Materiały źródłowe

• #1 Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study | The BMJ

  https://www.bmj.com/content/360/bmj.j5745

  Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). […] The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. […] The externally validated AUC was 0.80-0.81 for the models to predict functional outcome and 0.76-0.78 for the models to predict mortality. […] The web based calculator could facilitate the adoption into clinical practice. […] Our prediction models for outcome in patients with subarachnoid haemorrhage performed robustly in a validation cohort from different geographical regions, time periods, and settings of care. […] The SAHIT prediction models performed well in the pooled validation dataset and the different constituent samples. […] The predictor items in the SAHIT prediction models are readily available at hospital admission, and their prognostic value is well recognised. […] The tool performed satisfactorily in a different set of patients who were treated at different regions and settings of care.

• #2 Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-024-05245-y

  Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies. […] The novel deep learning model demonstrated strong predictive performance in determining functional outcomes in patients with aSAH, making it a valuable tool for guiding early rehabilitation strategies. […] It is essential to accurately predict outcomes in patients with aSAH for guiding personalized care and optimizing resource allocation. […] The primary outcome of this study was the mRS score at the time of hospital discharge. This outcome was selected due to its clinical relevance, as discharge mRS serves a critical indicator for planning post-discharge rehabilitation strategies.

• #3 Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-024-51684-6

  The model proposed in this study can provide dynamic prognosis for spontaneous SAH patients and significant potential benefits in critical care management. […] The prognostication of spontaneous SAH is intricate and multi-factorial. […] Close monitoring of disease progression and prediction of adverse clinical outcomes in the critical care are thus required to identify risks of deterioration and track changes in neurological status, which can evolve rapidly. […] This allows for timely neurological intervention, adjustments in treatment strategies, and proactive preventive measures to optimise critical care management and improve patient outcomes. […] The prognostication of spontaneous SAH can be potentially improved in several aspects. […] Incorporating these intermediate events in prognostication of spontaneous SAH can help improve the prediction accuracy of prognostic models, allowing for a more comprehensive and dynamic approach to prognostication to enhance the management and prognosis of patients suffering spontaneous SAH.

• #4 Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-024-05245-y

  Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies. […] The novel deep learning model demonstrated strong predictive performance in determining functional outcomes in patients with aSAH, making it a valuable tool for guiding early rehabilitation strategies. […] It is essential to accurately predict outcomes in patients with aSAH for guiding personalized care and optimizing resource allocation. […] The primary outcome of this study was the mRS score at the time of hospital discharge. This outcome was selected due to its clinical relevance, as discharge mRS serves a critical indicator for planning post-discharge rehabilitation strategies.

• #5 Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11519170/

  Aneurysmal subarachnoid haemorrhage (aSAH) is a life-threatening event with major complications. […] To determine the most predictive radiological scales in grading subarachnoid or ventricular haemorrhage or both for functional outcome at 3 months in a large aSAH population, we conducted a single-centre retrospective study. […] The Hijdra scale was the best predictor for DCI, with a receiver operating characteristic area under the curve (ROCAUC) of 0.80 (95% confidence interval (CI), 0.740.85). […] Although these results have yet to be prospectively confirmed, our findings suggest that the Hijdra scale may be a good predictor of DCI and could be useful in daily clinical practice. […] Better assessment of subarachnoid haemorrhage patients would allow for better prognostication and management of expectations, as well as referral for appropriate services and helping to appropriate use limited critical care resources.

