Validation and Optimization of Barrow Neurological Institute Score in Prediction of Adverse Events and Functional Outcome After Subarachnoid Hemorrhage-Creation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score

A Mediation Analysis of the Association Between Systemic Inflammation Response Index, in-Hospital Complications, and Poor Long-Term Functional Outcomes in Patients with Aneurysmal Subarachnoid Hemorrhage: Insights from a Large Prospective Cohort Study

Purpose

Early systemic inflammatory changes are increasingly recognized as factors influencing outcomes after aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammation response index (SIRI), an inflammation biomarker, was thought to be associated with adverse outcomes in many other diseases. However, in aSAH, research on SIRI remains limited. Thus, our objective was to investigate the association between SIRI and poor long-term functional outcomes while evaluating the mediating role of in-hospital complications in this association.

Patients and Methods

SIRI was defined as neutrophil count × monocyte count/lymphocyte count. Patients were categorized according to SIRI quartiles. Stabilized inverse probability of treatment weighting (sIPTW) was utilized to minimize group differences. The association between SIRI and in-hospital complications as well as poor 90-day functional outcomes (mRS 3-6) was estimated by multivariable logistic regression analyses. Mediation analysis was performed to investigate the relationship between SIRI and poor functional outcomes mediated by in-hospital complications.

Results

A total of 650 patients were prospectively included. After sIPTW, compared to the lowest quartile, an elevated SIRI was associated with delayed cerebral ischemia (DCI) (OR 2.12, 95% CI 1.20-3.74), post-operative pneumonia (POP) (OR 2.16, 95% CI 1.29-3.62) and poor 90-day functional outcomes (OR 3.03, 95% CI 1.55-5.91). In-hospital complications including DCI (mediation proportion, 18.18% before sIPTW and 20.0% after sIPTW) and POP (mediation proportion, 18.18% before sIPTW and 26.7% after sIPTW) partially mediated the association between SIRI and poor 90-day functional outcomes. Mediation analysis yielded comparable results in subgroups stratified by age and sex.

Conclusion

In this study, SIRI was associated with poor long-term functional outcomes in aSAH, which was partially mediated by DCI and POP with a mediation proportion exceeding 18%. Our findings might underscore the potential utility of SIRI in prompting physicians to address systemic inflammatory status timely to prevent in-hospital complications, including DCI and POP, and ultimately improve long-term functional outcomes.

Construction and verification of risk predicting models to evaluate the possibility of hydrocephalus following aneurysmal subarachnoid hemorrhage

BACKGROUND

Hydrocephalus following a ruptured aneurysm portends a poor prognosis. The authors aimed to establish a nomogram to predict the risk of hydrocephalus after aneurysmal subarachnoid hemorrhage (aSAH).

METHODS

A total of 421 patients with aSAH who were diagnosed by digital subtraction angiography in The General Hospital of Northern Theater Command center from January 2020 to June 2021 were screened to establish the training cohort. An additional 135 patients who enrolled between July 2021 and May 2022 were used for the validation cohort. Variate difference analysis and stepwise logistic regression (model A) and univariate and multivariate logistic regressions (model B) were respectively used to construct two models. Then, the net reclassification improvement (NRI), integrated discrimination improvement (IDI), and receiver operating characteristic (ROC) curve were used to compare the predictive abilities of the two models. Finally, two nomograms were constructed and externally validated.

RESULTS

After screening, 556 patients were included. The area under the ROC curve of models A and B in the training cohort were respectively 0.884 (95 % confidence interval [CI]: 0.847-0.921) and 0.834 (95 % CI: 0.787-0.881). The prediction ability of the model A was superior to model B (NRI > 0, IDI > 0, p < 0.05). The C-index of models A and B was 0.8835 and 0.8392, respectively. Regarding clinical usefulness, the two models offered a net benefit with a threshold probability of between 0.12 and 1 in the decision curve analysis, suggesting that the two models can accurately predict hydrocephalus events.

CONCLUSIONS

Both models have good prediction accuracy. Compared with model B, model A has better discrimination and calibration. Further, the easy-to-use nomogram can help neurosurgeons to make rapid clinical decisions and apply early treatment measures in high-risk groups, which ultimately benefits patients.

Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention

Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings.

The Role of Early Serum Biomarkers and Clinical Rating Scales in the Prediction of Delayed Cerebral Ischaemia and Short-Term Outcome after Aneurysmal Subarachnoid Haemorrhage: Single Centre Experience

