Abstract
Cancer patients with acute pulmonary embolism (APE) admitted to the intensive care unit (ICU) face a high short-term mortality rate. The simplified pulmonary embolism severity index (sPESI) is tool for predicting adverse outcomes. However, its effectiveness in ICU cancer patients with APE remains unclear. This study aimed to validate the sPESI score and develop a predictive model for 30-day mortality in this specific patient group. We conducted a retrospective analysis using data from the MIMIC-IV database, focusing on ICU patients with cancer and APE. The primary outcome of interest was 30-day mortality. Predictors were initially selected using Least Absolute Shrinkage and Selection Operator (LASSO) analysis. A multivariable logistic regression model was then developed. The performance of the nomogram was assessed using calibration, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis to evaluate accuracy, clinical utility, and discrimination, respectively. A total of 286 cancer patients with APE were included in the study, with an average age of 68.9 years; the cohort comprised 137 males (47.9%) and 149 females (52.1%), and the 30-day mortality rate was 32.2%. Multivariable logistic regression analysis identified SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease as independent predictors of 30-day mortality. The area under the curves (AUCs) of ROC for sPESI and the nomogram model were 0.568 (95% CI, 0.500-0.637) and 0.761 (95% CI, 0.701–0.821). The nomogram model had a higher predictive value for 30-day mortality in patients with acute pulmonary embolism and cancer compared to the sPESI score (P < 0.05). We developed a nomogram to predict the probability of 30-day mortality for ICU patients with acute pulmonary embolism and cancer. This nomogram demonstrated robust performance and serves as a valuable tool for clinicians to identify patients at high risk of 30-day mortality.
Introduction
Venous thromboembolism (VTE), which includes deep venous thrombosis (DVT) and pulmonary embolism (PE), is a common complication affecting up to 20% of patients with cancer1. Individuals with cancer have a seven-fold higher risk of developing VTE compared to those without cancer2. The relationship between cancer and VTE has also been extensively investigated3,4. The heightened risk of VTE in cancer patients is attributed to factors such as hypercoagulability, anticancer therapies, and decreased physical activity5,6,7,8. Cancer patients with VTE have poorer short-term and long-term survival than cancer patients without VTE9,10. Notably, the 30-day mortality rate for cancer patients with acute pulmonary embolism (APE) is as high as 24%11. The poor short-term prognosis in cancer and APE patients is influenced by factors such as tumor progression and PE-related complications12,13. Therefore, identifying the risk of early mortality in cancer patients with APE is essential for guiding clinical management and improving short-term outcomes. Identifying the risk of early mortality in cancer patients with APE is crucial for guiding clinical management and improving short-term outcomes.
The simplified pulmonary embolism severity index (sPESI) is a widely used risk stratification tool to predict whether patients with APE should be classified as high or low risk for 30-day mortality. The sPESI score is based on six clinical parameters, a total score of 1 or more indicates a high-risk classification14. Yugo Yamashita reported that Among PE patients with active cancer, patients with sPESI score = 1 had a lower 30-day mortality rate compared with patients with sPESI score ≥ 2 (6.3% versus 13.1%)15. ICU patients with APE are at a higher risk of short-term mortality. However, the applicability of the sPESI has not yet been validated in this population. The aim of this study was to assess the effectiveness of the sPESI score in predicting 30-day mortality risk among ICU patients with cancer and APE, and to develop a prognostic model tailored for this group. This prognostic model will enable clinicians to identify patients with cancer and APE at higher risk of 30-day mortality early.
Methods
Data source
This study utilized data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) https://mimic.physionet.org/. The MIMIC-IV database contains comprehensive, high-quality information on patients admitted to ICU at the Beth Israel Deaconess Medical Center between 2008 and 2019. Data extraction was performed by the first author (certification number: 13425982), who successfully passed the Collaborative Institutional Training Initiative examination and was granted access to the database for this purpose. As this study exclusively used publicly available, anonymized data, informed consent and ethical review were not required.
