Deep vein thrombosis in severe community-acquired pneumonia patients undergoing thromboprophylaxis: Prevalence, risk factors, and outcome

doi:10.21203/rs.3.rs-4376169/v1

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Research Article

Deep vein thrombosis in severe community-acquired pneumonia patients undergoing thromboprophylaxis: Prevalence, risk factors, and outcome

https://doi.org/10.21203/rs.3.rs-4376169/v1

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Background:

Even with adherence to thromboprophylaxis recommended by guidelines, the incidence of deep vein thrombosis (DVT) remains high among patients with severe community-acquired pneumonia (SCAP). There is an urgent need to identify the risk factors for DVT in these patients to optimize preventive strategies.

Study Design and Methods: We retrospectively enrolled 309 adults with SCAP admitted to Beijing Chao-Yang Hospital between 1 January 2015 and 30 June 2023. All patients received guideline-recommended thromboprophylaxis and lower extremity venous compression ultrasound scanning. Clinical characteristics, including demographic information, clinical history, vital signs, laboratory findings, treatments, complications, and outcomes, were analyzed for patients with and without DVT in these two cohorts.

Results: Of the 309 patients, 110 (35.6%) developed 1ower extremity DVT. There was no significant difference in the incidence of DVT among the different prophylactic measures (P = 0.393). Multivariate logistic regression analysis showed an association between a history of VTE (OR, 20.056, 95% CI: 3.740 ~ 107.540; P < 0.001), longer bedridden time (3 days < bedridden times ≤ 7 days: OR, 6.580, 95% CI: 1.884 ~ 22.988, P = 0.003; bedridden times ≥ 7 days: OR, 32.050, 95% CI: 9.629 ~ 106.675, P < 0.001), D-dimer levels ≥ 1.0 µg/mL(OR, 2.433, 95% CI: 1.123 ~ 5.272; P = 0.024), LDH levels ≥ 400 U/L (OR, 2.269, 95% CI: 1.002 ~ 5.138; P = 0.049), IMV (OR, 2.248, 95% CI: 1.081 ~ 4.672; P = 0.030) and the occurrence of DVT. A new prediction model, including age, history of VTE, bedridden time, D-dimer levels, LDH levels and IMV, showed a better performance in predicting DVT (AUC = 0.830; 95% CI: 0.746 ~ 0.913; sensitivity: 66.1%; specificity: 90.0%) than Padua prediction score (AUC = 0.666) and Caprini prediction score (AUC = 0.688) for patients with SCAP. The 30-day mortality and in-hospital mortality in the DVT group were significantly higher than those in the non-DVT group.

Conclusions: Even received guideline-recommended thromboprophylaxis, the prevalence of DVT among patients with SCAP remains unexpectedly high which is also associated with a poor prognosis. It is necessary to identify people at high risk of DVT early and refine the preventive strategies accordingly to improve patient outcomes.

Community-acquired pneumonia

Severe

Deep vein thrombosis

Thromboprophylaxis

Invasive mechanical ventilation

Community-acquired pneumonia (CAP) is an infectious inflammatory condition of the pulmonary parenchyma that is contracted outside of a healthcare facility. Although the in-hospital mortality for CAP has declined over the past decade [1], the 30-day mortality for severe community-acquired pneumonia (SCAP) remains distressingly high [2–4]. Venous thromboembolism (VTE), encompassing both deep vein thrombosis (DVT) and pulmonary embolism (PE), has been increasingly recognized as a complication following acute respiratory infections since 2006 [5], with the risk positively correlating with the severity of the infection [6–10]. The interplay of inflammation, coagulation and fibrinolytic abnormalities, endothelial injury, and thrombotic events constitutes a significant pathophysiological process in severe infections.

The risk of VTE increases with the number of precipitating factors [11, 12], with different factors contributing to varying degrees of VTE risk [12]. Patients with SCAP exhibit a profound inflammatory response, hypercoagulability and blood stasis caused by reduced mobility [8, 13], all of which are risk factors for VTE [14]. Our previous research found that patients with acute respiratory distress syndrome (ARDS) due to bacterial pneumonia and Corona Virus Disease 2019 (COVID-19) had an alarming DVT incidence rate of 41.5% and 57.1%, respectively [15]. Additionally, some studies have indicated that the preventive strategies recommended by current guidelines are not entirely effective in preventing DVT in critically ill patients[15–19]. Given the already high mortality among SCAP patients [2–4], the risk of death is further increased by the complication of DVT. Our previous research has indicated that among ARDS patients caused by SCAP, those with concurrent DVT experienced a significantly higher 28-day mortality compared to those without DVT [15, 18, 19]. Therefore, for this specific cohort of critically ill patients with SCAP, especially those who develop DVT despite undergoing thromboprophylaxis, the early identification of DVT risk factors and targeted prevention is of paramount importance. Consequently, we conducted a retrospective analysis of SCAP patients who received guideline-recommended VTE prophylaxis to elucidate the risk factors for this population, with the aim of implementing more optimized preventive strategies for those at high risk of DVT.

Study Design and Population

We retrospectively enrolled adult patients (≥ 18 years old) with SCAP who were admitted into Department of Pulmonary and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China, between 1 January 2015 and 30 June 2023. All patients were included consecutively and met the criteria of SCAP according to Clinical Practice Guideline of the American Thoracic Society (ATS) and Infectious Diseases Society of America (IDSA) [20].

The exclusion criteria were: active malignant tumor, cerebral stroke, acute myocardial infarction, serious trauma (injury severity score > 16), major operation lasting longer than 45 minutes, fracture of the lower limb, and joint replacement of hip or knee within one month before admission. Patients who had already been diagnosed with VTE prior to admission and were undergoing anticoagulant therapy, patients with a survival time less than one day and patients without lower extremity venous compression ultrasound data were also excluded.

The first ultrasound examination was performed within 1–3 days after admission. After intensive treatment, if the patient’s condition was unstable (e.g., due to unexplained hypoxemia or cardiac insufficiency), ultrasound was performed again. If there was more than one ultrasound scan for a single patient, all the results were recorded. Patients were divided into a DVT and a non-DVT group according to the results of the venous compression ultrasound of the lower extremities. The study flow chart is shown in Fig. 1 (A, B).

The study was approved by the ethics committees of the Beijing Chao-Yang Hospital (2024-ke-335), and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Clinical Data

Medical records, including demographic information, clinical history, vital signs, laboratory findings, treatments, complications, and outcomes of the patients during hospitalization, were collected and analyzed for all patients. We also analyzed the survival rates of all patients within 30 days after a diagnosis of SCAP. For patients discharged within 30 days, we followed up by phone concerning their survival status after discharge.

