Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors

Evaluation of PHASES Score for Predicting Rupture of Intracranial Aneurysms: Significance of Aneurysm Size

BACKGROUND

Recent data have identified that certain risk factors for rupture differ between small and larger intracranial aneurysms (IAs). Such differing risk factors make up 5 out of the 6 predictor variables used in the PHASES score, which raises the question on whether IA size has a significant effect on the score's performance.

METHODS

Patients who were diagnosed with an IA incidentally or due to a subarachnoid hemorrhage between 2015 and 2023 were selected for potential inclusion. The median IA size of the cohort was chosen as the cutoff point to categorize small and large (6 mm). The PHASES score was calculated for all patients, and a receiver operating characteristic curve analysis was performed to evaluate the classification accuracy of PHASES in predicting rupture for small and large IAs.

RESULTS

A total of 677 IAs were included. Among the IAs, 400 (58.9%) presented as UIAs and 279 (41.0%) as subarachnoid hemorrhage. The average PHASES score was 2.9 and 6.5 for small (n = 322) and large (n = 355) IAs, respectively. The PHASES score performed significantly lower for predicting rupture in smaller IAs (area under the curve: 0.634) compared with the larger (area under the curve: 0.741) (P = 0.00083).

CONCLUSIONS

PHASES was shown to underperform on small IAs. The decision to treat small unruptured IAs remains highly controversial, and the development of a new score to estimate the annual rupture rate while accounting for IA morphology is of great need. Our findings can help encourage future researchers to develop such a score.

Nomograms for assessing the rupture risk of anterior choroid artery aneurysms based on clinical, morphological, and hemodynamic features

Background and purpose

A notable prevalence of subarachnoid hemorrhage is evident among patients with anterior choroidal artery aneurysms in clinical practice. To evaluate the risk of rupture in unruptured anterior choroidal artery aneurysms, we conducted a comprehensive analysis of risk factors and subsequently developed two nomograms.

Methods

A total of 120 cases of anterior choroidal artery aneurysms (66 unruptured and 54 ruptured) from 4 medical institutions were assessed utilizing computational fluid dynamics (CFD) and digital subtraction angiography (DSA). The training set, consisting of 98 aneurysms from 3 hospitals, was established, with an additional 22 cases from the fourth hospital forming the external validation set. Statistical differences between the two data sets were thoroughly compared. The significance of 9 clinical baseline characteristics, 11 aneurysm morphology parameters, and 4 hemodynamic parameters concerning aneurysm rupture was evaluated within the training set. Candidate selection for constructing the nomogram models involved regression analysis and variance inflation factors. Discrimination, calibration, and clinical utility of the models in both training and validation sets were assessed using area under curves (AUC), calibration plots, and decision curve analysis (DCA). The DeLong test, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed to compare the effectiveness of classification across models.

Results

Two nomogram models were ultimately constructed: model 1, incorporating clinical, morphological, and hemodynamic parameters (C + M + H), and model 2, relying primarily on clinical and morphological parameters (C + M). Multivariate analysis identified smoking, size ratio (SR), normalized wall shear stress (NWSS), and average oscillatory shear index (OSIave) as optimal candidates for model development. In the training set, model 1 (C + M + H) achieved an AUC of 0.795 (95% CI: 0.706 ~ 0.884), demonstrating a sensitivity of 95.6% and a specificity of 54.7%. Model 2 (C + M) had an AUC of 0.706 (95% CI: 0.604 ~ 0.808), with corresponding sensitivity and specificity of 82.4 and 50.3%, respectively. Similarly, AUCs for models 1 and 2 in the external validation set were calculated to be 0.709 and 0.674, respectively. Calibration plots illustrated a consistent correlation between model evaluations and real-world observations in both sets. DCA demonstrated that the model incorporating hemodynamic parameters offered higher clinical benefits. In the training set, NRI (0.224, p = 0.007), IDI (0.585, p = 0.002), and DeLong test (change = 0.089, p = 0.008) were all significant. In the external validation set, NRI, IDI, and DeLong test statistics were 0.624 (p = 0.063), 0.572 (p = 0.044), and 0.035 (p = 0.047), respectively.

Conclusion

Multidimensional nomograms have the potential to enhance risk assessment and patient-specific treatment of anterior choroidal artery aneurysms. Validated by an external cohort, the model incorporating clinical, morphological, and hemodynamic features may provide improved classification of rupture states.

Predictors for Rupture of Small (<7mm) Intracranial Aneurysms: A Systematic Review and Meta-Analysis

INTRODUCTION

Identifying predictors for rupture of small intracranial aneurysms (sIAs) have become a growing topic in the literature given the relative paucity of data on their natural history. The authors performed a meta-analysis to identify reliable predictors.

METHODS

PubMed, Scopus, and Web of Science were used to systematically extract references which involved at least 10 IAs <7mm which including a control group experiencing no rupture. All potential predictors reported in the literature were evaluated in the meta-analysis.

