Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update

BACKGROUND

A systematic review of clinical prediction models for aneurysmal subarachnoid hemorrhage (aSAH) reported in 2011 noted that clinical prediction models for aSAH were developed using poor methods and were not externally validated. This study aimed to update the above review to guide the future development of predictive models in aSAH.

METHODS

We systematically searched Embase and MEDLINE databases (January 2010 to February 2022) for articles that reported the development of a clinical prediction model to predict functional outcomes in aSAH. Our reviews are based on the items included in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) checklist, and on data abstracted from each study in accord with the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) 2014 checklist. Bias and applicability were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).

RESULTS

We reviewed data on 30 466 patients contributing to 29 prediction models abstracted from 22 studies identified from an initial search of 7858 studies. Most models were developed using logistic regression (n=20) or machine learning (n=9) with prognostic variables selected through a range of methods. Age (n=13), World Federation of Neurological Surgeons (WFNS) grade (n=11), hypertension (n=6), aneurysm size (n=5), Fisher grade (n=12), Hunt and Hess score (n=5), and Glasgow Coma Scale (n=8) were the variables most frequently included in the reported models. External validation was performed in only four studies. All but one model had a high or unclear risk of bias due to poor performance or lack of validation.

CONCLUSION

Externally validated models for the prediction of functional outcome in aSAH patients have now become available. However, most of them still have a high risk of bias.

Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database

Aneurysmal subarachnoid hemorrhage (aSAH) develops quickly once it occurs and threatens the life of patients. We aimed to use machine learning to predict mortality for SAH patients at an early stage which can help doctors make clinical decisions. In our study, we applied different machine learning methods to an aSAH cohort extracted from a national EHR database, the Cerner Health Facts EHR database (2000-2018). The outcome of interest was in-hospital mortality, as either passing away while still in the hospital or being discharged to hospice care. Machine learning-based models were primarily evaluated by the area under the receiver operating characteristic curve (AUC). The population size of the SAH cohort was 6728. The machine learning methods achieved an average of AUCs of 0.805 for predicting mortality with only the initial 24 hours' EHR data. Without losing the prediction power, we used the logistic regression to identify 42 risk factors, -examples include age and serum glucose-that exhibit a significant correlation with the mortality of aSAH patients. Our study illustrates the potential of utilizing machine learning techniques as a practical prognostic tool for predicting aSAH mortality at the bedside.

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.

A visualized MAC nomogram online predicts the risk of three-month mortality in Chinese elderly aneurysmal subarachnoid hemorrhage patients undergoing endovascular coiling

OBJECTIVE

Aneurysmal subarachnoid hemorrhage (aSAH) is an aggressive disease with higher mortality rate in the elderly population. Unfortunately, the previous models for predicting clinical prognosis are still not accurate enough. Therefore, we aimed to construct and validate a visualized nomogram model to predict online the 3-month mortality in elderly aSAH patients undergoing endovascular coiling.

METHOD

We conducted a retrospective analysis of 209 elderly aSAH patients at People's Hospital of Hunan Province, China. A nomogram was developed based on multivariate logistic regression and forward stepwise regression analysis, then validated using the bootstrap validation method (n = 1000). In addition, the performance of the nomogram was evaluated by various indicators to prove its clinical value.

RESULT

Morbid pupillary reflex, age, and using a breathing machine were independent predictors of 3-month mortality. The AUC of the nomogram was 0.901 (95% CI: 0.853-0.950), and the Hosmer-Lemeshow goodness-of-fit test showed good calibration of the nomogram (p = 0.4328). Besides, the bootstrap validation method internally validated the nomogram with an area under the curve of the receiver operator characteristic (AUROC) of 0.896 (95% CI: 0.846-0.945). Decision curve analysis (DCA) and clinical impact curve (CIC) indicated the nomogram's excellent clinical utility and applicability.

CONCLUSION

An easily applied visualized nomogram model named MAC (morbid pupillary reflex-age-breathing machine) based on three accessible factors has been successfully developed. The MAC nomogram is an accurate and complementary tool to support individualized decision-making and emphasizes that patients with higher risk of mortality may require closer monitoring. Furthermore, a web-based online version of the risk calculator would greatly contribute to the spread of the model in this field.

Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases

Scientific advances have informed many aspects of acute stroke care but have also highlighted the complexity and heterogeneity of cerebrovascular diseases. While practice guidelines are essential in supporting the clinical decision-making process, they may not capture the nuances of individual cases. Personalized stroke care in ICU has traditionally relied on integrating clinical examinations, neuroimaging studies, and physiologic monitoring to develop a treatment plan tailored to the individual patient. However, to realize the potential of precision medicine in stroke, we need advances and evidence in several critical areas, including data capture, clinical phenotyping, serum biomarker development, neuromonitoring, and physiology-based treatment targets. Mathematical tools are being developed to analyze the multitude of data and provide clinicians with real-time information and personalized treatment targets for the critical care management of patients with cerebrovascular diseases. This review summarizes research advances in these areas and outlines principles for translating precision medicine into clinical practice.

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.

Denosumab for Prevention of Acute Onset Immobilization-Induced Alterations of Bone Turnover: A Randomized Controlled Trial

Metabolic bone disease is a devastating condition in critically ill patients admitted to an intensive care unit (ICU). We investigated the effects of early administration of the antiresorptive drug denosumab on bone metabolism in previously healthy patients. Fourteen patients with severe intracerebral or subarachnoid hemorrhage were included in a phase 2 trial. Within 72 hours after ICU admission, they were randomized in a 1:1 ratio to receive denosumab 60 mg or placebo subcutaneously. The primary endpoint was group differences in the percentage change of C-terminal telopeptide of type 1 collagen (CTX-1) levels in serum from denosumab/placebo application to 4 weeks thereafter. Changes in serum levels of bone formation markers and urinary calcium excretion were secondary outcome parameters. Regarding serum levels of CTX-1, changes over time averaged -0.45 ng/mL (95% confidence interval [CI] -0.72, -0.18) for the denosumab group and 0.29 ng/mL (95% CI -0.01, 0.58) for the placebo group. The primary endpoint, the group difference in changes between baseline and secondary measurement, adjusted for baseline serum levels and baseline neurological status, averaged -0.74 ng/mL (95% CI -1.14, -0.34; p = 0.002). The group difference in changes between baseline and secondary osteocalcin measurement averaged -5.60 ng/mL (95% CI -11.2, -0.04; p = 0.049). The group difference in averaged change between baseline and secondary measurement of 24-hour urine calcium excretion was significant (-1.77 mmol/L [95% CI -3.48, -0.06; p = 0.044]). No adverse events could be attributed to the study medication. The investigation proved that a single application of denosumab early after admission to an ICU prevents acute immobilization-associated increase in bone resorption among previously healthy individuals. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Clinical Outcome and Prognostic Factors of Patients with Perimesencephalic and Nonperimesencephalic Subarachnoid Hemorrhage

OBJECTIVE

To demonstrate the clinical outcome of patients with nonperimesencephalic subarachnoid hemorrhage (npSAH) compared with patients with aneurysmal SAH (aSAH) and perimesencephalic SAH (pSAH) and to evaluate predictive value of various clinical and radiological findings in patients with npSAH.

METHODS

We retrospectively identified patients with SAH who presented at our institution between 2009 and 2018. We analyzed demographic and clinical data and outcomes. Multivariable analysis was performed for outcome parameters.

RESULTS

Of 608 patients with confirmed SAH, 78% had aSAH, and 22% had nonaneurysmal SAH. Nonaneurysmal SAH was perimesencephalic in 30% of cases and nonperimesencephalic in 70%. Initial clinical status (Hunt and Hess score) was significantly worse in patients with aSAH compared with patients with nonaneurysmal SAH. Complications such as delayed cerebral ischemia occurred significantly more often in patients with aSAH. Patients with pSAH had a more favorable clinical course than patients with aSAH or npSAH. There was no significant difference in 30-day mortality between aSAH (29%) and npSAH (28%) patients (P = 0.835). Hunt and Hess score emerged as a strong predictor of unfavorable outcome in both aSAH and npSAH in multivariable regression.

CONCLUSIONS

Patients with npSAH had a similar clinical outcome as patients with aSAH, although there were significantly fewer clinical complications in patients with npSAH. Patients with pSAH demonstrated an overall good clinical course. Our multivariable analysis showed that initial Hunt and Hess score was an important predictor for clinical outcome in aSAH as well as npSAH.

