A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage

ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results.

Combination of Albumin/Fibrinogen Ratio and Admission Hunt-Hess Scale Score as an Independent Predictor of Clinical Outcome in Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

The albumin/fibrinogen ratio (AFR) is an independent predictor of clinical outcomes of some diseases; however, the prognostic value of AFR and the admission Hunt-Hess (HH) score is still unclear for patients with an aneurysmal subarachnoid hemorrhage (aSAH). This study aimed to assess the relationship between the AFR-HH score and 6-month outcomes of aSAH patients.

METHODS

The clinical characteristics of aSAH patients admitted to our department between December 2017 and December 2021 were retrospectively analyzed. The candidate risk factors were screened using univariate regression analysis, and the independence of the resultant risk factors was evaluated by binary logistic regression analysis. The predictive value of the combined AFR and HH score for unfavorable outcomes was assessed using receiver operating characteristic curve analysis.

RESULTS

A total of 112 aSAH patients were included. Binary logistic regression analysis showed the perioperative period AFR, Glasgow coma scale score, and admission HH score were independent risk factors for unfavorable outcomes for aSAH patients. The receiver operating characteristic curve analysis showed the predictive capacity of AFR plus the admission HH score outperformed the AFR, Glasgow coma scale score, and admission HH scale alone and the combination of the AFR and Glasgow coma scale score.

CONCLUSIONS

A low AFR during the perioperative period is associated with unfavorable outcomes for aSAH patients at 6 months. The combination of the AFR and admission HH scale score provides superior predictive capacity to either the AFR or HH scale score alone.

Mini review: Current status and perspective of S100B protein as a biomarker in daily clinical practice for diagnosis and prognosticating of clinical outcome in patients with neurological diseases with focus on acute brain injury

Prognosticating the clinical outcome of neurological diseases is essential to guide treatment and facilitate decision-making. It usually depends on clinical and radiological findings. Biomarkers have been suggested to support this process, as they are deemed objective measures and can express the extent of tissue damage or reflect the degree of inflammation. Some of them are specific, and some are not. Few of them, however, reached the stage of daily application in clinical practice. This mini review covers available applications of the S100B protein in prognosticating clinical outcome in patients with various neurological disorders, particularly in those with traumatic brain injury, spontaneous subarachnoid hemorrhage and ischemic stroke. The aim is to provide an understandable picture of the clinical use of the S100B protein and give a brief overview of the current limitations that require future solutions.

The incidence and risk factors of perioperative cardiac complications in noncardiac major surgery in high-altitude areas: A prospective trial in Tibet autonomous region, China

Background

The risk of perioperative cardiac complications (PCCs) in patients living in high-altitude areas may increase with more adverse clinical outcomes due to the special geographical environment, which has not yet been studied. We aimed to determine the incidence and analyze risk factors for PCCs in adult patients undergoing major noncardiac surgery in the Tibet Autonomous Region.

Methods

This prospective cohort study enrolled resident patients from high-altitude areas receiving major noncardiac surgery in Tibet Autonomous Region People's Hospital in China. Perioperative clinical data were collected, and the patients were followed up until 30 days after surgery. The primary outcome was PCCs during the operation and within 30 days after the surgery. Logistic regression was used to build the prediction models for PCCs. A receiver operating characteristic (ROC) curve was used to evaluate the discrimination. A prognostic nomogram was constructed to generate a numerical probability of PCCs for patients undergoing noncardiac surgery in high-altitude areas.

Results

Among the 196 patients living in high-altitude areas involved in this study, 33 (16.8%) suffered PCCs perioperatively and within 30 days after surgery. Eight clinical factors were identified in the prediction model, including older age (P = 0.028), extremely high altitude above 4,000 m (P = 0.442), preoperative metabolic equivalent (MET) < 4 (P = 0.153), history of angina within 6 months (P = 0.037), history of great vascular disease (P = 0.073), increased preoperative high sensitivity C-reactive protein (hs-CRP) (P = 0.072), intraoperative hypoxemia (P = 0.025) and operation time >3 h (P = 0.043). The area under the curve (AUC) was 0.766 (95% confidence interval: 0.785–0.697). The score calculated from the prognostic nomogram predicted the risk of PCCs in high-altitude areas.

Conclusion

The incidence of PCCs in resident patients living in high-altitude areas who underwent noncardiac surgery was high, and the risk factors included older age, high altitude above 4,000 m, preoperative MET < 4, history of angina within 6 months, history of great vascular disease, increased preoperative hs-CRP, intraoperative hypoxemia, and operation time >3 h. The prognostic nomogram of this study could help to assess the PCCs for patients in high-attitude areas undergoing noncardiac surgery.

Clinical Trial Registration

ClinicalTrials.gov ID: NCT04819698.

Reproducible manuscript preparation with RMarkdown application to JMSACL and other Elsevier Journals

INTRODUCTION

With the rising complexity of modern multimarker analytical techniques and notable scientific publication retractions required for erroneous statistical analysis, there is increasing awareness of the importance of research transparency and reproducibility. The development of mature open-source tools for literate programming in multiple langauge paradigms has made fully-reproducible authorship possible.

OBJECTIVES

We describe the procedure for manuscript preparation using RMarkdown and the R statistical programming language with application to JMSACL or any other Elsevier journal.

METHODS

An instructional manuscript has been prepared in the RMarkdown markup language with stepwise directions on preparing sections, subsections, lists, tables, figures and reference management in an entirely reproducible format.

RESULTS

From RMarkdown code, a submission-ready PDF is generated and JMSACL-compatible LaTeX code is generated. These can be uploaded to the Editorial Manager.

CONCLUSION

A completely reproducible manuscript preparation pipeline using the R and RMarkdown is described.

