Skip to main content
Clinical and Applied Thrombosis/Hemostasis logoLink to Clinical and Applied Thrombosis/Hemostasis
. 2024 Aug 25;30:10760296241278353. doi: 10.1177/10760296241278353

Utilization of a Novel Scoring System in Predicting 30-day Mortality in Acute Pulmonary Embolism, the CLOT-5 Pilot Study

Alexandru Marginean 1, Punit Arora 2,, Kevin Walsh 1, Elizabeth Bruno 2, Cathryn Sawalski 2, Riya Gupta 2, Frances Greathouse 2, Jacob Clarke 2, Quinn Mallery 2, Myoung Hyun Choi 2, Waddah Malas 1, Parth Shah 1, David Sutherland 1, Amudha Kumar 1, Igor Wroblewski 1, Ahmed Elkaryoni 1, Parth Desai 1, Yevgeniy Brailovsky 3, Amir Darki 1
PMCID: PMC11348478  PMID: 39183532

Abstract

Objectives

To construct a new scoring system utilizing biomarkers, vitals, and imaging data to predict 30-day mortality in acute pulmonary embolism (PE).

Background

Acute PE, a well-known manifestation of venous thromboembolic disease, is responsible for over 100,000 deaths worldwide yearly. Contemporary management algorithms rely on a multidisciplinary approach to care via PE response teams (PERT) in the identification of low, intermediate, and high-risk patients. The PESI and sPESI scores have been used as cornerstones of the triage process in assigning risk of 30-day mortality for patients presenting with acute PE; however, the specificity of these scoring systems has often come into question.

Methods

This study retrospectively analyzed 488 patients with acute PE who were managed at a tertiary care institution with either conservative therapy consisting of low molecular weight or unfractionated heparin, advanced therapies consisting of catheter directed therapies, aspiration thrombectomy, or a combination of these therapies, or surgical embolectomy. The CLOT-5 score was designed to include vital signs, biomarkers, and imaging data to predict 30-day mortality in patients presenting with acute PE.

Results

The CLOT-5 score had an area under the curve (AUC) of 0.901 with a standard error of 0.29, while the PESI and sPESI scores had an AUC and standard errors of 0.793 ±­ 0.43 and 0.728 ± 0.55, respectively.

Conclusions

When incorporated into the management algorithms of national PERT programs, the CLOT-5 score may allow for rapid and comprehensive assessment of patients with acute PE at high risk for clinical decompensation, leading to early escalation of care where appropriate.

Keywords: pulmonary embolism, mortality, PESI, scoring system

Introduction

Acute pulmonary embolism (PE) is a common manifestation of venous thromboembolic disease, responsible for over 100,000 deaths worldwide in 2018 alone. 1 Patients with acute PE can have a variety of presentations, ranging from no symptoms to obstructive shock leading to right heart failure and death.2,3 Clinical presentation is directly linked to the severity of the acute PE. Low-risk acute PE has no evidence of hemodynamic compromise and no evidence of RV strain, defined as a right to left ventricular diameter ratio (RV/LV) > 0.9 on computed tomography (CT) or transthoracic echocardiography (TTE) or evidence of RV systolic dysfunction. Submassive or intermediate risk PE presents with preserved hemodynamics but with evidence of RV strain on imaging modalities. Massive PE is associated with compromised hemodynamics, defined as a systolic blood pressure <90 mm Hg for more than 15 min, more than 40 mm Hg drop in blood pressure from a patient's baseline, or hypotension requiring vasoactive support, placing these patients in the highest risk category.4,5 Over the last 15 years the diagnosis and categorization of acute PE has been inextricably linked with the development and utilization of scoring systems designed to identify high risk individuals who may benefit from early escalation of care and low risk individuals who may be candidates for early discharge. The Pulmonary Embolism Severity Index (PESI) score utilized 11 variables of interest to categorize patients presenting with acute PE into five risk classes to predict 30-day mortality. 6 The simplified PESI score (sPESI) removed several of the PESI variables to predict 30-day mortality in patients with acute PE based on their categorization as low (sPESI score 0) or high (sPESI score ≥1) risk. 7 The 2019 European Society of Cardiology guidelines incorporated the sPESI score along with imaging or biomarker evidence of RV strain such as troponin and brain natriuretic peptide (BNP) elevation to further subclassify acute PE patients based on their in-hospital or 30-day mortality risk into high, intermediate low/high, or low risk categories. 8 The Bova score, utilizing four variables of interest, has been validated to predict 30-day PE related outcomes consisting of PE related death, hemodynamic collapse, or recurrent nonfatal PE. 9 The accuracy of these scores, however, has recently come into question, with a head-to-head validation study demonstrating a wide range of predictive ability between the scores. 10 For instance, the strength of the PESI and sPESI scores lies in the identification of low-risk individuals, with a negative predictive value of 94.7% and 97.1%. The positive predictive value, however, is poor at 31.7% and 30.4%, respectively. 11

