Risk factors for pulmonary embolism: a case-control study
Original Article

Risk factors for pulmonary embolism: a case-control study

Geng Yang1, Shaoping Nie2

1Emergency Rescue Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China; 2Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China

Contributions: (I) Conception and design: Both authors; (II) Administrative support: Both authors; (III) Provision of study materials or patients: G Yang; (IV) Collection and assembly of data: G Yang; (V) Data analysis and interpretation: Both authors; (VI) Manuscript writing: Both authors; (VII) Final approval of manuscript: Both authors.

Correspondence to: Shaoping Nie, MD, PhD, FESC, FACC, FSCAI. Director, Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing 100029, China. Email: gengyangeason@gmail.com.

Background: Pulmonary embolism (PE) is characterized by a high rate of misdiagnosis and poor prognosis. This descriptive epidemiological study aimed to identify modifiable risk factors for PE through an age- and sex-matched case-control study.

Methods: The case group consisted of patients diagnosed with PE at Beijing Anzhen Hospital, within a 3-year period. Age- and sex-matched controls were randomly selected from individuals who participated in health check-ups at the same institute during the same period. Clinical variables, including histories of hypertension, diabetes, body mass index (BMI), smoking, systolic blood pressure (SBP), and obstructive sleep apnea-hypopnea syndrome (OSAHS), were analyzed in 129 case-control pairs.

Results: In univariate analysis, significant risk factors for PE included OSAHS, smoking, triglycerides, and estimated glomerular filtration rate (eGFR). In multivariate analysis, using a conditional logistic regression model, OSAHS [P=0.01; odds ratio (OR), 3.100; 95% confidence interval (CI): 1.202–7.994], hypertension (P=0.02; OR, 2.212; 95% CI: 1.107–4.420), and smoking (P<0.001; OR, 7.167; 95% CI: 3.302–15.556) were identified as independent risk factors for PE. No significant associations were observed between triglycerides or eGFR and PE. Additionally, a negative correlation between arterial partial pressure of carbon dioxide (PCO2) and PE was observed in the multivariate analysis (P=0.02; OR, 0.946; 95% CI: 0.904–0.990). A risk model and scoring system with strong discriminatory power were developed (adjusted OR, 1.129; 95% CI: 1.021–1.248).

Conclusions: The findings suggest that OSAHS, hypertension, and smoking are strongly associated with PE, while arterial PCO2 may exhibit a protective correlation with PE risk.

Keywords: Pulmonary embolism (PE); risk factors; obstructive sleep apnea-hypopnea syndrome (OSAHS); hypertension; smoking

Submitted Aug 23, 2024. Accepted for publication Jan 17, 2025. Published online Mar 13, 2025.

doi: 10.21037/jtd-24-1293

Highlight box

Key findings

• Obstructive sleep apnea-hypopnea syndrome (OSAHS), hypertension, and smoking may be potential risk factors of pulmonary embolism (PE) occurrence in this clinical based age- and sex-matched case-control study.

What is known and what is new?

• PE is typically associated with a low survival rate and risk factors for PE remain poorly understood in cold climate-related regions.

• OSAHAS, hypertension, smoking history, and arterial partial pressure of carbon dioxide (PCO2) were independent risk factors of PE.

What is the implication, and what should change now?

• Regarding OSAHS, further well-designed cohort studies should be conducted, including many PE-specific epidemiological studies.

Introduction

Pulmonary embolism (PE) is a leading cause of sudden death resulting from impaired pulmonary blood flow (1). It is the most frequently reported pulmonary vascular disease and substantially contributes to the burden of cardiovascular disease in public health during the 21st century. With an aging population, the incidence of PE has steadily increased in recent years. Current estimates suggest an annual incidence of 60 to 120 cases per 100,000 individuals, with an in-hospital mortality rate of 14% and a 90-day mortality rate of 20% (2). Furthermore, diagnostic challenges remain significant, with misdiagnosis or missed diagnosis rates reaching 60–80%, and fewer than 10% of patients suspected of having PE ultimately receiving a confirmed diagnosis (3,4). Early identification of high-risk patients and implementation of preventive or interventional therapies are critical in PE management. Surgery-related factors, such as total hip or knee replacement and interventions for hip fractures, cancer, trauma, or spinal cord injuries, are well-documented etiological contributors to PE (5). Patient-specific risk factors include advanced age, cancer diagnosis, estrogen-containing oral contraceptive use, pregnancy, obesity, prior PE episodes, and sex (6-9). A recent retrospective study of 17,903 PE patients in China identified low ambient temperature as a novel risk factor, increasing PE onset risk by 84% with an effect lasting up to 72 hours (10). While the biological mechanism underlying cold-induced PE remains unclear, it is hypothesized that exposure to low temperatures (e.g., cold air inhalation) may cause pulmonary vasoconstriction and hematologic alterations, thus inducing PE.

