	0.75	0.46-1.24	0.265
PTA (> higher limit)	0.85	0.33-2.20	0.742
APTT (˂ lower limit)	1.36	0.85-2.19	0.203
TT (> higher limit)	1.14	0.66-1.97	0.644
FIB (> higher limit)	1.01	0.76-1.34	0.949
AT III (˂ lower limit)	0.83	0.51-1.37	0.466
D-D (>0.5 mg./L)	1.03	0.45-2.16	0.938
FDP (>5 mg/L)	1.49	0.70-3.20	0.302

Abbreviations: RR, relative ratio; 95% CI, 95% confidence interval; RDW, red blood cell distribution width; BMI, body mass index; ALB, albumin; GLOB, globulin; WBC, white blood cell; NEU, neutrophil; LYM, lymphocyte; MON, monocyte count; RBC, red blood cell; HGB, hemoglobin; HCT, hematocrit; PLT, blood platelet; MPV, mean platelet volume; PDW, platelet distribution width; PT, prothrombin time; PTA, prothrombin activity; APTT, partial thromboplastin time; TT, thrombin time; FIB, fibrinogen; AT III, anti-thrombase III; D-D, D-Dimer; FDP, fibrinogen degradation product.

*: P<0.05, it indicates statistically significant differences.

TABLE 4: Comparison of the RR Value in Different Settings

Outcome	Preoperative RDW (%)	Crude			PSM
		Event/N	RR (95% CI)	P	Event/N	Before Adjusted		After Adjusted
						RR (95% CI)	P	RR (95% CI)	P
RDW	˂14.23 ≥14.23	150/1641 168/879	1.28 （1.15-1.43）	<0.001*	90/747 142/747	1.58 (1.24-2.01)	<0.001*	1.83 (1.28-2.62)	<0.001*

Abbreviations: RR, relative ratio; 95% CI, 95% confidence interval; PSM, propensity score matching.

*: P<0.05, it indicates statistically significant differences.

There were two main findings in this study. Firstly, the increased RDW was associated with an increased risk of preoperative DVT either before or after PSM. Secondly, intertrochanteric fracture, BMI, and a long time to surgery also were confirmed to be independent risk factors in our cohort. As far as we know, the research was the first to show a relatively clear elucidation that RDW was associated with preoperative DVT in elderly patients with hip fractures.

The previous studies had widely reported that RDW was related to the DVT in different population of patients [24, 25]. The mechanism was not clear, but there were some potential explanations. Some scholars believed that increased RDW generally represented higher levels of oxidative stress damage and inflammatory status [26], thus contributing an important role in DVT formation [27]. Hip fractures and senility could create a large number of free radicals, and RBC was damaged by oxidative stress, resulting in increased fragility, decreased viability, and decreased deformability [28]. When immature RBC was involved in the regulation of blood circulation, RDW subsequently increased [29]. On the other hand, increased RDW reflected the higher inflammation status in elderly patients with hip fractures. The fracture healing started with the inflammatory process, and pro-inflammatory cytokines increased dramatically with the onset of initial fracture [30-32]. The pro-inflammatory cytokines hampered RBC maturation by inhibiting iron metabolism and erythropoiesis, and thus resulting a higher RDW [33]. As was well-established that oxidative stress and inflammatory status were risk factors for DVT, which could change the morphology, deformability, and half-life of red blood cells, leading to the tendency of RBC to aggregate, causing increased blood viscosity, slowing blood flow, and ultimately promoting thrombosis [34-36].

In this study we have identified other factors associated with preoperative DVT, including BMI, fracture type, and time to surgery, consistent with most prior studies. Zuo et al. found the 2.9-fold increased the risk of incident DVT in the elderly patients with intertrochanteric fracture who had a BMI greater than 28 kg/m² [37]. But this relationship might not be applicable to patients undergoing elective arthroplasty procedures [38]. Different from prior findings, we found that the BMI ≥22.04 kg/m²was associated with an increased risk of preoperative DVT by 2.4-fold, a somewhat strange result and we have not found a reasonable explanation for this finding, which deserved further investigation. Intertrochanteric fracture as an independent risk factor for preoperative DVT had been repeatedly investigated in literature, including a recent retrospective study which showed a comparable OR of 2.58 with ours [39]. The delayed surgery should be attributed to multiple steps involving the referral, delayed diagnosis, and a shortage of surgical resources or personnel, sometimes the prolonged preoperative time was unavoidable in most tertiary referral hospitals in China [40-42]. Accordingly, to reduce the number of thrombotic events, minimizing waiting time by optimizing clinic flow, weight control and meticulous fracture evaluation (type, displacement, soft-tissue, and hidden blood loss) should be considered, and patients anticipated to be substantially delayed to surgery should be treated a priority in DVT risk evaluation.