Pulmonary embolism scoring systems not only allow for the prediction of adverse outcomes, but they also facilitate an early discussion between healthcare providers regarding the type of management strategy and optimal timing to treat such patients. Over the last decade, multiple tertiary care centers in the United States have begun implementing a multidisciplinary approach to their treatment of acute PE patients, resulting in the formation of Pulmonary Embolism Response Teams (PERT). 12 Such teams are designed to analyze each case uniquely and to decide between a conservative approach to care with systemic anticoagulation versus an early escalation of care with catheter directed therapies (CDT) or surgical management. CDT have been proven safe and effective in multiple studies and have become the mainstay of therapy in patients presenting with massive PE.1315 Controversy remains, however, as to the best approach to care in regard to treatment strategy and timing for patients presenting with submassive PE. Contemporary scoring systems lack the ability to reliably predict adverse outcomes in high-risk submassive PE. 16 As such, this study aimed to design a new scoring system to predict adverse outcomes in patients presenting with low risk, submassive, and massive PE and to compare it to contemporary scoring systems such as PESI and sPESI as validation standards.

Methods

From March 2017 until May 2021, 488 patients with acute PE were evaluated and managed at a tertiary care institution with either conservative therapy consisting of low molecular weight heparin (LMWH) or unfractionated heparin, or advanced therapies consisting of CDT, aspiration thrombectomy, or a combination of these therapies, or surgical embolectomy. All patients were evaluated following activation of the PERT. Criteria for inclusion into the study consisted of confirmed acute on CT pulmonary angiography scan and no evidence of coagulopathy precluding anticoagulation or invasive therapies. Patients were managed by the PERT consult service for the duration of the hospitalization. All data was analyzed retrospectively. The study design was approved by the Loyola University Medical Center Institutional Review Board.

Parameters of interest were collected at initial presentation and consisted of patient age, gender, history of coronary artery disease (CAD), congestive heart failure (CHF), hypertension, chronic kidney disease (CKD), diabetes mellitus (DM), presence or history of cancer, history of chronic obstructive pulmonary disease (COPD), history of deep vein thrombosis (DVT) or PE, systolic blood pressure (SBP), heart rate, respiratory rate (RR), and oxygen saturation (SpO2) (Table 1). Biomarkers of interest at initial presentation included lactic acid, troponin, BNP, neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), and red cell distribution width (RDW). The severity of acute PE was classified as low risk, submassive, or massive as previously described in the 2019 European Society of Cardiology guidelines on acute PE management. 8

Table 1.

Baseline Demographics and Vital Signs of Living and Deceased Patients Presenting with Acute Pulmonary Embolism at 30 Days.

