Development and Clinical Translation of a Perioperative Nomogram Incorporating Free Fatty Acids to Predict Poor Outcome of Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment

Comparison of prediction for short-term and long-term outcomes in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis

Despite extensive research on prediction models for outcomes in aneurysmal subarachnoid hemorrhage (aSAH) patients, the distinction between models for short- and long-term outcomes remains insufficiently explored. This study aims to compare these models, identify the risk factors of poor outcomes, summarize the predictors of outcomes, and assess the performance of the prediction models for short- and long-term outcomes in aSAH patients. PubMed, Web of Science, the Cochrane Library, and Embase were searched to identify studies investigating risk factors for developed and/or validated prediction models for short-term (< 12 months) and long-term (≥ 12 months) outcomes in aSAH patients. The main outcome was neurological function, defined as poor if the Glasgow Outcome Scale (GOS) score was ≤ 3, or if the modified Rankin Scale (mRS) score was ≥ 3. Fifty-six studies reporting 61 models with 36,879 aSAH patients were included. A total of 93 predictors were examined and categorized into six domains including demographic factors, scoring systems, clinical factors, aneurysm characteristics, laboratory examinations, and imaging features. Among these, laboratory examinations were included in 57.45% (27/47) of models predicting short-term outcomes, while only 14.29% (2/14) of long-term prediction models incorporated them. An mFisher score of 3-4 [OR = 1.95, 95%CI (1.43, 2.64), P < 0.01] and the presence of multiple aneurysms [OR = 1.56, 95% CI (1.25, 1.94), P < 0.01] were identified as risk factors for poor short-term outcomes, however, this association was weakened in predicting poor long-term outcomes. All studies were found to have a high risk of bias, primarily due to inappropriate data sources and inadequate reporting of the analysis domain. This review suggested that aSAH patients with poor clinical scores and hypertension are at a higher risk of poor outcomes. The majority of the included prediction models perform well, but generally lack reporting in the analysis domain, which may hinder their clinical applicability.

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.

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.

A novel visual dynamic nomogram to online predict the risk of unfavorable outcome in elderly aSAH patients after endovascular coiling: A retrospective study

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a significant cause of morbidity and mortality throughout the world. Dynamic nomogram to predict the prognosis of elderly aSAH patients after endovascular coiling has not been reported. Thus, we aimed to develop a clinically useful dynamic nomogram to predict the risk of 6-month unfavorable outcome in elderly aSAH patients after endovascular coiling.

Methods

We conducted a retrospective study including 209 elderly patients admitted to the People's Hospital of Hunan Province for aSAH from January 2016 to June 2021. The main outcome measure was 6-month unfavorable outcome (mRS ≥ 3). We used multivariable logistic regression analysis and forwarded stepwise regression to select variables to generate the nomogram. We assessed the discriminative performance using the area under the curve (AUC) of receiver-operating characteristic and the risk prediction model's calibration using the Hosmer-Lemeshow goodness-of-fit test. The decision curve analysis (DCA) and the clinical impact curve (CIC) were used to measure the clinical utility of the nomogram.

Results

The cohort's median age was 70 (interquartile range: 68-74) years and 133 (36.4%) had unfavorable outcomes. Age, using a ventilator, white blood cell count, and complicated with cerebral infarction were predictors of 6-month unfavorable outcome. The AUC of the nomogram was 0.882 and the Hosmer-Lemeshow goodness-of-fit test showed good calibration of the nomogram (p = 0.3717). Besides, the excellent clinical utility and applicability of the nomogram had been indicated by DCA and CIC. The eventual value of unfavorable outcome risk could be calculated through the dynamic nomogram.

Conclusion

This study is the first visual dynamic online nomogram that accurately predicts the risk of 6-month unfavorable outcome in elderly aSAH patients after endovascular coiling. Clinicians can effectively improve interventions by taking targeted interventions based on the scores of different items on the nomogram for each variable.

A nomogram for predicting the risk of poor prognosis in patients with poor-grade aneurysmal subarachnoid hemorrhage following microsurgical clipping

Objective

Aneurysmal subarachnoid hemorrhage (aSAH) is a common and potentially fatal cerebrovascular disease. Poor-grade aSAH (Hunt-Hess grades IV and V) accounts for 20-30% of patients with aSAH, with most patients having a poor prognosis. This study aimed to develop a stable nomogram model for predicting adverse outcomes at 6 months in patients with aSAH, and thus, aid in improving the prognosis.

Method

The clinical data and imaging findings of 150 patients with poor-grade aSAH treated with microsurgical clipping of intracranial aneurysms on admission from December 2015 to October 2021 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO), logistic regression analyses, and a nomogram were used to develop the prognostic models. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow tests were used to assess discrimination and calibration. The bootstrap method (1,000 repetitions) was used for internal validation. Decision curve analysis (DCA) was performed to evaluate the clinical validity of the nomogram model.

Result

LASSO regression analysis showed that age, Hunt-Hess grade, Glasgow Coma Scale (GCS), aneurysm size, and refractory hyperpyrexia were potential predictors for poor-grade aSAH. Logistic regression analyses revealed that age (OR: 1.107, 95% CI: 1.056-1.116, P < 0.001), Hunt-Hess grade (OR: 8.832, 95% CI: 2.312-33.736, P = 0.001), aneurysm size (OR: 6.871, 95% CI: 1.907-24.754, P = 0.003) and refractory fever (OR: 3.610, 95% CI: 1.301-10.018, P < 0.001) were independent predictors of poor outcome. The area under the ROC curve (AUC) was 0.909. The calibration curve and Hosmer-Lemeshow tests showed that the nomogram had good calibration ability. Furthermore, the DCA curve showed better clinical utilization of the nomogram.

Conclusion

This study provides a reliable and valuable nomogram that can accurately predict the risk of poor prognosis in patients with poor-grade aSAH after microsurgical clipping. This tool is easy to use and can help physicians make appropriate clinical decisions to significantly improve patient prognosis.

Assessment of Sustained Systemic Inflammatory Response Syndrome and CSF Markers as Predictive Values Associated with Shunt-Dependent Hydrocephalus after Aneurysmal Subarachnoid Hemorrhage

Aim of the study: This study was conducted to detect incidence and risk factors of shunt-dependent hydrocephalus, including Systemic Inflammatory Response Syndrome. Patients and methods: After obtaining ethical approval from the research ethics committee of Cairo University, this study was conducted in two phases, phase I in the form of follow up study to detect the incidence of shunt dependent hydrocephalus in patients with ruptured subarachnoid aneurysm then phase II in the form of comparative one to detect the risk factors of acquisition of shunt dependent hydrocephalus and detect the predictive role of SIRS in SDH. The study included 90 patients with ruptured subarachnoid aneurysms followed up in the department of neurosurgery of Cairo university hospital from April 2018 to April 2020. Results: The incidence of shunt-dependent hydrocephalus was 28% among the studied patients with significant association with high-grade SIRS, Fisher score, Hunt and Hess score, and leukocytosis. The CSF white blood cells and protein were significantly higher in the hydrocephalus group. Also, there was significant hypernatremia among the hydrocephalic group. Conclusion: Despite the study's analytical design, we observed a link between high fisher, SIRS, hypernatremia, and shunt-dependent hydrocephalus in aneurysmal SAH patients. Serum sodium, CSF WBCs, and protein may all be used to predict HC.