Antiseizure Medication Treatment and Outcomes in Patients with Subarachnoid Hemorrhage Undergoing Continuous EEG Monitoring

BACKGROUND

Patients with aneurysmal subarachnoid hemorrhage (aSAH) with electroencephalographic epileptiform activity (seizures, periodic and rhythmic patterns, and sporadic discharges) are frequently treated with antiseizure medications (ASMs). However, the safety and effectiveness of ASM treatment for epileptiform activity has not been established. We used observational data to investigate the effectiveness of ASM treatment in patients with aSAH undergoing continuous electroencephalography (cEEG) to develop a causal hypothesis for testing in prospective trials.

METHODS

This was a retrospective single-center cohort study of patients with aSAH admitted between 2011 and 2016. Patients underwent ≥ 24 h of cEEG within 4 days of admission. All patients received primary ASM prophylaxis until aneurysm treatment (typically within 24 h of admission). Treatment exposure was defined as reinitiation of ASMs after aneurysm treatment and cEEG initiation. We excluded patients with non-cEEG indications for ASMs (e.g., epilepsy, acute symptomatic seizures). Outcomes measures were 90-day mortality and good functional outcome (modified Rankin Scale scores 0-3). Propensity scores were used to adjust for baseline covariates and disease severity.

RESULTS

Ninety-four patients were eligible (40 continued ASM treatment; 54 received prophylaxis only). ASM continuation was not significantly associated with higher 90-day mortality (propensity-adjusted hazard ratio [HR] = 2.01 [95% confidence interval (CI) 0.57-7.02]). ASM continuation was associated with lower likelihood for 90-day good functional outcome (propensity-adjusted HR = 0.39 [95% CI 0.18-0.81]). In a secondary analysis, low-intensity treatment (low-dose single ASM) was not significantly associated with mortality (propensity-adjusted HR = 0.60 [95% CI 0.10-3.59]), although it was associated with a lower likelihood of good outcome (propensity-adjusted HR = 0.37 [95% CI 0.15-0.91]), compared with prophylaxis. High-intensity treatment (high-dose single ASM, multiple ASMs, or anesthetics) was associated with higher mortality (propensity-adjusted HR = 6.80 [95% CI 1.67-27.65]) and lower likelihood for good outcomes (propensity-adjusted HR = 0.30 [95% CI 0.10-0.94]) compared with prophylaxis only.

CONCLUSIONS

Our findings suggest the testable hypothesis that continuing ASMs in patients with aSAH with cEEG abnormalities does not improve functional outcomes. This hypothesis should be tested in prospective randomized studies.

External Validation of a Neural Network Model in Aneurysmal Subarachnoid Hemorrhage: A Comparison With Conventional Logistic Regression Models

BACKGROUND

Interest in machine learning (ML)-based predictive modeling has led to the development of models predicting outcomes after aneurysmal subarachnoid hemorrhage (aSAH), including the Nijmegen acute subarachnoid hemorrhage calculator (Nutshell). Generalizability of such models to external data remains unclear.

OBJECTIVE

To externally validate the performance of the Nutshell tool while comparing it with the conventional Subarachnoid Hemorrhage International Trialists (SAHIT) models and to review the ML literature on outcome prediction after aSAH and aneurysm treatment.

METHODS

A prospectively maintained database of patients with aSAH presenting consecutively to our institution in the 2013 to 2018 period was used. The web-based Nutshell and SAHIT calculators were used to derive the risks of poor long-term (12-18 months) outcomes and 30-day mortality. Discrimination was evaluated using the area under the curve (AUC), and calibration was investigated using calibration plots. The literature on relevant ML models was surveyed for a synopsis.

RESULTS

In 269 patients with aSAH, the SAHIT models outperformed the Nutshell tool (AUC: 0.786 vs 0.689, P = .025) in predicting long-term functional outcomes. A logistic regression model of the Nutshell variables derived from our data achieved adequate discrimination (AUC = 0.759) of poor outcomes. The SAHIT models outperformed the Nutshell tool in predicting 30-day mortality (AUC: 0.810 vs 0.636, P < .001). Calibration properties were more favorable for the SAHIT models. Most published aneurysm-related ML-based outcome models lack external validation and usable testing platforms.