Ultrasound Assessment

Bedside ultrasound examinations were performed using a portable color ultrasound scanner (CX50, Philips Medical Systems, the Netherlands, equipped with an L12-3/S5-1 probe). The lower extremity venous compression ultrasound was obtained from the institution’s Picture Archiving and Communication System. The levels of DVT included the bilateral common femoral, deep and superficial femoral veins, the popliteal veins, and the anterior tibial, posterior tibial, peroneal, and calf muscle veins. Left ventricular and right ventricular function parameters were captured. The presence of pulmonary artery hypertension was evaluated by adding a tricuspid regurgitation pressure gradient to the estimated right atrial pressure [21]. The left ventricular ejection fraction was calculated using Simpson’s biplane method.

Definitions

SCAP was defined according to Clinical Practice Guideline of ATS/IDSA [20]. COVID-19 was diagnosed according to the Chinese Management Guideline for COVID-19 (version 10) [22]. A distal thrombosis was defined as thrombosis in the veins of the calf muscle or in at least 1 branch of the 3 pairs of deep calf veins (anterior tibial vein, posterior tibial vein, or peroneal vein); a proximal thrombosis was defined as thrombosis in the popliteal vein or above. ARDS was defined according to the Berlin definition [23]. Acute kidney injury was identified according to Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines: serum creatinine levels increased by ≥ 0.3 mg/dl (≥ 26.5 µmol/l) within 48 hours or by 1.5 times baseline[24]. Cardiac injury was defined as the serum levels of cardiac troponin I above the upper limit of the reference. The Padua prediction score was defined according to the Barbar model [25]. The Caprini score was defined according to the updated Caprini Risk Assessment Model (2013 Version)[26]. The Wells score for DVT was defined according to the Di Nisio model [27]. We applied APACHE II score and SOFA score to assess the severity of disease [28, 29].

Statistical Analyses

Categorical variables were described as number and percentage (%) and continuous variables, like mean, standard deviation, median, and interquartile range. The Shapiro-Wilk test was used to evaluate if a continuous variable follows a normal distribution. Differences between the DVT and the non-DVT groups were assessed by a two-sample t-test for normally distributed continuous variables, while the Mann-Whitney U test was used for non-normally distributed continuous variables; the χ² or Fisher exact test was used for categorical variables. To determine risk factors for DVT, multivariable logistic regression analysis which was based on the factors with significant differences between DVT and non-DVT groups in univariate analysis and the factors that may be related to dependent variables from the perspective of professional knowledge was performed. The adjusted odds ratio (OR) with 95% confidence intervals (CI) was reported. A receiver operating characteristic (ROC) analysis was performed to calculate the sensitivity and specificity of risk factors for screening for DVT. The comparison methods of diagnostic accuracy for screening for DVT of different ROCs are as follows: Patients were split by generating random numbers to produce a training data set (n*0.7) and a validation data set (n*0.3), and the area under receiver operating characteristic curves (ROC-AUCs) for different models were compared using the method of DeLong et al [30]. A nomogram based on the predictors was drew in order to enhance the practicability of the prediction model. Calibration was evaluated with calibration plots, which used the bootstrap method of 1000 resampling to show the relationship between the observed frequency and the prediction probability through a graph. In a well-calibrated model, the prediction should fall on a 45-degree diagonal. Survival curves were plotted using the Kaplan-Meier method and compared between patients with or without DVT using the log-rank test. Statistical analyses were performed using SPSS version 23.0 (Statistical Package for the Social Sciences, Chicago, IL USA) and R software version 4.3.3. All tests were two-tailed; P < 0.05 was considered statistically significant.

A total of 309 patients with SCAP were enrolled in this study, including 84 patients with COVID-19 and 225 patients without COVID-19. The study ﬂow chart is shown in Fig. 1 (A, B). We followed up the survival of all patients within 30 days after a diagnosis of SCAP. No patients were lost to follow-up. All enrolled patients received thromboprophylaxis recommended by guideline, including 166 cases with sole low molecular weight heparin (LMWH) prophylaxis (57 patients: dalteparin sodium, 5000 IU once daily; 107 patients: nadroparin calcium, 0.1ml/kg once daily; 2 patients: enoxaparin sodium, 40mg once daily), 53 cases with sole physical prophylaxis (20 patients: active bleeding;10 patients: platelet count less than 50×10⁹/L; 23 patients: high risk of bleeding for other causes), and 90 cases with a combination of LMWH (51 patients: dalteparin sodium, 5000 IU once daily; 39 patients: nadroparin calcium, 0.1ml/kg once daily) and physical prophylaxis. There was no significant difference in the incidence of DVT among the different prophylactic measures (38.6% [64/166],35.8% [19/53] and 30.0% [27/90], respectively; P = 0.393). The incidence of DVT in LMWH combined with physical prophylaxis group was slightly lower than that in LMWH alone group, but the difference was not statistically significant (P = 0.172).

Ultrasound scan for screening for DVT

Lower extremity venous compression ultrasound scanning was performed for 309 patients regardless of clinical symptoms of the lower limbs (Fig. 1B). The median number of ultrasound scans was 2 (range, 1-5). Twenty-seven (27/309) developed DVT was found and the other 282 was a negative result at the first ultrasound scan. Subsequently, 186 patients underwent more than one ultrasound scan; among those, 83 developed DVT and 103 had no DVT with 2 (range, 2 ~ 5) ultrasound examinations. There was no difference in the number of ultrasound examinations between the two groups (P = 0.306).

Finally, of the 309 patients, 110 (35.6%) developed DVT, including 8 with proximal DVT and 102 with distal DVT. The incidence of asymptomatic DVT was 97 (31.4%), including 6 (2.0%) proximal DVT and 91 (29.4%) distal DVT. For all the 309 patients, the interval from onset to the occurrence of DVT for the 110 patients who developed DVT was 14 (9, 21) days, and the interval from onset to the last ultrasound examination for the 199 cases without DVT was 13 (8, 22) days. There was no difference between the two groups (P = 0.454). Among the patients suspected of having PE, 11 underwent computed tomographic pulmonary angiography (CTPA), and 9 of these cases were confirmed as PE finally.

