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

Development and Validation of a Nomogram Prediction Model for Key Symptoms of Post-Intensive Care Syndrome-Family in Family Members of Critically-Ill Patients: Focusing on Sleep Disturbance, Fatigue, Anxiety, and Depression

Purpose

To construct and validate a nomogram model predicting the risk of post-intensive care syndrome-family (PICS-F) in family members of critically ill patients.

Methods

This study was conducted on family members (parents, spouses, or children) of critically ill patients in the three intensive care units of Binzhou Medical University Hospital from December 2023 to June 2024, responsible for medical decisions and primary care. The sleep disturbances, fatigue, anxiety, and depression were assessed using the Pittsburgh Sleep Quality Index, the Subscale of Fatigue Assessment Instrument, and the Hospital Anxiety and Depression Scale, respectively. Predictive factors were identified through univariate and multivariate logistic regression, and a nomogram was constructed using R4.2.3. Internal validation used the Bootstrap sampling method, and external validation employed the time-period method.

Results

The study involved 567 participants divided into a modeling group (n = 432; median age = 46 years; 209 males, 223 females) and an external validation group (n = 135; median age = 45 years; 70 males, 65 females). The model included five predictive factors: family age, patient age, APACHE II score, average monthly income per family member, and PSSS score. The AUC of the modeling group was 0.894 (0.864 ~ 0.924), with a specificity of 85.4%, a sensitivity of 78.0%, and a maximum Youden index of 0.634. The H-L test revealed a good fit (X 2 value = 9.528, P = 0.300). The internal validation results of the Bootstrap sampling method showed that the calibration curve of the model was close to the ideal curve, and the DCA curve results indicated high clinical practicality. Moreover, the external validation results showed that AUC was 0.847 (0.782 ~ 0.912), with sensitivity and specificity of 74.5% and 86.3%, respectively. The H-L test results indicated a good fit (X 2 value = 9.625, P = 0.292).

Conclusion

The nomogram demonstrated strong predictive performance for PICS-F risk in ICU patients' families, offering a valuable tool for clinical assessment.

Development of a Clinical and Laboratory-Based Predictive Nomogram Model for Unfavorable Functional Outcomes Among Patients Who Undergo Interventions for Aneurysmal Subarachnoid Hemorrhage

Objective: This study elucidates the prognostic significance of perioperative changes in laboratory indicators for aneurysmal SAH and develops a nomogram model for outcome prediction. Methods: Aneurysmal SAH patients who received clipping or coiling at our institution between January 2016 and December 2022 were included. All patients were randomly assigned to derivation and validation cohorts. Independent predictors of unfavorable outcomes were identified by multivariate analyses. Three models were conducted to evaluate whether perioperative laboratory changes improve prediction performance. A nomogram including all independent predictors was developed in the derivation cohort and verified in both cohorts. Results: Diabetes mellitus [OR (95% CI) = 2.84 (1.44-5.59)], WFNS grade 3-5 [OR: (95% CI), 9.17 (5.49-15.33)], clipping [OR (95% CI) = 1.71 (1.03-2.85)], perioperative changes in white blood cell count [OR (95% CI) = 2.15 (1.17-3.96)], and concentrations of ALT [OR (95% CI) = 1.41 (1.04-1.91)], sodium [OR (95% CI) = 5.40 (3.01-9.71)], and glucose [OR (95% CI) = 2.18 (1.05-4.53)] were independent predictors of an unfavorable outcome. The predictive nomogram incorporated the aforementioned predictors and performed well in the derivation cohort (AUC, 0.839; 95% CI: 0.810-0.866) and the validation cohort (AUC, 0.797; 95% CI: 0.734-0.850). Conclusions: Perioperative changes in laboratory indicators can be predictors of unfavorable outcomes in aneurysmal SAH patients. The nomogram based on clinical and laboratory risk factors can be used as a convenient tool to facilitate individualized decision making.

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.

