CRP (C-Reactive Protein) in Outcome Prediction After Subarachnoid Hemorrhage and the Role of Machine Learning

Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models

OBJECTIVE

Machine learning and deep learning techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The novel transfer learning approach mitigates the time constraints associated with the traditional techniques and demonstrates a superior performance. This study aims to evaluate the effectiveness of convolutional neural networks (CNNs) and CNN-based transfer learning models in differentiating between aneurysmal SAH and nonaneurysmal SAH.

METHODS

Data from Istanbul Ümraniye Training and Research Hospital, which included 15,600 digital imaging and communications in medicine images from 123 patients with aneurysmal SAH and 7793 images from 80 patients with nonaneurysmal SAH, were used. The study employed 4 models: Inception-V3, EfficientNetB4, single-layer CNN, and three-layer CNN. Transfer learning models were customized by modifying the last 3 layers and using the Adam optimizer. The models were trained on Google Collaboratory and evaluated based on metrics such as F-score, precision, recall, and accuracy.

RESULTS

EfficientNetB4 demonstrated the highest accuracy (99.92%), with a better F-score (99.82%), recall (99.92%), and precision (99.90%) than the other methods. The single- and three-layer CNNs and the transfer learning models produced comparable results. No overfitting was observed, and robust models were developed.

CONCLUSIONS

CNN-based transfer learning models can accurately diagnose the etiology of SAH from computed tomography images and is a valuable tool for clinicians. This approach could reduce the need for invasive procedures such as digital subtraction angiography, leading to more efficient medical resource utilization and improved patient outcomes.

Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV

Background

Postoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and increased medical fee. Early identification of pneumonia and more aggressive treatment can improve patient outcomes. We aimed to develop a model to predict POP in aSAH patients using machine learning (ML) methods.

Methods

This internal cohort study included 706 patients with aSAH undergoing intracranial aneurysm embolization or aneurysm clipping. The cohort was randomly split into a train set (80%) and a testing set (20%). Perioperative information was collected from participants to establish 6 machine learning models for predicting POP after surgical treatment. The area under the receiver operating characteristic curve (AUC), precision-recall curve were used to assess the accuracy, discriminative power, and clinical validity of the predictions. The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Results

In this study, 15.01% of patients in the training set and 12.06% in the testing set with POP after underwent surgery. Multivariate logistic regression analysis showed that mechanical ventilation time (MVT), Glasgow Coma Scale (GCS), Smoking history, albumin level, neutrophil-to-albumin Ratio (NAR), c-reactive protein (CRP)-to-albumin ratio (CAR) were independent predictors of POP. The logistic regression (LR) model presented significantly better predictive performance (AUC: 0.91) than other models and also performed well in the external validation set (AUC: 0.89).

Conclusion

A machine learning model for predicting POP in aSAH patients was successfully developed using a machine learning algorithm based on six perioperative variables, which could guide high-risk POP patients to take appropriate preventive measures.

Association of Performance on Multiple Cognitive Domains with Sarcopenia among Middle-Aged and Older Adults

INTRODUCTION

The relationship between cognitive function and subsequent sarcopenia remains unclear. Therefore, this study aimed to examine the associations of performance on multiple cognitive domains with sarcopenia in the middle-aged and older adults.

METHODS

This longitudinal analysis (wave 2011-2013) included 2,934 participants from the CHARLS study. Sarcopenia was defined by the Asian Sarcopenia Working Group 2019 criteria. Cognitive function was measured by the Chinese version of the Mini-Mental State Examination (MMSE). Three interpretable techniques, namely SHapley Additive exPlanations (SHAP) and two built-in methods (coefficients of logistic regression and Gini importance of random forest), were used to assess the relationship between MMSE, its components (orientation, attention, episodic memory, and visuospatial ability) and sarcopenia. In addition, the association of MMSE score and its components with sarcopenia was further validated using stepwise regression.

RESULTS

All interpretable methods showed that MMSE score was important predictors of sarcopenia, especially the SHAP (MMSE score ranked top one). For its components, episodic memory, visuospatial ability, and attention showed high predictive value compared with orientation. Stepwise regression analyses showed that MMSE score and its components of episodic memory and visuospatial ability were correlated with sarcopenia, with their odds ratios of 0.93 (95% CI: 0.91-0.96, p < 0.001), 0.87 (95% CI: 0.82-0.93, p < 0.001), and 1.32 (95% CI: 1.05-1.65, p = 0.016), respectively.

CONCLUSIONS

Better cognitive function especially episodic memory and visuospatial ability was negatively associated with incident sarcopenia among community middle-aged and older adults.

