Ethical Considerations in Neuroprognostication Following Acute Brain Injury

Lissak, India A.; Edlow, Brian L.; Rosenthal, Eric; Young, Michael J.

doi:10.1055/s-0043-1775597

Cited by 4 publications

(1 citation statement)

References 122 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the ethical challenges involved in the personalized prognostication of life-threatening conditions like SAH, particularly in terms of interpreting and conveying the inherent uncertainty to those making decisions on behalf of patients, must be considered. In this scenario, the advent of artificial-intelligence-assisted prognostication calls for a contemporary and enduring framework [38]. Such a framework should ensure that physicians, patients and their families are provided with reassurance amidst the uncertainties surrounding the unfathomable question of life and death in critically ill patients.…”

Section: Discussionmentioning

confidence: 99%

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

García-García,

Cepeda,

Müller

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

Background: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. Methods: We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model’s architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Results: Images from 219 patients were processed; 175 for training and validation and 44 for the model’s evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). Conclusion: Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.

show abstract

Section: Discussionmentioning

confidence: 99%

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

García-García,

Cepeda,

Müller

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

show abstract

Equity in Clinical Care and Research Involving Persons with Disorders of Consciousness

Rubin,

Lewis,

Creutzfeldt

et al. 2024

Neurocrit Care

View full text Add to dashboard Cite

Limitation of life sustaining therapy in disorders of consciousness: ethics and practice

Lissak,

Young

2024

Brain

View full text Add to dashboard Cite

Clinical conversations surrounding the continuation or limitation of life-sustaining treatments (LLST) are both challenging and tragically necessary for patients with Disorders of Consciousness (DoC) following severe brain injury. Divergent cultural, philosophical, and religious perspectives contribute to vast heterogeneity in clinical approaches to LLST – as reflected in regional differences and inter-clinician variability. Here we provide an ethical analysis of factors that inform LLST decisions among patients with DoC. We begin by introducing the clinical and ethical challenge and clarifying the distinction between withdrawing and withholding life-sustaining therapy. We then describe relevant factors that influence LLST decision-making including diagnostic and prognostic uncertainty, perception of pain, defining a “good” outcome, and the role of clinicians. In concluding sections, we explore global variation in LLST practices as they pertain to patients with DoC and examine the impact of religious perspectives on approaches to LLST. Understanding and respecting the cultural and religious perspectives of patients and surrogates is essential for protecting patient autonomy and advancing goal-concordant care during critical moments of medical decision making.

show abstract

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

Cited by 4 publications

References 122 publications

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan

Equity in Clinical Care and Research Involving Persons with Disorders of Consciousness

Limitation of life sustaining therapy in disorders of consciousness: ethics and practice

Contact Info

Product

Resources

About