Elevated d-dimer cut-off values for computed tomography pulmonary angiography-d-dimer correlates with location of embolism

Quality of referrals for lower extremity ultrasonography and computed tomography pulmonary angiography and associations with positive findings of venous thromboembolism

INTRODUCTION

The referral is the basis for radiologists' assessment of modality, protocol and urgency, and insufficient information may threaten patient safety. The aim of this study was to assess the completeness of referrals for lower extremity venous duplex ultrasonography (LEVDUS) and computed tomography pulmonary angiography (CTPA), and to investigate associations between the provided clinical information including risk factors, symptoms and lab results in the referrals and positive findings of deep vein thrombosis (DVT) and pulmonary embolism (PE), respectively.

METHODS

Referrals for LEVDUS (801) and CTPA (800) performed from 2016 to 2019 were obtained. Three categories of clinical information from the referrals were recorded: symptoms, risk factors and laboratory results, as well as positive imaging findings of venous thromboembolism (VTE). Referral completeness was rated from zero to three according to how many categories of clinical information the referral provided.

RESULTS

Information from all three clinical information categories was provided in 15% and 25% of referrals for LEVDUS and CTPA, respectively, while 2% and 10% of referrals did not contain any clinical information. Symptoms were provided most often (85% for LEVDUS and 94% for CTPA). Provided information about risk factors was significantly associated with positive findings for LEVDUS, (p = 0.02) and CTPA (p < 0.001).

CONCLUSION

A great majority of referrals failed to provide one or more categories of clinical information. Risk factors were associated with a positive finding of VTE on LEVDUS and CTPA.

IMPLICATIONS FOR PRACTICE

Improving clinical information in referrals may improve justification, patient safety and quality of radiology services.

Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis

BACKGROUND

Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles.

OBJECTIVE

We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts.

METHODS

PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile.

RESULTS

There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression.

CONCLUSIONS

Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.

Diagnostic Accuracy of Multiple D-Dimer Cutoff Thresholds and Other Clinically Applicable Biomarkers for the Detection and Radiographic Evaluation of Pulmonary Embolism

Background: Diagnostic work-up of pulmonary embolism (PE) remains a challenge. Methods: We retrospectively studied all patients referred for computed tomography pulmonary angiography (CTPA) with suspicion of PE during a 12-month period (2018). The diagnostic accuracy of different D-dimer (Dd) cutoff thresholds for ruling out PE was evaluated. Furthermore, the association of Dd and red cell distribution width (RDW) with embolus location, CTPA findings, and patient outcome was recorded. Results: One thousand seventeen (n = 1017) patients were finally analyzed (mean age: 64.6 years (SD = 11.8), males: 549 (54%)). PE incidence was 18.7%. Central and bilateral embolism was present in 44.7% and 59.5%, respectively. Sensitivity and specificity for conventional and age-adjusted Dd cutoff was 98.2%, 7.9%, and 98.2%, 13.1%, respectively. A cutoff threshold (2.1 mg/L) with the best (64.4%) specificity was identified based on Receiver Operating Characteristics analysis. Moreover, a novel proposed Dd cutoff (0.74 mg/L) emerged with increased specificity (20.5%) and equal sensitivity (97%) compared to 0.5 mg/L, characterized by concurrent reduction (17.2%) in the number of performed CTPAs. Consolidation/atelectasis and unilateral pleural effusion were significantly associated with PE (p < 0.05, respectively). Patients with consolidation/atelectasis or intrapulmonary nodule(s)/mass on CTPA exhibited significantly greater median Dd values compared to patients without the aforementioned findings (2.34, (IQR 1.29−4.22) vs. 1.59, (IQR 0.81−2.96), and 2.39, (IQR 1.45−4.45) vs. 1.66, (IQR 0.84−3.12), p < 0.001, respectively). RDW was significantly greater in patients who died during hospitalization (p = 0.012). Conclusions: Age-adjusted Dd increased diagnostic accuracy of Dd testing without significantly decreasing the need for imaging. The proposed Dd value (0.74 mg/L) showed promise towards reducing considerably the need of CTPA. Multiple radiographic findings have been associated with increased Dd values in our study.

Is COVID Evolution Due to Occurrence of Pulmonary Vascular Thrombosis?

In this hypothesis paper, we suggest that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may induce intravascular pulmonary thrombosis, which may result in the rapid worsening of clinical conditions and, eventually, exitus. Previously published papers have demonstrated that increased levels of D-dimer at hospital admission correlate with a more severe disease (0.5 mg/L) or occurrence of death (1 mg/L). The potential prothrombotic action of the SARS-CoV-2 is supported by the topographical involvement of the lung regions with a predilection for the lower lobe with peripheral involvement. If this hypothesis is demonstrated, this could suggest the benefit of using antithrombotic/coagulation regimens for SARS-CoV-2 and, at the same time, the urgency to identify drugs that could alter the inflammatory storm, thus protecting the vessel wall.

D-dimer Interval Likelihood Ratios for Pulmonary Embolism

OBJECTIVE

The objective was to estimate D-dimer interval likelihood ratios (iLRs) for diagnosing pulmonary embolism (PE).

METHODS

The authors used pooled patient-level data from five PE diagnostic management studies to estimate iLRs for the eight D-dimer intervals with boundaries 250, 500, 750, 1,000, 1,500, 2,500, and 5,000 ng/mL. Logistic regression was used to fit the data so that an interval increase corresponds to increasing the likelihood ratio by a constant factor.

RESULTS

The iLR for the D-dimer interval 1,000-1,499 ng/mL was essentially 1.0 (0.98 with 95% confidence interval [CI] = 0.82-1.18). In the logistic regression model, the constant between-interval factor was 2.0 (95% CI = 1.9-2.1). Using these iLR estimates, if the pre-D-dimer probability of PE is 15%, only a D-dimer less than 500 ng/mL will result in a posttest probability below 3%; if the pretest probability is 5%, the threshold for a "negative" D-dimer is 1,000 ng/mL.

CONCLUSIONS

A decision strategy based on these approximate iLRs agrees with several published strategies.