Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

Deep vein thrombosis (DVT) is a common but preventable complication in hospitalized stroke patients [1‐3]. Although the incidence of clinically evident DVT after stroke is generally between 2 and 20% [4, 5], this rate has been reported to increase to 75% in immobilized post-stroke patients who do not receive prophylaxis [6]. In the absence of preventive measures, first-time DVT may occur as early as the second day after stroke onset and occurrence peaks from 2 to 7 days post-stroke [5, 7].

In particular, post-stroke DVT predominantly affects the paretic or plegic leg [5] and approximately two-thirds of cases are observed below the knee [8]. Isolated distal deep vein thrombosis (IDDVT) is thrombosis restricted to the infra-popliteal deep veins that account for approximately 23–59% of all DVT cases [9]. The incidence of IDDVT in inpatients range from 12 to 17%, and it can lead to poor prognosis including high rate of death and the recurrence rate of venous thromboembolism (VTE) [10‐13]. Meanwhile, a review summarized that about 25–33% of IDDVT patients without treatment extended proximally, which would lead to a high risk of pulmonary embolism (PE) [14]. A prospective study showed that the rate of PE in patients with IDDVT was 8.7% [15]. Besides, long-standing IDDVT is associated with post-thrombotic syndrome (PTS), which presents as venous insufficiency and reduces the patient’s quality of life [16].

The identification of patients with a high risk of early IDDVT is crucial due to its adverse clinical outcomes. Currently, compression ultrasound (CUS) is the most common tool for the diagnosis of IDDVT. It is performed in patients with suspected leg DVT by complete CUS examination of all deep veins [14]. However, the clinical judgment of “suspected” had subjectivity and recent study found the sensitivity and specificity of CUS was not satisfactory for IDDVT [12]. Furthermore, due to the increasing number of asymptomatic post-stroke IDDVT cases [17], CUS may sometimes be relatively too late to detect early IDDVT and perform beneficial interventions, which results in poor clinical outcomes. Currently, due to the lack of objective and reliable clinical signs and symptoms, there are no accurate prediction methods for the early detection of IDDVT to initiate timely CUS monitoring and interventions. Hence, a simple and evidence-based method for assessing a patient’s individual risk of early IDDVT after stroke becomes much needed in the healthcare settings.

Recently, with the advantages of easily accessible format, nomograms have been used to provide accurate risk assessments of specific clinical outcomes for individual patients based on significant factors. Although nomograms appear to be a tool that can assist clinical staff in determining diagnostic and therapeutic strategies for various diseases [18, 19], they have not yet been used for the prediction of IDDVT.

The early detection of patients at a high risk of IDDVT would be helpful for disease management and improving the prognoses of patients. Thus, the present study aimed to establish and validate a predictive nomogram model that could effectively identify early IDDVT risk in acute ischemic stroke (AIS) patients. This would aid clinicians in identifying patients with a higher risk of early IDDVT and enable these patients to receive CUS and appropriate therapies in time.

In this study, we established and validated a novel and simple nomogram-derived score for predicting the occurrence of early IDDVT in AIS patients within 7 days of stroke onset. And this score was applicable to AIS patients who did not have previous DVT. The IDDVT nomogram prediction model identified several factors that were associated with the patient profile, including age, gender, lower limb paralysis, malignant tumor, pneumonia, and atrial fibrillation. This model exhibited good discrimination in both the training and validation cohorts. The present nomogram model can be easily applied to daily clinical practice and can be helpful for the screening of AIS patients who may be at a high risk of IDDVT, which would be benefit to initiate CUS and interventions timely.

DVT, as a common complication in hospitalized AIS patients, may delay the rehabilitation of stroke, cause postphlebitic leg, varicose ulcers and PE [2]. As is known, IDDVT accounts for approximately 23–59% of all DVT cases [9] and the incidence of IDDVT in inpatients range from 12 to 17%. 25–33% of IDDVTs who without treatment extended proximally [14]. Thus, consistent with DVT, IDDVT could lead to poor prognosis including high rate of death, the recurrence rate of VTE, PE and PTS [10‐14, 16]. Meanwhile, proximal DVT (symptomatic) and PE are easily to recognize and diagnosis in time. While IDDVT used to present as asymptomatic and indiscoverable; and the most common tool for the diagnosis of IDDVT is CUS with unsatisfactory sensitivity and specificity [12]. Our predictive nomogram model could effectively help clinicians in identifying patients with a higher risk of early IDDVT and enable these patients to receive CUS and appropriate therapies in time.