Demographics and clinical characteristics of DVT vs non-DVT patients with SCAP

Among 309 patients with SCAP, patients with DVT had longer bedridden time, higher CURB 65 (abbreviation of “confusion, urea, respiratory rate, blood pressure, age 65 years”) scores, higher pneumonia severity index (PSI) scores, higher acute physiology and chronic health evaluation (APACHE) II scores, higher SOFA scores, higher Caprini scores, higher Padua prediction scores, higher Wells scores, higher D-dimer levels, higher neutrophil counts, higher lactate dehydrogenase (LDH) levels, and lower PaO₂/FiO₂ratios compared to patients without DVT (all P < 0.05). Also, more patients with DVT had symptoms of dyspnea and leg pain, had history of VTE, chronic respiratory disease, more received sedative therapy and invasive mechanical ventilation (IMV), and more developed acute respiratory distress syndrome (ARDS) (all P < 0.05). (Table 1)

A total of 305 (98.7%) patients received echocardiogram examinations, with 108 patients in the DVT group and 197 patients in the non-DVT group. Compared to patients without DVT, those with DVT exhibited wider internal diameters of the right atrium, right ventricle, and pulmonary artery, with a higher prevalence of tricuspid regurgitation and pulmonary artery hypertension (all P < 0.05). (Table 2)

Independent risk factors associated with DVT for patients with SCAP

The results of univariate and multivariate logistic regression models are shown in Table 3. In order to reduce data duplication, we did not include thrombus prediction scores and disease severity scores in the multiple regression models. Since all patients with sedative therapy received IMV, there was a certain degree of overlap between these two variables, so we did not incorporate sedative therapy in the multivariate regression. For the 309 patients with SCAP, the independent contributors to DVT were history of VTE (OR, 20.056, 95% CI: 3.740 ~ 107.540; P < 0.001), longer bedridden time (3 days < bedridden times ≤ 7 days: OR, 6.580, 95% CI: 1.884 ~ 22.988, P = 0.003; bedridden times ≥ 7 days: OR, 32.050, 95% CI: 9.629 ~ 106.675, P < 0.001), D-dimer levels ≥ 1.0 µg/mL (OR, 2.433, 95% CI: 1.123 ~ 5.272; P = 0.024), LDH levels ≥ 400 U/L (OR, 2.269, 95% CI: 1.002 ~ 5.138; P = 0.049), and IMV (OR, 2.248, 95% CI: 1.081 ~ 4.672; P = 0.030).

Comparison of diagnostic accuracy for assessing the risk of DVT of different ROCs

Patients were split by generating random numbers to produce a training data set (n *0.7) and a validation data set (n*0.3), and there were 219 and 90 patients in each set respectively. The demographics, clinical characteristics, laboratory data, treatments, complications, and prognoses of training set and validation set patients with SCAP were shown in Supplementary Table 1. There were no differences in clinical manifestations of pneumonia, symptoms of DVT, history of venous VTE, chronic respiratory diseases, bedridden time, laboratory indicators such as LDH and the PaO₂/FiO₂ ratio, thrombus prediction scores and disease severity scores, the proportion of sedation treatment and IMV, the proportion of intensive care unit (ICU) admissions, and mortality both in-hospital and 30-day between the two sets. The demographics, clinical characteristics, laboratory data, treatments, complications, and prognoses of DVT patients and non-DVT patients with SCAP in training set were shown in Supplementary Table 2.

We selected the risk factors based on the test results of the logistic regression models in training set and proposed a new way of combining forecasting model for assessing the risk of DVT in patients with SCAP. The new prediction model, including age, history of VTE, bedridden time, D-dimer levels, LDH levels and IMV, showed a better performance in predicting DVT (AUC = 0.830; 95% CI: 0.746 ~ 0.913; sensitivity: 66.1%; specificity: 90.0%) than Padua prediction score (AUC = 0.666; P = 0.011 for these two curves) and Caprini prediction score (AUC = 0.688; P = 0.045 for these two curves) for patients with SCAP who had received guideline-recommended thromboprophylaxis (Fig. 2); yet, there was no significant difference between Padua prediction score and Caprini prediction score (P = 0.668 for these two curves) when predicting DVT (Fig. 2).

Nomogram for assessing the risk of DVT

In order to increase the practicability of the prediction model, we created a nomogram based on the selected predictors (Fig.3). There were six prediction variables. The corresponding points were obtained by making a vertical line upward based on the value of each variable. The total points were obtained by adding the points of the six variables. The predicted probability of DVT in hospital was obtained by making a vertical line downward based on the total points. The calibration plots showed good consistency of DVT between the actual observation and the nomogram prediction (Supplementary Fig. 1).

Prognosis of DVT patients with SCAP

Compared to patients without DVT, more patients with DVT were admitted to ICU. Moreover, patients with DVT had a significantly higher 30-day mortality (45.5% [50/110] vs 19.1% [38/199], respectively; P < 0.001) and in-hospital mortality (52.7% [58/110] vs 22.6% [45/199] respectively; P < 0.001). There was no significant difference in length of ICU stay and length of hospital stay between the DVT group and the non-DVT group. (Table 1 and Fig 4).

Prevalence of DVT in patients with SCAP undergoing thromboprophylaxis

In recent years, there have been numerous studies on COVID-19 related VTE or DVT, but few papers on VTE or DVT caused by non-COVID-19 related SCAP, especially in patients who had already received VTE prophylaxis. In this single-center study, we found a high incidence of DVT in SCAP patients undergoing thromboprophylaxis recommended by guideline, with no significant difference in the incidence of both COVID-19 and non-COVID-19 induced SCAP. The occurrence of DVT is independently associated with personal history of VTE, longer bedridden time, higher D-dimer levels, higher LDH levels, and IMV.

In 2002, Greets et al. reported that the rates of objectively confirmed DVT in 4 prospective studies ranged from 13–31% and suggested a potential role of thromboprophylaxis in patients requiring critical care [31]. In recent years, some research has shown that, despite receiving guideline-recommended thromboprophylaxis, the incidence of DVT is still as high as 14–37.2% in critically ill patients [15–19].

In our study, even with VTE prophylaxis, the incidence of DVT in patients with SCAP was as high as 35.6%. Several reasons probably account for the high prevalence of DVT in our study. First, most of the above studies generally focused on critically ill patients. SCAP is a more serious type of critical illness that shows an overwhelming systemic inflammatory process accompanied by vascular endothelial injury and hypercoagulability, and may have a higher risk of DVT [5, 7, 16, 32, 33]. Second, immobility in these patients leads to stasis of blood flow [8], further intensifying their predisposition to DVT. Third, while some studies define DVT as either proximal DVT or symptomatic DVT [34], yet, in our research, we conducted lower extremity venous compression ultrasonography on all patients regardless of whether they exhibited symptoms of DVT, which might increase the detection rate of DVT.