Genomic Prediction of Two Complex Orthopedic Traits Across Multiple Pure and Mixed Breed Dogs

Canine hip dysplasia (CHD) and rupture of the cranial cruciate ligament (RCCL) are two complex inherited orthopedic traits of dogs. These two traits may occur concurrently in the same dog. Genomic prediction of these two diseases would benefit veterinary medicine, the dog’s owner, and dog breeders because of their high prevalence, and because both traits result in painful debilitating osteoarthritis in affected joints. In this study, 842 unique dogs from 6 breeds with hip and stifle phenotypes were genotyped on a customized Illumina high density 183 k single nucleotide polymorphism (SNP) array and also analyzed using an imputed dataset of 20,487,155 SNPs. To implement genomic prediction, two different statistical methods were employed: Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian method called BayesC. The cross-validation results showed that the two methods gave similar prediction accuracy (r = 0.3–0.4) for CHD (measured as Norberg angle) and RCCL in the multi-breed population. For CHD, the average correlation of the AUC was 0.71 (BayesC) and 0.70 (GBLUP), which is a medium level of prediction accuracy and consistent with Pearson correlation results. For RCCL, the correlation of the AUC was slightly higher. The prediction accuracy of GBLUP from the imputed genotype data was similar to the accuracy from DNA array data. We demonstrated that the genomic prediction of CHD and RCCL with DNA array genotype data is feasible in a multiple breed population if there is a genetic connection, such as breed, between the reference population and the validation population. Albeit these traits have heritability of about one-third, higher accuracy is needed to implement in a natural population and predicting a complex phenotype will require much larger number of dogs within a breed and across breeds. It is possible that with higher accuracy, genomic prediction of these orthopedic traits could be implemented in a clinical setting for early diagnosis and treatment, and the selection of dogs for breeding. These results need continuous improvement in model prediction through ongoing genotyping and data sharing. When genomic prediction indicates that a dog is susceptible to one of these orthopedic traits, it should be accompanied by clinical and radiographic screening at an acceptable age with appropriate follow-up.

A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve

The area under the receiver operating characteristic curve is a widely used measure for evaluating the performance of a diagnostic test. Common approaches for inference on area under the receiver operating characteristic curve are usually based upon approximation. For example, the normal approximation based inference tends to suffer from the problem of low accuracy for small sample size. Frequentist empirical likelihood based approaches for area under the receiver operating characteristic curve estimation may perform better, but are usually conducted through approximation in order to reduce the computational burden, thus the inference is not exact. By contrast, we proposed an exact inferential procedure by adapting the empirical likelihood into a Bayesian framework and draw inference from the posterior samples of the area under the receiver operating characteristic curve obtained via a Gibbs sampler. The full conditional distributions within the Gibbs sampler only involve empirical likelihoods with linear constraints, which greatly simplify the computation. To further enhance the applicability and flexibility of the Bayesian empirical likelihood, we extend our method to the estimation of partial area under the receiver operating characteristic curve, comparison of multiple tests, and the doubly robust estimation of area under the receiver operating characteristic curve in the presence of missing test results. Simulation studies confirm the desirable performance of the proposed methods, and a real application is presented to illustrate its usefulness.

Never forget a face: Verbalization facilitates recollection as evidenced by flexible responding to contrasting recognition memory tests

Verbal facilitation occurs when describing a face improves its subsequent recognition; but there are several theoretical explanations debated in the literature. The results of the present studies support a relatively unrestricted, parsimonious theory that verbal facilitation occurs because describing a face supports recollection of several different facets of the face-viewing experience. This recollection is then demonstrated by flexibly responding to two competing types of recognition task demands. Participants studied a list of faces and, following each face, performed a nonverbalization task (Experiment 1) or described its features or traits (Experiment 2). Two subsequent recognition tests included intact faces, new faces, and conjunctions (each of which recombined features of two studied faces). Inclusion test instructions emphasized featural information: respond "yes" to both intact and conjunction faces (both of which contained studied features), but "no" to new faces. Exclusion test instructions emphasized configural information: respond "yes" only to intact faces (which were the only test items that matched studied configurations), and "no" to both conjunctions and new faces. Both yes/no responses and confidence ratings supported our hypothesis that verbalization improved discrimination between (a) conjunctions and new faces in the inclusion test, and (b) intact faces and conjunctions in the exclusion test. Additional secondary responses about face type elucidated that verbalization at study improves the ability to recollect either featural or configural information, depending on which type of response the recognition test required. We discuss these findings about practical applications of improved face memory in real-world contexts.

Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36-60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of 'Maximum naïve CAR-T cell concentrations/Baseline tumor burden' ratio and propose a CCSTN-value > 0.00136 (cells·µL-1·mL-1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.

Validation and Optimization of Barrow Neurological Institute Score in Prediction of Adverse Events and Functional Outcome After Subarachnoid Hemorrhage-Creation of the HATCH (Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus) Score

BACKGROUND

The Barrow Neurological Institute (BNI) score, measuring maximal thickness of aneurysmal subarachnoid hemorrhage (aSAH), has previously shown to predict symptomatic cerebral vasospasms (CVSs), delayed cerebral ischemia (DCI), and functional outcome.

OBJECTIVE

To validate the BNI score for prediction of above-mentioned variables and cerebral infarct and evaluate its improvement by integrating further variables which are available within the first 24 h after hemorrhage.

METHODS

We included patients from a single center. The BNI score for prediction of CVS, DCI, infarct, and functional outcome was validated in our cohort using measurements of calibration and discrimination (area under the curve [AUC]). We improved it by adding additional variables, creating a novel risk score (measure by the dichotomized Glasgow Outcome Scale) and validated it in a small independent cohort.

RESULTS

Of 646 patients, 41.5% developed symptomatic CVS, 22.9% DCI, 23.5% cerebral infarct, and 29% had an unfavorable outcome. The BNI score was associated with all outcome measurements. We improved functional outcome prediction accuracy by including age, BNI score, World Federation of Neurologic Surgeons, rebleeding, clipping, and hydrocephalus (AUC 0.84, 95% CI 0.8-0.87). Based on this model we created a risk score (HATCH-Hemorrhage, Age, Treatment, Clinical State, Hydrocephalus), ranging 0 to 13 points. We validated it in a small independent cohort. The validated score demonstrated very good discriminative ability (AUC 0.84 [95% CI 0.72-0.96]).

CONCLUSION

We developed the HATCH score, which is a moderate predictor of DCI, but excellent predictor of functional outcome at 1 yr after aSAH.

Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls

BACKGROUND AND PURPOSE

Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring.

METHODS

We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life.

RESULTS

Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory.

CONCLUSIONS

Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.

What Room for Two-Dimensional Gel-Based Proteomics in a Shotgun Proteomics World?

Two-dimensional gel electrophoresis was instrumental in the birth of proteomics in the late 1980s. However, it is now often considered as an outdated technique for proteomics—a thing of the past. Although this opinion may be true for some biological questions, e.g., when analysis depth is of critical importance, for many others, two-dimensional gel electrophoresis-based proteomics still has a lot to offer. This is because of its robustness, its ability to separate proteoforms, and its easy interface with many powerful biochemistry techniques (including western blotting). This paper reviews where and why two-dimensional gel electrophoresis-based proteomics can still be profitably used. It emerges that, rather than being a thing of the past, two-dimensional gel electrophoresis-based proteomics is still highly valuable for many studies. Thus, its use cannot be dismissed on simple fashion arguments and, as usual, in science, the tree is to be judged by the fruit.

Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury

Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without clinical predictors. Methods: Blood samples from patients with acute TBI (all severities) were collected <24 h post trauma. The outcome was measured >6 months post injury using the Glasgow Outcome Scale Extended (GOSE) score, dichotomizing patients into: (i) those with favorable (GOSE≥5)/unfavorable outcome (GOSE ≤ 4) and complete (GOSE = 8)/incomplete (GOSE ≤ 7) recovery, and (ii) patients with mild TBI (mTBI) and patients with TBIs of all severities. Results: When sensitivity was set at 95-100%, the proteins' individual specificities remained low. H-FABP showed the best specificity (%) and sensitivity (100%) in predicting complete recovery in patients with mTBI. IL-10 had the best specificity (50%) and sensitivity (96%) in identifying patients with favorable outcome in patients with TBIs of all severities. When individual proteins were combined with clinical parameters, a model including H-FABP, NF-L, and ISS yielded a specificity of 56% and a sensitivity of 96% in predicting complete recovery in patients with mTBI. In predicting favorable outcome, a model consisting IL-10, age, and TBI severity reached a specificity of 80% and a sensitivity of 96% in patients with TBIs of all severities. Conclusion: Combining novel TBI biomarkers H-FABP and IL-10 with GFAP, NF-L and S100β and clinical parameters improves outcome prediction models in TBI.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.

The association between serum adipocyte fatty acid-binding protein and 3-month disability outcome after aneurysmal subarachnoid hemorrhage

BACKGROUND

Adipocyte fatty acid-binding protein (FABP4) is an adipokine that plays an important role in development of cardiovascular and metabolic diseases. The aim of this study was to assess the 3-month prognostic value of serum levels of FABP4 in Chinese patients with aneurysmal subarachnoid hemorrhage (aSAH) on hospital admission.

METHODS

This was a prospective observational study from a stroke treatment center in Zhengzhou, China. From October 2016 to May 2018, patients with aSAH who were hospitalized within 24 h were included. In addition, 202 age- and gender-matched healthy volunteers were assigned to the healthy control group. At admission, serum levels of FABP4 were measured, and patients' characteristics, Hunt-Hess grade, and modified Fisher grade evaluated. At 3-month follow-up, functional outcome (Glasgow Outcome Scale score; dichotomized as poor [score 1-3] or good [score 4-5]) and all-cause mortality were recorded. Univariate and multivariate logistic regression models were used to investigate the association of FABP4 with the two endpoints.

RESULTS

A total of 418 patients with aSAH were included in this study. The median age was 58 years (interquartile range, 49-66 years), and 57.9% were women. FABP4 serum levels were related to Hunt-Hess score (r[Spearman] = 0.381; P < 0.001). Patients with a poor outcome and non-survivors had significantly increased serum FABP4 levels on admission (P < 0.001 for all). In multivariate logistic regression analysis, FABP4 was an independent predictor of poor outcome and mortality, with increased risks of 7% (odds ratios 1.07, 95% confidence interval [CI] 1.02-1.13; P = 0.001) and 5% (odds ratio 1.05, 95% CI, 1.01-1.12; P = 0.003), respectively. Receiver operating characteristics to predict functional outcome and mortality were significantly different between conventional risk factors (difference area under the curve 0.024, 95% CI 0.018-0.032) and FABP4 plus conventional risk factors (area under the curve 0.015, 95%CI 0.011-0.020). After FABP4 was added to the existing risk factors, mortality was better reclassified and was associated with the net reclassification improvement statistic (P = 0.009), while poor outcome was better reclassified and associated with both the integrated discrimination improvement and net reclassification improvement statistics (P < 0.05 for all).

CONCLUSIONS

Elevated serum FABP4 levels were related to poor outcome and mortality in a cohort of patients with aSAH.

You shall not pass: how facial variability and feedback affect the detection of low-prevalence fake IDs

In many real-world settings, individuals rarely present another person's ID, which increases the likelihood that a screener will fail to detect it. Three experiments examined how within-person variability (i.e., differences between two images of the same person) and feedback may have influenced criterion shifting, thought to be one of the sources of the low-prevalence effect (LPE). Participants made identity judgments of a target face and an ID under either high, medium, or low mismatch prevalence. Feedback appeared after every trial, only error trials, or no trials. Experiment 1 used two controlled images taken on the same day. Experiment 2 used two controlled images taken at least 6 months apart. Experiment 3 used one controlled and one ambient image taken at least 1 year apart. Importantly, receiver operating characteristic curves revealed that feedback and greater within-person variability exacerbated the LPE by affecting both criterion and discriminability. These results carry implications for many real-world settings, such as border crossings and airports, where identity screening plays a major role in securing public safety.

Prediction of Outcome After Aneurysmal Subarachnoid Hemorrhage

Background and Purpose- Early prediction of clinical outcome after aneurysmal subarachnoid hemorrhage (aSAH) is still lacking accuracy. In this observational cohort study, we aimed to develop and validate an accurate bedside prediction model for clinical outcome after aSAH, to aid decision-making at an early stage. Methods- For the development of the prediction model, a prospectively kept single-center cohort of 1215 aSAH patients, admitted between 1998 and 2014, was used. For temporal validation, a prospective cohort of 224 consecutive aSAH patients from the same center, admitted between 2015 and 2017, was used. External validation was performed using the ISAT (International Subarachnoid Aneurysm Trial) database (2143 patients). Primary outcome measure was poor functional outcome 2 months after aSAH, defined as modified Rankin Scale score 4-6. The model was constructed using multivariate regression analyses. Performance of the model was examined in terms of discrimination and calibration. Results- The final model included 4 predictors independently associated with poor outcome after 2 months: age, World Federation of Neurosurgical Societies grade after resuscitation, aneurysm size, and Fisher grade. Temporal validation showed high discrimination (area under the receiver operating characteristic curve, 0.90; 95% CI, 0.85-0.94), external validation showed fair to good discrimination (area under the receiver operating characteristic curve, 0.73; 95% CI, 0.70-0.76). The model showed satisfactory calibration in both validation cohorts. The SAFIRE grading scale was derived from the final model: size of the aneurysm, age, Fisher grade, world federation of neurosurgical societies after resuscitation. Conclusions- The SAFIRE grading scale is an accurate, generalizable, and easily applicable model for early prediction of clinical outcome after aSAH.