The incidence of obstructive sleep apnea-hypopnea syndrome (OSAHS) has also increased annually. Approximately 175 million adults (23.6%) aged 30–69 years are estimated to have OSAHS in China (11), while the prevalence in the United States reaches 25% of the adult population (12). OSAHS and PE share common risk factors, including obesity and aging (13,14). Studies indicate that OSAHS may predispose individuals to coagulation disorders, characterized by elevated fibrinogen, D-dimer, and platelet activity, as well as other hematologic abnormalities (13,14). Furthermore, altered hemodynamics, vascular endothelial injury (VEI), oxidative stress (OS), and hyperinflammatory responses likely contribute to the development of PE (15). Early reports in the 1970s suggested a potential association between OSAHS and PE, which has since been supported by cross-sectional case-control studies (16,17). These studies propose shared etiological mechanisms and indicate that OSAHS might be an independent risk factor for PE. Building upon this foundation, we hypothesized that regional factors, such as cold exposure, influence PE incidence. Consequently, we conducted a case-control study to evaluate these risk factors in the Beijing population and develop a quantitative risk model and scoring system for healthcare-related exposure. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1293/rc).

Methods

Subjects

Cases were defined as patients newly diagnosed with PE at the Emergency Rescue Center of Beijing Anzhen Hospital, Beijing, China, between 2017 and 2020. Controls were 1:1 matched with cases based on age and sex and were randomly selected from health check-up participants at the same institute during the same period. A total of 129 PE patients and 129 matched controls were included in the analysis, drawn from 2,710 health check-up participants (Figure 1).

Figure 1 Schematic flow diagram of enrollment and matching process of the case and the control groups. CTPA, computer tomographic pulmonary angiography.

Inclusion criteria included meeting the diagnostic criteria for PE. Exclusion criteria included: (I) missing computed tomographic pulmonary angiography (CTPA) data; (II) missing baseline data. PE diagnosis was established based on clinical symptoms, laboratory findings, and imaging studies, including CTPA. For patients whose imaging results were unavailable, PE diagnosis was confirmed through clinical follow-up. Diagnosis reviews were independently conducted by a clinician and a radiologist, with final results verified by an expert panel. A complete medical history was collected for all patients. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the ethics committee of Beijing Anzhen Hospital (No. 2024259x). Individual consent for this retrospective analysis was waived.

Data collection

Demographic characteristics, medical histories, smoking status, body mass index (BMI), and conventional cardiovascular risk factors, including OSAHS, were extracted from medical records for both the case and the control groups. All participants or their family caregivers were interviewed by well-trained research personnel using a structured questionnaire to confirm the aforementioned information.

Medical history data included hypertension, diabetes, hyperlipidemia, and deep vein thrombosis (DVT). Smoking behavior, which was not consistently recorded in the medical histories, was ascertained through participant interviews. Smoking history was classified into two categories: ever-smokers and never-smokers. Hypertension was defined as either a documented medical history of antihypertensive medication use or repeated blood pressure measurements of systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg on at least three occasions, taken on separate days. Smoking was defined as the consumption of at least one cigarette per day for a duration exceeding six months.

Statistical analysis

Continuous variables were expressed as the mean ± standard deviation (SD) or, for skewed data, as the median with an interquartile range (IQR). Differences between the case and control groups were assessed using Student’s t-test for normally distributed data and the Mann-Whitney test for non-normally distributed data. Categorical variables were presented as counts and percentages and analyzed using either the Chi-squared (χ2) test or Fisher’s exact test, as appropriate.

To identify potential risk factors for PE, variables that were statistically significant in univariate analysis and other relevant confounding factors were included in a multivariate analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a conditional logistic regression model. The discrimination power of the risk score was evaluated via receiver operating characteristic (ROC) curve analysis. An additive risk score for PE prediction was developed using coefficients from the final logistic regression model. Model fit was assessed with the Hosmer-Lemeshow test. A logistic exposure risk calculator was subsequently constructed. All statistical tests were two-tailed, and a type I error rate of 0.05 was employed. Statistical analyses were performed using SPSS software version 23 (SPSS Inc., Chicago, IL, USA).