Patients selection
All adult patients diagnosed with pulmonary embolism were identified and extracted from the MIMIC-IV database. The inclusion criteria were as follows: (1) APE diagnosed using the International Classification of Diseases codes, ICD9 : 41,519; ICD10 : I2609, I2690, I2693, I2699; (2) Patients diagnosed with cancer based on the Charlson comorbidity index; (3) For patients with multiple ICU admissions, only data from the first ICU hospitalization were used; (4) Patients admitted to the ICU who were 18 years of age or older. Exclusion criteria included: (1) Individuals with an ICU stay duration of less than 24 h; (2) Patients without white blood cell or platelet data within 24 h; (3) Patients without weight data and sufficient data on key variables required for the sPESI score calculations.
Data extraction and management
We utilized Navicat Premium (version 15.0.29 )https://www.navicat.com to extract relevant data from the MIMIC-IV database. The extracted data included demographic information (age, race, sex), comorbidities (myocardial infarction, heart failure, cerebrovascular disease, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, paraplegia, liver disease, diabetes, renal disease, sepsis), presence of tumor metastasis, vital signs (heart rate, respiratory rate, mean blood pressure (MBP), systolic blood pressure (SBP), and pulse oxygen saturation (SpO2)), body weight, and results from laboratory tests (hemoglobin (HGB), white blood cell count (WBC), platelet count, anion gap, blood urea nitrogen (BUN), calcium, chloride, creatinine, glucose, sodium, and potassium), treatment of thrombus. Additionally, we collected data on the Sequential Organ Failure Assessment (SOFA) score and sPESI score. The scoring criteria for the sPESI scores are detailed in Table 1.
Table 1 sPESI score.
Full size table
Statistical analysis
Variables with more than 20% missing data, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), B-type natriuretic peptide(BNP), troponin T were excluded from the analysis, while those with fewer than 20% missing data (including calcium with 10 missing cases, accounting for 3.5%) were imputed using multiple imputation by chained equations (MICE) to ensure unbiased estimates. Continuous variables with a normal distribution were expressed as mean ± standard deviation (SD), while those with a non-normal distribution were described using medians and interquartile ranges (IQR). The normality of continuous variables was assessed using the T-test, and the Wilcoxon rank-sum test was employed for continuous variables with non-normal distributions. Categorical variables were compared using the chi-square test or Fisher’s exact test. First, LASSO regression was used to preliminarily screen predictors based on variables listed in Table 1 with a significance threshold of P < 0.05. Second, the variables selected by LASSO regression were further analyzed through univariate logistic regression, and those with P < 0.05 were included in the multivariate logistic regression analysis. Using the independent risk factors identified through multivariable logistic regression, a nomogram prediction model was developed. The receiver operating characteristic (ROC) curve was employed to evaluate the predictive performance of both the sPESI score and the nomogram model. The calibration curve, assessed through the Hosmer–Lemeshow test, was utilized to evaluate the goodness of fit of these models. Clinical decision curve analysis (DCA) was performed to assess the clinical utility and net benefit. We assessed the risk of overfitting in the nomogram model using 5-fold cross-validation and Leave-One-Out cross-validation. Statistical analyses were conducted using IBM SPSS Statistics (version 23) https://www.ibm.com/products/spss-statistics and R software (version 4.2.2) https://www.r-project.org/. A two-sided P-value < 0.05 was considered statistically significant.
Results
The characteristics of study patients
Following the defined inclusion and exclusion criteria, a total of 286 cancer patients with APE were enrolled in the study, as depicted in Fig. 1. The average age of the patients was 68.9 years. The cohort consisted of 137 males (47.9%) and 149 females (52.1%), with 194 patients (67.8%) identifying as white. Within 30 days, 92 patients (32.2%) had died. Compared to the 30-day survivors, the nonsurvivors had significantly higher white blood cell counts, anion gap, lower hemoglobin, calcium, glucose, lower mean blood pressure and wight, as well as higher SOFA scores (P < 0.05). However, there was no significant difference in the sPESI scores between the two groups (2 vs. 2, *P* = 0.05). Furthermore, a higher prevalence of liver disease, sepsis and tumor metastasis was observed among the 30-day nonsurvivors (*P* < 0.05), however, there was no statistically significant difference in the treatment methods for pulmonary embolism, including anticoagulation and thrombolysis (*P* > 0.05).as detailed in Table 2.