Correlation of Inflammatory Parameters with the Development of Cerebral Vasospasm, Takotsubo Cardiomyopathy, and Functional Outcome after Spontaneous Subarachnoid Hemorrhage

Background: The present work aimed to determine whether a relationship exists between inflammatory parameters and the development of vasospasm (VS) and Takotsubo cardiomyopathy (TTC), as well as clinical outcome, in patients suffering from spontaneous subarachnoid hemorrhage (SAH). Methods: In this study, the authors processed the prospectively collected laboratory and clinical data of spontaneous SAH patients admitted to the neurointensive care unit between March 2015 and October 2023. The highest values of neutrophils (NEUpeak), monocytes (MONOpeak), neutrophil-to-lymphocyte ratio (NLRpeak), and CRP (CRPpeak) during the initial 7 days were correlated with the occurrence of VS and TTC, and with the outcome measures at day 30 after onset. Results: Data were collected from 175 SAH patients. Based on ROC analysis, for the development of VS, MONOpeak was the most accurate indicator (AUC: 0.619, optimal cut-off: 1.45 G/L). TTC with severe left ventricular dysfunction (ejection fraction < 40%) was indicated most sensitively by NEUpeak (ROC: 0.763, optimal cut-off: 12.34 G/L). Both for GOS and Barthel Index at day 30, CRPpeak was the best predictor for the outcome (GOS: ROC: 0.846, optimal cut-off: 78.33 mg/L and Barthel Index: ROC: 0.819, optimal cut-off: 78.33 mg/L). Conclusions: Laboratory parameters referring to inflammation during the initial 7 days after SAH correlate with the development of VS and TTC, and thus may predict functional outcome.

CLR (C-Reactive Protein to Lymphocyte Ratio) Served as a Promising Predictive Biomarker for Cerebral Vasospasm in Aneurysmal Subarachnoid Hemorrhage (aSAH): A Retrospective Cohort Study

Background: Subarachnoid hemorrhage is a devastating disease. Even after state-of-the-art treatment patients suffer from complications, including cerebral vasospasm (CVS), delayed cerebral ischemia (DCI), and chronic hydrocephalus (CH) following aneurysmal subarachnoid hemorrhage (aSAH). The aim of our study is to identify the predictive value of the C-reactive protein to lymphocyte ratio (CLR) for neurological functional outcome and complications after aSAH. Methods: We retrospectively analyzed a total of 166 aSAH patients who met the inclusion criteria enrolled in our study. Multivariate logistic regression analyses were performed to evaluate the independent risk factors. The predictive value of different models was compared by calculating the areas under the receiver operating characteristic (ROC) curve. Results: On-admission levels of CLR in patients with poor outcomes (6 months mRS 3-6), CVS, DCI, and CH were significantly higher than those in patients with good outcomes (6 months mRS 0-2), non-CVS, non-DCI, and non-CH. Multivariate logistic regression analysis revealed that admission CLR was independently associated with CVS (OR [95% CI] 2.116 [1.507-2.971]; p < 0.001), and DCI (OR [95% CI] 1.594 [1.220-2.084]; p = 0.001). In ROC analysis, the area under the curve (AUC) of CLR for poor outcomes (6 months mRS 3-6), CVS, DCI, and CH prediction were (AUC [95% CI] 0.639 [0.555-0.724]; p = 0.002), (AUC [95% CI] 0.834 [0.767-0.901]; p < 0.001), (AUC [95% CI] 0.679 [0.581-0.777]; p < 0.001), and (AUC [95% CI] 0.628 [0.543-0.713]; p = 0.005) revealing that admission CLR had a favorable predictive value for CVS after aSAH. The sensitivity and specificity of admission CLR for CVS prediction were 77.1% and 75.4%. On-admission CLR of 0.757 mg × 10-6 was identified as the best cutoff threshold to discriminate between CVS and non-CVS (CVS: CLR < 0.757 mg × 10-6 11/100 [11.0%] vs. CLR ≥ 0.757 mg × 10-6 37/66 [56.1%]; p < 0.001). Conclusions: High levels of on-admission CLR serve as an independent risk factor for CVS and DCI after aSAH. Admission CLR is an easy-to-quantify laboratory parameter that efficiently predicts the CVS after aSAH, which can provide some guidance for clinicians to evaluate for possible progression and treatment strategies in patients with aSAH.

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model.

Clinical value of the low-grade inflammation score in aneurysmal subarachnoid hemorrhage

BACKGROUND AND PURPOSE

Multiple inflammatory biomarkers have been shown to predict symptomatic cerebral vasospasm (SCVS) and poor functional outcome in patients with aneurysmal subarachnoid hemorrhage. However, the impact of the low-grade inflammation (LGI) score, which can reflect the synergistic effects of five individual inflammatory biomarkers on SCVS and poor functional outcome on aneurysmal subarachnoid hemorrhage (aSAH), has not yet been well established. The aim of this study was to evaluate the impact of the LGI score on SCVS and poor functional outcome in aSAH patients.

