Abstract
Background
Acute pulmonary thromboembolism (PTE) is the third leading cause of cardiovascular mortality with increased incidence. Comorbidities are prevalent among PTE patients, yet their characteristics and impacts have not been thoroughly investigated. Thus, this retrospective study aims to explore the comorbidity characteristics and impact in patients with acute PTE, so as to provide a reference for comorbidity management and risk assessment in acute PTE patients.
Methods
A retrospective study was conducted on 243 patients with acute PTE in Beijing Hospital between January 2009 and December 2015. Univariate and multivariate Logistic regression analysis were performed to analyze the associations between comorbidity, and risk factors, mortality and adverse events risk.
Results
The mean age of the 243 acute PTE patients was 67.18 ± 12.96 years, with 123 (50.6%) females. Univariate analysis showed patients aged 65–75 years had 5.37-fold (vs. <45years, p = 0.024), 26.60-fold (vs.<45years, p = 0.005) and 6.00-fold (vs. 45-55years, p = 0.031) risks of having 1,2, and ≥ 3 comorbidities, respectively. Patients aged ≥ 75 years had 17.11-fold (vs. <45years, p = 0.013) and 5.00-fold (vs. 45-55years, p = 0.036) risks of having 2 or ≥ 3 comorbidities. Multivariate analysis showed patients aged 65–75 years had 29.79-fold (vs. <45years, p = 0.008) and 6.31-fold (vs. 45-55years, p = 0.029) risks of having 2 or ≥ 3comorbidities, respectively, and patients aged ≥ 75 years had 17.38-fold (vs. <45years, p = 0.022) risk of having 2 comorbidities. Additionally, univariate analysis showed genitourinary tumors (OR = 4.75, p = 0.017), gastric cancer (OR = 8.96, p = 0.032), and chronic nephritis (OR = 18.00, p = 0.020) were significantly associated with increased mortality risk, and lung infection (OR = 3.07, p = 0.049),chronic obstructive pulmonary disease (OR = 3.63, p = 0.046),and nephrotic syndrome (OR = 14.00, p = 0.033) had higher risks of adverse events. Multivariate analysis showed that genitourinary tumor (OR = 7.86, p = 0.005),gastric cancer (OR = 16.81, p = 0.012), leukemia (OR = 22.00, p = 0.046), and chronic nephritis (OR = 28.62,p = 0.018) significantly increased mortality risk, and liver cirrhosis (OR = 8.99, p = 0.038) and nephrotic syndrome (OR = 26.58, p = 0.021) significantly increased adverse events risk.
Conclusions
Age is an independent risk factor for comorbidities burden in acute PTE patients. Genitourinary tumors, gastric cancer, leukemia, and chronic nephritis, significantly increase mortality risk, and liver cirrhosis and nephrotic syndrome are associated with higher adverse events risk. These findings highlight the need for personalized comorbidity management and risk assessment in acute PTE patients, particularly among the elderly.
Keywords: Acute pulmonary thromboembolism, Comorbidity, Risk factors, Mortality, Adverse events
Background
Venous thromboembolism (VTE) includes deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE). Acute PTE is currently the third leading cause of cardiovascular mortality [1]. The mortality rate of PTE patients varies widely, ranging from 4.1% to 14.5% [2], and the 3-month mortality rate in high-risk patients can reach up to 52% [3]. In recent years, advancements in diagnostic techniques and a deeper understanding of PTE have led to a significant increase in its diagnosis rate. Studies have shown that the incidence of PTE in China increased 5-fold from 1.1/100,000 in 2007 to 6.3/100,000 in 2016 [4]. A 2021 study showed that the incidence of PTE in China was 14.19/100,000, with a mortality rate of 1.00/100,000 [5].
Common risk factors for PTE include cancer, smoking, inflammatory bowel disease, infections (such as respiratory infections), hyperthyroidism, kidney disease, oral contraceptives and hormone replacement therapy, chronic obstructive pulmonary disease (COPD), atrial fibrillation, diabetes, stroke, trauma, a history of DVT, obstructive sleep apnea (OSA) [6, 7]. Many of these risk factors also sever as comorbidities in PTE patients. Research indicates that patients with COPD exacerbations, interstitial lung disease (ILD), OSA, and corona virus disease 2019 (COVID-19) have a higher prevalence of PTE [8]. Additionally, diabetes not only increases the risk of PTE but also associates with adverse events [9]. These Comorbidities significantly impact the decision-making and prognosis in PTE patients and should be considered in clinical management.