Baseline Demographics Living (n = 447) Deceased (n = 41) P value
Age (median, ± IQR) 62 (52–71) 66 (52.5–73.5) 0.14
Female, n (%) 221 (49.6) 21 (50.9) 0.48
CAD, n (%) 41 (9.2) 7 (17) 0.22
CHF, n (%) 42 (9.4) 3 (7.5) 0.14
Hypertension, n (%) 201 (45) 24 (58.5) 0.06
CKD, n (%) 36 (8) 8 (20.7) 0.05
DM2, n (%) 86 (19.3) 12 (28.3) 0.7
Cancer, n (%) 103 (23.2) 22 (52.8) <0.01
COPD, n (%) 25 (5.5) 8 (18.9) 0.05
Prior DVT, n (%) 67 (15) 6 (15) 0.70
Prior PE, n (%) 49 (11) 11 (28) 0.05
PE severity
Low, n (%) 188 (42) 2 (6) 0.04
Submassive, n (%) 228 (51) 23 (55)
Massive, n (%) 9 (2) 12 (30)
Vitals
SBP 106.7 (95–118) 89 (70.5–104.5) <0.01
Heart Rate 103.5 (92–118) 120 (108.5–135.5) <0.01
RR 23.1 (20–26.3) 31.3 (25 -34.3) <0.01
Oxygen saturation 93 (90–95) 88 (86–94) <0.01

Values are medians with interquartile ranges or frequency and percentages (n, %). CAD (coronary artery disease), CHF (congestive heart failure), CKD (chronic kidney disease), COPD (chronic obstructive pulmonary disease), DM2 (type 2 diabetes), DVT (deep vein thrombosis), PE (pulmonary embolism), RR (respiratory rate), SBP (systolic blood pressure). A p-value <0.05 implied significance.

The presence of acute PE was confirmed by CT pulmonary angiography scan on initial presentation or at the time of PERT activation. The RV/LV ratio was measured by an independent cardiologist at the time of patient assessment. The greatest right and left ventricular diameters were measured on non-reformatted images in the axial plane, 1 cm apical to the mitral and tricuspid valves. All CT scans were non-EKG gated for acquisition of images. RV strain was defined as an RV/LV ratio >0.9.

All patients obtained a baseline echocardiogram within 24 h of activation of the PERT and confirmation of an acute PE. Each TTE was performed by an independent sonographer and was evaluated by independent cardiologists. Parameters of interest that were measured specifically on TTE included the RV systolic function using S’ < 9.5 cm as a cutoff, tricuspid annular plane systolic excursion (TAPSE) (abnormal <17 mm), and visual assessment. The LV systolic function was measured by Simpson's biplane with values >55% signifying normal function. The right ventricular outflow tract velocity time integral (RVOT VTI) was measured in the parasternal short axis view at the level of the aortic valves, with values <9.5 cm representing decreased RV stroke volume, as previously published. 17 LV outflow tract (LVOT) VTI was measured using pulsed wave Doppler in the LVOT immediately adjacent to the aortic valve with values ≤15 cm associated with poor outcomes in acute PE. 18 The modified Bernoulli equation was used to estimate the pulmonary artery systolic pressure utilizing the tricuspid regurgitant velocity and adding the estimated right atrial (RA) pressure. RA pressure was estimated in accordance with previously published guidelines. 19

The Kolmogorov-Smirnov test was used to analyze normality of data. Continuous variables were expressed as median (IQR) and categorical variables were expressed as frequency and percentages (n, %). The chi-square test, student t test and Mann-Whitney U-test were used to examine differences in demographic data between patients. A two-tailed P value of < 0.05 was indicated statistical significance.

Logistic regression was used to identify variables independently associated with 30-day mortality. Variables that were significant in univariate analysis (P < 0.05) were entered into the multivariate model. Data was presented as adjusted odds ratios (ORs) and 95% confidence intervals (CIs) where appropriate. Model fit was evaluated using the Hosmer-Lemeshow goodness-of-fit test. For the composite endpoint, the cutoff value was determined by the receiver operating characteristic (ROC) curve with the highest sensitivity and specificity. SPSS software was used for all analyses (version 26.0; IBM SPSS, Armonk, NY).

