Pulmonary embolism critical care update: prognosis, treatment, and research gaps

Reduced-Dose Thrombolysis in Acute Pulmonary Embolism A Systematic Review

Pulmonary embolism (PE) is the third-leading cause of cardiovascular mortality and the second-leading cause of death in cancer patients. The clinical efficacy of thrombolysis for acute PE has been proven, yet the therapeutic window seems narrow, and the optimal dosing for pharmaceutical reperfusion therapy has not been established. Higher doses of systemic thrombolysis inevitably associated with an incremental increase in major bleeding risk. To date, there is no high-quality evidence regarding dosing and infusion rates of thrombolytic agents to treat acute PE. Most clinical trials have focused on thrombolysis compared with anticoagulation alone, but dose-finding studies are lacking. Evidence is now emerging that lower-dose thrombolytic administered through a peripheral vein is efficacious in accelerating thrombolysis in the central pulmonary artery and preventing acute right heart failure, with reduced risk for major bleeding. The present review will systematically summarize the current evidence of low-dose thrombolysis in acute PE.

Application of artificial neural network in daily prediction of bleeding in ICU patients treated with anti-thrombotic therapy

OBJECTIVES

Anti-thrombotic therapy is the basis of thrombosis prevention and treatment. Bleeding is the main adverse event of anti-thrombosis. Existing laboratory indicators cannot accurately reflect the real-time coagulation function. It is necessary to develop tools to dynamically evaluate the risk and benefits of anti-thrombosis to prescribe accurate anti-thrombotic therapy.

METHODS

The prediction model,daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy, was built using deep learning algorithm recurrent neural networks, and the model results and performance were compared with clinicians.

RESULTS

There was no significant statistical discrepancy in the baseline. ROC curves of the four models in the validation and test set were drawn, respectively. One-layer GRU of the validation set had a larger AUC (0.9462; 95%CI, 0.9147-0.9778). Analysis was conducted in the test set, and the ROC curve showed the superiority of two layers LSTM over one-layer GRU, while the former AUC was 0.8391(95%CI, 0.7786-0.8997). One-layer GRU in the test set possessed a better specificity (sensitivity 0.5942; specificity 0.9300). The Fleiss' k of junior clinicians, senior clinicians, and machine learning classifiers is 0.0984, 0.4562, and 0.8012, respectively.

CONCLUSIONS

Recurrent neural networks were first applied for daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy. Deep learning classifiers are more reliable and consistent than human classifiers. The machine learning classifier suggested strong reliability. The deep learning algorithm significantly outperformed human classifiers in prediction time.

[Sustainable anticoagulation in COVID-19: Review of severity prediction and clinical reasoning]

OBJECTIVE

To identify the procoagulant phenomenon in SARS-CoV-2 patients and propose sustainable therapeutic guidance for low-income countries.

METHODS

A systematic review was conducted. It identified 5 observational studies from a scrutiny from 78 results. 712 patients were examined and the results were grouped according to mortality and severity. The comparison of the groups was interpreted using descriptive statistics.

RESULTS

D-dimer values were significantly associated with greater severity and mortality. Prothrombin was associated in some observations with higher mortality, but in terms of severity it was inconclusive.

CONCLUSION

COVID-19 disease has significant procoagulant activity and its timely treatment can alter the prognosis. The explored evidence supports sustainable methods. More evidence is needed to improve management. An early systematic approach to patients with sustainable therapeutic measures tailored to the health system is recommended.

Thromboembolic Complications of SARS-CoV-2 and Metabolic Derangements: Suggestions from Clinical Practice Evidence to Causative Agents

Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2 is a recently identified positive sense single-strand RNA (ssRNA) β-coronavirus. The viral spike proteins infect human hosts by binding to the cellular receptor angiotensin-converting enzyme 2 (ACE2). The infection causes a systemic illness involving cell metabolism. This widespread involvement is implicated in the pathophysiology of the illness which ranges from mild to severe, requiring multi organ support, ranging from oxygen supplementation to full cardiovascular and respiratory support. Patients with multiple co-existing comorbidities are also at a higher risk. The aim of this review is to explore the exact mechanisms by which COVID-19 affects patients systemically with a primary focus on the bleeding and thrombotic complications linked with the disease. Issues surrounding the thrombotic complications following administration of the ChAdOx1 nCoV-19 (Astra-Zeneca-Oxford) vaccine have also been illustrated. Risk stratification and treatment options in these patients should be tailored according to clinical severity with input from a multidisciplinary team.