Risk factors for DVT in patients with SCAP undergoing thromboprophylaxis

Our study shows that the history of VTE, longer bedridden times, higher D-dimer levels, higher LDH levels, and IMV were independent risk factors for DVT. Using a ROC analysis, the combination of age, history of VTE, bedridden times, D-dimer levels and IMV yielded a sensitivity of 66.1% and a specificity of 90.0% for scanning for DVT in these hospitalized patients.

Aligning with prior studies and guidelines [35–38], our survey indicates that a history of VTE is an independent risk factor for the development of DVT. Despite the implementation of prudent preventive measures, the existence of a prior VTE history significantly contributes to the risk of DVT occurrence (with an adjusted OR as high as 20), which is a striking statistic. A history of VTE is a potentially strong risk factor for recurrent VTE events [12]. As early as the beginning of this century, Baglin and Hansson et al. highlighted that patients with a history of VTE had a 2- and 5-year recurrence rate of VTE higher than 10% [39, 40]. Subsequent research has also affirmed that a personal history of VTE stands as an independent risk factor for VTE recurrence, and this factor has been recognized as a non-modifiable risk factor, been integrated into various VTE risk assessment tools and authoritative guidelines [12, 25–27]. This may be attributable to the potential for underlying genetic and acquired thrombophilia in such patients, conferring a heightened likelihood of thrombus recurrence. It is regrettable that in our study, the cohort of patients with a personal history of VTE was limited to only 13 individuals, and we did not conduct a follow-up on their thrombophilia, particularly their genetic predisposition to VTE. This represents an area that warrants further investigation in future research.

Prolonged bed rest was also identified as a risk factor for the development of DVT in this study. Even with the implementation of reasonable preventive measures, patients who have been bedridden for more than 3 days face a 6-fold increase in the risk of DVT, and those immobilized for more than 7 days see more than a 30-fold increase in risk. As one of the significant contributing factors to the occurrence of VTE [41–43], prolonged bed rest or immobilization has been incorporated into various VTE risk assessment models and authoritative guidelines[12, 25–27]. This may be because bed rest leads to a reduction in physical activity, which in turn slows the return of blood from the lower extremities, causing stasis and thereby increasing the risk of thrombus formation. Furthermore, an extended period of bed rest often indicates a more severe condition in the patient, potentially accompanied by a more serious inflammatory response and a hypercoagulable state, which in turn raises the risk of developing DVT.

Within our research, it was discerned that elevated D-dimer levels at admission is indicative of an increased risk for DVT among patients afflicted with SCAP who were administered prophylaxis against VTE. D-dimer is a fibrin degradation product encompassing multiple cross-linked D domains and/or E domains present in the original fibrinogen molecule, whose generation is only theoretically possible when hemostasis and fibrinolysis pathways are concomitantly activated. Measurement of plasma D-dimer level has now become a basic element in the diagnosis or exclusion and prognostication of VTE when incorporated into validated clinical algorithms. In a comprehensive study by Tsaplin et al., a cohort of 168 patients with COVID-19 was evaluated to discern the efficacy of the original versus the revised Caprini scoring system in assessing the risk of VTE. The latter, which includes elevated D-dimer levels, demonstrated superior accuracy in predicting VTE risk among the studied population [44]. It is a well-documented phenomenon that patients afflicted with CAP, irrespective of whether the etiology is viral or bacterial, frequently present with increased D-dimer levels and an augmented risk of DVT [15, 45, 46], a concern that is particularly pronounced in those SCAP. Among patients exhibiting elevated D-dimer levels at admission, vigilant surveillance is paramount, notwithstanding the implementation of VTE preventative measures. This proactive approach is essential for the timely identification of DVT and the subsequent initiation of appropriate therapeutic measures.

We had concurrently identified an association between elevated serum LDH levels and the occurrence of DVT. LDH, a pivotal enzyme ubiquitously present within nearly every tissue of the human body, including red blood cells, heart, lungs, liver, muscles, and kidneys, plays an indispensable role in cellular metabolism by catalyzing the interconversion of pyruvate to lactate and vice versa[47]. Upon the onset of tissue injury or infection, LDH is expressed by the affected tissues or cells, leading to an increase in serum LDH levels. Consequently, elevated LDH levels are observed in a myriad of conditions, including infections, and are often correlated with the severity and prognosis of the disease[48, 49]. In concordance with previous findings in patients with COVID-19 [50–52], we had observed a higher expression of serum LDH in SCAP patients with concomitant DVT. The likely rationale for this phenomenon is the occurrence of DVT, which, due to impeded blood circulation, results in localized tissue ischemia, hypoxia, edema, degeneration, and potentially necrosis. This cascade of events leads to the release of intracellular LDH into the bloodstream, thereby manifesting as elevated serum LDH levels [53].

The utilization of IMV emerged as an additional independent risk factor associated with the occurrence of DVT in our study. For critically ill patients, the risk of hospital-acquired VTE escalates during the course of IMV treatment, with an increasing duration of IMV conferring a progressively higher risk for VTE [34, 54–56]. Due to the typical presence of severe respiratory failure, the necessity for IMV in the context of SCAP is associated with an increased predisposition towards VTE. As supported by literature reports and our statistical analysis, prolonged immobilization inherent to IMV potentiates the risk for VTE. The hypercoagulable state induced by critical illness, exacerbated by the inflammatory cascade of severe pneumonia, also augments the likelihood of thrombotic events. Nonetheless, within the parameters of our investigation, a discernible statistical correlation was not detected between the duration of IMV and the incidence of DVT. This absence of correlation could be ascribed to the relatively small sample size of our patient cohort.

ROC and nomogram for screening for DVT in SCAP undergoing thromboprophylaxis

The susceptibility to VTE intensifies with an increasing number of precipitating factors [11]. In the context of SCAP who have received VTE prophylaxis recommended by guidelines, the newly formulated predictive model demonstrates superior efficacy in forecasting the incidence of DVT. Existing models, such as the Caprini prediction score and Padua prediction score, which are prevalent in clinical practice, are designed for the general inpatient population that has not yet undergone thromboprophylaxis and do not account for specific medical conditions. The novel model, specifically crafted for the cohort of SCAP patients who have already been subjected to preventive measures for VTE, and which integrates characteristic indicators of the patients' condition, such as the requirement for IMV, provides a more precise prediction of DVT occurrence within this particular group. This enhanced predictive acumen could potentially facilitate the early identification of individuals at heightened risk, thereby allowing for the refinement and optimization of their preventive measures.