CONCLUSION

The Nutshell tool demonstrated limited performance on external validation in comparison with the SAHIT models. External validation and the dissemination of testing platforms for ML models must be emphasized.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Glycemic indices predict outcomes after aneurysmal subarachnoid hemorrhage: a retrospective single center comparative analysis

Although hyperglycemia is associated with worse outcomes after aneurysmal subarachnoid hemorrhage (aSAH), there is no consensus on the optimal glucose control metric, acceptable in-hospital glucose ranges, or suitable insulin regimens in this population. In this single-center retrospective cohort study of aSAH patients, admission glucose, and hospital glucose mean (MHG), minimum (MinG), maximum (MaxG), and variability were compared. Primary endpoints (mortality, complications, and vasospasm) were assessed using multivariate logistic regressions. Of the 217 patients included, complications occurred in 83 (38.2%), 124 (57.1%) had vasospasm, and 41 (18.9%) died. MHG was independently associated with (p < 0.001) mortality, MaxG (p = 0.017) with complications, and lower MinG (p = 0.015) with vasospasm. Patients with MHG ≥ 140 mg/dL had 10 × increased odds of death [odds ratio (OR) = 10.3; 95% CI 4.6–21.5; p < 0.0001] while those with MinG ≤ 90 mg/dL had nearly 2× increased odds of vasospasm (OR = 1.8; 95% CI 1.01–3.21; p = 0.0422). While inpatient insulin was associated with increased complications and provided no mortality benefit, among those with MHG ≥ 140 mg/dL insulin therapy resulted in lower mortality (OR = 0.3; 95% CI 0.1–0.9; p = 0.0358), but no increased complication risk. While elevated MHG and MaxG are highly associated with poorer outcomes after aSAH, lower MinG is associated with increased vasospasm risk. Future trials should consider initiating insulin therapy based on MHG rather than other hyperglycemia measures.

Prognostic value of the 2010 consensus definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

BACKGROUND AND PURPOSE

Delayed cerebral ischemia (DCI) complicates the recovery of approximately 30% of patients with aneurysmal subarachnoid hemorrhage (aSAH). The definition of DCI widely varies, even though a consensus definition has been recommended since 2010. This study aimed to evaluate the prognostic value of the 2010 consensus definition of DCI in a cohort of patients with aSAH.

METHODS

We conducted a single-center, retrospective, observational study that included consecutive adult patients with aSAH who were admitted to the intensive care unit from January 2010 to December 2014. DCI was evaluated 48 h to 14 days after onset of aSAH symptoms using the 2010 consensus criteria and outcome was assessed by the Glasgow Outcome Scale (GOS) at discharge from hospital.

RESULTS

A total of 340 patients were analyzed and the incidence of DCI was 37.1%. The median time from primary hemorrhage to the occurrence of DCI was 97 h. Neurological deterioration was observed in most (89.7%) of the patients who fulfilled the DCI criteria. The occurrence of DCI was strongly associated with an unfavorable outcome (GOS 1-3) at hospital discharge (OR 2.65, 95% CI 1.69-4.22, p < 0.001).

CONCLUSIONS

The incidence of DCI after aSAH is high and its occurrence is strongly associated with an unfavorable neurological outcome. This finding adds to the previous literature, which has shown that DCI appears to be a major contributor affecting the functional ability of survivors of aSAH. To further advance reliable knowledge of DCI, future studies should adhere to the consensus definition of DCI.

Sedation Patterns and Hyperosmolar Therapy in Emergency Departments were Associated with Blood Pressure Variability and Outcomes in Patients with Spontaneous Intracranial Hemorrhage

Background

Spontaneous intracranial hemorrhage (sICH) is associated with high mortality. Little information exists to guide initial resuscitation in the emergency department (ED) setting. However, blood pressure variability (BPV) and mechanical ventilation (MV) are known risk factors for poor outcome in sICH.

Objectives

The objective was to examine the associations between BPV and MV in ED (EDMV) and between two ED interventions - post-MV sedation and hyperosmolar therapy for elevated intracranial pressure - and BPV in the ED and in-hospital mortality.

Methods

We retrospectively studied adults with sICH and external ventricular drainage who were transferred to a quaternary academic medical center from other hospitals between January 2011 and September 2015. We used multivariable linear and logistic regressions to measure associations between clinical factors, BPV, and outcomes.