METHODS

The LGI score was calculated as the sum of 10 quantiles of each individual inflammatory biomarker. The association of the LGI score with the risk of SCVS and poor functional outcome was analyzed with multivariate logistical regression.

RESULTS

A total of 270 eligible aSAH patients were included in this study: 74 (27.4%) had SCVS, and 79 (29.3%) had poor functional outcomes. After adjusting for confounders, a higher LGI score was revealed to independently predict SCVS (OR, 1.083; 95% CI, 1.011-1.161; P = 0.024) and poor functional outcome (OR, 1.132; 95% CI, 1.023-1.252; P = 0.016), and the second and third tertile group had higher risk of SCVS than lowest tertile group (OR, 2.826; 95% CI, 1.090-7.327; P = 0.033) (OR, 3.243; 95% CI, 1.258-8.358; P = 0.015). The receiver operating characteristic (ROC) curve uncovered the ability of the LGI score to distinguish patients with and without SCVS (area under the curve [AUC] = 0.746; 95% CI, 0.690-0.797; P < 0.001) and poor functional outcomes (area under the curve [AUC] = 0.799; 95% CI, 0.746-0.845; P < 0.001), the predictive value of LGI on SCVS and poor functional outcome is superior than PLT, NLR and WBC, but there was no statistical difference between LGI and CRP for predicting SCVS (P = 0.567) and poor functional outcome (P = 0.171).

CONCLUSIONS

A higher LGI which represents severe low grade inflammation status is associated with SCVS and poor functional outcome at 3 months after aSAH.

Outcomes following poor-grade aneurysmal subarachnoid haemorrhage: a prospective observational study

BACKGROUND

Up to 35% of aneurysmal subarachnoid haemorrhage (aSAH) cases may present as poor grade, defined as World Federation of Neurosurgical Societies (WFNS) grades IV and V. In this study, we evaluate functional outcomes and prognostic factors.

METHODS

This prospective study included all patients referred to a national, centralized neurosurgical service with a diagnosis of poor-grade aSAH between 01/01/2016 and 31/12/2019. Multivariable logistic regression models were used to estimate probability of poor functional outcomes, defined as a Glasgow Outcome Scale (GOS) of 1-3 at 3 months.

RESULTS

Two hundred fifty-seven patients were referred, of whom 116/257 (45.1%) underwent treatment of an aneurysm, with 97/116 (84%) treated within 48 h of referral. Median age was 62 years (IQR 51-69) with a female predominance (167/257, 65%). Untreated patients tended to be older; 123/141 (87%) had WFNS V, 60/141 (45%) unreactive pupils and 21/141 (16%) circulatory arrest. Of all referred patients, poor outcome occurred in 169/230 (73.5%). Unreactive pupils or circulatory arrest conferred a universally poor prognosis, with mortality in 55/56 (98%) and 19/19 (100%), respectively. The risk of a poor outcome was 14.1% (95% CI 4.5-23.6) higher in WFNS V compared with WFNS IV. Age was important in patients without circulatory arrest or unreactive pupils, with risk of a poor outcome increasing by 1.8% per year (95% CI 1-2.7). In patients undergoing aneurysm securement, 48/101 (47.5%) had a poor outcome, with age, rebleeding, vasospasm and cerebrospinal fluid (CSF) diversion being important prognosticators. The addition of serum markers did not add significant discrimination beyond the clinical presentation.

CONCLUSIONS

The overall outcomes of WFNS IV and V aSAH remain poor, mainly due to the devastating effects of the original haemorrhage. However, in patients selected for aneurysm securement, good outcomes can be achieved in more than half of patients. Age, pre-intervention rebleeding, vasospasm, and CSF diversion are important prognostic factors.

Comparison of Prognostic Models for Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning

BACKGROUND

Controversy exists regarding the superiority of the performance of prognostic tools based on advanced machine learning (ML) algorithms for patients with aneurysmal subarachnoid hemorrhage (aSAH). However, it is unclear whether ML prognostic models will benefit patients due to the lack of a comprehensive assessment. We aimed to develop and evaluate ML models for predicting unfavorable functional outcomes for aSAH patients and identify the model with the greatest performance.

METHODS

In this retrospective study, a dataset of 955 patients with aSAH was used to construct and validate prognostic models for functional outcomes assessed using the modified Rankin scale during a follow-up period of 3-6 months. Clinical scores and clinical and radiological features on admission and secondary complications were used to construct models based on 5 ML algorithms (i.e., logistic regression [LR], k-nearest neighbor, extreme gradient boosting, random forest, and artificial neural network). For evaluation among the models, the area under the receiver operating characteristic curve, area under the precision-recall curve, calibration curve, and decision curve analysis were used.