Results

The study included 488 patients diagnosed with acute PE. The participants were divided into two groups, the “Living” group which included 447 patients that survived to hospital discharge and did not have 30-day mortality post discharge, and the “Deceased” group which included 41 patients that either did not survive to hospital discharge or expired within 30 days of discharge. The median age for the living participants was 62 years (IQR 52–71) while the median age of deceased participants was 66 (IQR of 52.5–73.5). There was no statistically significant difference in age, with a p-value >0.05. Likewise, there was no statistically significant difference in gender between the two groups, with 221 Living participants being female and 21 Deceased participants being female (p-value >0.05). There was no difference in baseline chronic health conditions such as a history of CAD, CHF, hypertension, CKD, diabetes, COPD, prior DVT, or prior PE (p value >0.05). A significantly higher proportion of deceased patients had a history of or the presence of active cancer (52.8%; p-value <0.01) (Table 1).

From 488 patients included in our study, 68 variables were collected that included demographic information, clinical comorbidities, laboratory data, echocardiographic, and CT findings. Univariate logistic regression model revealed several potential predictors of 30-day mortality: massive PE, use of systemic tissue plasminogen activator, CKD, presence of cancer, peripheral arterial disease, COPD, heart rate >100 bpm, systolic blood pressure <90 mm Hg, respiratory rate >22 breaths/min, need for mechanical ventilation, PaO2 ≤ 90%, lactic acid >2 mm/L, need for intensive care unit (ICU) admission, need for ≥1 pressor, acute renal injury (defined as increase in serum creatinine ≥0.3 unit mg/dL), RVOT VTI <9.5 cm, RV/LV ratio >1.4, pulmonary artery systolic pressure (PASP) > 35 mm Hg, RDW >15.6, NLR >5.46, and PLR >256.6. A multivariable logistic regression analysis yielded the following nine explanatory variables which were used to compile the CLOT-5 score: presence of cancer, need for ≥1 pressor, SPO2 < 90%, heart rate >120 bpm, lactic acid >2 mm/L, NLR >5.46, RDW >15% RV/LV ratio >1.4, RVOT VTI <9.5 cm. The CLOT-5 name is an acronym based on the presence of cancer, lactic acidosis, oxygen saturation <90%, tachycardia, and the five other variables making up the score. The need for vasoactive support, a RDW >15%, and the presence of cancer demonstrated to be the strongest predictors of 30-day mortality with an odds ratio of 19.1, 8.7, and 6.5, respectively (Table 2). The cutoff for the variables was determined by ROC curves unless already published. A receiver operator curve was calculated for the CLOT-5 score and was subsequently compared to the PESI and sPESI scores which were independently calculated for each patient in the study population (Figure 1). The area under the cure was calculated for each score and compared directly (Table 3). The CLOT-5 score demonstrated an AUC of 0.901 compared to an AUC of 0.728 for the sPESI and 0.793 for the PESI scores.

Table 2.

Components of the CLOT-5 Score as Predictors of 30-day Mortality.

Variable Beta P value Odds Ratio 95% Confidence Interval
Cancer 1.867 0.000 6.5 2.6–15.6
Pressor requirements 2.96 0.000 19.1 8.1–21.2
Oxygen saturation  < 90% 1.59 0.034 4.8 1.2–18.1
Tachycardia > 120 1.32 0.05 3.74 1.01–14.5
Lactate > 2 1.6 0.02 5.1 1.5–22.1
NLR >5.46 1 0.05 2.03 1.01–4.39
RDW >15 2.17 0.006 8.7 1.86–31.00
RV/LV > 1.4 0.79 0.04 2.1 1.02–4.4
RVOT VTI <9.5 cm 0.855 0.015 2.35 1.78–4.7

Nine explanatory variables identified by a multivariable logistic regression analysis to correlate with 30-day mortality, and which were used to compile the CLOT-5 score. NLR (neutrophile to lymphocyte ratio), RDW (red blood cell distribution width), RV/LV (right to left ventricular diameter ratio), RVOT VTI (right ventricular outflow tract velocity time integral). A p-value of <0.05 implied significance. Values are reported as odds ratios and 95% confidence interval.