Prognosis of DVT patients with SCAP undergoing thromboprophylaxis

Similar to prior investigations [15, 19, 46], our findings suggested a connection between DVT and a bleaker prognosis for patients. Notably, the 30-day and in-hospital mortality rates are markedly higher among patients with concurrent DVT compared to those without DVT. Univariate analysis revealed that patients with DVT exhibit elevated neutrophil counts and D-dimer levels compared to patients without DVT, indicating a potential escalation in inflammatory response and subsequent exacerbation of coagulation and fibrinolysis dysfunction [57, 58]. Additionally, individuals in the DVT cohort experience prolonged periods of immobility, lower oxygenation indices, higher rates of IMV therapy, and elevated severity scores such as APACHE II and SOFA, suggesting that these patients may represent a more severe subset of SCAP. In addition, there is a 50% chance for patients with untreated proximal DVT to develop symptomatic PE [59] which might aggravate the hypoxemia of SCAP patients and then result in lower actuarial survival rates. If there is any clinical suspicion of PE, a CTPA should be considered and obtained. Unfortunately, due to the critical condition of SCAP, CTPA examination was limited. CTPA examination was performed only for 11 patients with SCAP who were highly suspected of PE and 9 patients consequently diagnosed with PE. Using the figures given above, we may underestimate the incidence of PE. The potential presence of PE associated with DVT may be also associated with poor survival in patients with DVT. While this scenario may not be surprising given that our population consists of critically ill patients with poor prognosis, the high incidence of DVT and its associated worsened clinical outcomes, even in those who have received VTE prophylaxis, underscores the importance of screening for DVT-related risk factors in such patients. It is imperative to explore specific and optimized VTE prevention strategies to improve the prognosis of these individuals.

We have elucidated, in a timely manner, the incidence of DVT among patients with SCAP who have been administered guideline-recommended VTE prophylactic measures, as well as the associated risk factors. However, the study is not without its inherent limitations. First, the retrospective nature of our research precluded the tracking of genetic predispositions in patients afflicted with DVT. Second, due to the same retrospective design, an analysis of the initial causative factors in patients with a prior history of VTE was not conducted. Third, given the critical condition of SCAP patients, the application of CTPA was constrained, resulting in only 11 cases within our study cohort undergone CTPA for suspected PE, which may have led to an underestimation of PE prevalence. Finally, while the incidence of DVT was marginally lower in patients treated with a combination of LMWH and physical prophylaxis compared to those with LMEH alone (30.0% vs 38.6%), the difference was not statistically significant (P = 0.172). It is plausible that a larger sample size could yield more compelling results.

Despite adherence to guideline-recommended prophylactic measures for VTE, the incidence of DVT in patients with SCAP remains disproportionately high. The occurrence of DVT is also correlated with a poorer prognosis for these patients. A novel model encompassing a spectrum of factors, including age, history of VTE, duration of bedridden, levels of D-dimer, levels of LDH, and the requirement for IMV, has been demonstrated to effectively predict the occurrence of DVT in this cohort of SCAP patients. It is imperative to identify early those at a heightened risk for DVT and to tailor preventive strategies accordingly. This may involve, for instance, the judicious escalation of dosages of LMWH or the concomitant administration of LMWH with anti-inflammatory medications to enhance the preventive efficacy and ameliorate patient outcomes.

APACHE, Acute Physiology and Chronic Health Evaluation; ARDS, acute respiratory distress syndrome; AUC, area under the curve; BMI, body mass index; CCI, Charlson comorbidity index; CI, confidence interval; COVID-19, coronavirus disease 2019; CTPA, computed tomography pulmonary angiography; CURB-65, abbreviation of “confusion, urea, respiratory rate, blood pressure, age 65 years”; DVT, deep vein thrombosis; EF, ejection fraction; FiO₂, fraction of inspired oxygen; ICU, intensive care unit; IMV, invasive mechanical ventilation; IQR, interquartile range; LA, left atrial; LDH, lactate dehydrogenase; LV, left ventricular; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index; NIV, Noninvasive mechanical ventilation; OR, odds ratio; PA, pulmonary artery; PAH, pulmonary artery hypertension; PaO₂, partial pressure of arterial oxygen; PASP, pulmonary artery systolic pressure; PE, pulmonary embolism; PSI, pneumonia severity index; RA, right atrial; ROC, receiver operating characteristic; RV, right ventricular; SCAP, severe community-acquired pneumonia; SD, standard deviation; SOFA, Sequential Organ Failure Assessment; VTE, venous thromboembolism.

Acknowledgements

Not applicable.

Authors’ contributions

NC designed the study, collected clinical data, analyzed the data, and wrote the manuscript. JW and XF designed the study, and wrote the manuscript. YY and LZ helped manage the research, performed the statistical analyses, and revised the paper. YY and LZ contributed equally to this article and share corresponding authorship. All authors read and approved the final manuscript.

Funding

This work was supported by the Study on Risk assessment and Prevention management System construction of venous thromboembolism for high-risk in-patient and specific diseases (Grant No. 2023YFC2507200 to Dr Yuanhua Yang).

Availability of data and materials

All data analyzed during the study are presented in the main manuscript and supplementary file. In addition, the anonymous dataset is available from the corresponding author upon reasonable request.

Ethics and approval and consent to participate

This retrospective study involving human participants was approved by the ethics committee of the Beijing Chao-Yang Hospital, Capital Medical University (2024-ke-335) and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China; ²Department of Respiratory and Critical Care Medicine, Lhasa People’s Hospital, Lhasa, Tibet, China.

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Table 1. Demographics, clinical characteristics, laboratory data, treatments, complications, and prognoses of DVT vs non-DVT patients with SCAP