• #6 Predictive validity of the prognosis on admission aneurysmal subarachnoid haemorrhage scale for the outcome of patients with aneurysmal subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-023-33798-5

  The degree of neurologic impairment and the extent of subarachnoid bleeding at the time of admission are the most important predictors of neurologic complications and outcomes. […] A number of grading systems are used in clinical practice to standardize the classification of patients with SAH based upon the initial evaluation. […] The PAASH scale had a good discriminatory ability for the prognosis of patients with aneurysmal SAH and was slightly preferable to the WFNS scale. […] The aim of this study was to investigate the rate of poor outcomes of patients with aneurysmal SAH, to determine the relationships among the grades on the PAASH, WFNS, and HH scales and the actual outcomes and to compare the prognostic accuracy of these scales. […] The primary outcome of this study was poor neurological function (poor outcome) on day 90th after ictus, which was defined as mRS scores of 4 (moderately severe disability) to 6 (death). […] We also examined the following secondary outcomes: poor outcome on day 30th after ictus, 30- and 90-day mortality rates, and incidence rate of complications. […] In the present study, poor neurological function on day 90th after the ictus served as the primary outcome. […] Therefore, these findings support the superiority of the PAASH scale compared to the WFNS and HH scales in predicting poor outcomes.

• #7 Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11519170/

  Aneurysmal subarachnoid haemorrhage (aSAH) is a life-threatening event with major complications. […] To determine the most predictive radiological scales in grading subarachnoid or ventricular haemorrhage or both for functional outcome at 3 months in a large aSAH population, we conducted a single-centre retrospective study. […] The Hijdra scale was the best predictor for DCI, with a receiver operating characteristic area under the curve (ROCAUC) of 0.80 (95% confidence interval (CI), 0.740.85). […] Although these results have yet to be prospectively confirmed, our findings suggest that the Hijdra scale may be a good predictor of DCI and could be useful in daily clinical practice. […] Better assessment of subarachnoid haemorrhage patients would allow for better prognostication and management of expectations, as well as referral for appropriate services and helping to appropriate use limited critical care resources.

• #8 Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11519170/

  Accurate assessment of the amount of blood in the subarachnoid spaces on computed tomography with the Hijdra scale can better predict the risk of delayed cerebral infarct. […] The Hijdra scale could be a good triage tool for subarachnoid haemorrhage patients. […] The aim of our study is to evaluate the predictive performance of eight radiological scales used in aSAH for the occurrence of DCI, acute hydrocephalus and functional outcome at 3 months to enable better triage of patients and provide them with a care offer adapted to their severity. […] The Hijdra scale was the most effective scale for predicting DCI, with an ideal cut-off of 20/42 and excellent interobserver agreement. […] The Hijdra scale was the only variable with significant prognostic value for the presence of DCI (adjusted odds ratio per unit, 1.18; 95% CI, 1.101.25; p0.001). […] Radiological grading of SAH is useful for predicting DCI risk. Among these scales, the Hijdra scale seems to be the most effective at predicting the occurrence of DCI.

• #9 Predictive validity of the prognosis on admission aneurysmal subarachnoid haemorrhage scale for the outcome of patients with aneurysmal subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-023-33798-5

  The degree of neurologic impairment and the extent of subarachnoid bleeding at the time of admission are the most important predictors of neurologic complications and outcomes. […] A number of grading systems are used in clinical practice to standardize the classification of patients with SAH based upon the initial evaluation. […] The PAASH scale had a good discriminatory ability for the prognosis of patients with aneurysmal SAH and was slightly preferable to the WFNS scale. […] The aim of this study was to investigate the rate of poor outcomes of patients with aneurysmal SAH, to determine the relationships among the grades on the PAASH, WFNS, and HH scales and the actual outcomes and to compare the prognostic accuracy of these scales. […] The primary outcome of this study was poor neurological function (poor outcome) on day 90th after ictus, which was defined as mRS scores of 4 (moderately severe disability) to 6 (death). […] We also examined the following secondary outcomes: poor outcome on day 30th after ictus, 30- and 90-day mortality rates, and incidence rate of complications. […] In the present study, poor neurological function on day 90th after the ictus served as the primary outcome. […] Therefore, these findings support the superiority of the PAASH scale compared to the WFNS and HH scales in predicting poor outcomes.