Despite these insights, most current studies only describe the prevalence of different comorbidities in PTE patients without in-depth analysis of the correlation between comorbidity patterns and potential risk factors or prognosis. Therefore, this study aims to explore the comorbidity characteristics and impact on the prognosis of acute PTE patients, providing a reference for comorbidity management and risk assessment in this patient population, ultimately improving clinical outcomes.
Methods
Study design and patients
This single-center, retrospective, cross-sectional study included patients diagnosed with acute PTE in Beijing Hospital from January 2009 to December 2015. Inclusion criteria: (1) Patients hospitalized at Beijing Hospital from January 2009 to December 2015, diagnosed with acute PTE by computed tomographic pulmonary angiography (CTPA) or pulmonary ventilation-perfusion scan; (2) Age ≥ 18 years; (3) Complete electronic medical records. Exclusion criteria: (1) Age <18 years; (2) Non-thrombotic pulmonary embolism; (3) Incomplete or missing medical records; (4) No PTE risk stratification; (5) with chronic thromboembolic pulmonary hypertension (CTEPH). This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Hospital (approval number: 2012BJYYEC-050-02); informed consents were obtained from all participants, and all methods were performed in accordance with relevant guidelines and regulations.
Epidemiological, demographic, clinical characteristics, treatments, and prognosis of all patients were collected from electronic medical system, mainly including age, gender, body mass index (BMI), PTE risk stratification, age-adjusted Charlson Comorbidity Index (aCCI), length of stay (LOS), comorbidity, trauma history, surgical history, adverse events, and prognosis.Smoking status was collected in the dataset but not included in regression models due to incomplete data for some patients.
The enrolled patients were divided into five groups based on age: (1) < 45 years; (2) 45–55 years; (3) 55–65 years; (4) 65–75 years; (5) ≥ 75 years. Patients’ BMI (kg/m2) was divided into four groups: (1) < 18.5; (2) 18.5–24; (3) 24–28; (4) ≥ 28. Acute PTE risk stratification was based on the ESC Guideline [10, 11], which categorize patients into high-, intermediate-, and low-risk groups based on the presence of hemodynamic instability, signs of right ventricular dysfunction (on imaging or by elevated cardiac biomarkers). High-risk was defined as hemodynamic instability (cardiac arrest; obstructive shock [systolic blood pressure (BP) < 90 mmHg, or vasopressors required to achieve a BP ≥ 90 mmHg despite an adequate filling status, in combination with end-organ hypoperfusion]; or persistent hypotension (systolic BP < 90 mmHg, or a systolic pressure drop 40 mmHg for >15 min), not caused by new-onset arrhythmia, hypovolaemia, or sepsis.); Intermediate-risk was defined as presence of right ventricular dysfunction on imaging or elevated cardiac biomarkers, without hemodynamic instability; Low-risk was defined as absence of both hemodynamic instability and right ventricular dysfunction, negative cardiac biomarkers and a low clinical risk core(e.g., simplified pulmonary embolism severity index (sPESI) = 0). Comorbidities other than PTE were counted, and patients were divided into four subgroups according to the number of comorbidities: 0 comorbidity, 1 comorbidity, 2 comorbidities, and ≥ 3 comorbidities. The age-adjusted Charlson Comorbidity Index (aCCI) was calculated for each patient (Fig. 1).
Fig. 1.
Flowchart of the study design
The prognostic categories were defined based on a combination of clinical symptoms, imaging findings (CTPA or perfusion scan), and laboratory markers (e.g., D-dimer, arterial blood gas) at discharge: Healing: Complete resolution of PTE symptoms and imaging findings; Improved: Partial resolution of symptoms and/or imaging findings; No Change: No significant change in symptoms or imaging; Worsen: Deterioration in symptoms, imaging, or development of new complications.
Statistical analysis
Baseline data were described based on the type of variable. Quantitative data, such as age and BMI, were expressed as mean ± standard deviation (mean ± SD); categorical variables, such as gender, were expressed as frequency (percentage). One-way Analysis of Variance (ANOVA) was used to compare quantitative baseline data of the four subgroups, and Chi-Square analysis was used to compare the distribution of categorical data across different subgroups. Univariate binary Logistic regression analysis was used to explore the correlation between comorbidities and mortality and adverse events risk, then using multivariate regression analysis to explore interactions between all comorbidities and calculate corresponding OR values (95% CI). Gender, age, BMI, surgical history, trauma history, adverse events, and death were substituted into Logistic regression equation for univariate and multivariate regression analysis to explore the correlation between the number of comorbidities and potential risk factors and prognosis, and OR values (95% CI) were calculated. Age was included as a categorical variable in multivariate analysis to control for its effect, though residual confounding may still exist. Treatment modalities were collected and described in Table 1 but were not included as covariates in regression models due to study aims and sample size constraints. The results were represented by forest maps. All data were analyzed using IBM SPSS 26.0 (SPSS Inc., Chicago, USA), with a two-sided p < 0.05 considered statistically significant.