Characteristics	Total (N = 309)	DVT (n = 110)	Non-DVT (n = 199)	P value
Age (years)	73 (63, 81)	74 (64, 81)	72 (61, 82)	0.225^*
＜ 65 years , n (%)	93 (30.1)	28 (25.5)	65 (32.7)	0.390^#
≥ 65 years and＜75 years, n (%)	77 (24.9)	28 (25.5)	49 (24.6)
≥ 75 years, n (%)	139 (45.0)	54 (49.1)	85 (42.7)
Male, n (%)	207 (67.0)	71 (64.5)	136 (68.3)	0.497
BMI (kg/m²)	23.8 ± 2.9	23.9 ± 2.9	23.7 ± 3.0	0.505
COVID-19, n (%)	84 (27.2)	28 (25.5)	56 (28.1)	0.611
Length from onset to admission (days)	7 (3, 12)	7 (4, 12)	7 (3, 12)	0.371
Length from admission to DVT or last negative ultrasound (days)	3 (2, 10)	4 (2, 8)	3 (1, 10)	0.103
Length from onset to DVT or last negative ultrasound (days)	13 (8, 22)	14 (9, 21)	13 (8, 22)	0.454
Symptoms and signs of onset
Fever, n (%)	274 (88.7)	93 (84.5)	181 (91.0)	0.089
Cough, n (%)	290 (93.9)	106 (96.4)	184 (92.5)	0.172
Hemoptysis, n (%)	13 (4.2)	6 (5.5)	7 (3.5)	0.606
Chest pain, n (%)	8 (2.6)	3 (2.7)	5 (2.5)	1.000
Dyspnea, n (%)	289 (93.5)	108 (98.2)	181 (91.0)	0.013
Fatigue, n (%)	70 (22.7)	23 (20.9)	47 (23.6)	0.586
Unconsciousness, n (%)	64 (20.7)	27 (24.5)	37 (18.6)	0.216
Respiratory rate, times/minute	30 (27, 33)	30 (27, 34)	30 (26, 32)	0.176^*
Systolic blood pressure, mmHg	118 (94, 139)	113 (92, 135)	122 (94, 140)	0.146^*
DVT symptoms, n (%)	25 (8.1)	13 (11.8)	12 (6.0)	0.074
Leg pain, n (%)	6 (1.9)	5 (4.5)	1 (0.5)	0.042
Edema of low extremities, n (%)	24 (7.8)	12 (10.9)	12 (6.0)	0.125
Comorbidities
History of VTE, n (%)	13 (4.2)	9 (8.2)	4 (2.0)	0.022
Current smoker, n (%)	116 (37.5)	44 (40.0)	72 (36.2)	0.507
Chronic respiratory disease, n (%)	57 (18.4)	27 (24.5)	30 (15.1)	0.040
Hypertension, n (%)	164 (53.1)	59 (53.6)	105 (52.8)	0.883
Diabetes mellitus, n (%)	95 (30.7)	37 (33.6)	58 (29.1)	0.413
Coronary artery disease, n (%)	66 (21.4)	22 (20.0)	44 (22.1)	0.665
Congestive heart failure, n (%)	31 (10.0)	13 (11.8)	18 (9.0)	0.437
Cerebrovascular disease, n (%)	63 (20.4)	22 (20.0)	41 (20.6)	0.900
Chronic kidney disease, n (%)	25 (8.1)	7 (6.4)	18 (9.0)	0.408
Chronic liver disease, n (%)	1 (0.3)	0 (0)	1 (0.5)	1.000
History of solid malignancy, n (%)	21 (6.8)	11 (10.0)	10 (5.0)	0.096
History of hematological malignancy, n (%)	4 (1.3)	1 (0.9)	3 (1.5)	1.000
Hyperlipidaemia, n (%)	16 (5.2)	7 (6.4)	9 (4.5)	0.484
CCI	5 (4, 6)	5 (4, 6)	5 (3, 6)	0.106
Bedridden time (days)	7 (3, 12)	9 (8, 14)	5 (3, 10)	＜0.001^*
≤ 3 days, n (%)	83 (26.9)	4 (3.6)	79 (39.7)	＜0.001^#
＞3 and ≤ 7 days, n (%)	82 (26.5)	21 (19.1)	61 (30.7)
> 7 days, n (%)	144 (46.6)	85 (77.3)	59 (29.6)
Scores associated with pneumonia
CURB-65 score	3 (2, 3)	3 (3, 4)	3 (2, 3)	0.001
PSI score	131 (108, 154)	135 (114, 155)	128 (103, 150)	0.025
Scores associated with critical illness
APACHE II score	17 (14, 21)	17 (15, 22)	16 (13, 20)	0.029
SOFA score	5 (3, 8)	5 (4, 8)	5 (3, 7)	0.009
Scores associated with VTE
Caprini score	7 (6, 8)	7 (7, 9)	6 (5, 7)	＜0.001
Padua prediction score	6 (5, 6)	6 (5, 6)	6 (5, 6)	＜0.001
Wells score	1 (1, 1)	1(1, 1)	1 (1, 1)	＜0.001
Oxygenation stratification index
PaO₂/FiO₂ Ratio (mmHg)	174 (120.1, 224.3)	148.0 (75.0, 216.1)	188.0 (141.6, 230.0)	＜0.001^*
＞200 mmHg, n (%)	121 (39.2)	34 (30.9)	87 (43.7)	＜0.001^#
＞100mmHg and ≤ 200 mmHg, n (%)	126 (40.8)	40 (36.4)	86 (43.2)
≤ 100 mmHg, n (%)	62 (20.1)	36 (32.7)	26 (13.1)
Coagulation function index
D-dimer (μg/mL)	0.8 (0.4, 1.9)	1.0 (0.5, 2.4)	0.6 (0.3, 1.7)	＜0.001^*
＜ 0.5 μg/mL, n (%)	100 (32.4)	21 (19.1)	79 (39.7)	0.001^#
≥ 0.5 μg/mL and＜1.0μg/mL, n (%)	78 (25.2)	32 (29.1)	46 (23.1)
≥ 1.0 μg/mL, n (%)	131 (42.4)	57 (51.8)	74 (37.2)
Prothrombin time (seconds)	12.9 (11.8, 14.1)	13.1 (11.8, 14.2)	12.9 (11.7, 13.8)	0.540
Activated partial thromboplastin time (seconds)	30.0 (27.4, 33.7)	29.8 (27.0, 33.2)	30.3 (27.6, 34.0)	0.168
Hematological and infection-related indices
White blood cells (×10⁹/L)	11.0 (7.0, 16.9)	12.3 (7.2, 17.6)	10.2 (6.9, 15.8)	0.069