Consistent with previous studies, the present findings revealed that gender, age, and a history of atrial fibrillation could contribute to the development of IDDVT. In particular, the present study showed that atrial fibrillation played an important role in the development of DVT. A Northern Norway study found that atrial fibrillation is associated with an increased risk of subsequent VTE [24], which is similar to the findings of a population-based cohort study conducted in China [25]. Because atrial fibrillation can lead to venous stasis, this may lead to a subsequent increase in the risk of VTE through the activation of coagulation factors and platelets [26]. For example, a recent review found that inflammation and various growth factors may play potential roles in promoting a pro-thrombotic state during atrial fibrillation [27]. In terms of gender, many studies have found that female patients are more likely to develop DVT after stroke [2, 28]; this is consistent with our results. Age is also a well-known risk factor of IDDVT that many prediction models have identified as a predictive factor [2, 20‐22]. Additionally, some studies have noted that the incidence of VTE is expected to increase with age [29, 30]. The present results also suggest that malignant tumor is commonly related to IDDVT. Cancer is doubtlessly a major risk factor for VTE as it accounts for approximately 20% of VTE cases [31] and several studies found that patients with cancer-related IDDVT are more likely to relapse into VTE [32‐34]. Furthermore, a review study concluded that some cancer-associated pathways may be related to the onset of VTE. For example, cancers may increase the risk of VTE by producing neutrophil extracellular traps, inducing monocytes to express tissue factor (TF), and leading to thrombocytosis [35]. Similarly, cancer treatments, such as immunosuppressive or cytotoxic chemotherapy, may have similar effects. A case study of a patient with leukemia who received L-asparaginase therapy described the development of vein thrombosis due to therapy-induced reductions of plasma plasminogen and antithrombin III levels in the patient [36]. Additionally, two placebo-controlled studies showed that lenalidomide and dexamethasone for the treatment of multiple myeloma increase the incidence of venous thrombosis [37].

The present IDDVT prediction model identified lower limb paralysis as a risk factor. A case-control study with a large sample size conducted in the United States found that leg paresis is an independent risk factor of vein thrombosis, with an odds ratio of 6.10 [38]. Moreover, several previous studies have reported that the severity of paralysis is associated with DVT [5, 39]. This may be due to the fact that lower limb paralysis prolongs time spent in a hospital bed and leg immobility time, which may lead to venous stasis [40] and ultimately DVT. Interestingly, the present study showed that pulmonary infection was also an independent predictor of IDDVT. Several possible mechanisms may explain the link between pneumonia and DVT. An animal study in which platelets were extracted from wild-type mice and transplanted into Toll-like receptor (TLR)-4-deficient mice that then received either bacteria-produced lipopolysaccharide (LPS) or saline injections revealed that LPS initiates thrombosis via TLR-4 [41]. Furthermore, TF may contribute to thrombosis in pneumonia patients [42]. For example, TF protein levels in pulmonary edema fluid of acute lung injury patients are 100 times higher than their simultaneous plasma samples [43]. This finding provides another mechanism by which the infected alveolar epithelium may initiate thrombosis via the upregulation of TF. Furthermore, Levi et al. [44] showed that the protein C system is important during the coagulation/inflammatory response and that impairments in the activation of this system may lead to thrombosis in pneumonia patients. Taken together, these findings suggest that the process by which pneumonia causes DVT is complex and likely involve complementary actions of several mechanisms. Further research is required to validate these mechanisms.

Many models are available to predict DVT in stroke patients. The IMPROVE score is a model for predicting the risk of thrombosis in acutely ill hospitalized patients [21], whereas our model was primarily developed for stroke patients in common wards. The Padua score was empirically generated and has been verified in a cohort study [22]. However, relative to the two abovementioned models, our model is so straightforward that it is possible to understand it at a glance while still being being comprehensive. And our prediction object is IDDVT that few studies has predicted so far. The Liu score is a clinical risk assessment for predicting the incidence rate of DVT in patients with acute stroke within 2 weeks, which was developed using a multicenter prospective cohort with a large sample size [2]. Compared to the Liu score, we added two additional factors, pulmonary infection and atrial fibrillation, into a nomogram model developed specifically to predict the risk of early IDDVT in hospitalized patients with stroke. With the currently available prediction tools, nomograms represent a vital component of the modern medical decision-making model due to their high accuracy and good discrimination characteristics [19, 45]. To our knowledge, there are few models to predict early IDDVT. This nomogram is the first developed model for predicting the risk of IDDVT in patients with AIS. Compared to the IMPROVE score (AUROC = 0.750), Liu score (AUROC = 0.760), and Padua score (AUROC = 0.752) for predicting IDDVT in patients with AIS, the present nomogram model exhibited the best predictive accuracy for the occurrence of IDDVT (AUROC = 0.820). Additionally, the nomogram was validated using a larger cohort for IDDVT prediction in AIS patients.

The present study has several limitations that should be noted. First, the final nomogram was established based on a retrospective analysis of a single-center database. Thus, it is necessary to validate the generalizability of the final nomogram in other centers, and a prospective study is needed to further confirm its reliability. Second, the database used in the present study did not include potential predictors previously shown to affect DVT, such as ABO blood type, and sleep apnea [46, 47]; these markers are not routinely tested for in the clinical practice of AIS patients. Third, the effect of different dose levels of anticoagulation (prophylactic doses versus therapeutic doses) was not analyzed in our study. Fourth, the final nomogram was applicable to AIS patients who did not have previous DVT. Finally, the laboratory variables included in the nomogram were not dynamically measured.

In conclusion, the present study established and validated a reliable nomogram with good accuracy and discrimination for the individualized prediction of the risk of early IDDVT. And this score was applicable to AIS patients who did not have previous DVT. This nomogram is an easy-to-use tool for the early diagnosis and prevention of IDDVT and may be helpful for clinicians when making decisions and implementing effective preventive interventions according to the individual risks of specific patients.