• #10 Comparison of scales for the evaluation of aneurysmal subarachnoid haemorrhage: a retrospective cohort study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11519170/

  Accurate assessment of the amount of blood in the subarachnoid spaces on computed tomography with the Hijdra scale can better predict the risk of delayed cerebral infarct. […] The Hijdra scale could be a good triage tool for subarachnoid haemorrhage patients. […] The aim of our study is to evaluate the predictive performance of eight radiological scales used in aSAH for the occurrence of DCI, acute hydrocephalus and functional outcome at 3 months to enable better triage of patients and provide them with a care offer adapted to their severity. […] The Hijdra scale was the most effective scale for predicting DCI, with an ideal cut-off of 20/42 and excellent interobserver agreement. […] The Hijdra scale was the only variable with significant prognostic value for the presence of DCI (adjusted odds ratio per unit, 1.18; 95% CI, 1.101.25; p0.001). […] Radiological grading of SAH is useful for predicting DCI risk. Among these scales, the Hijdra scale seems to be the most effective at predicting the occurrence of DCI.

• #11 Predictive validity of the prognosis on admission aneurysmal subarachnoid haemorrhage scale for the outcome of patients with aneurysmal subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-023-33798-5

  The degree of neurologic impairment and the extent of subarachnoid bleeding at the time of admission are the most important predictors of neurologic complications and outcomes. […] A number of grading systems are used in clinical practice to standardize the classification of patients with SAH based upon the initial evaluation. […] The PAASH scale had a good discriminatory ability for the prognosis of patients with aneurysmal SAH and was slightly preferable to the WFNS scale. […] The aim of this study was to investigate the rate of poor outcomes of patients with aneurysmal SAH, to determine the relationships among the grades on the PAASH, WFNS, and HH scales and the actual outcomes and to compare the prognostic accuracy of these scales. […] The primary outcome of this study was poor neurological function (poor outcome) on day 90th after ictus, which was defined as mRS scores of 4 (moderately severe disability) to 6 (death). […] We also examined the following secondary outcomes: poor outcome on day 30th after ictus, 30- and 90-day mortality rates, and incidence rate of complications. […] In the present study, poor neurological function on day 90th after the ictus served as the primary outcome. […] Therefore, these findings support the superiority of the PAASH scale compared to the WFNS and HH scales in predicting poor outcomes.

• #12 Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-024-05245-y

  This study developed a machine learning model that can accurately predict functional outcomes in patients with aSAH. The model demonstrated excellent performance using key clinical features. Our findings emphasize the importance of early identification of predictors such as age, WFNS grade, and higher brain dysfunction (including aphasia) for guiding rehabilitation strategies.

• #13 Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage | Aging

  https://www.aging-us.com/full/205621

  Objective: Accurate prognostic prediction in patients with high-grade aneurysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). […] The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0.929). […] Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis.

• #14 Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage | Aging

  https://www.aging-us.com/full/205621

  This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. […] The feature importance analysis revealed WFNS grade, age, mFS, and treatment of coiling embolization as predominant predictors for poor prognosis in the RF model. Our findings indicate that higher WFNS grade, higher mFS grade, and advanced age are distinct predictors for poor prognosis in high-grade SAH patients; while the treatment modality of coiling embolization serves as a protective factor. […] In summary, our study established four ML models (LR, SVM, RF, XGBoost) and selected the RF model to conduct a comprehensive SHAP analysis based on its superior predictive performance. The SHAP analysis revealed the significant contributions of clinical features in predicting long-term prognosis in high-grade aSAH. Elevated WFNS grades and mFS, along with advanced age, were associated with unfavorable outcomes, indicating aggravated neurological impairment and bleeding severity. Conversely, the strategic implementation of endovascular coiling emerges as a promising method to improve patient prognosis by preventing rebleeding and mitigating associated complication. Incorporating these insights into clinical decision-making holds great potential to guide therapeutic strategies and optimize patient neurocritical care.