Multivariable and Bayesian Network Analysis of Outcome Predictors in Acute Aneurysmal Subarachnoid Hemorrhage: Review of a Pure Surgical Series in the Post-International Subarachnoid Aneurysm Trial Era

BACKGROUND

Following the International Subarachnoid Aneurysm Trial (ISAT), evolving treatment modalities for acute aneurysmal subarachnoid hemorrhage (aSAH) has changed the case mix of patients undergoing urgent surgical clipping.

OBJECTIVE

To update our knowledge on outcome predictors by analyzing admission parameters in a pure surgical series using variable importance ranking and machine learning.

METHODS

We reviewed a single surgeon's case series of 226 patients suffering from aSAH treated with urgent surgical clipping. Predictions were made using logistic regression models, and predictive performance was assessed using areas under the receiver operating curve (AUC). We established variable importance ranking using partial Nagelkerke R² scores. Probabilistic associations between variables were depicted using Bayesian networks, a method of machine learning.

RESULTS

Importance ranking showed that World Federation of Neurosurgical Societies (WFNS) grade and age were the most influential outcome prognosticators. Inclusion of only these 2 predictors was sufficient to maintain model performance compared to when all variables were considered (AUC = 0.8222, 95% confidence interval (CI): 0.7646-0.88 vs 0.8218, 95% CI: 0.7616-0.8821, respectively, DeLong's P = .992). Bayesian networks showed that age and WFNS grade were associated with several variables such as laboratory results and cardiorespiratory parameters.

CONCLUSION

Our study is the first to report early outcomes and formal predictor importance ranking following aSAH in a post-ISAT surgical case series. Models showed good predictive power with fewer relevant predictors than in similar size series. Bayesian networks proved to be a powerful tool in visualizing the widespread association of the 2 key predictors with admission variables, explaining their importance and demonstrating the potential for hypothesis generation.

PanelomiX for the Combination of Biomarkers

Proteomics has allowed the discovery and validation of a massive number of biomarkers. However most of them suffer from insufficient specificity and sensitivity and therefore didn't translate to clinical practice. Combining biomarkers with different properties into panels can be an efficient way to bypass these limitations and facilitate the translation of biomarkers into clinical practice.

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning

BACKGROUND

The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs.

RESULTS

RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues.

CONCLUSIONS

To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

Computation and Selection of Optimal Biomarker Combinations by Integrative ROC Analysis Using CombiROC

The diagnostic accuracy of biomarker-based approaches can be considerably improved by combining multiple markers. A biomarker's capacity to identify specific subjects is usually assessed using receiver operating characteristic (ROC) curves. Multimarker signatures are complicated to select as data signatures must be integrated using sophisticated statistical methods. CombiROC, developed as a user-friendly web tool, helps researchers to accurately determine optimal combinations of markers identified by a range of omics methods. With CombiROC, data of different types, such as proteomics and transcriptomics, can be analyzed using Sensitivity/Specificity filters: the number of candidate marker panels arising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Users have full control over initial selection stringency, then CombiROC computes sensitivity and specificity for all marker combinations, determines performance for the best combinations, and produces ROC curves for automatic comparisons. All steps can be visualized in a graphic interface. CombiROC is designed without hard-coded thresholds, to allow customized fitting of each specific dataset: this approach dramatically reduces computational burden and false-negative rates compared to fixed thresholds. CombiROC can be accessed at www.combiroc.eu .

Combining H-FABP and GFAP increases the capacity to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

Mild traumatic brain injury (mTBI) patients may have trauma-induced brain lesions detectable using CT scans. However, most patients will be CT-negative. There is thus a need for an additional tool to detect patients at risk. Single blood biomarkers, such as S100B and GFAP, have been widely studied in mTBI patients, but to date, none seems to perform well enough. In many different diseases, combining several biomarkers into panels has become increasingly interesting for diagnoses and to enhance classification performance. The present study evaluated 13 proteins individually-H-FABP, MMP-1, MMP-3, MMP-9, VCAM, ICAM, SAA, CRP, GSTP, NKDA, PRDX1, DJ-1 and IL-10-for their capacity to differentiate between patients with and without a brain lesion according to CT results. The best performing proteins were then compared and combined with the S100B and GFAP proteins into a CT-scan triage panel. Patients diagnosed with mTBI, with a Glasgow Coma Scale score of 15 and one additional clinical symptom were enrolled at three different European sites. A blood sample was collected at hospital admission, and a CT scan was performed. Patients were divided into two two-centre cohorts and further dichotomised into CT-positive and CT-negative groups for statistical analysis. Single markers and panels were evaluated using Cohort 1. Four proteins-H-FABP, IL-10, S100B and GFAP-showed significantly higher levels in CT-positive patients. The best-performing biomarker was H-FABP, with a specificity of 32% (95% CI 23-40) and sensitivity reaching 100%. The best-performing two-marker panel for Cohort 1, subsequently validated in Cohort 2, was a combination of H-FABP and GFAP, enhancing specificity to 46% (95% CI 36-55). When adding IL-10 to this panel, specificity reached 52% (95% CI 43-61) with 100% sensitivity. These results showed that proteins combined into panels could be used to efficiently classify CT-positive and CT-negative mTBI patients.

Mortality after Spontaneous Subarachnoid Hemorrhage: Causality and Validation of a Prediction Model

OBJECTIVE

To evaluate primary causes of death after spontaneous subarachnoid hemorrhage (SAH) and externally validate the HAIR score, a prognostication tool, in a single academic institution.

METHODS

We reviewed all patients with SAH admitted to our neuro-intensive care unit between 2010 and 2016. Univariate and multivariate logistic regressions were performed to identify predictors of in-hospital mortality. The HAIR score predictors were Hunt and Hess grade at treatment decision, age, intraventricular hemorrhage, and rebleeding within 24 hours. Validation of the HAIR score was characterized with the receiver operating curve, the area under the curve, and a calibration plot.

RESULTS

Among 434 patients with SAH, in-hospital mortality was 14.1%. Of the 61 mortalities, 54 (88.5%) had a neurologic cause of death or withdrawal of care and 7 (11.5%) had cardiac death. Median time from SAH to death was 6 days. The main causes of death were effect of the initial hemorrhage (26.2%), rebleeding (23%) and refractory cerebral edema (19.7%). Factors significantly associated with in-hospital mortality in the multivariate analysis were age, Hunt and Hess grade, and intracerebral hemorrhage. Maximum lumen size was also a significant risk factor after aneurysmal SAH. The HAIR score had a satisfactory discriminative ability, with an area under the curve of 0.89.

CONCLUSIONS

The in-hospital mortality is lower than in previous reports, attesting to the continuing improvement of our institutional SAH care. The major causes are the same as in previous reports. Despite a different therapeutic protocol, the HAIR score showed good discrimination and could be a useful tool for predicting mortality.