Fig. 1
figure 1
Flowchart of patient selection.
Full size image
Table 2 Baseline characteristics of the 30-day survivors and 30-day nonsurvivors.
Full size table
Predictors for 30-day mortality of ICU patients with cancer and APE
LASSO regression was performed on 11 candidates (hemoglobin, WBC, anion gap, calcium, glucose, SOFA, weight, MBP, prevalence of liver disease, tumor metastasis, sepsis) (Fig. 2). Eight variables were suggested by LASSO regression to be associated with 30-day mortality, including hemoglobin, anion gap, SOFA, weight, MBP, prevalence of liver disease, tumor metastasis, sepsis, which were included in the univariate logistic regression. Hemoglobin, WBC, anion gap, calcium, glucose, SOFA, weight, MBP, prevalence of liver disease, tumor metastasis, sepsis were in the multivariate logistic analysis. In the multivariate logistic regression analysis, the results highlighted the SOFA score [odds ratio (OR) 1.13, P = 0.021], tumor metastasis (OR 2.85, P = 0.001) hemoglobin level (OR 0.81, P = 0.006), anion gap (OR 1.15, P = 0.002), weight (OR 0.98, P = 0.011) and the prevalence of liver disease (OR 2.59, P = 0.045) as independent risk factors for 30-day mortality, as detailed in Table 3.
Fig. 2
figure 2
Texture feature selection using LASSO binary logistic regression model. (A) Each curve in the figure represents the change trajectory of each independent variable coefficient. The ordinate is the value of the coefficient, the lower abscissa is log(λ), and the upper abscissa is the number of non-zero coefficients in the model at this time. (B) The optimal parameter (lambda) selection was performed using LASSO with ten-fold cross-validation based on the minimum criteria. The average number of predicted variables is represented by the numbers along the upper x-axis. For each λ value, mean value of the target parameter shown by the red dot. A minimum criteria and a 1-SE criteria were used to draw the dotted vertical lines representing the optimal values.
Full size image
Table 3 Univariate and multivariate logistic analysis for 30-day mortality of ICU patients with cancer and APE.
Full size table
Nomogram for 30-day mortality of ICU patients with cancer and APE
Based on the findings from the multivariate analysis, a nomogram model was developed incorporating the SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease, as shown in Fig. 3.
Evaluation of the sPESI score and nomogram model
The area under the curves (AUCs) of ROC curves for the sPESI score and the nomogram model were 0.568 (95%CI, 0.500 − 0.637) and 0.761 (95%CI, 0.701 − 0.821), respectively. There was a significant difference in the area under the ROC curve for predicting 30-day mortality between the sPESI and the nomogram model (P < 0.001), as shown in Fig. 4. The calibration curve indicated good concordance between the predicted probabilities and observed death rates for the nomogram model, as shown in Fig. 5. The decision curve analysis (DCA) showed that the nomogram model had a higher net benefit compared to the sPESI, as shown in Fig. 6. The ROC curve for the nomogram model, based on 5-fold cross-validation and Leave-One-Out cross-validation, resulted in AUCs of 0.729 (95%CI, 0.673–0.785) and 0.727(95%CI, 0.668–0.786), respectively.
Fig. 3
figure 3
Nomogram for predicting 30-day mortality in patients with cancer and APE. The top row of the ‘Points’ represents a scale for each risk factors, and points of each predictor were acquired by drawing a straight line upwards from the corresponding value to the ‘Points’ line. Then, the points received from each predictor are summed, and the number is located on the ‘Total Points’ axis. To conclude the patient’s sort of probability for 30-day mortality, draw a straight line down to the corresponding ‘Mortality Risk’ axis. SOFA, sequential organ failure assessment.