Table 1.
Baseline characteristic of the subjects
| Variable | Total | 0comorbidity | 1comorbidity | 2comorbidities | ≥ 3comorbidities | χ2/F | P-value |
|---|---|---|---|---|---|---|---|
| Total | 243(100.0%) | 36(14.8%) | 78(32.1%) | 56(23.0%) | 73(30.0%) | ||
| Gender | |||||||
| Male | 120(49.4%) | 21(58.3%) | 34(43.6%) | 27(48.2%) | 38(52.1%) | 2.44 | 0.486 |
| Female | 123(50.6%) | 15(41.7%) | 44(56.4%) | 29(51.8%) | 35(47.9%) | ||
| Age Group(years) | 41.50 | < 0.001 | |||||
| < 45 | 14(5.8%) | 7(19.4%) | 6(7.7%) | 1(1.8%) | 0(0.0%) | ||
| 45–55 | 23(9.5%) | 5(13.9%) | 10(12.8%) | 4(7.1%) | 4(5.5%) | ||
| 55–65 | 52(21.4%) | 10(27.8%) | 23(29.5%) | 10(17.9%) | 9(12.3%) | ||
| 65–75 | 71(29.2%) | 5(13.9%) | 23(29.5%) | 19(33.9%) | 24(32.9%) | ||
| ≥ 75 | 83(34.2%) | 9(25.0%) | 16(20.5%) | 22(39.3%) | 36(49.3%) | ||
| Age(years) | 67.18 ± 12.96 | 58.67 ± 17.78 | 63.49 ± 11.95 | 69.75 ± 10.61 | 73.36 ± 8.81 | 16.07 | < 0.001 |
| Male | 65.90 ± 14.58 | 52.90 ± 18.25 | 61.74 ± 12.59 | 71.96 ± 11.61 | 72.50 ± 9.19 | 14.17 | < 0.001 |
| Female | 68.43 ± 11.08 | 66.73 ± 13.97 | 64.84 ± 11.40 | 67.69 ± 9.32 | 74.29 ± 8.41 | 5.51 | 0.001 |
| BMI(kg/m2) | 24.63 ± 4.10 | 23.97 ± 3.78 | 25.34 ± 4.48 | 24.54 ± 3.46 | 24.28 ± 4.24 | 1.29 | 0.280 |
| BMI Group | 4.57 | 0.870 | |||||
| < 18.5 | 10(4.1%) | 1(2.8%) | 2(2.6%) | 2(3.6%) | 5(6.8%) | ||
| 18.5–24 | 104(42.8%) | 19(52.8%) | 30(38.5%) | 23(41.1%) | 32(43.8%) | ||
| 24–28 | 81(33.3%) | 10(27.8%) | 29(37.2%) | 20(35.7%) | 22(30.1%) | ||
| ≥ 28 | 48(19.8%) | 6(16.7%) | 17(21.8%) | 11(19.6%) | 14(19.2%) | ||
| aCCI | 3.74 ± 1.62 | 2.33 ± 1.45 | 3.17 ± 1.16 | 3.82 ± 1.34 | 4.99 ± 1.43 | 39.76 | < 0.001 |
| Surgical History | |||||||
| Yes | 101(41.6%) | 10(27.8%) | 29(37.2%) | 25(44.6%) | 37(50.7%) | 6.15 | 0.104 |
| No | 142(58.4%) | 26(72.2%) | 49(62.8%) | 31(55.4%) | 36(49.3%) | ||
| Trauma History | |||||||
| Yes | 14(5.8%) | 3(8.3%) | 2(2.6%) | 2(3.6%) | 7(9.6%) | 4.37 | 0.224 |
| No | 229(94.2%) | 33(91.7%) | 76(97.4%) | 54(96.4%) | 66(90.4%) | ||
| PTE risk stratification | 6.61 | 0.358 | |||||
| High Risk | 18(7.4%) | 1(2.8%) | 10(12.8%) | 3(5.4%) | 4(5.5%) | ||
| Intermediate Risk | 72(29.6%) | 9(25.0%) | 25(32.1%) | 16(28.6%) | 22(30.6%) | ||
| Low Risk | 153(63.0%) | 26(72.2%) | 43(55.1%) | 37(66.1%) | 47(64.4%) | ||
| Length of Stay(days) | 20.51 ± 19.31 | 19.47 ± 16.76 | 21.29 ± 25.21 | 21.98 ± 13.01 | 19.84 ± 17.48 | 0.12 | 0.950 |
| Prognosis | 18.24 | 0.109 | |||||
| Healing | 23(9.5%) | 6(16.7%) | 8(10.3%) | 4(7.1%) | 5(6.8%) | ||
| Improved | 189(77.8%) | 28(77.8%) | 62(79.5%) | 46(82.1%) | 53(72.6%) | ||
| No Change | 4(1.6%) | 1(2.8%) | 0(0.0%) | 1(1.8%) | 2(2.7%) | ||
| Worsen | 1(0.4%) | 0(0.0%) | 0(0.0%) | 1(1.8%) | 0(0.0%) | ||
| Death | 26(10.7%) | 1(2.8%) | 8(10.3%) | 4(7.1%) | 13(17.8%) | ||