Figure 1.

Figure 1.

Comparison of receiver operator curves for the CLOT-5, PESI, and sPESI scores. (A) The receiver operator curve for the CLOT-5 Score as calculated based on the patient population included in the study. (B) A comparison of the CLOT-5, PESI, and sPESI receiver operator curves calculated for the patient population included in the study.

Table 3.

Area Under the Curve and Corresponding 95% Confidence Interval for the CLOT-5, PESI, and sPESI Scores.

Area Under the Curve
Asymptotic Sig. b Asymptotic 95% Confidence Interval
Test Result Variable (s) Area Std. Error a Lower Bound Upper Bound
CLOT-5 0.901 0.029 0.000 0.843 0.959
sPESI 0.728 0.055 0.000 0.620 0.836
PESI 0.793 0.043 0.000 0.709 0.877

The test results variable(s): CLOT-5, sPESI, PESI has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased.

a.

Under the nonparametric assumption.

b.

Null hypothesis: true area = 0.5.

PESI (pulmonary embolism severity index), sPESI (simplified pulmonary embolism severity index).

A logistic regression was conducted to identify variables with a p-value <0.05 to be included in a multivariable logistic regression analysis. The logistic regression analysis yielded the presence of atrial fibrillation, presence of cancer, COPD, systolic blood pressure <90 mm Hg, heart rate >120 bpm, respiratory rate >22 breaths/min, SpO2 < 90%, need for ICU admission, acute renal injury, RV/LV ratio >1.4, RVOT VTI <9.5 cm, NRL >5.46, RDW >15%, troponin >2 ng/mL, BNP >400 pg/mL, lactic acid >2 mm/L as significant variables (p-value <0.05). Subsequently, a multivariable logistic regression analysis was performed to include these variables and yielded ten explanatory variables as predictors of the composite endpoint: SpO2 < 90%, heart rate >120 bpm, need for ICU admission, acute kidney injury, lactic acid >2 mmol/dL, NLR >5.46, RVOT VTI <9.5 cm, RV/LV ratio >1.4, respiratory rate >22 breaths/min. The need for ICU admission, the presence of cancer, a RV/LV ratio >1.4, and a lactate >2 were the strongest predictors of the composite endpoint, with odds ratios of 45.8, 7.5, 5.7, and 4.44, respectively (Table 4). The cutoff for the variables was determined by ROC curves unless already published.

Table 4.

Components of the Composite Endpoints of Need for Thrombolysis, Inpatient Mortality, Need for Vasoactive Support, and Need for Mechanical Ventilation.

Variable Beta P Value Odds Ratio 95% Confidence Interval
Oxygen Saturation <90% 1.07 0.014 2.91 1.24–6.8
HR > 120 1.25 0.03 3.4 2.34–5.2
ICU admission 3.82 0.000 45.8 12.49–50.95
Acute Kidney injury 1.31 0.041 4.022 1.060–15.26
Cancer 1.4 0.04 7.5 4.2–17.1
Lactate >2 1.49 0.01 4.44 2.57–7.66
NLR >5.46 1.01 0.05 1.75 1.10–2.35
RVOT VTI <9.5 cm .917 0.044 2.5 1.03–6.09
RV/LV >1.4 1.74 0.01 5.6 3.4–9.4
RR >22 1.06 0.03 2.88 1.88–4.43

Nine explanatory variables identified by a multivariable logistic regression analysis to correlate with the composite endpoints. HR (heart rate), ICU (intensive care unit), NLR (neutrophil to lymphocyte ratio), RR (respiratory rate), RVOT VTI (right ventricular outflow tract velocity time integral), RVL/LV (right to left ventricular diameter ratio). A p-value of <0.05 implied significance. Values are reported as odds ratios and 95% confidence interval.