Considering the variety of complications that arise after aneurysmal subarachnoid haemorrhage (aSAH) and the complex pathomechanism of delayed cerebral ischaemia (DCI), the task of predicting the outcome assumes a profound complexity. Therefore, there is a need to develop early predictive and decision-making models. This study explores the effect of serum biomarkers and clinical scales on patients' outcomes and their interrelationship with DCI and systemic complications in aSAH. This was a retrospective analysis including aSAH patients admitted to the Wroclaw University Hospital (Wrocław, Poland) from 2011 to 2020. A good outcome was defined as a modified Rankin Scale (mRS) score of 0-2. The prediction of the development of DCI and poor outcome was conducted using logistic regression as a standard model (SM) and random forest as a machine learning method (ML). A cohort of 174 aSAH patients were included in the analysis. DCI was diagnosed in 79 (45%) patients. Significant differences between patients with poor vs. good outcome were determined from their levels of albumin (31 ± 7 vs. 35 ± 5 (g/L); p < 0.001), D-dimer (3.0 ± 4.5 vs. 1.5 ± 2.8 (ng/mL); p < 0.001), procalcitonin (0.2 ± 0.4 vs. 0.1 ± 0.1 (ng/mL); p < 0.001), and glucose (169 ± 69 vs. 137 ± 48 (nmol/L); p < 0.001). SM for DCI prediction included the Apache II scale (odds ratio [OD] 1.05; 95% confidence interval [CI] 1.00-1.09) and albumin level (OD 0.88; CI 0.82-0.95). ML demonstrated that low albumin level, high Apache II scale, increased D-dimer and procalcitonin levels had the highest predictive values for DCI. The integration of clinical parameters and scales with a panel of biomarkers may effectively facilitate the stratification of aSAH patients, identifying those at high risk of secondary complications and poor outcome.

Pre- and Post-Operative Online Prediction of Outcome in Patients Undergoing Endovascular Coiling after Aneurysmal Subarachnoid Hemorrhage: Visual and Dynamic Nomograms

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) causes long-term functional dependence and death. Early prediction of functional outcomes in aSAH patients with appropriate intervention strategies could lower the risk of poor prognosis. Therefore, we aimed to develop pre- and post-operative dynamic visualization nomograms to predict the 1-year functional outcomes of aSAH patients undergoing coil embolization.

METHODS

Data were obtained from 400 aSAH patients undergoing endovascular coiling admitted to the People's Hospital of Hunan Province in China (2015-2019). The key indicator was the modified Rankin Score (mRS), with 3-6 representing poor functional outcomes. Multivariate logistic regression (MLR)-based visual nomograms were developed to analyze baseline characteristics and post-operative complications. The evaluation of nomogram performance included discrimination (measured by C statistic), calibration (measured by the Hosmer-Lemeshow test and calibration curves), and clinical usefulness (measured by decision curve analysis).

RESULTS

Fifty-nine aSAH patients (14.8%) had poor outcomes. Both nomograms showed good discrimination, and the post-operative nomogram demonstrated superior discrimination to the pre-operative nomogram with a C statistic of 0.895 (95% CI: 0.844-0.945) vs. 0.801 (95% CI: 0.733-0.870). Each was well calibrated with a Hosmer-Lemeshow p-value of 0.498 vs. 0.276. Moreover, decision curve analysis showed that both nomograms were clinically useful, and the post-operative nomogram generated more net benefit than the pre-operative nomogram. Web-based online calculators have been developed to greatly improve the efficiency of clinical applications.

CONCLUSIONS

Pre- and post-operative dynamic nomograms could support pre-operative treatment decisions and post-operative management in aSAH patients, respectively. Moreover, this study indicates that integrating post-operative variables into the nomogram enhanced prediction accuracy for the poor outcome of aSAH patients.

External Validation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score for Prediction of Functional Outcome After Subarachnoid Hemorrhage

BACKGROUND

The Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus (HATCH) Score has previously shown to predict functional outcome in aneurysmal subarachnoid hemorrhage (aSAH).

OBJECTIVE

To validate the HATCH score.

METHODS

This is a pooled cohort study including prospective collected data on 761 patients with aSAH from 4 different hospitals. The HATCH score for prediction of functional outcome was validated using calibration and discrimination analysis (area under the curve). HATCH score model performance was compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score.

RESULTS

At the follow-up of at least 6 months, favorable (Glasgow Outcome Score 4-5) and unfavorable functional outcomes (Glasgow Outcome Score 1-3) were observed in 512 (73%) and 189 (27%) patients, respectively. A higher HATCH score was associated with an increased risk of unfavorable outcome with a score of 1 showing a risk of 1.3% and a score of 12 yielding a risk of 67%. External validation showed a calibration intercept of -0.07 and slope of 0.60 with a Brier score of 0.157 indicating good model calibration and accuracy. With an area under the curve of 0.81 (95% CI 0.77-0.84), the HATCH score demonstrated superior discriminative ability to detect favorable outcome at follow-up compared with the World Federation of Neurosurgical Societies and Barrow Neurological Institute score with 0.72 (95% CI 0.67-0.75) and 0.63 (95% CI 0.59-0.68), respectively.

CONCLUSION

This multicenter external validation analysis confirms the HATCH score to be a strong independent predictor for functional outcome. Its incorporation into daily practice may be of benefit for goal-directed patient care in aSAH.

Preoperative predictors of poor outcomes in Thai patients with aneurysmal subarachnoid hemorrhage

Objective

A scoring system for aneurysmal subarachnoid hemorrhage (aSAH) is useful for guiding treatment decisions, especially in urgent-care limited settings. This study developed a simple algorithm of clinical conditions and grading to predict outcomes in patients treated by clipping or coiling.

