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Neurocritical Care: Clinical Advances and Practice Updates
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Neurocritical Care: Clinical Advances and Practice Updates

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: 25 June 2025 | Viewed by 1740

Special Issue Editor

Dr. Ali Seifi

E-Mail Website
Guest Editor
Department of Neurosurgery, Division of Neurocritical Care, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, Mail Code 7843, San Antonio, TX 78229, USA
Interests: traumatic brain injury; ICH; stroke; SAH; spinal cord injury; encephalitis; meningitis; seizures

Special Issue Information

Dear Colleagues,

The field of neurocritical care has seen significant advances in recent years, with emerging research shedding light on the complex mechanisms and innovative treatment approaches for various neurological emergencies. Despite these advances, many challenges remain, particularly in optimizing patient outcomes in conditions such as traumatic brain injury (TBI), intracerebral hemorrhage (ICH), stroke, subarachnoid hemorrhage (SAH), and spinal cord injury. Additionally, there is a growing recognition of the potential of artificial intelligence (AI) to revolutionize neurocritical care. However, the field is still in its early stages, and more research is urgently needed to harness AI's full potential in neuroscience, patient monitoring, and the development of new medical devices. This blossoming area holds great promise but requires further exploration to truly transform patient care. Current research continues to push the boundaries of our understanding, yet there are still critical knowledge gaps that need to be addressed.

The aim of this Special Issue is to address these gaps by focusing on the latest clinical advances and practice updates in neurocritical care. We seek to highlight innovative research and clinical practices that aim to improve patient outcomes, reduce complications, and enhance the overall management of neurocritical conditions. Core problems such as early diagnosis, effective treatment strategies, and long-term care management remain at the forefront of our discussion.

This Special Issue will encompass a diverse array of topics, including, but not limited to, advanced research on TBI, ICH, stroke, SAH, spinal cord injury, encephalitis, meningitis, and seizures. Beyond these clinical areas, we are particularly interested in the integration of cutting-edge technology within neurocritical care, such as the application of machine learning, artificial intelligence, and innovations in medical devices.

We are soliciting original research articles and reviews that provide new insights and contribute to the evolving landscape of neurocritical care. I encourage all researchers and clinicians in the field to contribute to this Special Issue. Your participation is vital to advancing the field and ultimately improving the care of patients with neurocritical conditions.

Dr. Ali Seifi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • traumatic brain injury (TBI)
  • intracerebral hemorrhage (ICH)
  • neurocritical care practice
  • neurocritical care artificial intelligence
  • medical device and technology innovation in neurocritical care
  • stroke management
  • subarachnoid hemorrhage (SAH)
  • status epilepticus

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Published Papers (3 papers)