Discussion

We demonstrate a derivation of a novel CLOT-5 risk score for the identification of a high-risk cohort of patients presenting with acute PE. Traditional scoring systems rely on a patient's history and basic vitals to calculate a patient's 30-day risk of PE related mortality; however, they present an incomplete picture of the patient's risk of mortality. Therefore, to mitigate this possibility, the CLOT-5 score was developed to include more objective measures of risk such as biomarkers and imaging parameters. Studies have evaluated the changes in biomarkers associated with PE and have found that markers such as CRP, IL-6, and fibrinogen are elevated early during the VTE/PE cascade, prior to symptom onset. 20 Others have identified the NLR and PLR as markers of poor short- and long-term prognosis in acute PE.21,22 The CLOT-5 score identified a lactic acid value >2 mm/L, a NLR ratio >5.46, and a RDW >15 as strongly predictive of 30-day mortality, with odds ratios of 5.1, 2.03, and 8.7 respectively. These markers are easily obtained from an initial blood draw when the patient initially presents, making the data available within minutes to assist with triaging the patient appropriately, potentially providing insight into the level of care and intensity of treatment needed.

While reducing 30-day PE-related mortality remains the strongest driving factor for fast, targeted management of acute PE, clinical decompensation and escalation of care prove just as important in the management of patients. Our study compiled variables of interest to determine the strongest predictors of the composite outcome of need for thrombolysis, need for vasoactive support, need for mechanical ventilation, and inpatient mortality. Our data shows that a need for admission to the ICU, the presence of cancer, RV/LV ratio >1.4, and lactic acid values >2 mm/L were the strongest predictors of the composite outcome, with odds ratios of 45.8, 7.5, 5.6, and 4.44, respectively. These variables, along with the six others noted in Table 4 are easy to obtain via biomarkers drawn on admission, the CT PE study diagnosing the acute PE or the TTE. As a result, the team managing the patient with acute PE can make a rapid and informed decision about the patient's overall risk of clinical decompensation and can escalate care in a timely manner, such as with ICU admission or through the use of catheter directed therapies, to help reduce the risk of the composite outcome noted in our study.

While the PESI and sPESI scores have been commonly utilized over the last decade in predicting 30-day mortality in patients presenting with acute PE, our study shows that the CLOT-5 score holds promise to be a more reliable metric in the assessment of 30-day mortality in this patient population. Figure 1 and Table 3 demonstrates the ROC curves for the CLOT-5, PESI, and sPESI scores with their respective AUC. The CLOT-5 score had an AUC of 0.901 with a standard error of 0.29. In comparison, the PESI and sPESI scores had an AUC and standard errors of 0.793 ±­ 0.43, 0.728 ± 0.55. The CLOT-5 score may be better equipped at predicting 30-day mortality as it incorporates more objective data, including biomarkers and imaging compared to the PESI and sPESI scores. The use of this objective data allows for a more tangible assessment of patient risk, with variables that are more strongly correlated to 30-day mortality than otherwise noted in the PESI and sPESI scores.

Despite the positive outcomes in this study, several limitations should be noted. This was a single center study that did not include a validation cohort, and thus should be considered a pilot study. The 30-day mortality reported incorporates all-cause mortality. A more accurate assessment of the predictive ability of the CLOT-5 score would assess specifically for PE-related mortality. This study is ongoing and will incorporate data obtained from the National Death Index into its analysis. Our sample included a very large proportion of low-risk PE. While this is reflective of the typical proportion of patients seen at our institution, a more robust analysis of the data would include a higher proportion of submassive, particularly submassive intermediate-high and intermediate-low risk, and massive PE patients. Finally, some of the parameters of the CLOT-5, specifically the imaging components, are not obtained as easily as parameters in the PESI and sPESI scores; however, they provide added value that the PESI and sPESI scores lack.