Full size image
Fig. 4
figure 4
ROC curve of the sPESI score and Nomogram model. The AUCs of ROC curves for the sPESI score and the nomogram model are 0.568 (95%CI, 0.500-0.637) and 0.761 (95%CI, 0.701–0.821), respectively. ROC, receiver operating characteristic; AUC, area under the curve; sPESI, simplified pulmonary embolism severity index; TPR, true positive rate; FPR, false positive rate.
Full size image
Fig. 5
figure 5
Calibration of the sPESI score and Nomogram model. The x-axis represents the predicted probability of 30-day mortality of patients. The y-axis represents the actual 30-day mortality of patients. The diagonal dotted line represents a perfect prediction by an ideal model. The orange solid line represents the performance of the sPESI, and the blue solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction. The figure shows that the nomogram model has a good predictive ability. sPESI, simplified pulmonary embolism severity index.
Full size image
Fig. 6
figure 6
Decision Curve Analysis (DCA) of the sPESI Score and Nomogram Model. The blue line represents the nomogram model, while the orange line represents the sPESI score. The Y-axis shows the net benefit. Our nomogram demonstrates higher clinical benefits for patients at high 30-day mortality risk compared to the sPESI. sPESI, simplified pulmonary embolism severity index.
Full size image
Discussion
This study addressed a critical gap in the understanding of APE in cancer patients, particularly those in ICU. Cancer patients are at an elevated risk of APE due to both malignancy and its treatments, making it essential to identify effective prognostic tools. Our study revealed that the SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease were independent predictors of 30-day mortality in this vulnerable cohort. Moreover, our findings indicated that the nomogram model—including the SOFA score, tumor metastasis, hemoglobin level, anion gap, weight, and liver disease prevalence—provided superior predictive value for 30-day mortality in patients with APE and cancer, compared to the sPESI score (AUC: 0.568 vs. 0.761, P < 0.001). By developing this scale, we can assess the impact of changes in each variable, calculate a total score, and predict the likelihood of 30-day mortality.
The emergence of targeted therapies and immunotherapies over the past few decades has significantly improved cancer prognosis. However, this progress has been accompanied by a concerning increase in the incidence of venous thromboembolism (VTE) among cancer patients, which has risen from 1% to over 3%9. This increase can be attributed to several factors, including the aging demographic of the cancer population, improved detection of asymptomatic clots during diagnostic imaging, and the prothrombotic effects of cancer treatments themselves. Consequently, in-hospital mortality due to pulmonary embolism (PE) remains distressingly high among cancer patients, with rates ranging from 20–45%2. A study examining a cohort of 1,556 patients with cancer-associated pulmonary embolism reported a 30-day mortality rate of 24%11. In our study, a similarly high 30-day mortality rate of 32.2% was observed among critically ill patients with cancer and APE, highlighting the need for improved risk stratification and management strategies.
Numerous risk stratification scores have been developed to predict 30-day mortality among patients with PE, including the RIETE-VTE and sPESI scores14,16. The RIETE-VTE score, in particular, was designed to assess the risk of adverse outcomes in patients with VTE associated with cancer. This score incorporates six predictors: leukocytosis (≥ 11.5 × 10^9/L), platelet count (≤ 160 × 10^9/L), metastasis, recent immobility, pulmonary embolism, and a Body Mass Index (BMI) of less than 18.5. The model classifies patients into three risk categories: low (score 0–3), moderate (score 4–6), and high (score ≥ 7)17. Identifying low-risk patients can facilitate home-based treatment, reducing the burden on healthcare systems. However, in ICU patients, height could not be measured due to the severity of their condition, making it impossible to calculate BMI. Additionally, the database does not provide information on whether the patients had a history of immobility prior to the occurrence of pulmonary embolism. Therefore, we were unable to assess the prognostic predictive value of the RIETE-VTE score for ICU patients with pulmonary embolism and cancer. This study was the first to validate the use of sPESI scores specifically in critically ill patients with cancer and APE. Our findings indicated that the sPESI score for predicting 30-day mortality in this patient population had a limited predictive power, which AUC was 0.568 (95%CI, 0.500 − 0.637), with a sensitivity of 45.65% and a specificity of 63.4% at a cutoff of > 2 points. The predictive value of nomogram model was notably enhanced, achieving an AUC of 0.761 (95%CI, 0.701 − 0.821). The ROC curve for the nomogram model, based on 5-fold cross-validation and Leave-One-Out cross-validation, resulted in an AUC of 0.729 (95%CI, 0.673–0.785) and 0.727(95%CI, 0.668–0.786), respectively.