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18 pages, 921 KiB  
Article
Trends in Ischemic Stroke Hospitalization and Outcomes in the United States Pre- and Peri-COVID-19 Pandemic: A National Inpatient Sample Study
by Alibay Jafarli, Mario Di Napoli, Rachel S. Kasper, Jeffrey L. Saver, Louise D. McCullough, Setareh Salehi-Omran, Behnam Mansouri, Vasileios Arsenios Lioutas, Mohammed Ismail and Afshin A. Divani
J. Clin. Med. 2025, 14(4), 1354; https://doi.org/10.3390/jcm14041354 - 18 Feb 2025
Viewed by 405
Abstract
Background/Objectives: The COVID-19 pandemic impacted healthcare systems globally, disrupting the management and treatment of acute ischemic stroke (AIS). Understanding how AIS admissions, treatments, and outcomes were affected is critical for improving stroke care in future crises. The objective of this work was to [...] Read more.
Background/Objectives: The COVID-19 pandemic impacted healthcare systems globally, disrupting the management and treatment of acute ischemic stroke (AIS). Understanding how AIS admissions, treatments, and outcomes were affected is critical for improving stroke care in future crises. The objective of this work was to assess the COVID-19 pandemic’s impact on AIS admissions, treatment patterns, complications, and patient outcomes in the U.S. from 2016 to 2021, focusing on differences between pre-pandemic (2016–2019) and peri-pandemic (2020–2021) periods. Methods: This is a retrospective cohort study using the National Inpatient Sample (NIS) database, analyzing weighted discharge records of AIS patients over six years. Data encompass U.S. hospitals, including urban, rural, teaching, and non-teaching facilities. The study included AIS patients aged 18 and older (N = 3,154,154). The cohort’s mean age was 70.0 years, with an average hospital stay of 5.1 days and an adjusted mean cost of $16,765. Men comprised 50.5% of the cohort. We analyzed temporal trends in AIS hospitalizations from 2016 to 2021, comparing pre- and peri-COVID-19 periods. The primary outcome was the AIS admissions trend over time, with secondary outcomes including reperfusion therapy utilization, intubation rates, discharge disposition, and complications. Trends in risk factors and NIH Stroke Scale (NIHSS) severity were also evaluated. Results: AIS admissions rose from 507,920 in 2016 to 535,694 in 2021. Age and sex distribution shifted, with a growing proportion of male AIS cases (from 49.8% to 51.4%) and a decrease in mean age from 70.3 to 69.7 years. Although not statistically significant, White patients were the majority (68.0%), though their proportion declined as Black, Hispanic, and Asian/Pacific Islander cases increased. Reperfusion therapy, especially mechanical thrombectomy, rose from 2.2% to 5.6% over the study period. Intubation rates increased from 4.8% pre-COVID-19 to 5.5% peri-COVID, with higher rates among COVID-positive patients. NIHSS severity declined over time, with severe strokes (NIHSS ≥ 16) decreasing from 14.5% in 2017 to 12.6% in 2021. Conclusions: The COVID-19 pandemic brought significant shifts in AIS patterns, with younger, more diverse patients, increased reperfusion therapy use, and rising complication rates. These changes underscore the importance of resilient healthcare strategies and resource allocation to maintain stroke care amid future public health emergencies. Full article
(This article belongs to the Special Issue Neurocritical Care: Clinical Advances and Practice Updates)
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12 pages, 607 KiB  
Article
Personalizing Prediction of High Opioid Use in the Neurointensive Care Unit: Development and Validation of a Stratified Risk Model for Acute Brain Injury Due to Stroke or Traumatic Brain Injury
by Wei Yun Wang, Ian C. Holland, Christine T. Fong, Samuel N. Blacker and Abhijit V. Lele
J. Clin. Med. 2024, 13(23), 7055; https://doi.org/10.3390/jcm13237055 - 22 Nov 2024
Viewed by 754
Abstract
Background/Objectives: This study aimed to develop and validate a stratified risk model for predicting high opioid use in patients with acute brain injury due to stroke or traumatic brain injury (TBI) admitted to a neurocritical care intensive care unit. Methods: We examined [...] Read more.
Background/Objectives: This study aimed to develop and validate a stratified risk model for predicting high opioid use in patients with acute brain injury due to stroke or traumatic brain injury (TBI) admitted to a neurocritical care intensive care unit. Methods: We examined the factors associated with the use of high-opioids (≥75th quartile, ≥17.5 oral morphine equivalent/ICU day) in a retrospective cohort study including patients with acute ischemic stroke, spontaneous intracerebral hemorrhage, spontaneous subarachnoid hemorrhage, and TBI. We then developed, trained, and validated a risk model to predict high-dose opioids. Results: Among 2490 patients aged 45–64 years (β = −0.25), aged 65–80 years (β = −0.97), and aged ≥80 years (β = −1.17), a history of anxiety/depression (β = 0.57), a history of illicit drug use (β = 0.79), admission diagnosis (β = 1.21), lowest Glasgow Coma Scale Score (GCSL) [GCSL 3–8 (β = −0.90], {GCS L 9–12 ((β = −0.34)], mechanical ventilation (β = 1.21), intracranial pressure monitoring (β = 0.69), craniotomy/craniectomy (β = 0.6), and paroxysmal sympathetic hyperactivity (β = 1.12) were found to be significant predictors of high-dose opioid use. When validated, the model demonstrated an area under the curve ranging from 0.72 to 0.82, accuracy ranging from 0.68 to 0.91, precision ranging from 0.71 to 0.94, recall ranging from 0.75 to 1, and F1 ranging from 0.74 to 0.95. Conclusions: A personalized stratified risk model may allow clinicians to predict the risk of high opioid use in patients with acute brain injury due to stroke or TBI. Findings need validation in multi-center cohorts. Full article
(This article belongs to the Special Issue Neurocritical Care: Clinical Advances and Practice Updates)
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27 pages, 4371 KiB  
Systematic Review
Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis
by Armin Karamian and Ali Seifi
J. Clin. Med. 2025, 14(7), 2377; https://doi.org/10.3390/jcm14072377 - 30 Mar 2025
Viewed by 100
Abstract
Background: Intracranial hemorrhage (ICH) is a life-threatening medical condition that needs early detection and treatment. In this systematic review and meta-analysis, we aimed to update our knowledge of the performance of deep learning (DL) models in detecting ICH on non-contrast computed tomography [...] Read more.
Background: Intracranial hemorrhage (ICH) is a life-threatening medical condition that needs early detection and treatment. In this systematic review and meta-analysis, we aimed to update our knowledge of the performance of deep learning (DL) models in detecting ICH on non-contrast computed tomography (NCCT). Methods: The study protocol was registered with PROSPERO (CRD420250654071). PubMed/MEDLINE and Google Scholar databases and the reference section of included studies were searched for eligible studies. The risk of bias in the included studies was assessed using the QUADAS-2 tool. Required data was collected to calculate pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the corresponding 95% CI using the random effects model. Results: Seventy-three studies were included in our qualitative synthesis, and fifty-eight studies were selected for our meta-analysis. A pooled sensitivity of 0.92 (95% CI 0.90–0.94) and a pooled specificity of 0.94 (95% CI 0.92–0.95) were achieved. Pooled PPV was 0.84 (95% CI 0.78–0.89) and pooled NPV was 0.97 (95% CI 0.96–0.98). A bivariate model showed a pooled AUC of 0.96 (95% CI 0.95–0.97). Conclusions: This meta-analysis demonstrates that DL performs well in detecting ICH from NCCTs, highlighting a promising potential for the use of AI tools in various practice settings. More prospective studies are needed to confirm the potential clinical benefit of implementing DL-based tools and reveal the limitations of such tools for automated ICH detection and their impact on clinical workflow and outcomes of patients. Full article
(This article belongs to the Special Issue Neurocritical Care: Clinical Advances and Practice Updates)
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