Results

Table 1 summarizes the characteristics of the 129 matched case-control pairs. No significant differences were observed in the prevalence of hyperlipidemia, diabetes, or venous thromboembolism (VTE) between the case and control groups. However, the case group exhibited a significantly higher prevalence of hypertension, smoking, and OSAHS compared with the control group. Triglyceride levels were significantly elevated in the case group, whereas estimated glomerular filtration rate (eGFR) and arterial partial pressure of carbon dioxide (PCO2) levels were notably lower.

Table 1

Distribution of clinical risk factors of pulmonary embolism in the case-control pairs

Risk factors Case (n=129) Control (n=129) P value
Age (years) 51.73±8.977 51.73±8.977 >0.99
Male 96 (74.4) 96 (74.4) >0.99
BMI (kg/m2) 26.45±2.738 26.45±2.982 >0.99
SBP (mmHg) 128.8±18.366 129.2±18.600 0.87
DBP (mmHg) 79.8±10.055 77.5±9.891 0.058
Hypertension 88 (68.2) 72 (55.8) 0.04
Diabetes 41 (31.8) 40 (31.0) 0.89
Hyperlipidemia 76 (58.9) 67 (51.9) 0.26
Smoking 108 (83.7) 70 (54.3) <0.001
Stroke 7 (5.4) 7 (5.4) >0.99
MI 10 (7.8) 13 (10.1) 0.51
DVT 24 (18.6) 24 (18.6) >0.99
OSAHS 27 (20.9) 9 (7.0) 0.001
Total cholesterol (mmol/L) 4.562±1.278 4.481±1.052 0.58
Triglyceride (mmol/L) 2.774±2.087 2.257±1.729 0.03
HDL-C (mmol/L) 0.918±0.211 0.948±0.222 0.28
LDL-C (mmol/L) 2.787±0.866 2.867±0.812 0.44
Glucose (mmol/L) 7.593±2.462 8.177±2.901 0.08
eGFR (mL/min/1.73 m2) 96.63±24.503 104.98±25.760 0.008
Arterial PO2 (mmol/L) 101.5±11.989 100.3±12.276 0.42
Arterial PCO2 (mmHg) 44.467±6.890 46.241±6.732 0.04
LVEF (%) 71.922±6.256 70.605±6.427 0.09
Aspirin 128 (99.2) 129 (100.0) 0.32
Statins 109 (84.5) 117 (90.7) 0.13

Data are presented as mean ± standard deviation or n (%). BMI, body mass index; DBP, diastolic blood pressure; DVT, deep vein thrombosis; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MI, myocardial infarction; OSAHS, obstructive sleep apnea-hypopnea syndrome; PCO2, partial pressure of carbon dioxide; PO2, partial pressure of oxygen; SBP, systolic blood pressure.

In the multivariate analysis using a conditional logistic regression model, OSAHS, hypertension, and smoking were identified as significant predictors of PE (OSAHS: OR, 3.100; 95% CI: 1.202–7.994; P=0.01; hypertension: OR, 2.212; 95% CI: 1.107–4.420; P=0.02; smoking: OR, 7.167; 95% CI: 3.302–15.556; P<0.001) (Table 2). A negative association between arterial PCO2 and PE risk was also identified (OR, 0.946; 95% CI: 0.904–0.990; P=0.02).

Table 2

Matched OR for multiple predictive variables associated with PE, based on conditional logistic regression (129 matched pairs)