Short-term prognosis in patients with cancer and APE is influenced by multiple factors, including patients, tumor, and PE-related characteristics, and treatments. In our study, SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease were identified as risk factors for 30-day mortality in ICU patients with cancer and APE. SOFA score evaluated the severity of illness by describing the time course of multiple organ dysfunction18. Previous studies supposed the SOFA score was associated with the severity of pulmonary embolism in ICU patients19. In this study, the SOFA score of non-survivors was statistically higher than survivors (3 vs. 4, P = 0.002) and SOFA score was an independent risk factor for predicting 30-day mortality in critically ill cancer patients with pulmonary embolism (OR 1.13, P = 0.021). This nomogram indicated a risk of 30-day mortality was 10% when the SOFA score was eleven. Tumor metastasis is a well-established indicator of disease progression and a standalone risk factor for mortality in cancer patients. Noteworthy, of the six variables, tumor metastasis had the highest weight in the simplified model (OR 2.85, P = 0.001). Life expectancy in patients with advanced or metastatic disease is significantly decreased20,21,22. Previous findings by Paul L et al. reported that weight < 60 kg was an independent risk factor for all-cause mortality within 30 days in patients with cancer and pulmonary embolism11. In our study, patients who died within 30 days had lower body weight (70.30 (58.48, 84.40) vs. 77.40 (66.32, 91.90), P = 0.002). Low body weight was an independent risk factor for 30-day mortality in patients with pulmonary embolism and cancer (OR 0.98, P = 0.011). In cancer patients, low body weight may be a manifestation of cachexia. Cachexia is closely associated with a high tumor burden, advanced disease stage, and poor prognosis. Patients with low body weight may be more prone to complications of pulmonary embolism due to malnutrition and muscle loss, such as right heart failure, respiratory failure, or multiple organ dysfunction. The coexistence of liver disease emerged in our research as an independent risk factor for early death in ICU patients with cancer and pulmonary embolism. As reported by Kadir İdin, the end-stage liver disease score is a potent predictor of 30-day mortality in high-risk patients with acute pulmonary embolism admitted to intensive care units23. Severe liver dysfunction is often accompanied by coagulopathy, which includes reduced levels of clotting factors, increased fibrinolysis, and decreased platelet production. These abnormalities can lead to a dual risk of both increased thrombosis and heightened bleeding, complicating the management of patients with pulmonary embolism. In this study, the hemoglobin in 30-day survivors was lower than 30-day survivors (8.8 vs. 9.8 p < 0.001). In this nomogram, when hemoglobin levels were below 9 g/L, every 1 g/L decrease in hemoglobin was associated with a 10–20% increase in 30-day mortality risk. Anemia is a common complication in cancer patients and is closely related to their prognosis. Anemia may be caused by the tumor itself, cancer treatment, or other comorbidities. Meanwhile, anemia is associated with a poorer prognosis in patients with pulmonary embolism, as anemia exacerbates the insufficiency of oxygen delivery and increases the burden on cardiac and pulmonary function24. Anion gap has been widely used in clinical practice to reflect disturbances in the acid-base balance of patients and has been associated with mortality and length of stay in the intensive care unit25,26,27. In our study, an elevated anion gap was identified as an independent risk factor for 30-day mortality in ICU patients with cancer and acute pulmonary embolism (OR 1.15, P = 0.002). In the nomogram, for patients with an anion gap greater than 16 mmol/L, every 2mmol/L increase in anion gap concentration was associated with a 10–20% increase in mortality risk.