| Adverse Events | |||||||
| Yes | 32(13.2%) | 2(5.6%) | 9(11.5%) | 6(10.7%) | 15(20.5%) | 5.78 | 0.123 |
| No | 211(86.8%) | 34(94.4%) | 69(88.5%) | 50(89.3%) | 58(79.5%) | ||
| Bleeding | 13(5.3%) | 0(0.0%) | 3(3.8%) | 0(0.0%) | 10(13.7%) | 15.60 | 0.001 |
| Thrombocytopenia | 3(1.2%) | 0(0.0%) | 1(1.3%) | 0(0.0%) | 2(2.7%) | 2.51 | 0.474 |
| Liver Dysfunction | 19(7.8%) | 2(5.6%) | 6(7.7%) | 6(10.7%) | 5(6.8%) | 1.00 | 0.800 |
| Treatment | |||||||
| Thrombolysis | 10(4.1%) | 0(0.0%) | 6(7.7%) | 4(7.1%) | 0(0.0%) | 8.51 | 0.037 |
| Anticoagulation | 214(88.1%) | 33(91.7%) | 68(87.2%) | 50(89.3%) | 63(86.3%) | 0.80 | 0.850 |
| Intervention | 11(4.5%) | 2(5.6%) | 5(6.4%) | 3(5.4%) | 1(1.4%) | 2.50 | 0.475 |
| Surgery | 1(0.4%) | 1(2.8%) | 0(0.0%) | 0(0.0%) | 0(0.0%) | 5.77 | 0.123 |
Results
Baseline clinical characteristics
From January 2009 to December 2015, a total of 288 patients diagnosed with acute PTE were screened in Beijing Hospital. After excluding 1 patient with a non-PTE discharge diagnosis, 24 patients without PTE risk stratification, 11 patients with missing medical records, and 9 patients with CTEPH, the remaining 243 patients were included in the analysis and were divided into four groups according to the number of comorbidities: 0 comorbidity (36,14.8%), 1 comorbidity (78,32.1%), 2 comorbidities (56,23.0%) and ≥ 3 comorbidities (73,30.0%).
The average age of the 243 patients with123 (50.6%) females was 67.18 ± 12.96 years and the average length of stay was 20.51 ± 19.31 days. The average number of comorbidities was 1.91 ± 1.47, with 207 (85.2%) patients having at least one comorbidity. The older patients presented with more comorbidities (p < 0.001). The average aCCI was 3.74 ± 1.62However, BMI, PTE risk stratification, length of stay, prognosis, and adverse events showed no significant difference with the number of comorbidities (all p > 0.05) (Table 1).
Logistic regression analysis of the number of comorbidity and risk factors and prognosis in patients with acute pulmonary thromboembolism
Univariate Logistic regression analysis showed that gender, BMI group, trauma history, PTE risk stratification, mortality, and adverse events were not significantly associated with comorbidities burden in acute PTE patients (all p > 0.05). However, compared to those aged < 45 years, patients aged 65–75 years had significantly higher risk of having 1 comorbidity (OR = 5.37, p = 0.024), 2 comorbidities (OR = 26.60, p = 0.005), and ≥ 3comorbidities (OR = 6.00 vs. 45-55years, p = 0.031). For patients aged ≥ 75 years, the risks of having 2 or ≥ 3 comorbidities were 17.11-fold (vs. <45 years, p = 0.013) and 5.00-fold (vs. 45–55 years, p = 0.036),respectively. Additionally, surgical history significantly increased the risk of having ≥ 3 comorbidities (OR = 2.67, p = 0.025) (Fig. 2).
Fig. 2.
