Development and Internal Validation of the ARISE Prediction Models for Rebleeding After Aneurysmal Subarachnoid Hemorrhage

Rebleeding following aneurysmal subarachnoid hemorrhage before 'endovascular first' treatment: a retrospective case-control study of published scoring systems

BACKGROUND

Pre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage (aSAH) affects up to 7.2% of patients even with ultra-early treatment within 24 hours. We retrospectively compared the utility of three published re-bleed prediction models and individual predictors between cases who re-bled matched to controls using size and parent vessel location from a cohort of patients treated in an ultra-early, 'endovascular first' manner.

METHODS

On retrospective analysis of our 9-year cohort of 707 patients suffering 710 episodes of aSAH, there were 53 episodes of pre-treatment re-bleeding (7.5%). Forty-seven cases who had a single culprit aneurysm were matched to 141 controls. Demographic, clinical and radiological data were extracted and predictive scores calculated. Univariate, multivariate, area under the receiver operator characteristic curve (AUROCC) and Kaplan-Meier (KM) survival curve analyses were performed.

RESULTS

The majority of patients (84%) were treated using endovascular techniques at a median 14.5 hours post-diagnosis. On AUROCC analysis the score of Liu et al. had minimal utility (C-statistic 0.553, 95% confidence interval (CI) 0.463 to 0.643) while the risk score of Oppong et al. (C-statistic 0.645 95% CI 0.558 to 0.732) and the ARISE-extended score of van Lieshout et al. (C-statistic 0.53 95% CI 0.562 to 0.744) had moderate utility. On multivariate modeling, the World Federation of Neurosurgical Societies (WFNS) grade was the most parsimonious predictor of re-bleeding (C-statistic 0.740, 95% CI 0.664 to 0.816).

CONCLUSIONS

For aSAH patients treated in an ultra-early timeframe matched on size and parent vessel location, WFNS grade was superior to three published models for re-bleed prediction. Future re-bleed prediction models should incorporate the WFNS grade.

Monitoring of Perioperative Microcirculation Dysfunction by Near-Infrared Spectroscopy for Neurological Deterioration and Prognosis of Aneurysmal Subarachnoid Hemorrhage: An Observational, Longitudinal Cohort Study

INTRODUCTION

No evidence has established a direct causal relationship between early microcirculation disturbance after aneurysmal subarachnoid hemorrhage (aSAH) and neurological function prognosis, which is the key pathophysiological mechanism of early brain injury (EBI) in patients with aSAH.

METHODS

A total of 252 patients with aSAH were enrolled in the Neurosurgical Intensive Care Unit of Southwest Hospital between January 2020 and December 2022 and divided into the no neurological deterioration, early neurological deterioration, and delayed neurological deterioration groups. Indicators of microcirculation disorders in EBI included regional cerebral oxygen saturation (rSO2) measured by near-infrared spectroscopy (NIRS), brain oxygen monitoring, and other clinical parameters for evaluating neurological function and determining the prognosis of patients with aSAH.

RESULTS

Our data suggest that the rSO2 is generally lower in patients who develop neurological deterioration than in those who do not and that there is at least one time point in the population of patients who develop neurological deterioration where left and right cerebral hemisphere differences can be significantly monitored by NIRS. An unordered multiple-classification logistic regression model was constructed, and the results revealed that multiple factors were effective predictors of early neurological deterioration: reoperation, history of brain surgery, World Federation of Neurosurgical Societies (WFNS) grade 4-5, Fisher grade 3-4, SAFIRE grade 3-5, abnormal serum sodium and potassium levels, and reduced rSO2 during the perioperative period. However, for delayed neurological deterioration in patients with aSAH, only a history of brain surgery and perioperative RBC count were predictive indicators.

CONCLUSIONS

The rSO2 concentration in patients with neurological deterioration is generally lower than that in patients without neurological deterioration, and at least one time point in the population with neurological deterioration can be significantly monitored via NIRS. However, further studies are needed to determine the role of microcirculation and other predictive factors in the neurocritical management of EBI after aSAH, as these factors can reduce the incidence of adverse outcomes and mortality during hospitalization.

Pre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage: A systematic review of published prediction models with risk of bias and clinical applicability assessment

BACKGROUND

Pre-treatment rebleeding following aneurysmal subarachnoid hemorrhage (aSAH) increases the risk of death and a poor neurological outcome. Current guidelines recommend aneurysm treatment "as early as feasible after presentation, preferably within 24 h of onset" to mitigate this risk, a practice termed ultra-early treatment. However, ongoing debate regarding whether ultra-early treatment is independently associated with reduced re-bleeding risk, together with the recognition that re-bleeding occurs even in centres practicing ultra-early treatment due to the presence of other risk-factors has resulted in a renewed need for patient-specific re-bleed risk prediction. Here, we systematically review models which seek to provide patient specific predictions of pre-treatment rebleeding risk.