We evaluated the predictive accuracy of the nomogram and the previously developed sPESI score for 30-day mortality in ICU patients with acute pulmonary embolism and cancer. The nomogram outperformed the sPESI score (AUC: 0.761 vs. 0.568, P < 0.001). The calibration curve indicated that the nomogram had good calibration. The DCA curve further demonstrated that the nomogram effectively identified true positive high-risk patients for 30-day mortality. Our study provided a robust tool for assessing the 30-day mortality risk in ICU patients with cancer and acute pulmonary embolism. Therefore, our prediction model may assist physicians in screening patients who are at high risk of short-term mortality, thereby improving patient management. However, there were some limitations in our study. Firstly, as a single-center, retrospective study based on the MIMIC-IV database, our research was inherently prone to selection bias, which may limit the general applicability of the fndings. Secondly, The exclusion of patients with missing data, including those lacking weight and laboratory values, could introduce selection bias, which may have influenced the outcomes and the generalizability of our findings. Thirdly, our analysis was constrained by the absence of certain clinical data in the MIMIC-IV database, such as the stage of cancer, degree of differentiation, tumor size, time since diagnosis, and specifics of therapies including chemotherapy and radiotherapy, right ventricular (RV) dysfunction assessed by echocardiography, and levels of troponin and BNP/NT-proBNP, which were crucial for the severity and prognosis of cancer and pulmonary embolism. Fourthly, limitation was the inability to differentiate mortality specifically attributable to pulmonary embolism from other causes of death within the MIMIC-IV database. Therefore, our study did not account for competing risks between death from different causes versus PE-specific mortality. Fifthly, due to the small sample size of 286 patients after exclusions, which was limited for developing and validating a predictive model, the reported performance metrics of the model may be biased due to overfitting. However, we performed cross-validation on the model, which demonstrated its robustness. In conclusion, while this study provides valuable insights, the findings should be validated in larger, multi-center studies with more robust statistical methods before clinical implementation.
Conclusion
In this analysis, we harnessed data from the MIMIC-IV database, specifically focusing on ICU patients with cancer and APE, to identify SOFA score, tumor metastasis, hemoglobin level, anion gap, weight and the prevalence of liver disease as key predictors of 30-day mortality risk. The model we developed by integrating these parameters provides valuable evidence for identifying patients at high risk of 30-day mortality.
Data availability
The data supporting the findings of this study are available in the MIMIC-IV database, which is accessible via PhysioNet. The direct link to the dataset is https://mimic.physionet.org/. Access to the database requires approval of a data use agreement.
Abbreviations
APE:
Acute pulmonary embolism
ICU:
Intensive care unit
sPESI:
Simplified pulmonary embolism severity index
DCA:
Decision curve analysis
ROC:
Receiver operating characteristic
AUC:
Area under the curve
CI:
Confidence intervals
VTE:
Venous thromboembolism
DVT:
Deep venous thrombosis
PE:
Pulmonary embolism
MIMIC:
Medical Information Mart for Intensive Care
MBP:
Mean blood pressure
SBP:
Systolic blood pressure
SpO2:
Pulse oxygen saturation
WBC:
White blood cell
HGB:
Hemoglobin
BUN:
Blood urea nitrogen
SOFA:
Sequential Organ Failure Assessment
ALT:
Alanine aminotransferase
AST:
Aspartate aminotransferase
BNP:
B-type natriuretic peptide(BNP)
rtPA:
Recombinant tissue plasminogen activator
OR:
Odds ratio
TPR:
True positive rate
FPR:
False positive rate
References
Grilz, E. et al. Relative risk of arterial and venous thromboembolism in persons with cancer vs. persons without cancer-a nationwide analysis. Eur. Heart J. 42 (23), 2299–2307 (2021).