RESULTS

Composite models had significantly higher area under the receiver operating characteristic curves than did simple models in predicting unfavorable functional outcomes. Compared with other composite models (random forest and extreme gradient boosting) with good calibration, LR had the highest area under the precision-recall score and showed the greatest benefit in decision curve analysis.

CONCLUSIONS

Of the 5 studied ML models, the conventional LR model outperformed the advanced algorithms in predicting the prognosis and could be a useful tool for health care professionals.

Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard

Aneurysmal subarachnoid hemorrhage (aSAH) frequently causes long-term disability, but predicting outcomes remains challenging. Routine parameters such as demographics, admission status, CT findings, and blood tests can be used to predict aSAH outcomes. The aim of this study was to compare the performance of traditional logistic regression with several machine learning algorithms using readily available indicators and to generate a practical prognostic scorecard based on machine learning. Eighteen routinely available indicators were collected as outcome predictors for individuals with aSAH. Logistic regression (LR), random forest (RF), support vector machines (SVMs), and fully connected neural networks (FCNNs) were compared. A scorecard system was established based on predictor weights. The results show that machine learning models and a scorecard achieved 0.75~0.8 area under the curve (AUC) predicting aSAH outcomes (LR 0.739, RF 0.749, SVM 0.762~0.793, scorecard 0.794). FCNNs performed best (~0.95) but lacked interpretability. The scorecard model used only five factors, generating a clinically useful tool with a total cutoff score of ≥5, indicating poor prognosis. We developed and validated machine learning models proven to predict outcomes more accurately in individuals with aSAH. The parameters found to be the most strongly predictive of outcomes were NLR, lymphocyte count, monocyte count, hypertension status, and SEBES. The scorecard system provides a simplified means of applying predictive analytics at the bedside using a few key indicators.

Predictive nomogram models for unfavorable prognosis after aneurysmal subarachnoid hemorrhage: Analysis from a prospective, observational cohort in China

AIM

The aim of the study was to identify predictors for 3-month poor functional outcome or death after aSAH and develop precise and easy-to-use nomogram models.

METHODS

The study was performed at the department of neurology emergency in Beijing Tiantan Hospital. A total of 310 aSAH patients were enrolled between October 2020 and September 2021 as a derivation cohort, while a total of 208 patients were admitted from October 2021 to March 2022 as an external validation cohort. Clinical outcomes included poor functional outcome defined as modified Rankin Scale score (mRS) of 4-6 or all-cause death at 3 months. Least absolute shrinkage and selection operator (LASSO) analysis, as well as multivariable regression analysis, were applied to select independent variables associated with poor functional outcome or death and then to construct two nomogram models. Model performance were evaluated through discrimination, calibration, and clinical usefulness in both derivation cohort and external validation cohort.

RESULTS

The nomogram model to predict poor functional outcome included seven predictors: age, heart rate, Hunt-Hess grade on admission, lymphocyte, C-reactive protein (CRP), platelet, and direct bilirubin levels. It demonstrated high discrimination ability (AUC, 0.845; 95% CI: 0.787-0.903), satisfactory calibration curve, and good clinical usefulness. Similarly, the nomogram model combining age, neutrophil, lymphocyte, CRP, aspartate aminotransferase (AST) levels, and treatment methods to predict all-cause death also revealed excellent discrimination ability (AUC, 0.944; 95% CI: 0.910-0.979), satisfactory calibration curve, and clinical effectiveness. Internal validation showed the bias-corrected C-index for poor functional outcome and death was 0.827 and 0.927, respectively. When applied to the external validation dataset, both two nomogram models exhibited high discrimination capacity [poor functional outcome: AUC = 0.795 (0.716-0.873); death: AUC = 0.811 (0.707-0.915)], good calibration ability, and clinical usefulness.

CONCLUSIONS

Nomogram models constructed for predicting 3-month poor functional outcome or death after aSAH are precise and easily applicable, which can help physicians to identify patients at risk, guide decision-making, and provide new directions for future studies to explore the novel treatment targets.

Serum amyloid A is a potential predictor of prognosis in acute ischemic stroke patients after intravenous thrombolysis

Objectives

Inflammation shows a notable relationship to acute ischemic stroke's (AIS) occurrence and prognosis. However, existing research has confirmed that serum amyloid A (SAA) is an inflammatory biomarker. The aim of this paper was to investigate the association between SAA and the three-month clinical results of acute AIS patients after intravenous thrombolysis (IVT).