Neutrophil (×10⁹/L)	9.6 (5.9, 14.7)	10.5 (6.2, 15.5)	8.9 (5.6, 13.9)	0.032^*
≥ 10.0×10⁹/L, n (%)	144 (46.6)	62 (56.4)	82 (41.2)	0.011^#
Lymphocytes (×10⁹/L)	0.8 (0.5, 1.2)	0.8 (0.5, 1.2)	0.8 (0.5, 1.2)	0.612
Platelets (×10⁹/L)	191 (127, 283)	186 (121, 273)	198 (131, 285)	0.427
Hemoglobin (g/L)	115 (96, 131)	116 (96, 132)	114 (96, 130)	0.752
Hematocrit (%)	34.0 (29.4, 38.7)	34.1 (30.0, 39.2)	33.9 (29.1, 38.3)	0.466
C-reactive protein (mg/L)	104.1 (50.0, 120.0)	118 (61.2, 120.0)	95 (43, 120)	0.054
Serum procalcitonin (ng/mL)	0.5 (0.1, 3.2)	0.8 (0.1, 4.4)	0.4 (0.1, 2.3)	0.062
Biochemical test
Blood lactate (mmol/L)	1.5 (1.1, 2.2)	1.6 (1.2, 2.4)	1.5 (1.0, 2.2)	0.100
Albumin (g/L)	28.5 (24.5, 33.1)	28.0 (24.6, 31.9)	29.0 (24.3, 34.0)	0.237
Aspartate aminotransferase (U/L)	33.2 (22.7, 59.5)	34.1 (23.1, 59.0)	32.4 (22.2, 59.8)	0.843
Alanine aminotransferase (U/L)	25.7 (17.4, 47.4)	24.6 (16.5, 46.5)	26.0 (17.3, 48.0)	0.777
Lactic dehydrogenase (U/L)	330.5 (229.9, 481.1)	400.1 (245.5, 575.3)	297.0 (220.6, 403.4)	＜0.001^*
＜250 U/L, n (%)	103 (33.9)	30 (27.8)	73 (37.2)	＜0.001^#
≥ 250 U/L and ＜400 U/L, n (%)	96 (31.6)	24 (22.2)	72 (36.7)
≥ 400 U/L, n (%)	105 (34.5)	54 (50.0)	51 (26.0)
Fasting blood glucose (mmol/L)	8.2 (6.5, 11.6)	9.2 (6.7, 12.6)	7.9 (6.4, 11.2)	0.068^*
≥ 9.0 mmol/L, n (%)	134 (43.4)	58 (52.7)	76 (38.2)	0.014^#
Blood urea nitrogen (mmol/L)	8.7 (6.1, 13.0)	9.3 (6.2, 13.0)	8.5 (6.0, 13.2)	0.698
Uric acid (μmol/L)	254.8 (181.0, 386.0)	242.1 (161.1, 368.3)	263.8 (188.0, 388.8)	0.395
Serum creatinine (μmol/L)	75.2 (57.6, 113.4)	71.6 (56.1, 102.1)	76.3 (59.4, 129.3)	0.155
Creatinine kinase-myocardial band (U/L)	14.8 (10.4, 24.7)	16.3 (10.6, 25.1)	14.6 (10.3, 24.5)	0.433
Cardiac troponin I, (ng/mL)	0.05 (0.02, 0.12)	0.05 (0.03, 0.15)	0.04 (0.02, 0.12)	0.126
N terminal pro B-type natriuretic peptide (pg/mL)	1498.8 (448.3, 3297.5)	1562.0 (706.4, 2765.1)	1356.1 (310.8, 998.7)	0.232
Cholesterol (mmol/L)	3.6 (2.9, 4.3)	3.6 (2.9, 4.4)	3.6 (2.9, 4.3)	0.328
Low density cholesterol (mmol/L)	1.9 (1,3, 2.5)	2.0 (1.4, 2.5)	1.8 (1.2, 2.5)	0.334
High density lipoprotein (mmol/L)	1.0 (0.7, 1.2)	1.0 (0.7, 1.2)	0.9 (0.7, 1.2)	0.526
Triglyceride (mmol/L)	1.4 (0.9, 2.0)	1.4 (0.9, 2.0)	1.4 (0.9, 2.0)	0.822
Treatments
Glucocorticoid therapy, n (%)	135 (43.7)	54 (49.1)	81 (40.7)	0.155
Immunoglobulin, n (%)	12 (3.9)	6 (5.5)	6 (3.0)	0.450
Sedative therapy, n (%)	74 (23.9)	42 (38.2)	32 (16.1)	＜0.001
Vasoactive agent therapy, n (%)	46 (14.9)	15 (13.6)	31 (15.6)	0.646
Intravenous nutrition, n (%)	57 (18.4)	21 (19.1)	36 (18.1)	0.828
Blood transfusion, n (%)	22 (7.1)	4 (3.6)	18 (9.0)	0.077
Continuous renal replacement therapy, n (%)	19 (6.1)	5 (4.5)	14 (7.0)	0.383
Central Venous Catheterization, n (%)	72 (23.3)	32 (29.1)	40 (20.1)	0.073
Nasal high-flow oxygen therapy, n (%)	31 (10.0)	11 (10.0)	20 (10.1)	0.989
Mechanical ventilation, n (%)	178 (57.6)	73 (66.4)	105 (52.8)	0.021
IMV, n (%)	99 (32.0)	52 (47.3)	47 (23.6)	＜0.001
Length of IMV (days)	4 (2, 10)	5 (2, 10)	4 (1, 10)	0.687
Length of IMV ≥ 3 days, n (%)	56 (56.6)	30 (57.7)	26 (55.3)	0.812
NIV, n (%)	115 (37.2)	40 (36.4)	75 (37.7)	0.818
Major complications
Acute respiratory distress syndrome, n(%)	203 (65.7)	87 (79.1)	116 (58.3)	＜0.001
Septic shock, n (%)	62 (20.1)	21 (19.1)	41 (20.6)	0.751
Acute cardiac injury, n (%)	238 (77.0)	90 (81.8)	148 (74.4)	0.136
Acute kidney injury, n (%)	97 (31.4)	36 (32.7)	61 (30.7)	0.707
Liver disfunction, n (%)	170 (55.0)	65 (59.1)	105 (52.8)	0.284
Stress ulcer with bleeding, n (%)	56 (18.1)	21 (19.1)	35 (17.6)	0.743
Prognosis
Stay in ICU, n (%)	260 (84.1)	101 (91.8)	159 (79.9)	0.006
Length of ICU stay (days)	15 (9, 25)	17 (12, 27)	13 (9, 23)	0.054
Length of hospital stay (days)	17 (11, 26)	17 (13, 27)	17 (11, 26)	0.334
30-day mortality, n (%)	88 (28.5)	50 (45.5)	38 (19.1)	＜0.001
In-hospital mortality, n (%)	103 (33.3)	58 (52.7)	45 (22.6)	＜0.001