Prognostic Value of Early S100 Calcium Binding Protein B and Neuron-Specific Enolase in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage: A Pilot Study

BACKGROUND

This prospective study was undertaken to investigate the value of early S100 calcium binding protein B (S100B) and neuron-specific enolase (NSE) in prognosticating outcome in patients with poor-grade aneurysmal subarachnoid hemorrhage and to develop a statistical model and cutoff values for clinical practice.

METHODS

Between 2012 and 2014, patients with poor-grade subarachnoid hemorrhage (Hunt and Hess grade 3-5) who were admitted within 24 hours after hemorrhage were prospectively enrolled. Serum NSE and S100B levels were assayed once daily during the first 3 days after hemorrhage. Patient characteristics, Glasgow Coma Scale score, Hunt and Hess grade, and Fisher grade at admission were recorded. Glasgow Outcome Scale (GOS) score was obtained at 6 months and dichotomized as poor (score 1-3) or good (score 4-5). Logistic regression and receiver operating characteristic curve were used to assess the value of S100B and NSE in predicting outcome, and cutoff values were calculated using conditional interference trees.

RESULTS

The study included 52 patients. Hunt and Hess grade was 3 in 23 patients, 4 in 15 patients, and 5 in 14 patients. S100B range was 0.07-5.62 μg/L (mean 0.87 μg/L ± 1.06). NSE range was 5.7-94.2 μg/L (mean 16.1 μg/L ± 10.5). At 6-month follow-up, 23 patients (44.2%) had a poor outcome, and 29 patients (55.8%) had a good outcome. Both S100B at day 1 (P = 0.004; cutoff 0.202 μg/L) and NSE at day 1 (P = 0.047; cutoff 9.4 μg/L) predicted good outcome with a specificity of 100%. The specificity of mean S100B in detecting patients with poor outcome reached 100% (P = 0.003) when combined with mean NSE levels.

CONCLUSIONS

S100B and NSE measured during the first 3 days after hemorrhage showed, separately and combined, a significant predictive value in prognosticating clinical outcome in patients with poor-grade subarachnoid hemorrhage. A multicenter study with a large patient cohort is necessary to validate the above-mentioned cutoff values for clinical practice.

The accuracy of postoperative, non-invasive Air-Test to diagnose atelectasis in healthy patients after surgery: a prospective, diagnostic pilot study

OBJECTIVE

To assess the diagnostic accuracy of peripheral capillary oxygen saturation (SpO₂) while breathing room air for 5 min (the 'Air-Test') in detecting postoperative atelectasis.

DESIGN

Prospective cohort study. Diagnostic accuracy was assessed by measuring the agreement between the index test and the reference standard CT scan images.

SETTING

Postanaesthetic care unit in a tertiary hospital in Spain.

PARTICIPANTS

Three hundred and fifty patients from 12 January to 7 February 2015; 170 patients scheduled for surgery under general anaesthesia who were admitted into the postsurgical unit were included.

INTERVENTION

The Air-Test was performed in conscious extubated patients after a 30 min stabilisation period during which they received supplemental oxygen therapy via a venturi mask. The Air-Test was defined as positive when SpO₂ was ≤96% and negative when SpO₂ was ≥97%. Arterial blood gases were measured in all patients at the end of the Air-Test. In the subsequent 25 min, the presence of atelectasis was evaluated by performing a CT scan in 59 randomly selected patients.

MAIN OUTCOME MEASURES

The primary study outcome was assessment of the accuracy of the Air-Test for detecting postoperative atelectasis compared with the reference standard. The secondary outcome was the incidence of positive Air-Test results.

RESULTS

The Air-Test diagnosed postoperative atelectasis with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.82 to 0.98) with a sensitivity of 82.6% and a specificity of 87.8%. The presence of atelectasis was confirmed by CT scans in all patients (30/30) with positive and in 5 patients (17%) with negative Air-Test results. Based on the Air-Test, postoperative atelectasis was present in 36% of the patients (62 out of 170).

CONCLUSION

The Air-Test may represent an accurate, simple, inexpensive and non-invasive method for diagnosing postoperative atelectasis.

TRIAL REGISTRATION

NCT02650037.

H-FABP: A new biomarker to differentiate between CT-positive and CT-negative patients with mild traumatic brain injury

The majority of patients with mild traumatic brain injury (mTBI) will have normal Glasgow coma scale (GCS) of 15. Furthermore, only 5%-8% of them will be CT-positive for an mTBI. Having a useful biomarker would help clinicians evaluate a patient's risk of developing intracranial lesions. The S100B protein is currently the most studied and promising biomarker for this purpose. Heart fatty-acid binding protein (H-FABP) has been highlighted in brain injury models and investigated as a biomarker for stroke and severe TBI, for example. Here, we evaluate the performances of S100B and H-FABP for differentiating between CT-positive and CT-negative patients. A total of 261 patients with a GCS score of 15 and at least one clinical symptom of mTBI were recruited at three different European sites. Blood samples from 172 of them were collected ≤ 6 h after trauma. Patients underwent a CT scan and were dichotomised into CT-positive and CT-negative groups for statistical analyses. H-FABP and S100B levels were measured using commercial kits, and their capacities to detect all CT-positive scans were evaluated, with sensitivity set to 100%. For patients recruited ≤ 6 h after trauma, the CT-positive group demonstrated significantly higher levels of both H-FABP (p = 0.004) and S100B (p = 0.003) than the CT-negative group. At 100% sensitivity, specificity reached 6% (95% CI 2.8-10.7) for S100B and 29% (95% CI 21.4-37.1) for H-FABP. Similar results were obtained when including all the patients recruited, i.e. hospital arrival within 24 h of trauma onset. H-FABP out-performed S100B and thus seems to be an interesting protein for detecting all CT-positive mTBI patients with a GCS score of 15 and at least one clinical symptom.

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis

Background

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance.

Methods

We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUC_SP) and sensitivity (pAUC_SE) intervals of 90–100%. Probabilistic associations were depicted using Bayesian networks.

Results

Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138–0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUC_SP = 0.6523, 95% CI: 0.6402–0.6641 and pAUC_SE = 0.6332, 95% CI: 0.62–0.6477) compared to the complete models (pAUC_SP = 0.6664, 95% CI: 0.6543–0.679, pAUC_SE = 0.6436, 95% CI: 0.6289–0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did.

Conclusion

We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Early and persistent high level of PS 100β is associated with increased poor neurological outcome in patients with SAH: is there a PS 100β threshold for SAH prognosis?

