Background And Purpose: The aim was to evaluate the temporal trends, characteristics and in-hospital outcomes of patients hospitalized with acute ischaemic stroke (AIS) between those with and without current or historical malignancies.
Methods: Adult hospitalizations with a primary diagnosis of AIS were identified from the National Inpatient Sample database 2007-2017. Logistic regression was used to compare the differences in the utilization of AIS interventions and in-hospital outcomes. For further analysis, subgroup analyses were performed stratified by cancer subtypes.
Results: There were 892,862 hospitalizations due to AIS, of which 108,357 (12.14%) had a concurrent diagnosis of current cancer (3.41%) or historical cancer (8.72%). After adjustment for confounders, patients with current malignancy were more likely to have worse clinical outcomes. The presence of historical cancers was not associated with an increase in poor clinical outcomes. Additionally, AIS patients with current malignancy were less likely to receive intravenous thrombolysis (adjusted odds ratio 0.66, 95% confidence interval 0.63-0.71). Amongst the subgroups of AIS patients treated with intravenous thrombolysis or mechanical thrombectomy, outcomes varied by cancer types. Notably, despite these acute stroke interventions, outcome remains poor in AIS patients with lung cancer.
Conclusions: Although AIS patients with malignancy generally have worse in-hospital outcomes versus those without, there were considerable variations in these outcomes according to different cancer types and the use of AIS interventions. Finally, treatment of these AIS patients with a current or historical cancer diagnosis should be individualized.
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http://dx.doi.org/10.1111/ene.15699 | DOI Listing |
BMJ Open
December 2024
Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xian, China
Introduction: Despite the implementation of mechanical thrombectomy, acute ischaemic stroke with large vessel occlusion (AIS-LVO) remains a significant health concern, characterised by substantial morbidity and mortality. Our trial aims to evaluate the efficacy and safety of minocycline in reducing infarct volume and improving functional outcomes in patients undergoing mechanical thrombectomy for anterior circulation AIS-LVO.
Methods And Analysis: The MIST-A trial is a prospective, randomised, open-label, blinded-endpoint trial to be conducted across 12 medical centres.
Sci Rep
January 2025
Department of Orofacial Pain and Oral Medicine, Yonsei University College of Dentistry, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
This study aimed to develop an artificial intelligence (AI) model for the screening of degenerative joint disease (DJD) using temporomandibular joint (TMJ) panoramic radiography and joint noise data. A total of 2631 TMJ panoramic images were collected, resulting in a final dataset of 3908 images (2127 normal (N) and 1781 DJD (D)) after excluding indeterminate cases and errors. AI models using GoogleNet were evaluated with six different combinations of image data, clinician-detected crepitus, and patient-reported joint noise.
View Article and Find Full Text PDFEur J Radiol
January 2025
School of Biomedical Engineering & Imaging Sciences, King's College London, London, the United Kingdom of Great Britain and Northern Ireland; Department of Neuroradiology, King's College Hospital National Health Service Foundation Trust, London, the United Kingdom of Great Britain and Northern Ireland. Electronic address:
Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
ETH Zurich, Zurich, Switzerland.
Background: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging.
View Article and Find Full Text PDFNeurology
February 2025
Department of Neurology, John Hunter Hospital, Newcastle, Australia.
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