Results

We analyzed ED records from 259 patients. There were 143 (55%) EDMV patients who had more severe clinical factors and significantly higher values of all BPV indices than NoEDMV patients. Two clinical factors and none of the severity scores (i.e., Hunt and Hess, World Federation of Neurological Surgeons Grades, ICH score) correlated with BPV. Hyperosmolarity therapy without fluid resuscitation positively correlated with all BPV indices, whereas propofol infusion plus a narcotic negatively correlated with one of them. Two BPV indices, i.e., successive variation of blood pressure (BP_SV) and absolute difference in blood pressure between ED triage and departure (BP_{Depart - Triage}), were significantly associated with increased mortality rate.

Conclusion

Patients receiving MV had significantly higher BPV, perhaps related to disease severity. Good ED sedation, hyperosmolar therapy, and fluid resuscitation were associated with less BPV and lower likelihood of death.

Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes

OBJECTIVE

To determine which findings on routine clinical EEGs correlate with delirium severity across various presentations and to determine whether EEG findings independently predict important clinical outcomes.

METHODS

We prospectively studied a cohort of nonintubated inpatients undergoing EEG for evaluation of altered mental status. Patients were assessed for delirium within 1 hour of EEG with the 3-Minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) and 3D-CAM severity score. EEGs were interpreted clinically by neurophysiologists, and reports were reviewed to identify features such as theta or delta slowing and triphasic waves. Generalized linear models were used to quantify associations among EEG findings, delirium, and clinical outcomes, including length of stay, Glasgow Outcome Scale scores, and mortality.

RESULTS

We evaluated 200 patients (median age 60 years, IQR 48.5-72 years); 121 (60.5%) met delirium criteria. The EEG finding most strongly associated with delirium presence was a composite of generalized theta or delta slowing (odds ratio 10.3, 95% confidence interval 5.3-20.1). The prevalence of slowing correlated not only with overall delirium severity (R 2 = 0.907) but also with the severity of each feature assessed by CAM-based delirium algorithms. Slowing was common in delirium even with normal arousal. EEG slowing was associated with longer hospitalizations, worse functional outcomes, and increased mortality, even after adjustment for delirium presence or severity.

CONCLUSIONS

Generalized slowing on routine clinical EEG strongly correlates with delirium and may be a valuable biomarker for delirium severity. In addition, generalized EEG slowing should trigger elevated concern for the prognosis of patients with altered mental status.

Interventions affecting blood pressure variability and outcomes after intubating patients with spontaneous intracranial hemorrhage

INTRODUCTION

Spontaneous intracranial hemorrhage (sICH) that increases intracranial pressure (ICP) is a life-threatening emergency often requiring intubation in Emergency Departments (ED). A previous study of intubated ED patients found that providing ≥5 interventions after initiating mechanical ventilation (pMVI) reduced mortality rate. We hypothesized that pMVIs would lower blood pressure variability (BPV) in patients with sICH and thus improve survival rates and neurologic outcomes.

METHOD

We performed a retrospective study of adults, who were transferred to a quaternary medical center between 01/01/2011 and 09/30/2015 for sICH, received an extraventricular drain during hospitalization. They were identified by International Classification of Diseases, version 9 (430.XX, 431.XX), and procedure code 02.21. Outcomes were BPV indices, death, and being discharged home.

RESULTS

We analyzed records from 147 intubated patients transferred from 40 EDs. Forty-one percent of patients received ≥5 pMVIs and was associated with lower median successive variation in systolic blood pressure (BP_SV) (31,[IQR 18-45) compared with those receiving 4 or less pMVIs (38[IQR 16-70]], p = 0.040). Three pMVIs, appropriate tidal volume, sedative infusion, and capnography were significantly associated with lower BPV. In addition to clinical factors, BP_SV (OR 26; 95% CI 1.2, >100) and chest radiography (OR 0.3; 95% CI 0.09, 0.9) were associated with mortality rate. Use of quantitative capnography (OR 8.3; 95%CI, 4.7, 8.8) was associated with increased likelihood of being discharged home.

CONCLUSIONS

In addition to disease severity, individual pMVIs were significantly associated with BPV and patient outcomes. Emergency physicians should perform pMVIs more frequently to prevent BPV and improve patients' outcomes.