ArticlePubMedMATHGoogle Scholar
Mokart, D. et al. Acute pulmonary embolism in cancer patients admitted to intensive care unit: impact of anticoagulant treatment on 90-day mortality and risk factors, results of a multicentre retrospective study. Thromb. Res. 237, 129–137 (2024).
ArticlePubMedGoogle Scholar
Hisada, Y. & Mackman, N. Cancer-associated pathways and biomarkers of venous thrombosis. Blood 130 (13), 1499–1506 (2017).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Lyman, G. H. et al. Morbidity, mortality and costs associated with venous thromboembolism in hospitalized patients with cancer. Thromb. Res. 164 (Suppl 1), S112–S118 (2018).
ArticleMathSciNetPubMedMATHGoogle Scholar
Konstantinides, S. V. et al. 2019 ESC guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European respiratory society (ERS). Eur. Heart J. 41 (4), 543–603 (2020).
ArticlePubMedMATHGoogle Scholar
Khorana, A. A. et al. Thromboembolism in hospitalized neutropenic cancer patients. J. Clin. Oncol. 24 (3), 484–490 (2006).
ArticlePubMedMATHGoogle Scholar
Zhao, H. et al. Prevalence and treatment of venous thromboembolism in patients with solid tumors. Exp. Ther. Med. 24 (6), 743 (2022).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Key, N. S. et al. Venous thromboembolism prophylaxis and treatment in patients with cancer: ASCO guideline update. J. Clin. Oncol. 41 (16), 3063–3071 (2023).
ArticlePubMedMATHGoogle Scholar
Sorensen, H. T. et al. Impact of venous thromboembolism on the mortality in patients with cancer: a population-based cohort study. Lancet Reg. Health Eur. 34, 100739 (2023).
ArticlePubMedPubMed CentralGoogle Scholar
Nishikawa, T. et al. Prognostic effect of incidental pulmonary embolism on Long-Term mortality in cancer patients. Circ. J. 88 (2), 198–204 (2024).
ArticlePubMedMATHGoogle Scholar
den Exter, P. L. et al. A clinical prognostic model for the identification of low-risk patients with acute symptomatic pulmonary embolism and active cancer. Chest 143 (1), 138–145 (2013).
ArticleGoogle Scholar
Pritchard, E. R. et al. Single-center, retrospective evaluation of safety and efficacy of direct oral anticoagulants versus low-molecular-weight heparin and vitamin K antagonist in patients with cancer. J. Oncol. Pharm. Pract. 25 (1), 52–59 (2019).
ArticlePubMedMATHGoogle Scholar
Chee, C. E. et al. Predictors of venous thromboembolism recurrence and bleeding among active cancer patients: a population-based cohort study. Blood 123 (25), 3972–3978 (2014).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Jimenez, D. et al. Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism. Arch. Intern. Med. 170 (15), 1383–1389 (2010).
ArticlePubMedMATHGoogle Scholar
Barra, S. et al. LR-PED rule: low risk pulmonary embolism decision rule - a new decision score for low risk pulmonary embolism. Thromb. Res. 130 (3), 327–333 (2012).
ArticlePubMedGoogle Scholar
Surov, A. et al. A new index for the prediction of 30-Day mortality in patients with pulmonary embolism: the pulmonary embolism mortality score (PEMS). Angiology 72 (8), 787–793 (2021).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Fuentes, H. E. et al. Prediction of early mortality in patients with cancer-associated thrombosis in the RIETE database. Int. Angiol. 38 (3), 173–184 (2019).
ArticlePubMedMATHGoogle Scholar
Roedl, K. et al. Epidemiology of intensive care unit cardiac arrest: characteristics, comorbidities, and post-cardiac arrest organ failure - A prospective observational study. Resuscitation 156, 92–98 (2020).