Early Microcirculatory Dysfunction on Perfusion CT Is Related to Prognosis After Aneurysmal Subarachnoid Hemorrhage

Microcirculatory dysfunction is an important pathophysiology mechanism of early brain injury after aneurysmal subarachnoid hemorrhage (aSAH), which contributes to poor outcomes. The study was performed in Beijing Tiantan Hospital from October 2020 to July 2023. Patients with aSAH who underwent computed tomographic perfusion (CTP) within 24 h after ictus were enrolled prospectively. The peak time of arterial inflow (PTA), peak time of venous outflow (PTV), total venous outflow time (TVT), and difference value of arteriovenous peak time (DV) were collected from the time-density curve of CTP. Primary outcome was 3-month unfavorable functional outcome (modified Rankin Scale score of 4-6). Secondary outcomes included 3-month all-cause death and delayed cerebral ischemia. Multivariable logistic regression analysis and restricted cubic splines were performed to explore the relationship between cerebral hemodynamic parameters and outcomes. We also assessed the prognostic performance of incorporating hemodynamic parameters into previous nomogram models for 3-month poor clinical outcomes. A total of 612 patients were enrolled, among whom the mean age was 56.9 ± 12.3 years old and 391 (63.9%) were female. On multivariable analysis, prolonged TVT could significantly predict 3-month poor functional outcome (adjusted OR 1.074, 95%CI 1.013-1.139), while prolonged PTA was an independent predictor of 3-month all-cause death (adjusted OR 1.293, 95%CI 1.099-1.521). The addition of TVT or PTA to previous nomogram models led to improvements in C-statistics, net reclassification (NRI), and integrated discrimination improvement (IDI). Our study underscores the vital role of arterial inflow and venous outflow in sustaining microcirculation during the acute phase after aSAH, thereby offering new directions for future investigations into therapeutic targets.

Analysis and prediction of subarachnoid hemorrhage burden in global, China, and Japan

BACKGROUND

Subarachnoid hemorrhage (SAH) remains a serious public health problem worldwide, especially in economically developed regions/countries. This study intends to thoroughly analyze the incidence, mortality, and disability-adjusted life years (DALYs) rate of SAH at the global, regional, and national levels. This study focused on the differences in SAH incidence between China and Japan from 1990 to 2019, and projected global, Chinese, and Japanese SAH incidence rates until 2030.

METHODS

Data on the disease burden owing to SAH from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) Study 2019. linear regression analysis was used to calculate the estimated annual percentage change (EAPC) and linear regression method was used to calculate the average annual percentage change (AAPC). Bayesian age-period-cohort (BAPC) model was used to predict the disease burden from 2020 to 2030.

RESULTS

Globally, age-standardised incidence, mortality, and DALYs rates was declined from 1990 to 2019. In 1990-2019, the incidence of SAH decreased in China, while it increased in Japan, especially among middle-aged and elderly women. Projections suggest that the global incidence of SAH will decrease by 2.06% in 2030, with an increase of 6.24% in China and 13.82% in Japan, with the highest increase among Japanese women being 16.19%.

CONCLUSIONS

Global SAH incidence, mortality, and DALYs rates declined over the 1990-2019 period, with regional/national SAH mortality and DALYs rates negatively correlated with socio-demographic index (SDI), while SAH incidence was positively correlated with SDI. The incidence of SAH decreased in China and increased in Japan during this period. The predictions show that over the next 10 years, while the incidence of SAH continues to decline globally, the incidence of SAH in China and Japan has increased. Thus, SAH remains a serious disease burden that requires early intervention targeting risk factors and populations at risk that may have increased because of economic development.

Interrelationships Between Inflammatory Score, Delayed Cerebral Ischemia and Unfavorable Outcome in Patients with aSAH: A Four-Way Decomposition

Background

To identify biomarkers and develop an inflammatory score based on proper integration to improve risk prediction of delayed cerebral ischemia (DCI) and poor outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). We also further explore the mediation and interaction of DCI within the chain of events using the four-way effect decomposition.

Methods

Machine learning algorithms are used for biomarker selection and constructed the inflammatory score. Multivariate logistic regression was performed to identify the association of inflammatory score with DCI and poor outcome. Next, we employed a four-way decomposition to assess the extent to which the inflammation effect on the risk of poor outcome is mediated by or interacts with DCI. Finally, the additive value of inflammatory score was measured using the area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

In total, 368 aSAH patients were included. The inflammatory score was calculated with the combination of lymphocyte, pan-immune-inflammation value (PIV), red blood cell distribution width (RDW), and lactate dehydrogenase (LDH). Multivariate analysis identified that inflammatory score was independently associated with DCI and poor outcome. The effect of high inflammatory score on poor outcome may be partly explained by DCI, where there is both pure mediation and mediated interaction. With DCI as a potential mediator, the excess relative risk could be decomposed into 30.86% controlled direct effect, 3.60% mediation only, 26.64% interaction only, and 38.89% mediated interaction. Adding the inflammatory score to the predictive model improved the AUC from 0.772 to 0.822, with an NRI of 5.3% and IDI of 6.9%.

