Pulmonary Embolism in COVID-19 Patients: Which Diagnostic Algorithm Should We Use?

A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study

The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 10³/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.

Revision of Clinical Pre-Test Probability Scores in Hospitalized Patients with Pulmonary Embolism and SARS-CoV-2 Infection

Background

The need for computed tomography pulmonary angiography (CTPA) to rule out pulmonary embolism (PE) is based on clinical scores in association with D-dimer measurements. PE is a recognized complication in patients with SARS-CoV-2 infection due to a pro-thrombotic state which may reduce the usefulness of preexisting pre-test probability scores.

Aim

The purpose was to analyze new clinical and laboratory parameters while comparing existing and newly proposed scoring system for PE detection in hospitalized COVID-19 patients (HCP).

Methods

We conducted a retrospective study of 270 consecutive HCPs who underwent CTPA due to suspected PE. The Modified Wells, Revised Geneva, Simplified Geneva, YEARS, 4-Level Pulmonary Embolism Clinical Probability Score (4PEPS), and PE rule-out criteria (PERC) scores were calculated and the area under the receiver operating characteristic curve (AuROC) was measured.

Results

Overall incidence of PE among our study group of HCPs was 28.1%. The group of patients with PE had a significantly longer COVID-19 duration upon admission, at 10 vs 8 days, p = 0.006; higher D-dimer levels of 10.2 vs 5.3 μ g/L, p < 0.001; and a larger proportion of underlying chronic kidney disease, at 16% vs 7%, p = 0.041. From already established scores, only 4PEPS and the modified Wells score reached statistical significance in detecting the difference between the HCP groups with or without PE. We proposed a new chronic kidney disease, D-dimers, 10 days of illness before admission (CDD-10) score consisting of the three aforementioned variables: C as chronic kidney disease (0.5 points if present), D as D-dimers (negative 1.5 points if normal, 2 points if over 10.0 μ g/L), and D-10 as day-10 of illness carrying 2 points if lasting more than 10 days before admission or 1 point if longer than 8 days. The CDD-10 score ranged from -1.5 to 4.5 and had an AuROC of 0.672, p < 0.001 at cutoff value at 0.5 while 4PEPS score had an AuROC of 0.638 and Modified Wells score 0.611. The clinical probability of PE was low (0%) when the CDD-10 value was negative, moderate (24%) for CDD-10 ranging 0-2.5 and high (43%) when over 2.5.

Conclusions

Better risk stratification is needed for HCPs who require CTPA for suspected PE. Our newly proposed CDD-10 score demonstrates the best accuracy in predicting PE in patients hospitalized for SARS-CoV-2 infection.

Utility of probability scores for the diagnosis of pulmonary embolism in patients with SARS-CoV-2 infection: A systematic review

BACKGROUND AND OBJECTIVE

Clinical prediction models determine the pre-test probability of pulmonary embolism (PE) and assess the need for tests for these patients. Coronavirus infection is associated with a greater risk of PE, increasing its severity and conferring a worse prognosis. The pathogenesis of PE appears to be different in patients with and without SARS-CoV-2 infection. This systematic review aims to discover the utility of probability models developed for PE in patients with COVID-19 by reviewing the available literature.

METHODS

A literature search on the PubMed, Scopus, and EMBASE databases was carried out. All studies that reported data on the use of clinical prediction models for PE in patients with COVID-19 were included. Study quality was assessed using the Newcastle-Ottawa scale for non-randomized studies.

RESULTS

Thirteen studies that evaluated five prediction models (Wells score, Geneva score, YEARS algorithm, and PERC and PEGeD clinical decision rules) were included. The different scales were used in 1,187 patients with COVID-19. Overall, the models showed limited predictive ability. The two-level Wells score with low (or unlikely) clinical probability in combination with a D-dimer level <3000ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE.

CONCLUSIONS

Our systematic review suggests that the clinical prediction models available for PE that were developed in the general population are not applicable to patients with COVID-19. Therefore, their use is in clinical practice as the only diagnostic screening tool is not recommended. New clinical probability models for PE that are validated in these patients are needed.

[Utility of probability scores for the diagnosis of pulmonary embolism in patients with SARS-CoV-2 infection: A systematic review]

Background and objective

Clinical prediction models determine the pre-test probability of pulmonary embolism (PE) and assess the need for tests for these patients. Coronavirus infection is associated with a greater risk of PE, increasing its severity and conferring a worse prognosis. The pathogenesis of PE appears to be different in patients with and without SARS-CoV-2 infection. This systematic review aims to discover the utility of probability models developed for PE in patients with COVID-19 by reviewing the available literature.

Methods

A literature search on the PubMed, Scopus, and EMBASE databases was carried out. All studies that reported data on the use of clinical prediction models for PE in patients with COVID-19 were included. Study quality was assessed using the Newcastle-Ottawa scale for non-randomized studies.

Results

Thirteen studies that evaluated five prediction models (Wells score, Geneva score, YEARS algorithm, and PERC and PEGeD clinical decision rules) were included. The different scales were used in 1,187 patients with COVID-19. Overall, the models showed limited predictive ability. The two-level Wells score with low (or unlikely) clinical probability in combination with a D-dimer level < 3000 ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE.

Conclusions

Our systematic review suggests that the clinical prediction models available for PE that were developed in the general population are not applicable to patients with COVID-19. Therefore, their use is in clinical practice as the only diagnostic screening tool is not recommended. New clinical probability models for PE that are validated in these patients are needed.

The potential role of ischaemia-reperfusion injury in chronic, relapsing diseases such as rheumatoid arthritis, Long COVID, and ME/CFS: evidence, mechanisms, and therapeutic implications

Ischaemia–reperfusion (I–R) injury, initiated via bursts of reactive oxygen species produced during the reoxygenation phase following hypoxia, is well known in a variety of acute circumstances. We argue here that I–R injury also underpins elements of the pathology of a variety of chronic, inflammatory diseases, including rheumatoid arthritis, ME/CFS and, our chief focus and most proximally, Long COVID. Ischaemia may be initiated via fibrin amyloid microclot blockage of capillaries, for instance as exercise is started; reperfusion is a necessary corollary when it finishes. We rehearse the mechanistic evidence for these occurrences here, in terms of their manifestation as oxidative stress, hyperinflammation, mast cell activation, the production of marker metabolites and related activities. Such microclot-based phenomena can explain both the breathlessness/fatigue and the post-exertional malaise that may be observed in these conditions, as well as many other observables. The recognition of these processes implies, mechanistically, that therapeutic benefit is potentially to be had from antioxidants, from anti-inflammatories, from iron chelators, and via suitable, safe fibrinolytics, and/or anti-clotting agents. We review the considerable existing evidence that is consistent with this, and with the biochemical mechanisms involved.