Risk factors Unadjusted Adjusted
OR 95% CI P value OR 95% CI P value
OSAHS 3.529 1.587–7.849 0.002 3.100 1.202–7.994 0.01
Age 1.000 0.572–1.749 >0.99 1.031 0.990–1.072 0.14
Male 1.000 0.973–1.028 >0.99 0.889 0.429–1.842 0.75
BMI 1.000 0.918–1.089 >0.99 0.983 0.880–1.097 0.76
SBP 0.999 0.986–1.012 0.87 0.989 0.969–1.010 0.32
DBP 1.024 0.999–1.050 0.059 1.023 0.987–1.060 0.22
Hypertension 1.699 1.022–2.824 0.41 2.212 1.107–4.420 0.02
Diabetes 1.037 0.613–1.754 0.89 1.277 0.633–2.380 0.54
Hyperlipidemia 1.327 0.811–2.171 0.26 1.49 0.725–3.060 0.28
Smoking 4.335 2.423–7.756 <0.001 7.167 3.302–15.556 <0.001
Stroke 1.000 0.341–2.937 >0.99 2.721 0.653–11.343 0.17
MI 0.75 0.316–1.778 0.51 0.908 0.309–2.671 0.86
DVT 1.000 0.543–1.872 >0.99 0.647 0.292–1.435 0.28
Total cholesterol 1.062 0.860–1.311 0.58 1.187 0.671–2.102 0.56
Triglyceride 1.171 1.006–1.364 0.042 1.035 0.776–1.379 0.82
HDL-C 0.531 0.170–1.658 0.28 1.564 0.301–8.141 0.59
LDL-C 0.892 0.665–1.194 0.44 0.58 0.279–1.209 0.15
Glucose 0.92 0.837–1.012 0.09 0.894 0.794–1.006 0.06
eGFR 0.987 0.976–0.997 0.01 0.995 0.982–1.008 0.44
Arterial PO2 1.008 0.988–1.029 0.42 1.006 0.980–1.032 0.66
Arterial PCO2 0.963 0.928–0.998 0.04 0.946 0.904–0.990 0.02
LVEF (%) 1.034 0.994–1.075 0.09 1.039 0.988–1.093 0.13
Statins 0.559 0.261–1.197 0.13 0.555 0.210–1.466 0.23

BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; DVT, deep vein thrombosis; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MI, myocardial infarction; OR, odds ratio; OSAHS, obstructive sleep apnea-hypopnea syndrome; PCO2, partial pressure of carbon dioxide; PE, pulmonary embolism; PO2, partial pressure of oxygen; SBP, systolic blood pressure.

Following prescreening with univariate logistic regression, these factors were included in the preliminary main-effect model (Table 2). Given the age- and sex-matched design of the study, and the established role of these variables as risk factors for PE, five factors were retained in the final logistic model. Independent associations between these factors and PE risk are shown in Table 3. To estimate the predicted probability of PE, an additive risk score was calculated, with factor scores summing to approximate a percentage likelihood of PE, as outlined in Table 4.

Table 3

Independent risk factors for PE and corresponding points for the risk score

Risk factors Categories Points
Age (years) 30–39 0
40–49 2
50–59 4
60–69 6
70–79 8
Sex Female 0
Male −1
OSAHS No 0
Yes 8
Hypertension No 0
Yes 5
Smoking No 0
Yes 13

OSAHS, obstructive sleep apnea-hypopnea syndrome; PE, pulmonary embolism.

Table 4

Predictive risk score for PE following occupational exposure

Point total Estimate of risk Point total Estimate of risk
−1 0.10888 17 0.54349
1 0.13596 18 0.57466
2 0.15152 19 0.60524
3 0.1685 20 0.63502
4 0.18697 21 0.66381
5 0.20696 22 0.69142
6 0.22849 23 0.71774
7 0.25155 24 0.74264
8 0.2761 25 0.76606
9 0.30208 26 0.78796
10 0.32939 27 0.80833
11 0.35791 28 0.82716
12 0.38746 29 0.8445
13 0.41787 30 0.8604
14 0.44892 31 0.87491
15 0.48037 32 0.88811
16 0.51198 – –

PE, pulmonary embolism.

Model fit was confirmed via the Hosmer-Lemeshow test (χ2=10.701, P=0.15). The discrimination power of the risk scoring system, as evaluated by ROC curve analysis, is shown in Figure 2, demonstrating robust predictive accuracy. The exposure risk model also exhibited strong predictive performance (adjusted OR, 1.129; 95% CI: 1.021–1.248).

Figure 2 Areas under the ROC curve for the risk scoring system. CI, confidence interval; ROC, receiver operating characteristic.

Discussion

This case-control study identified cold region-specific risk factors associated with the increased incidence of PE in a population matched for age and sex. The findings demonstrated significant associations between OSAHS, hypertension, and smoking and the risk of PE, while arterial PCO2 exhibited a negative correlation with PE risk.

