Comparison of the Wells score and the revised Geneva score as a tool to predict pulmonary embolism in outpatients over age 65

A new nomogram prediction model for pulmonary embolism in older hospitalized patients

Purpose

Diagnosing pulmonary embolism (PE) in older adults is relatively difficult because of the atypical clinical symptoms of PE in older adults accompanied by multiple complications. This study aimed to establish a nomogram model to better predict the occurrence of PE in older adults.

Methods

Data were collected from older patients (≥65 years old) with suspected PE who were hospitalized between January 2012 and July 2021 and received confirmatory tests (computed tomographic pulmonary angiography or ventilation/perfusion scanning). The PE group and non-PE (control) group were compared using univariable and multivariable analyses to identify independent risk factors. A nomogram prediction model was constructed with independent risk factors and verified internally. The effectiveness of the nomogram model, Wells score, and revised Geneva score was assessed using the area under the receiver operating characteristic curve (AUC).

Results

In total, 447 eligible older patients (290 PE patients and 157 non-PE patients) were enrolled. Logistic regression analysis revealed nine independent risk factors: smoking, inflammation, dyspnea, syncope, mean corpuscular hemoglobin concentration, indirect bilirubin, uric acid, left atrial diameter, and internal diameter of the pulmonary artery. The AUC, sensitivity, and specificity of the nomogram prediction model were 0.763 (95 % confidence interval, 0.721-0.802), 74.48 %, and 67.52 %, respectively. The nomogram showed superior AUC compared to the Wells score (0.763 vs. 0.539, P < 0.0001) and the revised Geneva score (0.763 vs. 0.605, P < 0.0001).

Conclusions

This novel nomogram may be a useful tool to better recognize PE in hospitalized older adults.

Comparison of VTE risk scores in guidelines for VTE diagnosis in nonsurgical hospitalized patients with suspected VTE

BACKGROUND

The assessment of VTE likelihood with VTE risk scores is essential prior to imaging examinations during VTE diagnostic procedure. Little is known with respect to the disparity of predictive power for VTE diagnosis among VTE risk scores in guidelines for nonsurgical hospitalized patients with clinically suspected VTE.

METHODS

A retrospective study was performed to compare the predictive power for VTE diagnosis among the Wells, Geneva, YEARS, PERC, Padua, and IMPROVE scores in the leading authoritative guidelines in nonsurgical hospitalized patients with suspected VTE.

RESULTS

Among 3168 nonsurgical hospitalized patients with suspected VTE, VTE was finally excluded in 2733(86.3%) ones, whereas confirmed in 435(13.7%) ones. The sensitivity and specificity resulted from the Wells, Geneva, YEARS, PERC, Padua, and IMPROVE scores were (90.3%, 49.8%), (88.7%, 53.6%), (73.8%, 50.2%), (97.7%,16.9%), (80.9%, 44.0%), and (78.2%, 47.0%), respectively. The YI were 0.401, 0.423, 0.240, 0.146, 0.249, and 0.252 for the Wells, Geneva, YEARS, PERC, Padua, and IMPROVE scores, respectively. The C-index were 0.694(0.626-0.762), 0.697(0.623-0.772), 0.602(0.535-0.669), 0.569(0.486-0.652), 0.607(0.533-0.681), and 0.609(0.538-0.680) for the Wells, Geneva, YEARS, PERC, Padua, and IMPROVE scores, respectively. Consistency was significant in the pairwise comparison of Wells vs Geneva(Kappa 0.753, P = 0.565), YEARS vs Padua(Kappa 0.816, P = 0.565), YEARS vs IMPROVE(Kappa 0.771, P = 0.645), and Padua vs IMPROVE(Kappa 0.789, P = 0.812), whereas it did not present in the other pairs. The YI was improved to 0.304, 0.272, and 0.264 for the PERC(AUC 0.631[0.547-0.714], P = 0.006), Padua(AUC 0.613[0.527-0.700], P = 0.017), and IMPROVE(AUC 0.614[0.530-0.698], P = 0.016), with a revised cutoff of 5 or less, 6 or more, and 4 or more denoting the VTE-likely, respectively.

CONCLUSIONS

For nonsurgical hospitalized patients with suspected VTE, the Geneva and Wells scores perform best, the PERC scores performs worst despite its significantly high sensitivity, whereas the others perform intermediately, albeit the absolute predictive power of all isolated scores are mediocre. The predictive power of the PERC, Padua, and IMPROVE scores are improved with revised cutoffs.

Pulmonary embolism diagnosis: clinical assessment at the front door

This first of two practice reviews addresses pulmonary embolism (PE) diagnosis considering important aspects of PE clinical presentation and comparing evidence-based PE testing strategies. A companion paper addresses the management of PE. Symptoms and signs of PE are varied, and emergency physicians frequently use testing to 'rule out' the diagnosis in people with respiratory or cardiovascular symptoms. The emergency clinician must balance the benefit of reassuring negative PE testing with the risks of iatrogenic harms from over investigation and overdiagnosis.

Predictive scores for the diagnosis of Pulmonary Embolism in COVID-19: A systematic review

Objectives

During the COVID-19 pandemic, several studies described an increased chance of developing pulmonary embolism (PE). Several scores have been used to predict the occurrence of PE. This systematic review summarizes the literature on predicting rules for PE in hospitalized COVID-19 patients (HCPs).

Methods

PUBMED and EMBASE databases were searched to identify articles (1 January 2020-28 April 2021) presenting data pertaining to the use of a prediction rule to assess the risk for PE in adult HCPs. The investigated outcome was the diagnosis of PE. Studies presenting data using a single laboratory assay for PE prediction were excluded. Included studies were appraised for methodological quality using the Newcastle - Ottawa Quality Assessment Scale for Cohort Studies (NOS).

Results

We obtained a refined pool of twelve studies for five scoring systems (Wells score, Geneva score, CHADS2/CHA2DS2VASc/M-CHA2DS2VASc, CHOD score, Padua Prediction Score), and 4,526 patients. Only one score was designed explicitly for HCPs. Three and nine included studies were prospective and retrospective cohort studies, respectively. Among the examined scores, the CHOD score seems promising for predictive ability.

Conclusion

New prediction rules, specifically developed and validated for estimating the risk of PE in HCP, differentiating ICU from non-ICU patients, and taking into account anticoagulation prophylaxis, comorbidities, and the time from COVID-19 diagnosis are needed.

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.