Univariate Logistic regression analysis of the number of comorbidities and risk factors and prognosis in patients with acute pulmonary thromboembolism
Multivariate Logistic regression analysis further confirmed that age was a significant predictors of comorbidity burden in PTE patients. Patients aged 65–75 years had 29.79-fold (vs.<45 years, p = 0.008) and 6.31-fold (vs.45-45years, p = 0.039) higher risks of having 2 or ≥ 3comorbidities, respectively, and patients aged ≥ 75 years had 17.38-fold (vs.45-55years, p = 0.022) higher risk of having 2 comorbidities (Fig. 3).
Fig. 3.
Multivariate Logistic regression analysis of the number of comorbidities and risk factors and prognosis in patients with acute pulmonary thromboembolism
Comorbidity
Comorbidity characteristics of patients with acute pulmonary thromboembolism
The six most common comorbidities were hypertension (52.3%), coronary heart disease (21.0%), Diabetes (18.9%), hyperlipidemia (14.0%), cerebrovascular disease (10.7%), and varicose veins (9.5%). Among these, cardiovascular disease was the most common type of comorbidity, accounting for 65.8% (Table 2).
Table 2.
Results of comorbidity
| Comorbidity | Total | Male | Female | χ2 | P-value |
|---|---|---|---|---|---|
| Total | 243(100.0%) | 120(49.4%) | 123(50.6%) | ||
| Cardiovascular Disease | 160(65.8%) | 75(62.5%) | 85(69.1%) | 1.18 | 0.278 |
| Hypertension | 127(52.3%) | 56(46.7%) | 71(57.7%) | 2.98 | 0.084 |
| Coronary Heart Disease | 51(21.0%) | 23(19.2%) | 28(22.8%) | 0.47 | 0.491 |
| Hyperlipidemia | 34(14.0%) | 16(13.3%) | 18(14.6%) | 0.09 | 0.770 |
| Heart Failure | 8(3.3%) | 2(1.7%) | 6(4.9%) | 1.97 | 0.161 |
| Cardiomyopathy | 2(0.8%) | 1(0.8%) | 1(0.8%) | 0.00 | 0.986 |
| Rheumatic Heart Disease | 1(0.4%) | 0(0.0%) | 1(0.8%) | 0.98 | 0.322 |
| Respiratory Disease | 52(21.4%) | 24(20.0%) | 28(22.8%) | 0.28 | 0.599 |
| Lung Infection | 17(7.0%) | 8(6.7%) | 9(7.3%) | 0.04 | 0.842 |
| COPD | 12(4.9%) | 7(5.8%) | 5(4.1%) | 0.41 | 0.525 |
| Tuberculosis | 13(5.3%) | 9(7.5%) | 4(3.3%) | 2.17 | 0.141 |
| Bronchietasis | 10(4.1%) | 3(2.5%) | 7(5.7%) | 1.57 | 0.211 |
| Asthma | 6(2.5%) | 4(3.3%) | 2(1.6%) | 0.74 | 0.391 |
| ILD | 3(1.2%) | 0(0.0%) | 3(2.4%) | 0.00 | 0.986 |
| Pulmonary Heart Disease | 2(0.8%) | 1(0.8%) | 1(0.8%) | 0.28 | 0.599 |
| Metabolic and Endocrine Disease | 50(20.6%) | 28(23.3%) | 22(17.9%) | 1.10 | 0.294 |
| Diabetes | 46(18.9%) | 27(22.5%) | 19(15.4%) | 1.97 | 0.161 |
| Hyperthyroidism | 1(0.4%) | 0(0.0%) | 1(0.8%) | 0.98 | 0.322 |
| Neurological Disease | 36(14.8%) | 17(14.2%) | 19(15.4%) | 0.08 | 0.779 |
| Cerebrovascular Disease | 26(10.7%) | 14(11.7%) | 12(9.8%) | 0.23 | 0.630 |
| Thrombotic and Vascular Disease | 63(25.9%) | 37(30.8%) | 26(21.1%) | 2.97 | 0.085 |
| Varicose Veins | 23(9.5%) | 15(12.5%) | 8(6.5%) | 2.55 | 0.110 |
| DVT | 18(7.4%) | 14(11.7%) | 4(3.3%) | 6.27 | 0.012 |
| Phlebitis | 3(1.2%) | 3(2.5%) | 0(0.0%) | 3.11 | 0.078 |
| Malignant Tumor | 47(19.3%) | 23(19.2%) | 24(19.5%) | 0.01 | 0.946 |
| Lung Cancer | 13(5.3%) | 9(7.5%) | 4(3.3%) | 2.17 | 0.141 |
| Genitourinary Tumors | 12(4.9%) | 4(3.3%) | 8(6.5%) | 1.30 | 0.254 |
| Gastric Cancer | 4(1.6%) | 2(1.7%) | 2(1.6%) | 0.00 | 0.980 |
| Leukemia | 2(0.8%) | 1(0.8%) | 1(0.8%) | 0.00 | 0.986 |
| Pancreatic Cancer | 1(0.4%) | 1(0.8%) | 0(0.0%) | 1.03 | 0.310 |
| Liver and Kidney Disease | 11(4.5%) | 4(3.3%) | 7(5.7%) | 0.78 | 0.377 |
| Liver Cirrhosis | 5(2.1%) | 2(1.7%) | 3(2.4%) | 0.18 | 0.672 |