In the mid-19th century, Rudolph Virchow identified a triad of factors contributing to thrombosis: stasis, vascular endothelial damage, and hypercoagulability. These mechanisms reflect interactions between major inherited factors (e.g., factor V Leiden, prothrombin gene mutation, antithrombin deficiency, protein C deficiency, protein S deficiency) and acquired factors (e.g., trauma, surgery, malignancy, pregnancy, aging, obesity) (18). Evidence suggests that OSAHS may exacerbate all three mechanisms of thrombosis in PE patients. Studies have reported elevated levels of thrombin-antithrombin (TAT) complexes (19), activated clotting factors [VIIa (20), XIIa (21)], and fibrinogen (22-24) in patients with OSAHS, potentially triggering the extrinsic coagulation pathway. Concurrently, significant increases in endogenous coagulation factors, tissue factors, thrombin levels, and plasminogen activator inhibitor-1 (PAI-1) activity have been observed, enhancing fibrinolytic activity (25).

OSAHS has also been associated with heightened platelet activity (26-28) and elevated levels of von Willebrand factor (vWF) (22,29-31), contributing to hypercoagulability. During obstructive sleep apnea episodes, airflow obstruction increases intrathoracic pressure, impeding venous blood return. Repeated hypoxia induces mitochondrial dysfunction, OS, pro-inflammatory cytokine release, and vascular endothelial damage (32). Díaz-García et al. (33) demonstrated that the activation of nucleotide-binding domain leucine-rich repeat-containing family pyrin domain-containing 3 (NLRP3) inflammasomes may mediate the interaction between intermittent hypoxia and systemic inflammation in OSAHS. Importantly, continuous positive airway pressure (CPAP) therapy has been shown to improve coagulation parameters, platelet function, and fibrinolysis in OSAHS patients (30,34), underscoring the role of hypercoagulability in PE pathogenesis among OSAHS patients.

DVT was the most frequent comorbid condition observed in the study. This finding aligns with the widely accepted view that DVT of the lower extremities and pulmonary thromboembolism (PTE) represent two manifestations of VTE syndrome (13). Accordingly, physicians managing patients with DVT should implement appropriate measures to reduce the risk of PTE. However, the relationship between DVT and PE was not evident in our study, which may be attributable to the unclear diagnosis of DVT in some patients. Our findings revealed that hypertension was a significant risk factor for the development of PE. This association may be explained by the propensity of elevated blood pressure to induce prothrombotic and hypercoagulable states (35). This underscores the importance of evaluating and monitoring hypertensive patients for thromboembolic complications and considering thromboprophylaxis when clinically appropriate.

Other factors, including diabetes mellitus, smoking, puerperium, dilated cardiomyopathy, obesity, acute myocardial infarction, alcohol consumption, fractures, stroke, pneumonia, and malignancy, were consistent with those reported in the existing literature (36,37). Although arterial PCO2 was identified as a risk factor, it is more likely to represent a compensatory finding indicative of tachypnea. These observations suggest that individuals with these conditions should be routinely monitored for coagulopathy and their potential risk of developing PTE.

Study limitations

This study has several notable limitations. First, the relatively small sample size of 129 subjects in both the case and control groups may limit the generalizability of the findings. Nevertheless, efforts were made to mitigate this limitation by employing a case-control design with age- and sex-matched participants. Second, the study was conducted at a single institution, raising questions regarding the representativeness of the PE control group for the general population of Beijing. Third, while Beijing serves as a representative cold-region environment, future research should aim to enhance the generalizability of the findings by including additional regions with similar climatic conditions. Lastly, as this was a retrospective cohort study, several unknown or unmeasured risk factors could not be accounted for in the analysis. Furthermore, this was a retrospective study, which itself inherits several biases which needs to be explored.

Conclusions

In conclusion, this clinical age- and sex-matched case-control study identified OSAHS, hypertension, and smoking as significant risk factors for PE in the population of Beijing. Additionally, a negative correlation was observed between arterial PCO2 and PE risk. The quantitative exposure risk model and risk score developed in this study demonstrated strong predictive value and may aid in the early detection of PE. Based on these findings, a detailed evaluation of predisposing factors for PE and early intervention is recommended to mitigate the incidence of this severe condition. Specifically, for OSAHS, further well-designed cohort studies or meta-analyses incorporating PE-specific epidemiological investigations are warranted.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1293/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1293/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1293/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-1293/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by the ethics committee of Beijing Anzhen Hospital (No. 2024259x), individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Yang G, Nie S. Risk factors for pulmonary embolism: a case-control study. J Thorac Dis 2025;17(3):1552-1560. doi: 10.21037/jtd-24-1293

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