BACKGROUND

Protein S100β (PS100 β) and neuron specific enolase (NSE) have been described as biological markers of neuronal damage. The purpose of our study was to assess the prognosis thresholds of these biomarkers in subarachnoid aneurysmal hemorrhage (SAH).

METHODS

Forty eight patients admitted following SAH were treated by endovascular coiling. Initial neurologic severity was assessed using the World Federation Neurologic Surgeons (WFNS), Fisher grades, initial Glasgow coma scale (GCS) and SAPS II. PS100β and NSE plasma concentration were measured daily within the first week. The primary endpoint of the study was the 6-month Glasgow Outcome Score (GOS) dichotomized as poor (GOS 1-3) or good (GOS 4-5).

RESULTS

A poor outcome at 6-months was associated with significant higher levels of S100β value from day 1 to day 7, whereas NSE values were significantly higher from day 5 to day 7. Best threshold value, for prognosis, was obtained at day 5 for PS100β >0.13 μg/L (specificity 0.95 95% confidence interval (CI) 0.74-1; sensitivity 0.83 95% CI 0.65-0.93) and day 7 for NSE >14.5 μg/L (specificity 0.90 95% CI 0.67-0.98); sensitivity (0.69 95% CI 0.51-0.83)). After multivariate logistic analysis, only PS100β at day 5 and SAPS II enabled to predict neurological outcome at 6 months (p<0.05).

CONCLUSION

PS100β >0.13 μg/L at day 5 is an independent predicting factor of poor neurological outcome at 6 months following SAH. This result could support the use of this biomarker at the acute phase of SAH to help physician determine the prognosis.

The simplified acute physiology score II to predict hospital mortality in aneurysmal subarachnoid hemorrhage

BACKGROUND

Early prediction of increased morbidity and mortality in aneurysmal subarachnoid hemorrhage (aSAH) remains crucial to improving patient management. Most prediction models lack external validation and focus on disease-specific items without considering physiological parameters and the past medical history. The aim was to assess the validity of the established Simplified Acute Physiology Score II (SAPS-II) in an aSAH cohort for the prediction of hospital mortality and to identify additional physiological and clinical predictors.

METHODS

The predictive value of SAPS-II for hospital mortality was assessed in a retrospective analysis of 263 consecutive patients with aSAH. Additional physiological and clinical parameters including the past medical history were analyzed by forward selection multivariate analysis to identify independent predictors of hospital mortality and to improve the prediction model.

RESULTS

The SAPS-II predicted hospital mortality with an area under the curve (AUC) of 0.834 with an odds ratio (OR) of 1.097 [95 % confidence interval 1.067-1.128) for each additional point. Forward selection multivariate analysis identified the Glasgow Coma Scale score (P < 0.001), history of chronic headache (P = 0.01) and medication with anticoagulants (P = 0.04) as independent predictors of hospital mortality. Adding these parameters to the SAPS-II, the AUC increased to 0.86.

CONCLUSION

This study validates the predictive accuracy of SAPS-II for hospital mortality in aSAH patients. Additional parameters from the past medical history increase its predictive power. From a practical viewpoint, SAPS-II alone already represents a sufficient and powerful score to predict hospital mortality at an early time point and may help to improve patient management.

Neuroimaging characteristics of ruptured aneurysm as predictors of outcome after aneurysmal subarachnoid hemorrhage: pooled analyses of the SAHIT cohort

OBJECT Neuroimaging characteristics of ruptured aneurysms are important to guide treatment selection, and they have been studied for their value as outcome predictors following aneurysmal subarachnoid hemorrhage (SAH). Despite multiple studies, the prognostic value of aneurysm diameter, location, and extravasated SAH clot on computed tomography scan remains debatable. The authors aimed to more precisely ascertain the relation of these factors to outcome. METHODS The data sets of studies included in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository were analyzed including data on ruptured aneurysm location and diameter (7 studies, n = 9125) and on subarachnoid clot graded on the Fisher scale (8 studies; n = 9452) for the relation to outcome on the Glasgow Outcome Scale (GOS) at 3 months. Prognostic strength was quantified by fitting proportional odds logistic regression models. Univariable odds ratios (ORs) were pooled across studies using random effects models. Multivariable analyses were adjusted for fixed effect of study, age, neurological status on admission, other neuroimaging factors, and treatment modality. The neuroimaging predictors were assessed for their added incremental predictive value measured as partial R(2). RESULTS Spline plots indicated outcomes were worse at extremes of aneurysm size, i.e., less than 4 or greater than 9 mm. In between, aneurysm size had no effect on outcome (OR 1.03, 95% CI 0.98-1.09 for 9 mm vs 4 mm, i.e., 75th vs 25th percentile), except in those who were treated conservatively (OR 1.17, 95% CI 1.02-1.35). Compared with anterior cerebral artery aneurysms, posterior circulation aneurysms tended to result in slightly poorer outcome in patients who underwent endovascular coil embolization (OR 1.13, 95% CI 0.82-1.57) or surgical clipping (OR 1.32, 95% CI 1.10-1.57); the relation was statistically significant only in the latter. Fisher CT subarachnoid clot burden was related to outcome in a gradient manner. Each of the studied predictors accounted for less than 1% of the explained variance in outcome. CONCLUSIONS This study, which is based on the largest cohort of patients so far analyzed, has more precisely determined the prognostic value of the studied neuroimaging factors. Treatment choice has strong influence on the prognostic effect of aneurysm size and location. These findings should guide the development of reliable prognostic models and inform the design and analysis of future prospective studies, including clinical trials.

Neopterin plasma concentrations in patients with aneurysmal subarachnoid hemorrhage: correlation with infection and long-term outcome

OBJECT Aneurysmal subarachnoid hemorrhage (aSAH) is associated with high rates of mortality and morbidity. The main predictor for the poor outcome is the World Federation of Neurosurgical Societies (WFNS) scale. However, this scale does not take into account proinflammatory events, such as infection occurring after the aSAH, which could modify the long-term status of patients. The aim of this study was to evaluate neopterin as an inflammatory biomarker for outcome and infection prediction in aSAH patients. METHODS Plasma concentrations of neopterin were measured in 61 aSAH patients (22 male and 39 female; mean age [± SD] 52.8 ± 11.8 years) using a commercial ELISA kit. Samples were collected daily for 10 days. Outcome at 12 months was determined using the Glasgow Outcome Scale (GOS) and dichotomized as poor (GOS score 1, 2, or 3) or good (GOS score 4 or 5). Infection was determined by the presence of a positive bacterial culture. RESULTS Patients with poor outcome at 12 months had higher concentrations of neopterin than patients with good outcome. In the same way, patients who had an infection during the hospitalization had significantly higher concentrations of neopterin than patients without infection (p = 0.001). Moreover, neopterin concentrations were significantly (p < 0.008) elevated in infected patients 2 days before infection detection and antibiotic therapy. CONCLUSIONS Neopterin is an efficient outcome predictor after aSAH. Furthermore, it is able to differentiate between infected and uninfected patients as early as 2 days before clinical signs of infection, facilitating earlier antibiotic therapy and better management.