| Chronic Nephritis | 3(1.2%) | 0(0.0%) | 3(2.4%) | 2.96 | 0.085 |
| Nephrotic Syndrome | 3(1.2%) | 1(0.8%) | 2(1.6%) | 0.31 | 0.576 |
| Chronic Hepatitis | 3(1.2%) | 1(0.8%) | 2(1.6%) | 0.31 | 0.576 |
DVT Deep Venous Thrombosis,COPD Chronic Obstructive Pulmonary Disease, ILD Interstitial Lung Disease
Mortality and adverse events risk analysis of comorbidities in patients with acute pulmonary thromboembolism
Univariate binary Logistic regression analysis was performed on all comorbidities to explore the correlation between comorbidity and mortality and adverse events risk in PTE patients. The results showed that patients with genitourinary tumors, gastric cancer, and chronic nephritis had a 4.75-fold (p = 0.017), 8.96-fold (p = 0.032), and 18.00-fold (p = 0.020) increased mortality risk, respectively; meanwhile, patients with lung infection (OR = 3.07, p = 0.049), COPD (OR = 3.63, p = 0.046), nephrotic syndrome (OR = 14.00, p = 0.033) had higher risks of adverse events. No significant association was found between other comorbidities and mortality and adverse events risk in acute PTE patients (all p > 0.05). Multivariate Logistic regression analysis showed that genitourinary tumors, gastric cancer, leukemia, and chronic nephritis significantly increased mortality risks, with odds ratio (OR) of 7.86 (p = 0.005), 16.81 (p = 0.012), 22.00 (p = 0.046), and 28.62 (p = 0.018), respectively, and liver cirrhosis and nephrotic syndrome increased adverse events risks with odds ratio (OR) of 8.99 (p = 0.038) and 26.58 (p = 0.021) (Figs. 4 and 5).
Fig. 4.
Univariate and Multivariate Logistic regression analysis of comorbidity and mortality risk in patients with acute pulmonary thromboembolism
Fig. 5.
Univariate and Multivariate Logistic regression analysis of comorbidity and adverse events risk in patients with acute pulmonary thromboembolism. COPD = Chronic Obstructive Pulmonary Disease; ILD = Interstitial Lung Disease; DVT = Deep Venous Thrombosis. In Figs. 4 and 5, the forest maps on the left and right are the result of univariate and multivariate Logistic regression analysis, respectively
Discussion
This single-center, retrospective, cross-sectional study provides important insights into the comorbidity burden and its impact on outcomes in patients with acute PTE. Our study identified age as an independent risk factor for comorbidity burden in acute PTE patients, with elderly patients (≥ 65 years) showing dramatically increased odds of multiple (≥ 3) comorbidities, likely due to the decline in physiological functions, such as physical activity, muscle strength, and cardiopulmonary function in elderly patients. This is consistent with previous studies by Ma et al. and Zhou et al., which reported a higher prevalence of comorbidities, such as diabetes, hypertension, COPD, and stroke, in the elderly PTE patients [7, 12]. Additionally, Pauley et al. demonstrated a significant increase in the number of comorbidities and Elixhauser comorbidity scores with age in a large cohort of PTE patients in the United States [13]. In our study, the age-adjusted Charlson Comorbidity Index also increased with age (< 45 years vs. 45–55 years vs. 55–65 years vs. 65–75 years vs. ≥75 years, 0.93 ± 1.21 vs. 2.04 ± 0.88 vs. 2.75 ± 1.01 vs. 4.38 ± 1.37 vs. 4.76 ± 0.93, p < 0.001). This age-comorbidity relationship has profound implications for clinical management, as elderly PTE patients represent a particularly vulnerable population requiring comprehensive assessment and individualized care strategies.
