Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage

Lim, Mervyn Jun Rui; Quek, Raphael Hao Chong; Ng, Kai Jie; Loh, Ne-Hooi Will; Lwin, Sein; Teo, Kejia; Nga, Vincent Diong Weng; Yeo, Tseng Tsai; Motani, Mehul

doi:10.1016/j.jstrokecerebrovasdis.2021.106234

Cited by 7 publications

(7 citation statements)

References 64 publications

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“…Here, as in many other areas, deep learning can help not only in the pure detection of bleeding but also in the immediate assignment of a possible cause of bleeding( 17 – 23 ).…”

Section: Discussionmentioning

confidence: 99%

CT and DSA for evaluation of spontaneous intracerebral lobar bleedings

et al. 2022

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PurposeThis study retrospectively examined the extent to which computed tomography angiography (CTA) and digital subtraction angiography (DSA) can help identify the cause of lobar intracerebral bleeding.Materials and methodsIn the period from 2002 to 2020, data from patients who were >18 years at a university and an academic teaching hospital with lobar intracerebral bleeding were evaluated retrospectively. The CTA DSA data were reviewed separately by two neuroradiologists, and differences in opinion were resolved by consensus after discussion. A positive finding was defined as an underlying vascular etiology of lobar bleeding.ResultsThe data of 412 patients were retrospectively investigated. DSA detected a macrovascular cause of bleeding in 125/412 patients (33%). In total, sixty patients had AVMs (15%), 30 patients with aneurysms (7%), 12 patients with vasculitis (3%), and 23 patients with dural fistulas (6%). The sensitivity, specificity, positive and negative predictive values, and accuracy of CTA compared with DSA were 93, 97, 100, and 97%. There were false-negative CTA readings for two AVMs and one dural fistula.ConclusionThe DSA is still the gold standard diagnostic modality for detecting macrovascular causes of ICH; however, most patients with lobar ICH can be investigated first with CTA, and the cause of bleeding can be found. Our results showed higher sensitivity and specificity than those of other CTA studies.

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“…Here, as in many other areas, deep learning can help not only in the pure detection of bleeding but also in the immediate assignment of a possible cause of bleeding( 17 – 23 ).…”

Section: Discussionmentioning

confidence: 99%

CT and DSA for evaluation of spontaneous intracerebral lobar bleedings

et al. 2022

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“…135,136 For assessing longterm functionality and mortality, a variety of supervised ML algorithms have demonstrated robust performance utilizing a mix of admission clinical and radiographic data, often outstripping conventional models in predictive accuracy. 133,[137][138][139][140] Moreover, deep neural networks have been successfully applied to predict outcomes in ICH patients postsurgical hematoma evacuation, incorporating demographics, laboratory results, and imaging features, and achieving higher accuracy (AUC: 0.88 vs. 0.69-0.80) with smaller patient samples. 141 In the wake of findings from the Early Minimally Invasive Removal of Intracerebral Hemorrhage (ENRICH) trial, which indicate that early minimally invasive hematoma removal may enhance outcomes, the precision of ML predictive models is increasingly vital.…”

Section: Intracerebral Hemorrhagementioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

Vitt,

Mainali

2024

Semin Neurol

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The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the “black box” nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

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“…Despite these barriers in identifying “reliable” predictors of ICH outcome as the word is strictly defined in this guideline, a number of different perspectives have nevertheless been offered in the literature with regards to research that might improve a multimodal approach to ICH prognostication [ 151 ], including: Additional external validation of existing major clinical grading scales [ 131 ], in particular those that have attempted to adjust for the self-fulfilling prophecy [ 150 ]; Comparisons of the accuracy of existing clinical grading scales against subjective clinician judgment [ 148 ]; Incorporation of frailty assessment [ 152 ] and/or newer imaging, fluid, or electrophysiology biomarkers [ 138 ] into novel grading scales; Incorporation of patient-reported outcome measures into novel grading scales [ 153 ]; Additional research regarding the optimal timing of prognostic assessment and the quality of post-acute rehabilitation services as a key predictor of interest [ 154 ]; Application of machine learning techniques for defining thresholds of interest for key clinical variables [ 155 ] and developing new predictive models [ 156 , 157 ]; Development of new clinical scales based on cohort of patients undergoing newer techniques for ICH evacuation [ 158 ]. …”

Section: Future Directionsmentioning

confidence: 99%

Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage

Hwang,

Kim,

Muehlschlegel

et al. 2023

Neurocrit Care

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Background The objective of this document is to provide recommendations on the formal reliability of major clinical predictors often associated with intracerebral hemorrhage (ICH) neuroprognostication. Methods A narrative systematic review was completed using the Grading of Recommendations Assessment, Development, and Evaluation methodology and the Population, Intervention, Comparator, Outcome, Timing, Setting questions. Predictors, which included both individual clinical variables and prediction models, were selected based on clinical relevance and attention in the literature. Following construction of the evidence profile and summary of findings, recommendations were based on Grading of Recommendations Assessment, Development, and Evaluation criteria. Good practice statements addressed essential principles of neuroprognostication that could not be framed in the Population, Intervention, Comparator, Outcome, Timing, Setting format. Results Six candidate clinical variables and two clinical grading scales (the original ICH score and maximally treated ICH score) were selected for recommendation creation. A total of 347 articles out of 10,751 articles screened met our eligibility criteria. Consensus statements of good practice included deferring neuroprognostication—aside from the most clinically devastated patients—for at least the first 48–72 h of intensive care unit admission; understanding what outcomes would have been most valued by the patient; and counseling of patients and surrogates whose ultimate neurological recovery may occur over a variable period of time. Although many clinical variables and grading scales are associated with ICH poor outcome, no clinical variable alone or sole clinical grading scale was suggested by the panel as currently being reliable by itself for use in counseling patients with ICH and their surrogates, regarding functional outcome at 3 months and beyond or 30-day mortality. Conclusions These guidelines provide recommendations on the formal reliability of predictors of poor outcome in the context of counseling patients with ICH and surrogates and suggest broad principles of neuroprognostication. Clinicians formulating their judgments of prognosis for patients with ICH should avoid anchoring bias based solely on any one clinical variable or published clinical grading scale.

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Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage

Cited by 7 publications

References 64 publications

CT and DSA for evaluation of spontaneous intracerebral lobar bleedings

CT and DSA for evaluation of spontaneous intracerebral lobar bleedings

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage

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