Conclusions

The data presented in this study supports the use of the CLOT-5 score as a new scoring system for the assessment of 30-day mortality in patients presenting with acute PE. The utilization of biomarker, vital signs, and imaging data strengthens the predictive ability of the CLOT-5 score when compared with traditional scoring systems, particularly the PESI and sPESI scores. When incorporated into the management algorithms of national PERT programs, the CLOT-5 score may allow for rapid and more comprehensive assessment of patients at high risk for clinical decompensation, leading to early escalation of care where appropriate. Studies are ongoing, utilizing the National Death Index, focusing specifically on PE related mortality.

Acknowledgements

Alexandru Marginean had access to the full data and takes responsibility for the content of this manuscript. PA, KW, EB, CS, RG, FG, JC, QM, MC, WM, PS, DS, AK, IW, AE, PD, YB, AD contributed substantially to the study design, data analysis and interpretation, and the writing of this manuscript.

Abbreviations

BNP

– brain natriuretic peptide

CAD

– coronary artery disease

CDT

– catheter directed therapies

CHF

– congestive heart failure

CI

– confidence interval

CKD

– chronic kidney disease

COPD

– chronic obstructive pulmonary disease

CT

– computed tomography

DM

– diabetes mellitus

DVT

– deep vein thrombosis

ICU

– intensive care unit

LMWH

– low molecular weight heparin

LV

– left ventricle

LVOT VTI

– left ventricular outflow tract velocity time integral

NLR

– neutrophil lymphocyte ration

OR

– odds ratio

PaO2

– arterial oxygen saturation

PASP

– pulmonary artery systolic pressure

PE

– pulmonary embolism

PERT

– pulmonary embolism response team

PESI

– pulmonary embolism severity index

PLR

– platelet lymphocyte ratio

RA

– right atrial

RDW

– red cell distribution width

RR

– respiratory rate

RV

– right ventricle

RVOT VTI

– right ventricular outflow tract velocity time integral

SBP

– systolic blood pressure

sPESI

– simplified pulmonary embolism severity index

SpO2

– oxygen saturation

TAPSE

– tricuspid annular plane systolic excursion

TTE

– transthoracic echocardiography

Footnotes

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval: All procedures performed were in accordance with the ethical standards of the institutional research committee (Loyola University Medical Center Institutional Review Board) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