METHODS

Following registration on the International prospective register of systematic reviews (PROSPERO) CRD 42023421235; Ovid Medline (Pubmed), Embase and Googlescholar were searched for English language studies between 1st May 2002 and 1st June 2023 describing pre-treatment rebleed prediction models following aSAH in adults ≥18 years. Of 763 unique records, 17 full texts were scrutinised with 5 publications describing 4 models reviewed. We used the semi-automated template of Fernandez-Felix et al. incorporating the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for data extraction, risk of bias and clinical applicability assessment. To further standardize risk of bias and clinical applicability assessment, we also used the published explanatory notes for the PROBAST tool and compared the aneurysm treatment practices each prediction model's formulation cohort experienced to a prespecified benchmark representative of contemporary aneurysm treatment practices as outlined in recent evidence-based guidelines and published practice pattern reports from four developed countries.

RESULTS

Reported model discriminative performance varied between 0.77 and 0.939, however, no single model demonstrated a consistently low risk of bias and low concern for clinical applicability in all domains. Only the score of Darkwah Oppong et al. was formulated using a patient cohort in which the majority of patients were managed in accordance with contemporary, evidence-based aneurysm treatment practices defined by ultra-early and predominantly endovascular treatment. However, this model did not undergo calibration or clinical utility analysis and when applied to an external cohort, its discriminative performance was substantially lower that reported at formulation.

CONCLUSIONS

No existing prediction model can be recommended for clinical use in centers practicing contemporary, evidence-based aneurysm treatment. There is a pressing need for improved prediction models to estimate and minimize pre-treatment re-bleeding risk.

Initial Temporal Muscle Thickness and Area: Poor Predictors of Neurological Outcome in Aneurysmal Subarachnoid Hemorrhage in a Central European Patient Cohort

The temporalis muscle area (TMA) has been proclaimed as a surrogate parameter for estimating skeletal muscle mass. Pilot studies in Asian populations suggested temporal muscle thickness (TMT) and TMA as prognostic factors for neurological outcomes in aneurysmal subarachnoid hemorrhage (aSAH) patients. This study aimed to validate these findings in a larger European patient cohort. We retrospectively analyzed age, sex, aneurysm location, treatment, World Federation of Neurosurgical Societies (WFNS) grade, Fisher score, and modified Rankin Score (mRS) at six months in patients with aSAH. TMT and TMA measurements were obtained from initial native CT scans. Logistic regression with the dichotomized six-month mRS as the outcome incorporating TMT, weighted average of TMT, or TMA as predictors was performed. Of the included 478 patients, 66% were female, the mean age was 56, and 48% of patients had an mRS of three to six after six months. The mean TMT at the level of the Sylvian fissure was 5.9 (±1.7) mm in males and 4.8 (±1.8) mm in females. The mean TMA was 234.5 (±107.9) mm2 in females and 380 (±134.1) mm2 in males. WFNS grade (p < 0.001), Fisher score (p < 0.001), and age (p < 0.05) correlated significantly with the mRS after six months. No correlation was found between mRS after six months and the TMT at the Sylvian fissure (p = 0.3), the weighted average of TMT (p = 0.1), or the TMA (p = 0.1). In this central European patient cohort of 478 individuals, no significant associations were found between TMT/TMA and neurological outcomes following aSAH. Further prospective studies in diverse patient populations are necessary to determine the prognostic value of TMA and TMT in aSAH patients.

Evolution of Making Clinical Predictions in Neurosurgery

Prediction of clinical outcomes is an essential task for every physician. Physicians may base their clinical prediction of an individual patient on their intuition and on scientific material such as studies presenting population risks and studies reporting on risk factors (prognostic factors). A relatively new and more informative approach for making clinical predictions relies on the use of statistical models that simultaneously consider multiple predictors that provide an estimate of the patient's absolute risk of an outcome. There is a growing body of literature in the neurosurgical field reporting on clinical prediction models. These tools have high potential in supporting (not replacing) neurosurgeons with their prediction of a patient's outcome. If used sensibly, these tools pave the way for more informed decision-making with or for individual patients. Patients and their significant others want to know their risk of the anticipated outcome, how it is derived, and the uncertainty associated with it. Learning from these prediction models and communicating the output to others has become an increasingly important skill neurosurgeons have to master. This article describes the evolution of making clinical predictions in neurosurgery, synopsizes key phases for the generation of a useful clinical prediction model, and addresses some considerations when deploying and communicating the results of a prediction model. The paper is illustrated with multiple examples from the neurosurgical literature, including predicting arachnoid cyst rupture, predicting rebleeding in patients suffering from aneurysmal subarachnoid hemorrhage, and predicting survival in glioblastoma patients.