Methods

The evaluation of AIS patients with complete medical records was carried out by prospectively investigating patients hospitalized in our department between January 2020 and February 2023. The SAA levels were examined with the use of an immunosorbent assay kit that shows a relationship with the enzyme (Invitrogen Corp). Patients were dichotomized into favorable (mRS score of 0, 1 or 2) and unfavorable (mRS score of 3, 4, 5, or 6) results with the use of the modified Rankin Scale (mRS).

Results

A total of 405 AIS patients who were subjected to IVT therapy were prospectively covered. To be specific, 121 (29.88%) patients had an unfavorable prognosis during the follow-up for 3 months. On that basis, patients achieving unfavorable results gained notably greater SAA levels (39.77 (IQR 38.32-46.23) vs.31.23 (IQR 27.44-34.47), p < 0.001) during hospitalization in comparison to patients with a better result. In the analysis with multiple variates, SAA was adopted to achieve the independent prediction of the three-month unfavorable clinical results of acute AIS patients after IVT [OR:2.874 (95% CI, 1.764-4.321), p < 0.001]. When the fundamental confounding factors were regulated, the odds ratio (OR) of unfavorable prognosis after AIS patients undergoing IVT therapy was 4.127 (95% CI = 1.695-10.464, p = 0.032) for the maximum tertile of SAA in terms of the minimal tertile. With an AUC of 0.703 (95% CI, 0.649-0.757), SAA revealed a notably more effective discriminating capability in terms of CRP, NLR, EMR, and WBC. SAA as a predictor in terms of the prediction of three-month unfavorable results after AIS patients undergoing IVT therapy achieved specificity and sensitivity of 84.45% and 77.23%, as well as an optimal cut-off value (COV) of 37.39.

Conclusion

SAA level that is up-regulated during hospitalization is capable of serving as an effective marker in terms of the prediction of unfavorable three-month results in AIS patients after IVT.

A correlation and prediction study of the poor prognosis of high-grade aneurysmal subarachnoid hemorrhage from the neutrophil percentage to albumin ratio

OBJECTIVE

Inflammatory response and nutritional status play crucial roles in patients with aneurysmal subarachnoid hemorrhage (aSAH). This study mainly investigated the correlation between neutrophil percentage to albumin ratio (NPAR) and clinical prognosis in aSAH patients with high-grade Hunt-Hess and its predictive model.

METHODS

A retrospective analysis was conducted based on 806 patients with aneurysmal subarachnoid hemorrhage who were admitted to the studied hospital from January 2017 to December 2021. Modified Fisher grade and Hunt-Hess grade were obtained according to their status at admission and hematological parameters within 48 h after hemorrhage. Univariate and multivariate logistic regression analysis were conducted to evaluate the relationship between NPAR and the clinical prognosis of patients with aSAH. And propensity matching analysis of patients with aSAH in the severe group. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off value of NPAR at admission to predict prognosis and its sensitivity and specificity. The nomogram diagram and Calibration curve were further used to examine the prediction model.

RESULTS

According to the mRS score at discharge, 184 (22.83 %) cases were classified as having poor outcomes (mRS > 2). Through multivariate logistic regression analysis, it was found that the Modified Fisher grade at admission, Hunt-Hess grade, eosinophils, neutrophil to lymphocyte ratio (NLR), and NPAR were independent risk factors for poor outcome in patients with aSAH (p < 0.05). The NPAR of aSAH patients with poor outcomes in the high-grade group was significantly higher than that in the low-grade group. The optimal cut-off value for NPAR was 21.90, the area under the ROC curve was 0.780 (95 % CI 0.700 - 0.861, p < 0.001). The Calibration curves show that the predicted probability of the drawn nomogram is overall consistent with the actual probability. (Mean absolute error = 0.031) CONCLUSION: The NPAR value of patients with aSAH at admission is significantly correlated with Hunt-Hess grade in a positive manner, namely, the higher the Hunt-Hess grade, the higher the NPAR value, and the worse the prognosis. Findings indicate that early NPAR value can be used as a feasible biomarker to predict the clinical prognosis of patients with aSAH.

Utility of serum amyloid A as a potential prognostic biomarker of aneurysmal subarachnoid hemorrhage

Objectives

Inflammation plays a vital role in the aneurysmal subarachnoid hemorrhage (aSAH), while serum amyloid A (SAA) has been identified as an inflammatory biomarker. The present study aimed to elucidate the relationship between SAA concentrations and prognosis in aSAH.

Methods

From prospective analyses of patients admitted to our department between March 2016 and August 2022, aSAH patients with complete medical records were evaluated. Meanwhile, the healthy control group consisted of the age and sex matched individuals who came to our hospital for healthy examination between March 2018 and August 2022. SAA level was measured by enzyme-linked immunosorbent assay kit (Invitrogen Corp). The Glasgow Outcome Scale (GOS) was used to classify patients into good (GOS score of 4 or 5) and poor (GOS score of 1, 2, or 3) outcome.