Previous studies have shown that surgical history is a risk factor for PTE, possibly due to vascular endothelial injury and postoperative hemodynamic changes that promote thrombus formation. However, the relationship between surgical history and comorbidity burden has been less explored. In this study, univariate analysis indicated that surgical history significantly increased the risk of having ≥ 3 comorbidities (OR = 2.67, p = 0.025), but this association disappeared after multivariate adjustment. This suggests surgical history may be a marker rather than a direct driver of comorbidity burden in PTE patients. Therefore, comprehensive risk assessment beyond isolated clinical events is needed when evaluating comorbidity burden in PTE patients.
This study is the first to explore the association between comorbidity burden and prognosis in PTE patients. Univariate analysis suggested that patients with ≥ 3 comorbidities might have higher mortality (OR = 7.58, p = 0.056) and adverse events risk (OR = 4.40, p = 0.059). However, no significant associations were found after multivariate adjustment with odds ratio (OR) of 4.36 (p = 0.218) and 4.16 (p = 0.113), respectively. This might be related to the lack of a significant correlation between comorbidity burden and PTE risk stratification in this study. Previous studies have shown that mortality and adverse event risk in acute PTE patients are significantly associated with PTE risk stratification [14, 15]. Therefore, after multivariate analysis adjustment, increased comorbidity burden did not independently elevate mortality and adverse events risk. Thus, future research should further explore this relationship and develop more precise risk assessment models to better guide clinical decision-making and patient management.
Our study identified genitourinary tumors, gastric cancer, and leukemia significantly increased mortality risk in PTE patients. This finding aligns with previous studies demonstrating an association between cancer and increased mortality risk in PTE patients [16, 17]. A study by Ramy et al., involving 28,547 high-risk PTE patients, found that metastatic genitourinary tumors, gastrointestinal tumors (excluding colon cancer), and lung cancer significantly increased in-hospital mortality [18]. However, our study did not find a similar association with lung cancer, potentially due to differences in study populations. Ramy et al. focused exclusively on metastatic lung cancer in high-risk PTE patients, whereas our study encompassed PTE patients across all risk strata without distinguishing metastatic status. The extremely high odds ratios(ORs) observed for these malignancies can be explained by their unique pathophysiological mechanisms that promote hypercoagulability. Genitourinary tumors, particularly renal cell carcinoma and urothelial cancers, are associated with venous compression by tumor mass, increased tissue factor expression on tumor cells, and paraneoplastic syndromes that promote coagulation through cytokine release and platelet activation [19]. Gastric cancer contributes to hypercoagulability through chronic inflammation, angiogenesis, release of procoagulant factors like tissue factor and cancer procoagulant, and the mucosal disruption and bacterial translation may trigger systemic inflammatory response that further promote thrombosis [20]. Leukemia creates a unique prothrombotic environment through extreme leukocytosis, cytokine release (particularly in acute promyelocytic leukemia), direct endothelial injury from circulating blast cells; and the treatment-related factors including chemotherapy-induced endothelial damage and frequent use of central venous catheters add additional thrombotic risk [21]. The high leukocyte count in leukemia promotes leukostasis and microvascular thrombosis, while treatment-related tumor lysis syndrome can further aggravate thrombotic risk [22]. As a hematologic malignancy, increased infection risk, due to weakened immunity, also increased the mortality risk [23–25]. Therefore, it is imperative to adopt more aggressive treatment strategies for patients with the these comorbidities to reduce the mortality risk and improve patients’ prognosis.
Furthermore, chronic nephritis and nephrotic syndrome presented exceptionally high risks for mortality and adverse events (ORs 28.62 and 26.58, respectively), highlighting the ‘dual challenge’ these conditions pose in PTE management. Patients with chronic nephritis experience loss of anticoagulant factors (particularly antithrombin III) through impaired renal function, creating a hypercoagulable state conductive to thrombus formation [26]. Nephrotic syndrome presents similar challenges with severe proteinuria leading to loss of albumin and anticoagulant factors (antithrombin, protein C and S) [27], while concurrent fluid overload and electrolyte imbalances increase the risk of arrhythmias and heart failure [28]. Simultaneously, renal impairment complicates anticoagulation management due to altered drug metabolism and increased bleeding risk, particularly with novel oral anticoagulants that require renal clearance [29, 30]. Additionally, the management dilemma is further compounded by the need for contrast-enhanced CT imaging for PTE diagnosis in patients with potentially compromised renal function, creating a risk of contrast-induced nephropathy [31].
Liver cirrhosis was also found to significantly increase the risk of adverse events. The pathophysiology involves a dual hemostatic defect: impaired hepatic synthesis of coagulation factors predisposes patients to bleeding, while portal hypertension sequelae, such as gastroesophageal varices, further elevate hemorrhage risk [32, 33]. Compounding this vulnerability, the cirrhosis-associated immune dysfunction (CAID) syndrome increases susceptibility to infections. Jalan et al. had revealed that patients with cirrhosis faced higher risks of infection and mortality [34]. These interconnected complications—bleeding and infection—collectively contribute to the elevated mortality observed in this patient population.
