First European Society of Cardiology Cardio-Oncology Guidelines: A Big Leap Forward for an Emerging Specialty

Nolan, Mark; Creati, Louise; Koczwara, Bogda; Kritharides, Leonard; Lyon, Alexander R.; Negishi, Kazuaki; Ngo, D.; Thomas, Liza; Vardy, Janette L.; Sverdlov, Aaron L.

doi:10.1016/j.hlc.2022.11.003

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Pulmonary embolism (PE) was defined in accordance with the criteria of the European Society of Cardiology Guidelines ( 20 ). The diagnosis of PE was based on a filling defect of the pulmonary artery system (including the subsegment pulmonary artery) in computed tomography pulmonary angiography (CTPA).…”

Section: Methodsmentioning

confidence: 99%

A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis

Jianling

Lulu²,

Liuyi³

et al. 2023

Front. Neurol.

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ObjectiveThe purpose of this retrospective study was to establish a numerical model for predicting the risk of pulmonary embolism (PE) in neurology department patients.MethodsA total of 1,578 subjects with suspected PE at the neurology department from January 2012 to December 2021 were considered for enrollment in our retrospective study. The patients were randomly divided into the training cohort and the validation cohort in the ratio of 7:3. The least absolute shrinkage and selection operator regression were used to select the optimal predictive features. Multivariate logistic regression was used to establish the numerical model, and this model was visualized by a nomogram. The model performance was assessed and validated by discrimination, calibration, and clinical utility.ResultsOur predictive model indicated that eight variables, namely, age, pulse, systolic pressure, hemoglobin, neutrophil count, low-density lipoprotein, D-dimer, and partial pressure of oxygen, were associated with PE. The area under the receiver operating characteristic curve of the model was 0.750 [95% confidence interval (CI): 0.721–0.783] in the training cohort and 0.742 (95% CI: 0.689–0.787) in the validation cohort, indicating that the model showed a good differential performance. A good consistency between the prediction and the real observation was presented in the training and validation cohorts. The decision curve analysis in the training and validation cohorts showed that the numerical model had a good net clinical benefit.ConclusionWe established a novel numerical model to predict the risk factors for PE in neurology department suspected PE patients. Our findings may help doctors to develop individualized treatment plans and PE prevention strategies.

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Section: Methodsmentioning

confidence: 99%

A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis

Jianling

Lulu²,

Liuyi³

et al. 2023

Front. Neurol.

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“…The diagnostic criteria of PE refer to the guidelines of the European Heart Association. 18 The patients who have undergone CTPA examination are suspected of PE. Determine whether PE has occurred based on the diagnostic conclusion of the radiologist.…”

Section: Methodsmentioning

confidence: 99%

Predictive Model for Pulmonary Embolism in Pregnant and Postpartum Women: A 10-Year Retrospective Study

Lili,

Shunlan,

Lixu

2023

Clin Appl Thromb Hemost

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Background: Pulmonary embolism (PE) in pregnant and postpartum women is fatal, and risk assessment is crucial for effective and safe management, the aim of this retrospective study was to establish a nomogram for predicting the risk of PE in pregnant and postpartum women. Methods: Totally 343 subjects suspected of PE at the Obstetrics Department of Affiliated Dongyang Hospital of Wenzhou Medical University from January 2012 to December 2021 were retrospective analyzed in our study. Pregnant women suspected of PE and who underwent computed tomographic pulmonary angiography examination were included in the study. The least absolute shrinkage and selection operator regression technique was used to select the best prediction features, and multivariate logistic regression is used to build the prediction model. Bootstrap resampling 1000 times was used to validate the model visualized by nomogram. Evaluate the performance of the model from three aspects: identification, calibration and clinical utility. Results: Our predictive model indicated that chest tightness, anhelation, lactate, and D-dimer were associated with PE. The area under the receiver operating characteristic curve of the model was 0.836 (95% CI: [0.770-0.902]), indicating that our model had a good differential diagnostic performance. Good consistency between prediction and real observation was presented as the calibration curve. Decision curve analysis indicated that our model had a good net clinical benefit. Conclusions: We developed a novel numerical model for selecting risk factors for PE in pregnant and postpartum women. Our results may help obstetricians and gynaecologists to develop individualized treatment plans and PE prevention strategies.

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“…The de nition of PE was in accordance with the criteria of the European Society of Cardiology guidelines (22). CTPA was used to con rm PE, including subsegmental PE, by identifying a lling defect in the pulmonary arterial system.…”

Section: Study Outcomes and Data Collectionmentioning

confidence: 99%

Establishing a Nomogram to Predict the Risk of Pulmonary Embolism in Oncology Patients

Liuyi

Chen

et al. 2023

Preprint

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Objective Pulmonary embolism (PE) is a serious disease that can result in high morbidity and mortality among cancer patients. The aim of this study was to create a nomogram to accurately predict PE risk in oncology patients to enhance their medical treatment and management. Methods This study was designed as a retrospective analysis; information on medical history, complications, specific clinical characteristics, and laboratory biomarker results was collected for suspected PE patients admitted to the oncology department at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2012 and December 2021. A total of 512 patients were randomly divided into training and validation groups based on a 6:4 ratio. LASSO and multivariate logistic regressions were used to develop a nomogram-based scoring model. Model performance was evaluated using receiver operating characteristic (AUC), calibration, and clinical decision curves. Results In our study, over 50 features from 512 patients were analyzed. The nomogram-based scoring model was established using five predictive features, including the neutrophil count, sex, systolic blood pressure, surgical status, and D-dimer levels, which achieved AUC values of 0.758 and 0.702 in the training (95% CI 0.695–0.804) and validation cohorts (95% CI 0.630–0.776), respectively. For our model, the sensitivity was 85.58%, the specificity was 35.78%, the positive predictive value was 72.44%, and the negative predictive value was 55.71%. The calibration curve results showed a strong consistency between the probability predicted by the nomogram and the actual probability. Decision curve analysis (DCA) also demonstrated that the nomogram-based scoring model produced a favorable net clinical benefit. Conclusions In this study, we successfully developed a novel numerical model that can predict PE risk in oncology patients, enabling appropriate selection of PE prevention strategies and reducing unnecessary computed tomography pulmonary angiography (CTPA) scans and their associated adverse effects.

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First European Society of Cardiology Cardio-Oncology Guidelines: A Big Leap Forward for an Emerging Specialty

Cited by 7 publications

References 11 publications

A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis

A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis

Predictive Model for Pulmonary Embolism in Pregnant and Postpartum Women: A 10-Year Retrospective Study

Establishing a Nomogram to Predict the Risk of Pulmonary Embolism in Oncology Patients

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