Results

456 patients were enrolled in the study, thereinto, 200 (43.86%) patients had a poor prognosis at the 3-months follow-up. Indeed, the SAA of poor outcome group were significantly increased compared to good outcome group and healthy control group [36.44 (32.23-41.00) vs. 28.99 (14.67-34.12) and 5.64 (3.43-7.45), P < 0.001]. In multivariate analyses, SAA served for independently predicting the poor outcome after aICH at 3 months [OR:1.129 (95% CI, 1.081-1.177), P < 0.001]. After adjusting the underlying confounding factors, the odds ratio (OR) of depression after aSAH was 2.247 (95% CI: 1.095-4.604, P = 0.021) for the highest tertile of SAA relative to the lowest tertile. With an AUC of 0.807 (95% CI, 0.623-0.747), SAA demonstrated an obviously better discriminatory ability relative to CRP, WBC, and IL-6. SAA as an indicator for predicting poor outcome after aSAH had an optimal cut-off value of 30.28, and the sensitivity and specificity were 61.9 and 78.7%, respectively.

Conclusions

Elevated level of SAA was associated with poor outcome at 3 months, suggesting that SAA might be a useful inflammatory markers to predict prognosis after aSAH.

The D-Dimer/Albumin Ratio Is a Prognostic Marker for Aneurysmal Subarachnoid Hemorrhage

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a severe neurological event with limited treatment options, and little is known about its pathophysiology. There are few objective tools for predicting outcomes of aSAH patients and further aiding in directing clinical therapeutic programs. This study aimed to determine whether an elevated serum D-dimer/albumin ratio (DAR) reflects disease severity and predicts aSAH outcomes. Methods: We included 178 patients with aSAH. Data included demographics; clinical severity of aSAH (World Federation of Neurological Societies (WFNS) grade and Hunt-Hess grade); levels of D-dimer, albumin, and c-reactive protein (CRP); leukocyte counts on admission; and three-month outcomes. The outcomes were dichotomized into good and poor. The predictive ability of DAR for outcomes was determined using receiver operating characteristic (ROC) curve analysis. Results: Serum DAR showed a positive correlation with disease severity. Univariate analysis revealed that DAR, WFNS grade, Hunt-Hess grade, delayed cerebral infarction (DCI), age, neutrophil-to-lymphocyte ratio (NLR), and CRP/albumin ratio (CAR) were associated with unfavorable outcomes. Multivariate regression analysis further revealed that elevated DAR predicted poor outcomes after adjusting for WFNS grade, Hunt-Hess grade, DCI, age, NLR, and CRP/albumin ratio. Receiver operating characteristic curve analysis revealed that DAR predicted outcomes at a level comparable with NLR and CAR and had superior predictivity than D-dimer alone. Conclusion: DAR is a promising objective tool for aSAH outcome prediction. A high content DAR was associated with disease severity and unfavorable short-term outcomes.

The value of comorbidities and illness severity scores as prognostic tools for early outcome estimation in patients with aneurysmal subarachnoid hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is a severe cerebrovascular disease not only causing brain injury but also frequently inducing a significant systemic reaction affecting multiple organ systems. In addition to hemorrhage severity, comorbidities and acute extracerebral organ dysfunction may impact the prognosis after aSAH as well. The study objective was to assess the value of illness severity scores for early outcome estimation after aSAH. A retrospective analysis of consecutive aSAH patients treated from 2012 to 2020 was performed. Comorbidities were evaluated applying the Charlson comorbidity index (CCI) and the American Society of Anesthesiologists (ASA) classification. Organ dysfunction was assessed by calculating the simplified acute physiology score (SAPS II) 24 h after admission. Modified Rankin scale (mRS) at 3 months was documented. The outcome discrimination power was evaluated. A total of 315 patients were analyzed. Significant comorbidities (CCI > 3) and physical performance impairment (ASA > 3) were found in 15% and 12% of all patients, respectively. The best outcome discrimination power showed SAPS II (AUC 0.76), whereas ASA (AUC 0.65) and CCI (AUC 0.64) exhibited lower discrimination power. A SAPS II cutoff of 40 could reliably discriminate patients with good (mRS ≤ 3) from those with poor outcome (p < 0.0001). Calculation of SAPS II allowed a comprehensive depiction of acute organ dysfunctions and facilitated a reliable early prognosis estimation in our study. In direct comparison to CCI and ASA, SAPS II demonstrated the highest discrimination power and deserves a consideration as a prognostic tool after aSAH.

Risk factors and predictive models of poor prognosis and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage complicated with hydrocephalus

Objective

To evaluate the correlation of serum biological markers and related scales to the occurrence of delayed cerebral ischemia and clinical prognosis in patients with aneurysmal subarachnoid hemorrhage (aSAH) complicated with acute hydrocephalus before admission.