While our study identified several comorbidities with exceptionally high odds ratios for mortality and adverse events, it is important to interpret these findings with appropriate caution. The extremely high ORs for conditions such as chronic nephritis (OR = 28.62) and leukemia (OR = 22.00) likely reflect both their genuine clinical impact and the statistical instability inherent in analyzing rare comorbidities within our sample size (chronic nephritis: 1.2%; leukemia: 0.8%). These estimates should be validated in larger, multicenter cohorts to establish more precise risk estimates.
This study also has some limitations that should be considered when interpreting the results. First, as a single-center study, our findings may not be fully generalizable to other populations or healthcare settings. The predominantly elderly population may limit the applicability of our results to younger PTE populations. Second, the exclusion of patients without risk stratification or incomplete records may introduce selection bias, potentially leading to underrepresentation of more severe or complex cases where risk stratification was not performed due to clinical urgency or missing data. This might result in an underestimation of the true comorbidity burden and its impact on mortality and adverse events. Third, the sample size, while adequate for detecting major associations, limited our ability to perform more detailed subgroup analyses and may have contributed to the instability of some risk estimates for rare comorbidities.Fourth, the retrospective design inherently carries risks of information bias and unmeasured confounding. Fifth, we were unable to account for competing risks (e.g., cancer-related deaths unrelated to PTE) due to the retrospective design and limited cause-specific mortality data, which may lead to overestimation of some comorbidity-mortality associations.Sixth, we did not adjust for treatment variations in our regression models, nor did we stratify surgical procedures by type and invasiveness, which could have provided more nuanced risk assessments. Seventh, residual confounding by age remains possible despite including age as a categorical variable in multivariate analysis. The continuous nature of age and its strong association with both comorbidity patterns and prognosis suggest that more sophisticated adjustment methods such as propensity score matching or age as a continuous variable might provide more robust estimates. Eighth, smoking status was collected in the dataset but was not included in the regression models due to incomplete data for some patients. Thus, future studies should include smoking as a covariate to better evaluate its role. Finally, the cross-sectional nature of our study precluded assessment of long-term outcomes and the evolution of comorbidity patterns over time. Therefore, future research should conduct prospective, multicenter studies to increase the sample sizes and populations diversity, as well as carry out long-term follow-up to overcome these limitations to further validate and refine the study results.
Conclusions
In conclusion, age is independently associated with increased comorbidity burden in acute PTE patients. Although overall comorbidity burden alone lacks prognostic independence, specific comorbidities, including genitourinary tumors, gastric cancer, chronic nephritis, leukemia, liver cirrhosis, and nephrotic syndrome, significantly increase mortality and adverse events risk. These results underscore the importance of individualized comorbidity assessment and management strategies in PTE patients, including comprehensive comorbidity screening at diagnosis, aggressive monitoring protocols for high-risk conditions, multidisciplinary co-management, and careful consideration of bleeding-thrombosis balance in anticoagulation decisions for patients with high-risk comorbidities, particularly for elderly patients.
Acknowledgements
We thank the healthcare workers who contributed to the data collection and management, and the participants in our study.
Authors’ contributions
CF.M: Writing - review & editing, Writing-original draft(Lead), Investigation, Formal analysis (Lead), Data curation, Conceptualization. XM.X: Writing - review & editing (Lead), Investigation, Funding acquisition, Data curation, Conceptualization. Y.Z: Data curation, Investigation, Writing - review & editing. YD.L: Data curation, Investigation, Writing - review & editing. LS.Q: Data curation, Investigation, Writing - review & editing. H.Y: Data curation, Investigation, Writing - review & editing.
Funding
This study was supported by Project of National Science &Technology Pillar Program of China during the 12th Five-Year Plan Period(2011BAI11B17), National High Level Hospital Clinical Research Funding (BJ-2023-071), Beijing Health Science and Technology Achievements and Appropriate Technology Promotion Project (BHTPP2022018) and “14th Five-Year” National Key Research and Development Program: “Research on the Prevention and Treatment of Common and Frequency Occurring Disease”. Key Project: “Systematic Research on the Construction of a Comprehensive Prevention and Treatment System for Pulmonary Thromboembolism and Long-Term Follow-Up Management” (2023YFC2507200).
Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Hospital (approval number:2012BJYYEC-050-02).
Consent of pubulication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.