Importance of computed tomography pulmonary angiography for predict 30-day mortality in acute pulmonary embolism patients

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The study provides a cut-off value of RV diameter and CT obstruction index by CTPA to predict acute PE patients' mortality.

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RV diameter of 53 mm or over and CT obstruction index >70 % is associated with increased 30-day mortality in APE patients.

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Increased RV diameter by CTPA is a better predictor of mortality than the clinical Pulmonary Embolism Severity Index (PESI).

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CTPA can be valuable as both the diagnostic and prognostic tool in APE patients.

Objective

The purpose of the present study was to assess the implications of different parameters of computed tomography pulmonary angiography (CTPA) to predict 30-day mortality in acute pulmonary embolism (APE) patients.

Material and Method

Patients who had clinical suspicion of APE and underwent CTPA were recruited in a retrospective cohort study. The findings of the CTPA included the parameters of right ventricular dysfunction (RVD), the severity of obstruction to the pulmonary artery by CT obstruction index, and the ratio of pulmonary trunk diameter and aorta. The endpoint of the study was established as the 30-day mortality associated with APE.

Results

A total of 238 patients with a confirmed APE diagnosis with CTPA were included in the study; 26 (10.9 %) of those patients died within 30 days. In patients with cancer and the Pulmonary Embolism Severity Index (PESI) class 5, the mortality rate was significantly higher. Compared with survivors, the mean CT obstruction index in the non-survivor group was significantly higher (p < 0.001). Higher mortality was associated with all RVD parameters identified by CTPA, such as the RV/LV ratio (p < 0.001), interventricular septum deviation grade 3 (p < 0.001), increased RV diameter (p < 0.001), and IVC contrast reflux (p < 0.001). The highest adjusted odds ratio was RV diameter at 1.094, followed by PESI and the CT obstruction index at 1.040.

Conclusion

CTPA-detected RVD parameters and CT obstruction index can predict a 30-day mortality rate in APE patients and be used for risk stratification. In APE patients, the RV diameter of 53 mm or greater and the CT obstruction index >70% is associated with increased 30-day mortality.

Effects of a Consensus-Based Pulmonary Embolism Treatment Algorithm and Response Team on Treatment Modality Choices, Outcomes, and Complications

Pulmonary embolism (PE) treatment depends on disease severity and risk of complications. Physician and institutional expertise may influence the use of reperfusion therapy (RT) such as systemic thrombolysis (SL) and catheter-directed interventions (CDI). We aimed to investigate the effects of a consensus-based treatment algorithm (TA) and subsequent implementation of PE response team (PERT) on RT modality choices and patient outcomes. A cohort of PE patients admitted to a tertiary care hospital between 2012 and 2017 was retrospectively evaluated. Demographics, clinical variables, RT selections, and patient outcomes during 3 consecutive 2-year periods (baseline, with TA, and with TA+PERT) were compared. Descriptive statistics were used for data analysis. A total of 1105 PE patients were admitted, and 112 received RT. Use of RT increased from 4.7% at baseline to 8.2% and 16.1% during the TA and TA+PERT periods. The primary RT modality transitioned from CDI to SL, and reduced-dose SL became most common. Treatment selection patterns remained unchanged after PERT introduction. Hospital length of stay decreased from 4.78 to 2.96 and 2.81 days (P < .001). Most of the hemorrhagic complications were minor, and their rates were similar across all 3 periods and between SL and CDI. No major hemorrhages occurred in patients treated with reduced-dose SL. In conclusion, TA and PERT represent components of a decision support system facilitating treatment modality selection, contributing to improved outcomes, and limiting complications. Treatment algorithm emerged as a factor providing consistency to PERT recommendations.