Methods

The clinical data of 227 patients with pre-admission aSAH complicated with acute hydrocephalus admitted to Henan Provincial People's Hospital from April 2017 to December 2020 were retrospectively analyzed. Patients were grouped according to the presence or absence of delayed cerebral ischemia (DCI) after surgery and the prognosis at 6 months after discharge. Univariate and multivariable logistic regression analysis were performed to analyze the relationship between serum biological indicators combined with aneurysm related clinical score scale and the occurrence and prognosis of delayed cerebral ischemia. ROC curves and nomogram were drawn.

Results

Multivariable Logistic regression analysis showed that high Hunt-Hess grade and surgical clipping were independent risk factors for postoperative DCI (P < 0.05). Older age, higher Hunt-Hess grade, higher CRP and neutrophil levels were independent risk factors for poor prognosis at 6 months after surgery (P < 0.05). ROC curve analysis showed that the area under the curve (AUC) of Hunt-Hess grade and surgical method for predicting DCI in patients with aSAH combined with hydrocephalus after surgery were 0.665 and 0.593. The combined AUC of Hunt-Hess grade and surgical method was 0.685, the sensitivity was 64.9%, and the specificity was 64.7%. The AUC of CRP, neutrophil, age and Hunt-Hess grade for predicting poor prognosis in patients with aSAH combined with hydrocephalus at 6 months after surgery were 0.804, 0.735, 0.596, 0.757, respectively. The combined AUC of CRP, neutrophil, age, Hunt-Hess grade was 0.879, the sensitivity was 79%, and the specificity was 84.5%. According to the correction curve, the predicted probability of the nomogram is basically consistent with the actual probability.

Conclusion

Hunt-Hess grade and surgical method are independent predictors of postoperative DCI in patients with aSAH complicated with hydrocephalus. “CRP,” “neutrophil,” “age” and “Hunt-Hess grade” at admission are independent predictors of clinical prognosis in patients with aSAH complicated with hydrocephalus. The combination of the above indicators has high predictive value.

Association between acute kidney injury and long-term mortality in patients with aneurysmal subarachnoid hemorrhage: A retrospective study

Background

Though acute kidney injury (AKI) in the context of aneurysmal subarachnoid hemorrhage (aSAH) worsens short-term outcomes, its impact on long-term survival is unknown.

Aim

We aimed to evaluate the association between long-term mortality and AKI during hospitalization for aSAH.

Methods

This was a retrospective study of patients who survived &gt;12 months after aSAH. All patients were evaluated at West China Hospital, Sichuan University, between December 2013 and June 2019. The minimum follow-up time was over 1 year. the maximum follow-up time was about 7.3 years. AKI was defined by the KDIGO (The Kidney Disease Improving Global Outcomes) guidelines, which stratifies patients into three stages of severity. The primary outcome was long-term mortality, which was analyzed with Kaplan-Meier curves and Cox proportional hazards models.

Results

During this study period, 238 (9.2%) patients had AKI among 2,592 patients with aSAH. We confirmed that AKI during care for aSAH significantly increased long-term mortality (median 4.3 years of follow-up) and that risk increased with the severity of the kidney failure, with an adjusted hazard ratio (HR) of 2.08 (95% CI 1.49–2.89) for stage 1 AKI, 2.15 (95% CI 1.05–4.43) for stage 2 AKI, and 2.66 (95% CI 1.08–6.53) for stage 3 AKI compared with patients without AKI. Among patients with an AKI episode, those with renal recovery still had increased long-term mortality (HR 1.96; 95% CI 1.40–2.74) compared with patients without AKI but had better long-term outcomes than those without renal recovery (HR 0.51, 95% CI 0.27–0.97).

Conclusions

Among 12-month survivors of aSAH, AKI during their initial hospitalization for aSAH was associated with increased long-term mortality, even for patients who had normal renal function at the time of hospital discharge. Longer, multidisciplinary post-discharge follow-up may be warranted for these patients.

Neutrophil to lymphocyte ratio predicting poor outcome after aneurysmal subarachnoid hemorrhage: A retrospective study and updated meta-analysis

Background

The relationship between neutrophil to lymphocyte ratio (NLR) and poor outcome of aneurysmal subarachnoid hemorrhage (aSAH) is controversial. We aim to evaluate the relationship between NLR on admission and the poor outcome after aSAH.

Method

Part I: Retrospective analysis of aSAH patients in our center. Baseline characteristics of patients were collected and compared. Multivariate analysis was used to evaluate parameters independently related to poor outcome. Receiver operating characteristic (ROC) curve analysis was used to determine the best cut-off value of NLR. Part II: Systematic review and meta-analysis of relevant literature. Related literature was selected through the database. The pooled odds ratio (OR) and corresponding 95% confidence interval (CI) were calculated to evaluate the correlation between NLR and outcome measures.