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

  https://www.mdpi.com/2076-3425/14/1/10

  Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. 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%). 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. The accurate prediction of outcomes in patients with moderate to poor grades remains a challenge. The highest grades on the WFNS, HH and mF scales demonstrated a strong association with mortality. The results demonstrated that a CNN predictive algorithm exclusively based on the initial CT outperformed a combination of images and clinical data. AI predictive models are a promising tool that could significantly improve the understanding of, and decision-making process in, complex pathologies like SAH. However, further optimization of these models through the inclusion of more data and patients is necessary to enhance their performance on complex tasks that are beyond the potential of conventional clinical knowledge.

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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10812955/

  Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. 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%). Accurate predictions in the medical field often require a large amount of data from large cohorts of patients. The accurate prediction of outcomes in patients with moderate to poor grades remains a challenge. The highest grades on the WFNS, HH and mF scales demonstrated a strong association with mortality. The results demonstrated that a CNN predictive algorithm exclusively based on the initial CT outperformed a combination of images and clinical data. Mortality and outcome predictions have classically relied on risk factors and clinical and radiological scales. AI predictive models are a promising tool that could significantly improve the understanding of, and decision-making process in, complex pathologies like SAH. However, further optimization of these models through the inclusion of more data and patients is necessary to enhance their performance on complex tasks that are beyond the potential of conventional clinical knowledge.

• #17 Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-024-51684-6

  Deterioration of neurovascular conditions can be rapid in patients with spontaneous subarachnoid haemorrhage (SAH) and often lead to poor clinical outcomes. […] This study incorporated baseline clinical conditions, repeatedly measured neurological grades and haematological biomarkers for dynamic outcome prediction in patients with spontaneous SAH. […] A dynamic prognostic model predicting outcome of patients was developed based on combination of Cox model and piecewise linear mixed-effect models to incorporate different types of prognostic information. […] Incorporation of neurological intervention as an intermediate event increases the prediction performance compared with baseline joint modelling approach. […] The average AUC of the optimal model proposed in this study is 0.7783 across different starting points of prediction and prediction intervals.

• #18 Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-024-51684-6

  The multivariate NIT joint model, incorporating GCS score, WBC and glucose as dynamic prognostic covariates is the optimal model in this study, increasing the predictive accuracy of outcome from 0.7422, in baseline joint model only considering the neurological grades of patients, to 0.7783. […] It can be used as an accurate and comprehensive clinical tool for dynamic personalised prognosis in patients suffering from spontaneous SAH, potentially benefiting disease progression monitoring, optimising treatment plans for better clinical outcomes.

• #19 Heart Rate Variability for Outcome Prediction in Intracerebral and Subarachnoid Hemorrhage: A Systematic Review

  https://www.mdpi.com/2077-0383/12/13/4355

  This systematic review presents clinical evidence on the association of heart rate variability with outcome prediction in intracerebral and subarachnoid hemorrhages. […] Heart rate variability was consistently associated with poor functional outcome and mortality, while controversial results were found regarding the association between heart rate variability and secondary brain injury and cardiovascular complications. […] The HRV analysis has been tested as a possible tool to assess the prognosis of patients presenting with ICH or SAH, but there is no conclusive evidence on the accuracy and value of this approach in the prediction of functional outcome, cardiovascular complications, secondary brain injury, and mortality of these patients or its association with clinical severity. […] A higher HRV is consistently associated with poor functional outcome both in ICH and SAH patients with an OR predictive value ranging between 1.14 and 1.31.

• #20 Heart Rate Variability for Outcome Prediction in Intracerebral and Subarachnoid Hemorrhage: A Systematic Review

  https://www.mdpi.com/2077-0383/12/13/4355

  A lower HRV was associated with a poor outcome in five studies (four on 266 with SAH and one on 47 patients with ICH). […] In conclusion, the HRV seems to have promising characteristics in the risk stratification of functional outcome, cardiovascular complications, secondary brain injury, and mortality.

