Anticoagulation reversal agents equally effective and safe after intracranial haemorrhage

25th November 2022

Anticoagulation reversal agents used for intracranial haemorrhage have similar levels of efficacy and safety according to a systematic review

Anticoagulant reversal agents use following an intracranial haemorrhage, all appear to have a similar level of efficacy and safety according to the findings of a meta-analysis by US researchers.

The direct oral anticoagulants (DOACs) are increasingly preferred over warfarin in DOAC-eligible patients with atrial fibrillation and patients with venous thromboembolism. In fact, the more widespread introduction of these agents has been generally well received by patients who report a high level of treatment satisfaction.

Nevertheless, anticoagulant agents are associated with an increased risk of bleeding and the annual rate of intracranial haemorrhage in those taking DOACs is 0.1% to 0.2%. Several anticoagulant reversal agents are currently available and approved. For example, andexanet alfa (AA) has been approved where reversal of anticoagulation is needed due to life-threatening or uncontrolled bleeding with apixaban or rivaroxaban.

Similarly, idarucizumab is approved for the specific reversal of dabigatran. Prothrombin complex concentrates have been developed to contain highly concentrated coagulation factors and one particular agent, 4-factor prothrombin complex concentrate (4F-PCC), has been found to be a good treatment option in patients requiring DOAC reversal.

However, the newer and traditional agents have not been directly compared. As a result, in the present study, the US researchers undertook a systematic review of the safety and efficacy of these non-specific (i.e., 4F-PCC) and targeted anticoagulant reversal agents in patients with DOAC-related intracranial haemorrhage (ICH).

Included studies were those in which patients with an intracranial haemorrhage had been treated with a DOAC, there was reversal of the DOAC and anticoagulant reversal outcomes such as thromboembolism and mortality were reported. The primary outcome was the successful reversal of anticoagulation.

Anticoagulant reversal of DOAC therapy

A total of 36 studies including 1832 patients with an ICH and a mean age of 76 years (57% male) met the inclusion criteria.

For patients treated with 4F-PCC, anticoagulant reversal occurred in 77% of patients, compared to 75% for AA and 82% for idarucizumab when reversing dabigatran.

In terms of safety, all-cause mortality was also broadly similar for 4F-PCC and AA (26% vs 24%) and lower for idarucizumab (11%). Thromboembolic events were similar for 4F-PCC and idarucizumab (8% vs 5%) and slightly higher for AA (14%).

The authors concluded that while there were no direct head-to-head comparisons available, their findings suggested that the overall anticoagulation reversal, mortality, and thromboembolic event rates appeared similar among available DOAC reversal agents for managing ICH.

Citation
Chaudary R et al. Evaluation of Direct Oral Anticoagulant Reversal Agents in Intracranial Hemorrhage: A Systematic Review and Meta-analysis. JAMA Netw Open 2022.

Convolutional neural network diagnosis of ICH equivalent to radiologists

13th December 2021

Convolutional neural network performance appears to be comparable to that of radiologists for the diagnosis of intracranial haemorrhage (ICH)

The use of convolutional neural networks (CNN) for diagnosing patients with an intracranial haemorrhage (ICH) appear to comparable to that of radiologists. This was the conclusion of a study by a team from the Faculty of Health and Medical Sciences, Copenhagen University, Denmark.

An ICH is usually caused by rupture of small penetrating arteries secondary to hypertensive changes or other vascular abnormalities and overall accounts for 10 – 20% of all strokes. However, this proportion varies across the world so that in Asian countries, an ICH is responsible for between 18 and 24% of strokes but only 8 – 15% in Westernised countries. An acute presentation of ICH can be difficult to distinguish from ischaemic stroke and non-contrast computerised tomography (CT) is the most rapid and readily available tool for the diagnosis of ICH.

As in many areas of medicine, artificial intelligence systems are becoming increasingly used and one such system is a Convolutional Neural Network (CNN), which represents a Deep Learning algorithm that is able to take an input image, assign importance to various aspects or objects within in the image and to differentiate one from the other. In fact, a 2019 systematic review of Deep Learning systems concluded that the ‘diagnostic performance of deep learning models to be equivalent to that of health-care professionals.’ Nevertheless, the authors added the caveat that ‘few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample.’

In the present study, the Danish team undertook a systematic review and meta-analysis to appraise the evidence of CNN in per-patient diagnosis of ICH. They performed a literature review and studies deemed suitable for inclusion were those where: patients had undergone non-contrast computed tomography of the cerebrum for the detection of an ICH; radiologists or a clinical report was used as the reference standard and finally where a CNN algorithm was deployed for the detection of ICH. For the purposes of their analysis, the minimum acceptable reference standard was defined as either manual, semi-automated or automated image labelling taken from radiology reports or electronic health records. For their analysis, the researchers calculated the pooled sensitivity, specificity and the receiver operating characteristics curves (SROC).

Findings

A total of six studies with 380,382 scans were included in the final analysis. When comparing the CNN performance to the reference standard, the pooled sensitivity was 96% (95% CI 93 – 97%), pooled specificity 97% (95% CI 90 – 99%) and an SROC of 98% (95% CI 97 – 99%). When combining both retrospective and external validation studies, for CNN, the performance was slightly worse with a pooled sensitivity of 95%, specificity 96% and pooled SROC 98%.

They concluded that CNN-algorithms accurately detect ICHs based on an analysis of both retrospective and external validation studies and that this approach seemed promising but highlighted the need for more studies using external validation test sets with uniform methods to define a more robust reference standard.

Citation

Jorgensen MD et al. Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis. Eur J Radiol 2021