Results

Part I: A total of 240 patients with aSAH were enrolled, and 52 patients had a poor outcome. Patients with poor outcome at 3 months had a higher admission NLR, Hunt & Hess score, Barrow Neurological Institute (BNI) scale score, Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), and proportion of hypertension history. After adjustment, NLR at admission remained an independent predictor of poor outcome in aSAH patients (OR 0.76, 95% CI 0.69-0.83; P < 0.001). The best cut-off value of NLR in ROC analysis is 12.03 (area under the curve 0.805, 95% CI 0.735 - 0.875; P < 0.001). Part II: A total of 16 literature were included. Pooled results showed that elevated NLR was significantly associated with poor outcome (OR 1.31, 95% CI 1.14-1.49; P < 0.0001) and delayed cerebral ischemia (DCI) occurrence (OR 1.32, 95% CI 1.11-1.56; P = 0.002). The results are more reliable in large sample sizes, low NLR cut-off value, multicenter, or prospective studies.

Conclusion

Elevated NLR is an independent predictor of poor outcome and DCI occurrence in aSAH.

Clinical Value and Prognosis of C Reactive Protein to Lymphocyte Ratio in Severe Aneurysmal Subarachnoid Hemorrhage

Objective

To investigate the relationship between CLR and disease severity and clinical prognosis of aSAH.

Methods

The authors retrospectively analyzed the clinical data of 221 patients with aSAH, who were admitted to the intensive care unit from January 2017 to December 2020. The indicators of inflammatory factors in the first blood routine examination within 48 h of bleeding were obtained. The prognosis was evaluated by mRS score at discharge, mRS>2 was a poor outcome. Through the receiver operating characteristic (ROC) curve, the area under the curve was calculated and the predicted values of inflammatory factors (CLR, CRP, WBC, and neutrophils) were compared. Univariate and multivariable logistic regression analyses were used to evaluate the relationship between CLR and the clinical prognosis of patients. ROC curve analysis was performed to determine the optimal cut-off threshold, sensitivity, and specificity of CLR in predicting prognosis at admission.

Results

According to the mRS score at discharge, 139 (62.90%) patients were classified with poor outcomes (mRS>2). The inflammatory factor with the best predictive value was CLR, which had an optimal cut-off threshold of 10.81 and an area under the ROC curve of 0.840 (95%CI.788–0.892, P < 0.001). Multivariable Logistic regression analysis showed that the Modified Fisher grade, Hunt-Hess grade, and CLR at admission were independent risk factors for poor outcomes of patients with aSAH (P < 0.05). According to Hunt-Hess grade, patients were divided into a mild group (Hunt-Hess ≤ 3) and a severe group (Hunt-Hess > 3), and the CLR value was significantly higher in severe patients with aSAH than in mild patients. The optimal cut-off threshold of CLR in the severe group was 6.87, and the area under the ROC curve was 0.838 (95% CI.752–0.925, P < 0.001).

Conclusions

The CLR value at the admission of patients with aSAH was significantly associated with Hunt-Hess grade, The higher Hunt-Hess grade, the higher the CL R-value, and the worse the prognosis. Early CLR value can be considered as a feasible biomarker to predict the clinical prognosis of patients with aSAH.

Genome-Wide Association Study of Clinical Outcome After Aneurysmal Subarachnoid Haemorrhage: Protocol

Aneurysmal subarachnoid haemorrhage (aSAH) results in persistent clinical deficits which prevent survivors from returning to normal daily functioning. Only a small fraction of the variation in clinical outcome following aSAH is explained by known clinical, demographic and imaging variables; meaning additional unknown factors must play a key role in clinical outcome. There is a growing body of evidence that genetic variation is important in determining outcome following aSAH. Understanding genetic determinants of outcome will help to improve prognostic modelling, stratify patients in clinical trials and target novel strategies to treat this devastating disease. This protocol details a two-stage genome-wide association study to identify susceptibility loci for clinical outcome after aSAH using individual patient-level data from multiple international cohorts. Clinical outcome will be assessed using the modified Rankin Scale or Glasgow Outcome Scale at 1–24 months. The stage 1 discovery will involve meta-analysis of individual-level genotypes from different cohorts, controlling for key covariates. Based on statistical significance, supplemented by biological relevance, top single nucleotide polymorphisms will be selected for replication at stage 2. The study has national and local ethical approval. The results of this study will be rapidly communicated to clinicians, researchers and patients through open-access publication(s), presentation(s) at international conferences and via our patient and public network.