ArticlePubMedGoogle Scholar
Leonhardi, J. et al. Computed tomography embolus texture analysis as a prognostic marker of acute pulmonary embolism. Angiology 74 (5), 461–471 (2023).
ArticlePubMedGoogle Scholar
Liu, C. et al. Colonization of Fusobacterium nucleatum is an independent predictor of poor prognosis in gastric cancer patients with venous thromboembolism: a retrospective cohort study. Thromb. J. 21 (1), 2 (2023).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Majmudar, K. et al. Outcomes after venous thromboembolism in patients with gastric cancer: analysis of the RIETE registry. Vasc Med. 25 (3), 210–217 (2020).
ArticlePubMedMATHGoogle Scholar
Law, C. et al. The impact of age on the Post-operative outcomes in patients undergoing resection for oesophageal and gastric cancer. World J. Surg. 47 (12), 3270–3280 (2023).
ArticlePubMedPubMed CentralMATHGoogle Scholar
Idin, K. et al. Modified model for end-stage liver disease score predicts 30-day mortality in high-risk patients with acute pulmonary embolism admitted to intensive care units. Scand. Cardiovasc. J. 55 (4), 237–244 (2021).
ArticlePubMedMATHGoogle Scholar
Ruan, Z. et al. The association between mean corpuscular hemoglobin concentration and prognosis in patients with acute pulmonary embolism: A retrospective cohort study. Clin. Appl. Thromb. Hemost. 28, 10760296221103867 (2022).
ArticlePubMedPubMed CentralGoogle Scholar
Xu, H. et al. Serum anion gap is associated with mortality in intensive care unit patients with diastolic heart failure. Sci. Rep. 13 (1), 16670 (2023).
ArticleADSPubMedPubMed CentralGoogle Scholar
Jian, L. et al. Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database. BMC Cardiovasc. Disord. 23 (1), 211 (2023).
ArticlePubMedPubMed CentralGoogle Scholar
Zhong, C. et al. Association between high serum anion gap and All-Cause mortality in Non-Traumatic subarachnoid hemorrhage: A retrospective analysis of the MIMIC-IV database. Front. Neurol. 13, 922099 (2022).
ArticlePubMedPubMed CentralGoogle Scholar
Download references
Author information
Author notes
Shuangping Li and Shenshen Huang contributed equally to this work.
Authors and Affiliations
College of Clinical Medicine, The First Affiliated Hospital, Henan University of Science and Technology, Luoyang, Henan, China
Shuangping Li, Shenshen Huang & Yuxuan Feng
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Henan University of Science and Technology, Luoyang, Henan, China
Yimin Mao
Authors
Shuangping Li
View author publications
You can also search for this author in PubMedGoogle Scholar
2. Shenshen Huang
View author publications
You can also search for this author in PubMedGoogle Scholar
3. Yuxuan Feng
View author publications
You can also search for this author in PubMedGoogle Scholar
4. Yimin Mao
View author publications
You can also search for this author in PubMedGoogle Scholar
Contributions
LSP, HSS, FYX and MYM contributed to design this study. LSP and FYX contributed to collect and integrate data. LSP and HSS analyzed the results and wrote the manuscript. LSP, HSS and MYM provided overall supervision and critically revised the manuscript. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Yimin Mao.
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Check for updates. Verify currency and authenticity via CrossMark
Cite this article
Li, S., Huang, S., Feng, Y. et al. Development of a nomogram model to predict 30-day mortality in ICU cancer patients with acute pulmonary embolism. Sci Rep 15, 9232 (2025). https://doi.org/10.1038/s41598-025-93907-4
Download citation
Received:08 September 2024
Accepted:10 March 2025
Published:18 March 2025
DOI:https://doi.org/10.1038/s41598-025-93907-4
Share this article
Anyone you share the following link with will be able to read this content:
Get shareable link
Sorry, a shareable link is not currently available for this article.
Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
Keywords
sPESI
Cancer and acute pulmonary embolism
Nomogram
30-day mortality