Cardiac Troponin Elevation and Outcome after Subarachnoid Hemorrhage: A Systematic Review and Meta-analysis

BACKGROUND

Cardiac abnormalities frequently occur after subarachnoid hemorrhage (SAH). Cardiac troponin (cTn) is a preferred biomarker for the diagnosis of cardiac damage, and the clinical significance of cTn elevation after SAH remains controversial. This meta-analysis was performed to assess the association between cTn elevation and patient outcomes, including delayed cerebral ischemia (DCI), poor outcome (death or dependency), and death in SAH patients.

METHODS

PubMed, Embase, and the Cochrane Library were searched for observational studies reporting an association between cTn elevation and outcome after SAH that were published before December 31, 2014. We extracted data regarding patient characteristics, cTn elevation, and outcome measurements (DCI, poor outcome, or death). Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated using a random-effects model.

RESULTS

Twelve studies involving 2214 patients were included in data analysis. Elevation of cTn was observed in 30% of the patients. The cTn elevation was associated with an increased risk of DCI (RR, 1.48; 95% CI, 1.23-1.79), poor outcome (RR, 1.91; 95% CI, 1.51-2.40), and death (RR, 2.53; 95% CI, 2.04-3.12). At the 3- and 12- month follow-ups, cTn elevation was associated with higher rates of DCI (RR, 1.51; 95% CI, 1.11-2.07), poor outcome (RR, 1.91; 95% CI, 1.51-2.40), and death (RR, 2.78; 95% CI, 1.80-4.29). At in-hospital follow-ups, cTn elevation was also associated with the higher rate of death (RR, 2.33; 95% CI, 1.76-3.07).

CONCLUSIONS

cTn elevation in SAH patients is associated with an increased risk of DCI, poor outcome, and death after SAH.

Systemic, local, and imaging biomarkers of brain injury: more needed, and better use of those already established?

Much progress has been made over the past two decades in the treatment of severe acute brain injury, including traumatic brain injury and subarachnoid hemorrhage, resulting in a higher proportion of patients surviving with better outcomes. This has arisen from a combination of factors. These include improvements in procedures at the scene (pre-hospital) and in the hospital emergency department, advances in neuromonitoring in the intensive care unit, both continuously at the bedside and intermittently in scans, evolution and refinement of protocol-driven therapy for better management of patients, and advances in surgical procedures and rehabilitation. Nevertheless, many patients still experience varying degrees of long-term disabilities post-injury with consequent demands on carers and resources, and there is room for improvement. Biomarkers are a key aspect of neuromonitoring. A broad definition of a biomarker is any observable feature that can be used to inform on the state of the patient, e.g., a molecular species, a feature on a scan, or a monitoring characteristic, e.g., cerebrovascular pressure reactivity index. Biomarkers are usually quantitative measures, which can be utilized in diagnosis and monitoring of response to treatment. They are thus crucial to the development of therapies and may be utilized as surrogate endpoints in Phase II clinical trials. To date, there is no specific drug treatment for acute brain injury, and many seemingly promising agents emerging from pre-clinical animal models have failed in clinical trials. Large Phase III studies of clinical outcomes are costly, consuming time and resources. It is therefore important that adequate Phase II clinical studies with informative surrogate endpoints are performed employing appropriate biomarkers. In this article, we review some of the available systemic, local, and imaging biomarkers and technologies relevant in acute brain injury patients, and highlight gaps in the current state of knowledge.

The prognostic significance of the serum biomarker heart-fatty acidic binding protein in comparison with s100b in severe traumatic brain injury

The outcome after severe traumatic brain injury (TBI) is largely unfavorable, with approximately two thirds of patients suffering from severe disabilities or dying during the first 6 months. Existing predictive models displayed only limited utility for outcome prediction in individual patients. Time courses of heart-fatty acidic binding protein (H-FABP) and their association with outcome were investigated and compared with S100b. Forty-nine consecutive patients with severe TBI (sTBI; Head component of the Abbreviated Injury Scale [HAIS] >3) with mono and multiple trauma were enrolled in this study. Enzyme-linked immunosorbent assay measured blood concentrations of H-FABP and S100b at 6, 12, 24, and 48 h after TBI. Outcome measures were conscious state at 14 days (Glasgow Coma Scale), disability (Glasgow Outcome Scale Extended; GOSE), and mortality at 3 months. Univariate logistic regression analysis and receiver operating characteristic curves analysis were carried out. Maximal H-FABP and S100b concentrations were observed at 6 h after TBI (34.4±34.0 and 0.64±0.99 ng/mL, respectively). Patients with multi-trauma had significantly higher H-FABP concentrations at 24 and 48 h (22.6±25.6 and 12.4±18.2 ng/mL, respectively), compared to patients with mono trauma (6.9±5.1 and 3.7±4.2 ng/mL, respectively). In the first 48 h, H-FABP and S100b were inversely correlated with the GOSE at 3 months; H-FABP at 48 h predicted mortality with 75% sensitivity and 93% specificity. Early blood levels of H-FABP after sTBI have prognostic significance for survival and disability.

Biomarker selection for medical diagnosis using the partial area under the ROC curve

Background

A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers.

Methods

Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance.

Results

The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers.

Conclusions

Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing.

Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review

BACKGROUND

Clinical prediction models can enhance clinical decision-making and research. However, available prediction models in aneurysmal subarachnoid hemorrhage (aSAH) are rarely used. We evaluated the methodological validity of SAH prediction models and the relevance of the main predictors to identify potentially reliable models and to guide future attempts at model development.

METHODS

We searched the EMBASE, MEDLINE, and Web of Science databases from January 1995 to June 2012 to identify studies that reported clinical prediction models for mortality and functional outcome in aSAH. Validated methods were used to minimize bias.

RESULTS

Eleven studies were identified; 3 developed models from datasets of phase 3 clinical trials, the others from single hospital records. The median patient sample size was 340 (interquartile range 149-733). The main predictors used were age (n = 8), Fisher grade (n = 6), World Federation of Neurological Surgeons grade (n = 5), aneurysm size (n = 5), and Hunt and Hess grade (n = 3). Age was consistently dichotomized. Potential predictors were prescreened by univariate analysis in 36 % of studies. Only one study was penalized for model optimism. Details about model development were often insufficiently described and no published studies provided external validation.