References

  • 1.Martinez Licha CR, McCurdy CM, Maldonado SM, et al. Current management of acute pulmonary embolism. Ann Thorac Cardiovasc Surg. 2020;26(2):65-71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rendina D, De Bonis S, Gallotta G, et al. Clinical, historical and diagnostic findings associated with right ventricular dysfunction in patients with central and non-massive pulmonary embolism. Intern Emerg Med. 2010;5(1):53-59. [DOI] [PubMed] [Google Scholar]
  • 3.Miller RL, Das S, Anandarangam T, et al. Association between right ventricular function and perfusion abnormalities in hemodynamically stable patients with acute pulmonary embolism. Chest. 1998;113(3):665-670. [DOI] [PubMed] [Google Scholar]
  • 4.Jaff MR, McMurtry MS, Archer SL, et al. Management of massive and submassive pulmonary embolism, iliofemoral deep vein thrombosis, and chronic thromboembolic pulmonary hypertension: A scientific statement from the American Heart Association. Circulation. 2011;123(16):1788-1830. [DOI] [PubMed] [Google Scholar]
  • 5.Pruszczyk P, Goliszek S, Lichodziejewska B, et al. Prognostic value of echocardiography in normotensive patients with acute pulmonary embolism. JACC Cardiovasc Imaging. 2014;7(6):553-560. [DOI] [PubMed] [Google Scholar]
  • 6.Aujesky D, Obrosky DS, Stone RA, et al. Derivation and validation of a prognostic model for pulmonary embolism. Am J Respir Crit Care Med. 2005;172(8):1041-1046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jimenez D, Aujesky D, Moores L, et al. Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism. Arch Intern Med. 2010;170(15):1383-1389. [DOI] [PubMed] [Google Scholar]
  • 8.Konstantinides SV, Meyer G, Becattini C, 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. 2020;41(4):543-603. [DOI] [PubMed] [Google Scholar]
  • 9.Fernández C, Bova C, Sanchez O, et al. Validation of a model for identification of patients at intermediate to high risk for complications associated with acute symptomatic pulmonary embolism. Chest. 2015;148(1):211-218. [DOI] [PubMed] [Google Scholar]
  • 10.Barnes GD, Muzikansky A, Cameron S, et al. Comparison of 4 acute pulmonary embolism mortality risk scores in patients evaluated by pulmonary embolism response teams. JAMA Netw Open. 2020;3(8):e2010779. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Dentali F, Riva N, Turato S, et al. Pulmonary embolism severity index accurately predicts long-term mortality rate in patients hospitalized for acute pulmonary embolism. J Thromb Haemost. 2013;11(12):2103-2110. [DOI] [PubMed] [Google Scholar]
  • 12.Galmer A, Weinberg I, Giri J, et al. The role of the pulmonary embolism response team: How to build one, who to include, scenarios, organization, and algorithms. Tech Vasc Interv Radiol. 2017;20(3):216-223. [DOI] [PubMed] [Google Scholar]
  • 13.Kucher N, Boekstegers P, Müller OJ, et al. Randomized, controlled trial of ultrasound-assisted catheter-directed thrombolysis for acute intermediate-risk pulmonary embolism. Circulation. 2014;129(4):479-486. [DOI] [PubMed] [Google Scholar]
  • 14.Piazza G, Hohlfelder B, Jaff MR, et al. A prospective, single-arm, multicenter trial of ultrasound-facilitated, catheter-directed, low-dose fibrinolysis for acute massive and submassive pulmonary embolism: The SEATTLE II study. JACC Cardiovasc Interv. 2015;8(10):1382-1392. [DOI] [PubMed] [Google Scholar]
  • 15.Kesselman A, Kuo WT. Catheter-Directed therapy for acute submassive pulmonary embolism: Summary of current evidence and protocols. Tech Vasc Interv Radiol. 2017;20(3):193-196. [DOI] [PubMed] [Google Scholar]
  • 16.Brailovsky Y, Allen S, Masic D, et al. Risk stratification of acute pulmonary embolism. Curr Treat Options Cardiovasc Med. 2021;23:48. [Google Scholar]
  • 17.Brailovsky Y, Lakhter V, Weinberg I, et al. Right ventricular outflow Doppler predicts low cardiac Index in intermediate risk pulmonary embolism. Clin Appl Thromb Hemost. 2019;25:1076029619886062. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Yuriditsky E, Mitchell OJ, Sibley RA, et al. Low left ventricular outflow tract velocity time integral is associated with poor outcomes in acute pulmonary embolism. Vasc Med. 2019;2:1-8. [DOI] [PubMed] [Google Scholar]
  • 19.Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American society of echocardiography and the European association of cardiovascular imaging. J Am Soc Echocardiogr. 2015;28(1):1-39 e14. [DOI] [PubMed] [Google Scholar]
  • 20.Galeano-Valle F, Ordieres-Ortega L, Oblitas CM, et al. Inflammatory Biomarkers in the Short-Term Prognosis of Venous Thromboembolism: A Narrative Review. Int J Mol Sci. 2021;22(5):2627. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wang Q, Ma J, Jiang Z, et al. Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute pulmonary embolism: A systematic review and meta-analysis. Int Angiol. 2018;37(1):4-11. [DOI] [PubMed] [Google Scholar]
  • 22.Phan T, Brailovsky Y, Fareed J, et al. Neutrophil-to-Lymphocyte and platelet-to-lymphocyte ratios predict all-cause mortality in acute pulmonary embolism. Clin Appl Thromb Hemost. 2020;26:1076029619900549. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Clinical and Applied Thrombosis/Hemostasis are provided here courtesy of SAGE Publications

RESOURCES