• #21

  https://scispace.com/papers/altered-expression-of-microrna-15a-and-kruppel-like-factor-4-1raefkwp8d

  Elevated miR-9 in Cerebrospinal Fluid Is Associated with Poor Functional Outcome After Subarachnoid Hemorrhage. […] MiR-9-3p and miR-5p are elevated in the CSF following SAH and this elevation is associated with a poor functional outcome, which has potential roles in the progression of cerebral injury and could add to early prognostication. […] MicroRNAs as Biomarkers for Predicting Complications following Aneurysmal Subarachnoid Hemorrhage. […] Aneurysmal subarachnoid hemorrhage (aSAH) is a high mortality hemorrhagic stroke that affects nearly 30,000 patients annually in the United States.

• #22 Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-024-04939-7

  Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. […] Aneurysmal subarachnoid hemorrhage (aSAH) remains a serious disease with often poor prognosis even after successful securing of the aneurysm. Patients who survive the initial hemorrhage remain at risk for developing secondary brain injury, such as delayed cerebral ischemia (DCI). DCI is a major cause of death and disability after aSAH. […] MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. […] The promising results of this study warrant further investigation into the cause of the resulting complexity as well as its association with important and potentially preventable complications (i.e. vasospasm and DCI).

• #23

  https://link.springer.com/article/10.1007/s12028-023-01879-y

  Despite intensive research on preventing and treating vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage (aSAH), mortality and morbidity rates remain high. […] This study aimed to identify early clinical and radiological events within 72 h after aSAH to develop a conclusive predictive EBI score for clinical practice. […] The analysis resulted in the Subarachnoid Hemorrhage Associated Early Brain Injury Outcome Prediction score (SHELTER-score), comprising seven clinical and radiological events: age (04 points), World Federation of Neurosurgical Societies (02.5 points), cardiopulmonary resuscitation (CPR) (2 points), mydriasis (12 points), midline shift (0.51 points), early deterioration (1 point), and early ischemic lesion (2 points). […] A SHELTER-score below 5 indicated a favorable outcome (mRS 02), 56.5 predicted a poor outcome (mRS 35), and 7 correlated with death (mRS 6) at 6 months.

• #24

  https://link.springer.com/article/10.1007/s12028-023-01879-y

  The novel SHELTER-score, incorporating seven clinical and radiological features of EBI, demonstrated strong predictive performance in determining clinical outcomes. […] This scoring system serves as a valuable tool for neurointensivists to identify patients with poor outcomes and guide treatment decisions, reflecting the great impact of EBI on the overall outcome of patients with aSAH. […] The SHELTER-score, which includes seven clinical and radiological features of EBI (age, WFNS grade, cardiopulmonary resuscitation, mydriasis, midline shift, early deterioration, and early ischemia), has high sensitivity and specificity for predicting clinical outcome. […] The SHELTER-score is an excellent tool for guiding neurointensivists to identify patients with poor outcomes and aid in treatment decision-making.

• #25 Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study | The BMJ

  https://www.bmj.com/content/360/bmj.j5745

  Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). […] The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. […] The externally validated AUC was 0.80-0.81 for the models to predict functional outcome and 0.76-0.78 for the models to predict mortality. […] The web based calculator could facilitate the adoption into clinical practice. […] Our prediction models for outcome in patients with subarachnoid haemorrhage performed robustly in a validation cohort from different geographical regions, time periods, and settings of care. […] The SAHIT prediction models performed well in the pooled validation dataset and the different constituent samples. […] The predictor items in the SAHIT prediction models are readily available at hospital admission, and their prognostic value is well recognised. […] The tool performed satisfactorily in a different set of patients who were treated at different regions and settings of care.