RESULTS

Fifteen studies yielding 4,739 sIAs were included in the meta-analysis. Four studies were prospective and 11 were retrospective. Univariate analysis identified 7 predictors which contradicted or are absent in the current scoring systems, while allowing to perform subgroup analysis for further reliability: patient age (MD -1.97, 95%CI -3.47-0.48; P = 0.01), the size ratio (MD 0.40, 95%CI 0.26-0.53; P < 0.00001), the aspect ratio (MD 0.16, 95%CI 0.11-0.22; P < 0.00001), bifurcation point (OR 3.76, 95%CI 2.41-5.85; P < 0.00001), irregularity (OR 2.95, 95%CI 1.91-4.55; P < 0.00001), the pressure loss coefficient (MD -0.32, 95%CI -0.52-0.11; P = 0.002), wall sheer stress (Pa) (MD -0.16, 95%CI -0.28-0.03; P = 0.01). All morphology related predictors listed above have been confirmed as independent predictors via multivariable analysis among the individual studies.

CONCLUSIONS

Morphology related predictors are superior to the classic patient demographic predictors present in most scoring systems. Given that morphology predictors take time to measure, our findings may be of great interest to developers seeking to incorporate artificial intelligence into the treatment decision-making process.

A nomogram to predict the risk of bleeding after discharge from stent-assisted ruptured aneurysm embolization in a Chinese population

The occurrence of bleeding events after stent-assisted embolization of a ruptured artery requiring continuous double antiplatelet therapy may seriously affect the prognosis of this group of patients. A nomogram can provide a personalized, more accurate risk estimate based on predictors. We, therefore, developed a nomogram to predict the probability of bleeding events in patients with stent-assisted ruptured aneurysm embolization. We performed a single-center retrospective analysis of data collected from patients undergoing stent-assisted ruptured aneurysm embolization between January 2018 and December 2021. Forward stepwise logistic regression was performed to identify independent predictors of adverse events of bleeding after stent-assisted embolization and to establish nomograms. Discrimination and calibration of this model were performed using the area under the ROC curve (AUC-ROC) and the calibration plot. The model is internally validated by using resampling (1000 replicates). A total of 131 patients were identified, and a total of 118 patients met the study criteria. The predictors included in the nomogram were body mass index (BMI), AAi, and MA-ADP. The model showed good resolving power with a ROC area of 0.893 (95% CI: 0.834 ~ 0.952) for this model with good calibration. The nomogram can be used to individualize, visualize, and accurately predict the risk probability of bleeding events after stent-assisted embolization of ruptured aneurysms.

Novel Bedside Dynamic Nomograms to Predict the Probability of Postoperative Cognitive Dysfunction in Elderly Patients Undergoing Noncardiac Surgery: A Retrospective Study

Purpose

Early and accurate prediction of elderly patients at high risk of postoperative cognitive dysfunction (POCD) after non-cardiac surgery will provide favorable evidence for rational perioperative management and long-term postoperative recovery. This study aimed to develop bedside dynamic nomograms to provide accurately an individualized prediction of the risk of POCD at 6-month postoperatively with patients undergoing non-cardiac surgery and to guide clinical decision-making and postoperative management.

Patients and Methods

We retrospectively collected patients undergoing surgical treatment at the Nanjing First Hospital between May 2020 and May 2021. We collected the data on preoperative, intraoperative, and postoperative variables. Clinical and laboratory data on admission and intraoperative variables and postoperative variables were used. We measured the performances of the nomograms using sensitivity, specificity of the receiver operating characteristic (ROC), the area under the ROC curves (AUC), the 10-fold cross-validation, and decision curve analysis (DCA).

Results

POCD was observed in 23 of 415 patients (5.6%) at 6-month postoperatively. The preoperative and postoperative models obtained 91.6% and 94.0% accuracy rates on the data. Compared to the preoperative model, the postoperative model had an area under the receiver characteristic curve (AUC) of 0.973 vs 0.947, corresponding to a specificity of 0.941 vs 0.918 and a sensitivity of 0.913 vs 0.870. The overall performance of the postoperative model was better than the preoperative model.

Conclusion

In this study, we developed novel bedside dynamic nomograms with reasonable clinical utility that can provide individualized prediction of POCD risk at 6-month postoperatively in elderly patients undergoing non-cardiac surgery at different time points based on patient admission and postoperative data. External validations are needed to ensure their value in predicting POCD in elderly patients.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Objective

Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods

From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results

A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion

Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

Nomogram-Based Risk Model of Small (≤5 mm) Intracranial Aneurysm Rupture in an Eastern Asian Study

Background and Purpose

Risk stratification of small unruptured intracranial aneurysms (IAs) (&lt; =5 mm) is important for clinical decision-making and management. The aim of this study was to develop an individualized rupture risk model for small IAs in an eastern Asian population.

Methods

This study retrospectively enrolled 343 patients with ruptured (n = 96) and unruptured (n = 285) small IAs. Clinical data and aneurysmal morphology were taken into consideration, regression analysis was performed to identify significant variables, and these variables were then incorporated into a predictive nomogram. The diagnostic performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot. Clinical effectiveness was validated by decision curve analysis (DCA). The PHASES score calculated for each case was used for comparison.

Results

The nomogram achieved an AUC of 0.849 (95% CI: 0.805–0.893), with a sensitivity of 86.5%, a specificity of 70.9%, and accuracy of 74.7%, which was superior to PHASES score system (AUC = 0.693, sensitivity = 83.6%, specificity = 48.8%, and accuracy = 57.5%). A good agreement between predicted rupture risk and actual rupture status in the small aneurysms was observed, and DCA illustrated the benefit of using the nomogram when decisions needed to be made clinically.

Conclusions

The nomogram based on clinical and morphological risk factors can be useful in assisting clinicians with individualized assessments and benefit-risk balancing in patients with small IAs (&lt; =5 mm).