Conclusion

The inflammatory score was significantly associated with DCI and poor outcome in patients with aSAH. Not only may be a potential synergistic interaction between high inflammatory score and DCI on the risk of poor outcome but also where DCI is an important mediating mechanism.

Insulin Resistance Predicts Prognosis in Patients With Subarachnoid Hemorrhage

OBJECTIVE

The objective of this study was to determine whether insulin resistance (IR) could be used as a predictor of poor prognosis at 3 months after subarachnoid hemorrhage (SAH).

METHODS

The study included patients aged 18 years or older with a confirmed diagnosis of SAH due to ruptured aneurysm from January 2021 to March 2024. Patients with confirmed diabetes mellitus and taking glucose-lowering drugs, or taking lipid-lowering drugs, or SAH not due to ruptured aneurysm, or comorbid systemic diseases were excluded. Patients were classified into good prognosis (modified Rankin scale [MRS] 0-2) and poor prognosis (MRS 3-6). Receiver operating characteristic curve (ROC), least absolute shrinkage and selection operator (LASSO) analysis, and multivariate logistic regression analysis were used to determine the potential of triglyceride-glucose (TyG) index and the triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio as predictors of poor prognosis. Finally, a prognostic prediction model based on IR was constructed.

RESULTS

A total of 358 patients were included in this study. Poor prognosis patients had higher age, BMI, hypertension percentage, glucose, triglycerides, TyG index and TG/HDL ratio, and lower HDL. ROC, LASSO, and multivariate logistic regression analysis revealed that age, glucose, TyG index, and TG/HDL ratio had significant potential to predict the prognosis of SAH patients. The prognostic prediction model constructed by combining age, glucose, TyG index, and TG/HDL ratio had high discriminatory power (area under the curve [AUC] = 0.80), satisfactory calibration curves, and good clinical utility.

CONCLUSION

IR is strongly associated with the prognosis of SAH patients, and the combination of age, glucose, TyG index, and TG/HDL ratio can provide a new direction for future treatment.

A nomogram for the prediction of short-term mortality in patients with aneurysmal subarachnoid hemorrhage requiring mechanical ventilation: a post-hoc analysis

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating stroke subtype with high morbidity and mortality. Although several studies have developed a prediction model in aSAH to predict individual outcomes, few have addressed short-term mortality in patients requiring mechanical ventilation. The study aimed to construct a user-friendly nomogram to provide a simple, precise, and personalized prediction of 30-day mortality in patients with aSAH requiring mechanical ventilation.

Methods

We conducted a post-hoc analysis based on a retrospective study in a French university hospital intensive care unit (ICU). All patients with aSAH requiring mechanical ventilation from January 2010 to December 2015 were included. Demographic and clinical variables were collected to develop a nomogram for predicting 30-day mortality. The least absolute shrinkage and selection operator (LASSO) regression method was performed to identify predictors, and multivariate logistic regression was used to establish a nomogram. The discriminative ability, calibration, and clinical practicability of the nomogram to predict short-term mortality were tested using the area under the curve (AUC), calibration plot, and decision curve analysis (DCA).

Results

Admission GCS, SAPS II, rebleeding, early brain injury (EBI), and external ventricular drain (EVD) were significantly associated with 30-day mortality in patients with aSAH requiring mechanical ventilation. Model A incorporated four clinical factors available in the early stages of the aSAH: GCS, SAPS II, rebleeding, and EBI. Then, the prediction model B with the five predictors was developed and presented in a nomogram. The predictive nomogram yielded an AUC of 0.795 [95% CI, 0.731-0.858], and in the internal validation with bootstrapping, the AUC was 0.780. The predictive model was well-calibrated, and decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusion

We have developed two models and constructed a nomogram that included five clinical characteristics to predict 30-day mortality in patients with aSAH requiring mechanical ventilation, which may aid clinical decision-making.

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.