• #26

  https://journals.lww.com/neurosurgery/fulltext/2021/05000/prediction_models_in_aneurysmal_subarachnoid.34.aspx

  Predicting outcome after aneurysmal subarachnoid hemorrhage (aSAH) is known to be challenging and complex. […] The area under the curve (AUC) of the ffANN showed to be 88%, 85%, and 72% for predicting mortality, an unfavorable mRS, and the occurrence of DCI, respectively. […] The presented ffANN showed equal performance when compared with VASOGRADE and SAHIT scoring systems while using less individual cases.

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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10812955/

  Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. 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%). Accurate predictions in the medical field often require a large amount of data from large cohorts of patients. The accurate prediction of outcomes in patients with moderate to poor grades remains a challenge. The highest grades on the WFNS, HH and mF scales demonstrated a strong association with mortality. The results demonstrated that a CNN predictive algorithm exclusively based on the initial CT outperformed a combination of images and clinical data. Mortality and outcome predictions have classically relied on risk factors and clinical and radiological scales. AI predictive models are a promising tool that could significantly improve the understanding of, and decision-making process in, complex pathologies like SAH. However, further optimization of these models through the inclusion of more data and patients is necessary to enhance their performance on complex tasks that are beyond the potential of conventional clinical knowledge.

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

  https://www.mdpi.com/2076-3425/14/1/10

  Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. 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%). 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. The accurate prediction of outcomes in patients with moderate to poor grades remains a challenge. The highest grades on the WFNS, HH and mF scales demonstrated a strong association with mortality. The results demonstrated that a CNN predictive algorithm exclusively based on the initial CT outperformed a combination of images and clinical data. AI predictive models are a promising tool that could significantly improve the understanding of, and decision-making process in, complex pathologies like SAH. However, further optimization of these models through the inclusion of more data and patients is necessary to enhance their performance on complex tasks that are beyond the potential of conventional clinical knowledge.

• #29

  https://link.springer.com/article/10.1007/s00701-024-05909-2

  Despite aneurysmal subarachnoid haemorrhage (aSAH) patients often experiencing physical and mental disabilities impacting their quality of life (QoL), routine assessment of long-term QoL data and predictive tools are limited. […] This study found no correlation between FRESH scores and validated QoL tools in a European population of aSAH patients. […] The study highlights the complexity of reliable long-term QoL prognostication in aSAH patients and emphasises the need for further prospective research to also focus on QoL as an important outcome parameter. […] No significant correlation could be detected between the actual clinical long-term outcome, as assessed by mRS on average 46 months after aneurysm rupture, and the prognosticated outcome after aSAH as calculated by the FRESH score.

• #30

  https://link.springer.com/article/10.1007/s00701-024-05909-2

  No significant correlation was found between the assessed parameters of cognitive performance and the QoL after an average follow-up of 46 months and the FRESH-cog, respectively, the FRESH-quol score. […] The absence of a correlation between the FRESH scores and the outcome, including QoL, in our patient cohort could potentially be a statistical and/or methodological artefact. However, we consider this possibility unlikely. […] In summary, our study results largely contrast with the existing data, highlighting the complexity of QoL research.

• #31 Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update | Journal of NeuroInterventional Surgery

  https://jnis.bmj.com/cgi/reprint/17/1/21

  A systematic review of clinical prediction models for aneurysmal subarachnoid hemorrhage (aSAH) reported in 2011 noted that clinical prediction models for aSAH were developed using poor methods and were not externally validated. […] Externally validated models for the prediction of functional outcome in aSAH patients have now become available. However, most of them still have a high risk of bias.

• #32

  https://link.springer.com/article/10.1007/s00701-024-05909-2

  Despite aneurysmal subarachnoid haemorrhage (aSAH) patients often experiencing physical and mental disabilities impacting their quality of life (QoL), routine assessment of long-term QoL data and predictive tools are limited. […] This study found no correlation between FRESH scores and validated QoL tools in a European population of aSAH patients. […] The study highlights the complexity of reliable long-term QoL prognostication in aSAH patients and emphasises the need for further prospective research to also focus on QoL as an important outcome parameter. […] No significant correlation could be detected between the actual clinical long-term outcome, as assessed by mRS on average 46 months after aneurysm rupture, and the prognosticated outcome after aSAH as calculated by the FRESH score.