Data are mean ± SD, median (IQR), or n (%). P values comparing DVT and non-DVT groups were from a two-sample t test, Mann-Whitney U test, χ² test or Fisher’s exact test. P < 0.05 was considered statistically significant

^* Mann-Whitney U test comparing DVT and non-DVT groups

^# χ² test or Fisher’s exact test comparing all subcategories

Abbreviations: APACHE, acute physiology and chronic health evaluation; BMI, body mass index; CCI, Charlson comorbidity index; CURB-65, abbreviation of “confusion, urea, respiratory rate, blood pressure, age 65 years”; DVT, deep vein thrombosis; FiO₂, fraction of inspired oxygen; ICU, intensive care unit; IMV, invasive mechanical ventilation; IQR, interquartile range; NIV, Noninvasive mechanical ventilation; PaO₂, partial pressure of arterial oxygen; PSI, pneumonia severity index; SCAP, severe community-acquired pneumonia; SD, standard deviation; SOFA, sequential organ failure assessment; VTE, venous thromboembolism

Table 2. Echocardiographic findings with DVT vs non-DVT patients in severe community-acquired pneumonia

Variable	Total (N = 305)	DVT (n =108)	non-DVT (n = 197)	P value
LA diameter (mm)	34 (32, 38)	34 (32, 37)	34 (31, 39)	0.429
LVESVI (mL/m²)	29 (26, 31)	29 (26, 31)	29 (27, 32)	0.405
LVEDVI (mL/m²)	46 (43, 49)	45 (43, 49)	46 (43, 49)	0.601
Simpson biplane EF (%)	67 (63, 70)	67 (64, 70)	67 (62, 70)	0.434
RA diameter (mm)	32 (30, 36)	33 (30, 36)	32 (29, 35)	0.016
RV diameter (mm)	30 (28, 33)	31 (29, 34)	30 (27, 33)	0.005
PA diameter (mm)	24 (22, 26)	25 (22, 27)	23 (21, 26)	0.003
Tricuspid regurgitation, n (%)	138 (45.2)	60 (55.6)	78 (39.6)	0.007
PASP (mmHg)	45 (40, 52)	45 (42, 57)	44 (38, 50)	0.066
PAH, n (%)	67 (22.0)	39 (36.1)	28 (14.2)	＜0.001
Pericardial effusion, n (%)	12 (3.9)	3 (2.8)	9 (4.6)	0.645

Data are median (IQR) or n (%). P values comparing DVT and non-DVT groups were from Mann-Whitney U test or χ² test. P<0.05 was considered statistically significant

Abbreviations: DVT, deep vein thrombosis; EF, ejection fraction; LA, left atrial; LV, left ventricular; LVEDVI, left ventricular end-diastolic volume index; LVESVI, left ventricular end-systolic volume index; PA, pulmonary artery; PAH, pulmonary artery hypertension; PASP, pulmonary artery systolic pressure; RA, right atrial; RV, right ventricular

Table 3. Risk Factors Associated with DVT in severe community-acquired pneumonia

Factors	Univariable OR (95% CI)	P value	Multivariable OR (95% CI)	P value
Age
＜ 65 years	Reference		Reference
≥ 65years and＜75 years	1.327 (0.698, 2.520)	0.388	1.534 (0.656, 3.590)	0.324
≥ 75 years	1.475 (0.843, 2.580)	0.173	2.128 (0.984, 4.604)	0.055
History of chronic respiratory diseases
No	Reference		Reference
Yes	1.833 (1.023, 3.281)	0.042	1.419 (0.644, 3.127)	0.386
History of VTE
No	Reference		Reference
Yes	4.344 (1.306, 14.452)	0.017	20.056 (3.740, 107.540)	＜0.001
DVT symptoms
No	Reference		Reference
Yes	2.088 (0.918, 4.752)	0.079	1.510 (0.521, 4.372)	0.448
Bedridden time
≤ 3 days	Reference		Reference
＞3 days and ≤ 7 days	6.799 (2.218, 20.843)	0.001	6.580 (1.884, 22.988)	0.003
＞7 days	28.453 (9.878, 81.960)	＜0.001	32.050 (9.629, 106.675)	＜0.001
D-dimer
＜0.5 μg/mL	Reference		Reference
≥ 0.5 μg/mL and ＜1.0 μg/mL	2.617 (1.353. 5.061)	0.004	2.232 (0.941, 5.294)	0.069
≥1.0 μg/mL	2.898 (1.603, 5.240)	＜0.001	2.433 (1.123, 5.272)	0.024
Neutrophil
＜10.0 × 10⁹/L	Reference		Reference
≥ 10.0 × 10⁹/L	1.843 (1.151, 2.951)	0.011	0.890 (0.466, 1.700)	0.724
Lactic dehydrogenase
＜250 U/L	Reference		Reference
≥250 U/L and ＜400 U/L	0.811 (0.433, 1.520)	0.513	0.736 (0.332, 1.635)	0.452
≥400U/L	2.576 (1.454, 4.565)	0.001	2.269 (1.002, 5.138)	0.049
Fasting blood-glucose
＜9.0 mmol/L	Reference		Reference
≥ 9.0 mmol/L	1.805 (1.127, 2.891)	0.014	1.234 (0.658, 2.315)	0.511
Oxygenation stratification index
＞200 mm Hg	Reference		Reference
＞100 mm Hg and ≤ 200 mm Hg	1.190 (0.690, 2.054)	0.532	0.587 (0.277, 1.245)	0.165
≤ 100 mm Hg	3.543 (1.865, 6.730)	＜0.001	1.381 (0.525, 3.634)	0.513
IMV
No	Reference		Reference
Yes	2.899 (1.764, 4.767)	＜0.001	2.248 (1.081, 4.672)	0.030

Multivariable logistic regression was performed in the overall cohort. P < 0.05 was considered statistically significant

Abbreviations: DVT, deep vein thrombosis; IMV, invasive mechanical ventilation; VTE, venous thromboembolism; OR = odds ratio

No competing interests reported.

SupplementaryFig.1.pdf
Supplementary Fig. 1Calibration curve of in-hospital DVT between the actual observation and the nomogram prediction The calibration plots showed good consistency of DVT between the actual observation and the nomogram prediction. Abbreviations: DVT, deep vein thrombosis.
Supplementaryfiles.docx

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Deep vein thrombosis in severe community-acquired pneumonia patients undergoing thromboprophylaxis: Prevalence, risk factors, and outcome

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Abstract

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Introduction

Methods

Study Design and Population

Clinical Data

Ultrasound Assessment

Definitions

Statistical Analyses

Results

Discussion

Prevalence of DVT in patients with SCAP undergoing thromboprophylaxis

Risk factors for DVT in patients with SCAP undergoing thromboprophylaxis

ROC and nomogram for screening for DVT in SCAP undergoing thromboprophylaxis

Prognosis of DVT patients with SCAP undergoing thromboprophylaxis

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1