CONCLUSIONS

While clinical prediction models for aSAH use a few simple predictors, there are substantial methodological problems with the models and none have had external validation. This precludes the use of existing models for clinical or research purposes. We recommend further studies to develop and validate reliable clinical prediction models for aSAH.

Physiological monitoring of the severe traumatic brain injury patient in the intensive care unit

Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. Despite encouraging animal research, pharmacological agents and neuroprotectants have disappointed in the clinical environment. Current TBI management therefore is directed towards identification, prevention, and treatment of secondary cerebral insults that are known to exacerbate outcome after injury. This strategy is based on a variety of monitoring techniques that include the neurological examination, imaging, laboratory analysis, and physiological monitoring of the brain and other organ systems used to guide therapeutic interventions. Recent clinical series suggest that TBI management informed by multimodality monitoring is associated with improved patient outcome, in part because care is provided in a patient-specific manner. In this review we discuss physiological monitoring of the brain after TBI and the emerging field of neurocritical care bioinformatics.

Bile carcinoembryonic cell adhesion molecule 6 (CEAM6) as a biomarker of malignant biliary stenoses

Differentiating malignant from nonmalignant biliary stenoses is challenging. This could be facilitated by the measurement of cancer biomarkers in bile. We aimed at (i) identifying new cancer biomarkers by comparative proteomic analysis of bile collected from patients with a malignant or benign biliary stenosis (exploratory phase) and (ii) verifying the accuracy of the newly identified potential biomarkers for discriminating malignant versus nonmalignant biliary stenoses in a larger group of patients (confirmation phase). Overall, 66 proteins were found overexpressed (ratio>1.5) in at least one cancer condition using proteomic analysis and 7 proteins were increased in all malignant/nonmalignant disease comparisons. Preliminary screening by immunoblot highlighted carcinoembryonic cell adhesion molecule 6 (CEAM6), a cell surface protein overexpressed in many human cancers, as an interesting candidate biomarker. ELISA subsequently confirmed CEAM6 as a potential bile biomarker for distinguishing malignant from benign biliary stenoses with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.92 (specificity 83%, sensitivity 93%, positive predictive value 93%, and negative predictive value 83%). No significant difference in serum CEAM6 level was found between malignant and nonmalignant samples. Combining bile CEAM6 and serum CA19-9 in a panel further improved diagnostic accuracy for malignant stenoses (AUC 0.96, specificity 83%, sensitivity 97%, positive predictive value 93%, and negative predictive value 91%). CEAM6 measurement in bile could be clinically useful to discriminate between malignant and nonmalignant causes of biliary stenosis. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Angiographic Coronary Spasm in a Case of Spontaneous Subarachnoid Hemorrhage Mimicking Acute Myocardial Infarction

Neurologic stunned myocardium after subarachnoid hemorrhage (SAH) has been evidenced. Clinical presentations manifested as ST segment elevation by electrocardiography (ECG), left ventricular wall motion abnormality by echocardiography, and abnormal cardiac markers. The pathophysiology remains controversial. Coronary artery spasm has been proposed as a possible mechanism. However, most SAH patients with ECG and echocardiographic findings suggestive of myocardial infarction were lacking of angiographic evidence of vasospasm. We present a case of 66-year-old man complained chest pain with transient conscious loss on the street. He was sent to our emergency room by witness with clear consciousness and electrocardiography showing prominent ST-segment elevation. Because chest tightness was complained, emergent catheterization was arranged immediately. Coronary angiography demonstrated a narrowing lesion on mid right coronary artery without atherosclerotic change on other site. He was successfully treated with primary coronary balloon angioplasty for the narrowing lesion. Then the patient was sent to intensive care unit for further care. His following ECG demonstrated sinus rhythm with ectopic beats without ST segment elevation. Unfortunately, he became irritable and deterioration of conscious level few hour later. Computer tomography revealed subdural and subarachnoid hemorrhage. Conservative treatment was suggested by neurological surgeon consulted. The clinical presentation of the SAH patient mimicked acute myocardial infarction and coronary spasm was evidenced by angiography. We report the case and review the articles.

Heart-fatty acid-binding and tau proteins relate to brain injury severity and long-term outcome in subarachnoid haemorrhage patients

BACKGROUND

Vasospasm and other secondary neurological insults may follow subarachnoid haemorrhage (SAH). Biomarkers have the potential to stratify patient risk and perhaps serve as an early warning sign of delayed ischaemic injury.

METHODS

Serial cerebrospinal fluid (CSF) samples were collected from 38 consecutive patients with aneurysmal SAH admitted to the neurosurgical intensive care unit. We measured heart-fatty acid-binding protein (H-FABP) and tau protein (τ) levels in the CSF to evaluate their association with brain damage, and their potential as predictors of the long-term outcome. H-FABP and τ were analysed in relation to acute clinical status, assessed by the World Federation of Neurological Surgeons (WFNS) scale, radiological findings, clinical vasospasm, and 6-month outcome.

RESULTS

H-FABP and τ increased after SAH. H-FABP and τ were higher in patients in poor clinical status on admission (WFNS 4-5) compared with milder patients (WFNS 1-3). Elevated H-FABP and τ levels were also observed in patients with early cerebral ischaemia, defined as a CT scan hypodense lesion visible within the first 3 days after SAH. After the acute phase, H-FABP, and τ showed a delayed increase with the occurrence of clinical vasospasm. Finally, patients with the unfavourable outcome (death, vegetative state, or severe disability) had higher peak levels of both proteins compared with patients with good recovery or moderate disability.

CONCLUSIONS

H-FABP and τ show promise as biomarkers of brain injury after SAH. They may help to identify the occurrence of vasospasm and predict the long-term outcome.

Clinical review: neuromonitoring - an update

Critically ill patients are frequently at risk of neurological dysfunction as a result of primary neurological conditions or secondary insults. Determining which aspects of brain function are affected and how best to manage the neurological dysfunction can often be difficult and is complicated by the limited information that can be gained from clinical examination in such patients and the effects of therapies, notably sedation, on neurological function. Methods to measure and monitor brain function have evolved considerably in recent years and now play an important role in the evaluation and management of patients with brain injury. Importantly, no single technique is ideal for all patients and different variables will need to be monitored in different patients; in many patients, a combination of monitoring techniques will be needed. Although clinical studies support the physiologic feasibility and biologic plausibility of management based on information from various monitors, data supporting this concept from randomized trials are still required.