• #33

  https://link.springer.com/article/10.1007/s00701-024-05909-2

  No significant correlation was found between the assessed parameters of cognitive performance and the QoL after an average follow-up of 46 months and the FRESH-cog, respectively, the FRESH-quol score. […] The absence of a correlation between the FRESH scores and the outcome, including QoL, in our patient cohort could potentially be a statistical and/or methodological artefact. However, we consider this possibility unlikely. […] In summary, our study results largely contrast with the existing data, highlighting the complexity of QoL research.

• #34

  https://link.springer.com/article/10.1007/s00701-024-05909-2

  Despite aneurysmal subarachnoid haemorrhage (aSAH) patients often experiencing physical and mental disabilities impacting their quality of life (QoL), routine assessment of long-term QoL data and predictive tools are limited. […] This study found no correlation between FRESH scores and validated QoL tools in a European population of aSAH patients. […] The study highlights the complexity of reliable long-term QoL prognostication in aSAH patients and emphasises the need for further prospective research to also focus on QoL as an important outcome parameter. […] No significant correlation could be detected between the actual clinical long-term outcome, as assessed by mRS on average 46 months after aneurysm rupture, and the prognosticated outcome after aSAH as calculated by the FRESH score.

• #35 Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-024-04939-7

  Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. […] Aneurysmal subarachnoid hemorrhage (aSAH) remains a serious disease with often poor prognosis even after successful securing of the aneurysm. Patients who survive the initial hemorrhage remain at risk for developing secondary brain injury, such as delayed cerebral ischemia (DCI). DCI is a major cause of death and disability after aSAH. […] MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. […] The promising results of this study warrant further investigation into the cause of the resulting complexity as well as its association with important and potentially preventable complications (i.e. vasospasm and DCI).

• #36 Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study | The BMJ

  https://www.bmj.com/content/360/bmj.j5745

  Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). […] The prediction models reliably estimate the outcome of patients who were managed in various settings for ruptured intracranial aneurysms that caused subarachnoid haemorrhage. […] The externally validated AUC was 0.80-0.81 for the models to predict functional outcome and 0.76-0.78 for the models to predict mortality. […] The web based calculator could facilitate the adoption into clinical practice. […] Our prediction models for outcome in patients with subarachnoid haemorrhage performed robustly in a validation cohort from different geographical regions, time periods, and settings of care. […] The SAHIT prediction models performed well in the pooled validation dataset and the different constituent samples. […] The predictor items in the SAHIT prediction models are readily available at hospital admission, and their prognostic value is well recognised. […] The tool performed satisfactorily in a different set of patients who were treated at different regions and settings of care.

• #37 Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage | Scientific Reports

  https://www.nature.com/articles/s41598-024-51684-6

  The model proposed in this study can provide dynamic prognosis for spontaneous SAH patients and significant potential benefits in critical care management. […] The prognostication of spontaneous SAH is intricate and multi-factorial. […] Close monitoring of disease progression and prediction of adverse clinical outcomes in the critical care are thus required to identify risks of deterioration and track changes in neurological status, which can evolve rapidly. […] This allows for timely neurological intervention, adjustments in treatment strategies, and proactive preventive measures to optimise critical care management and improve patient outcomes. […] The prognostication of spontaneous SAH can be potentially improved in several aspects. […] Incorporating these intermediate events in prognostication of spontaneous SAH can help improve the prediction accuracy of prognostic models, allowing for a more comprehensive and dynamic approach to prognostication to enhance the management and prognosis of patients suffering spontaneous SAH.

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