Methods

Data on patients with aSAH hospitalized in a university’s neurovascular center in Thailand from 2013 to 2018 were obtained for chart review. Factors associated with poor outcomes evaluated at one year were identified using a stepwise logistic regression model. For each patient, the rounded regression coefficients of independent risk factors were linearly combined into a total score, which was assessed for its performance in predicting outcomes using receiver operating characteristic analysis. An appropriate cutoff point of the scores for poor outcomes was based on Youden’s criteria, which maximized the summation between sensitivity or true positive rate and the specificity or true negative rate.

Results

Patients (n, 121) with poor outcomes (modified Rankin Scale, mRS score, 4–6) had a significantly higher proportion of old age, underlying hypertension, diabetes and chronic kidney disease, high clinical severity grading, preoperative rebleeding, and hydrocephalus than those (n, 336) with good outcomes (mRS score, 0–3). Six variables, including age >70 years, diabetes mellitus, World Federation of Neurosurgical Societies (WFNS) scaling of IV-V, modified Fisher grading of 3–4, rebleeding, and hydrocephalus, were identified as independent risk factors and were assigned a score weight of 2, 1, 2, 1, 3 and 1, respectively. Among the total possible scores ranging from 0–10, the cut point at score 3 yielded the maximum Youden’s index (0.527), which resulted in a sensitivity of 77.7% and specificity of 75.0%.

Conclusion

A simple 0–10 scoring system on six risk factors for poor outcomes was validated for aSAH and should be advocated for use in limited resource settings.

Subarachnoid Hemorrhage and Hydrocephalus

BACKGROUND

Hydrocephalus associated with subarachnoid hemorrhage is a common neurosurgical problem, the management of which is tailor-made to the patient. It is usually seen with an aneurysmal bleed and is independent of the primary modality of treatment.

AIM

This study aimed to provide a comprehensive overview of this important association and discuss the various available treatment modalities.

MATERIALS AND METHODS

A detailed review of the literature was done on the risk factors, pathogenesis, and treatment of hydrocephalus in the setting of subarachnoid hemorrhage.

RESULTS

Hydrocephalus occurs in 6% to 67% of subarachnoid hemorrhage (SAH). It may present as acute, subacute, or chronic at the time of presentation. Diagnosis is made with a plain computed tomography scan of the brain, and the treatment is observant, temporary, or permanent cerebrospinal fluid diversion.

CONCLUSION

Hydrocephalus associated with SAH interferes with the outcome of SAH. It prolongs the hospital stay, besides causing additional morbidity. The various risk factors, if present, should warn us to be vigilant, and management is definitely not uniform and is custom made to the patients' needs.

The Role of the Blood Neutrophil-to-Lymphocyte Ratio in Aneurysmal Subarachnoid Hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is an important type of stroke with the highest rates of mortality and disability. Recent evidence indicates that neuroinflammation plays a critical role in both early brain injury and delayed neural deterioration after aSAH, contributing to unfavorable outcomes. The neutrophil-to-lymphocyte ratio (NLR) is a peripheral biomarker that conveys information about the inflammatory burden in terms of both innate and adaptive immunity. This review summarizes relevant studies that associate the NLR with aSAH to evaluate whether the NLR can predict outcomes and serve as an effective biomarker for clinical management. We found that increased NLR is valuable in predicting the clinical outcome of aSAH patients and is related to the risk of complications such as delayed cerebral ischemia (DCI) or rebleeding. Combined with other indicators, the NLR provides improved accuracy for predicting prognosis to stratify patients into different risk categories. The underlying pathophysiology is highlighted to identify new potential targets for neuroprotection and to develop novel therapeutic strategies.

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

Introduction Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes and delayed cerebral ischemia (DCI) are needed to decide the treatment strategy. Automated artificial intelligence (AutoAI) is attractive, but there are few reports on AutoAI-based models for SAH functional outcomes and DCI. We herein made models using an AutoAI framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to other previous statistical prediction scores. Methods We used an open dataset of 298 SAH patients, who were with non-severe neurological grade and treated by coiling. Modified Rankin Scale 0-3 at six months was defined as a favorable functional outcome and DCI occurrence as another outcome. We randomly divided them into a 248-patient training dataset and a 50-patient test dataset. Prediction One made the model using training dataset with 5-fold cross-validation. We evaluated the model using the test dataset and compared the area under the curves (AUCs) of the created models. Those of the modified SAFIRE score and the Fisher computed tomography (CT) scale to predict the outcomes. Results The AUCs of the AutoAI-based models for functional outcome in the training and test dataset were 0.994 and 0.801, and those for the DCI occurrence were 0.969 and 0.650. AUCs for functional outcome calculated using modified SAFIRE score were 0.844 and 0.892. Those for the DCI occurrence calculated using the Fisher CT scale were 0.577 and 0.544. Conclusions We easily and quickly made AutoAI-based prediction models. The models' AUCs were not inferior to the previous prediction models despite the easiness.

Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission

BACKGROUND

Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score.

METHODS

We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0-3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared.

RESULTS

The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900-1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively.

CONCLUSION

We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.