There were some strengths of the study, including use of a cut-off value instead of the traditional reference range for defining RDW, PSM method to balance the variables and the conditional regression analysis to further minimize the confounding effects. However, several shortcomings also existed. Data collection in retrospective collection manner may have caused the risk of data inaccuracy. Second, it should be noted that the relationship between RDW and preoperative DVT should be interpreted as association rather than causation. Third, the study was conducted within a single institution, which weakened the generalizability of our findings to others.

This study demonstrated there existed a correlative relationship between RDW and preoperative DVT in elderly patients with hip fractures. This index was readily available, efficient and inexpensive, which can be considered for use in routine clinical practice for risk stratification and prognosis assessment.

DVT: Deep vein thrombosis; RDW: Red blood cell distribution width; ROC: Receiver operating characteristic; PSM: Propensity score matching; SMD:Standardized mean deviations; DUS: Doppler ultrasound; VTE:Venous thrombus embolism; BMI: Body mass index; TP: Total protein; ALB: Albumin; GLOB: Globulin; ALT: Alanine transaminase; AST: Aspartate aminotransferase; TBIL: Total bilirubin; DBIL: Direct bilirubin; IBIL: Indirect bilirubin; ALP: Alkaline phosphatase; LDH: Lactic dehydrogenase; TC: Total cholesterol; TG: Triglyceride; LDLC: High-density lipoprotein; VLDL: Very low density lipoprotein; A/G: Albumin to globulin ratio; WBC: White blood cell; LYM: Lymphocyte; MON: Monocyte count; HGB: Hemoglobin; HCT: Hematocrit; RBC: Red blood cell; MCV: Mean corpuscular volume; MCHC: Mean hemoglobin concentration; NEUT: Neutrophil count; PLT: Blood platelet; MPV: Mean platelet volume; PCT: Plateletcrit; PDW: Platelet distribution width; PT: Prothrombin time; PTA: Prothrombin activity; APTT: Partial thromboplastin time; TT: Thrombin time; FIB: Fibrinogen; AT III: Anti-thrombase III; FDP: Fibrinogen degradation product; SD: Standard deviation; IQR: Interquartile interval; AUC: Area under the curve; SMD: Standardized mean differences; RR: Relative ratio.

Acknowledgments

We are grateful to the Institute of Orthopedics of the Third Hospital of Hebei Medical University for providing data.

Authors’ Contributions

ZKX participated in the study design and drafted the manuscript. and ZYB participated in the statistical analysis and interpreted the results. ZKX and ZYB should be considered co-first authors. LXT provided the original idea and WJL revised the articles; TYX and TM participated in data collection.

Funding

This research has no financial relationship with commercial interests.

Availability of data and materials

Data is available from the authors upon request.

Ethic approval and consent to participate

The study was a database research project and did not involve any interventions or contact with patients, which is available to registered researchers Individual patients were not identifiable. All methods of this study were carried out in accordance with the guidelines of the Declaration of Helsinki and were approved by the Ethics Committee of the Third Hospital of Hebei Medical University, and written informed consent was obtained prior to participation of all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that there is no conflict of interest to disclose.

Author details

¹Department of orthopaedic surgery, the 3rd Hospital of Hebei Medical University, Shijiazhuang 050051, Hebei, P. R. China. ²Orthopaedic Institution of Hebei Province, Key Laboratory of Biomechanics of Hebei Province, Shijiazhuang 050051, Hebei, P.R. China. ³ Department of Orthopaedic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.

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Relationship Between red blood cell distribution width and Preoperative deep venous thrombosis of the lower extremity in elderly patients with hip fracture: A Propensity-Score Matched and Conditional Logistic Regression Analysis

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