Dataset Information

Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

ABSTRACT: Rigorous evidence generation with randomized controlled trials (RCTs) has lagged for aneurysmal subarachnoid hemorrhage (SAH) compared to other forms of acute stroke. Besides its lower incidence compared to other stroke subtypes, the presentation and outcome of SAH patients also differ. This must be considered and adjusted for in designing pivotal RCTs of SAH patients. Here, we show the effect of the unique expected distribution of the SAH severity at presentation (World Federation of Neurological Surgeons, WFNS, grade) on the outcome most used in pivotal stroke RCTs (modified Rankin Scale, mRS) and consequently on the sample size. Further, we discuss the advantages and disadvantages of different options to analyze the outcome and control the expected distribution of WFNS grades in addition to showing their effects on the sample size. Last, we offer methods that investigators can adapt to more precisely understand the effect of common mRS analysis methods and trial eligibility pertaining to the WFNS grade in designing their large-scale SAH RCTs.

SUBMITTER: Mistry AM

PROVIDER: S-EPMC10593043 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

Mistry Akshitkumar M AM Saver Jeffrey J Mack William W Kamel Hooman H Elm Jordan J Beall Jonathan J

medRxiv : the preprint server for health sciences 20231002

Rigorous evidence generation with randomized controlled trials (RCTs) has lagged for aneurysmal subarachnoid hemorrhage (SAH) compared to other forms of acute stroke. Besides its lower incidence compared to other stroke subtypes, the presentation and outcome of SAH patients also differ. This must be considered and adjusted for in designing pivotal RCTs of SAH patients. Here, we show the effect of the unique expected distribution of the SAH severity at presentation (World Federation of Neurologic ...[more]

PMID: 37873354

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Project description:The diagnosis of cerebral vasospasm after Subarachnoid-Hemorrhage is currently very difficult, additional tools such as blood biomarkers are necessary. We tested the ability of gene expression profiles of blood cells to predict vasospasm. 32 patients suffering subarachnoid-hemorrhage were included in this prospective monocentre study. They were grouped according to have a complicated cerebral vasospasm (Vasospasm) or not (Control) and Paired according to age (+/- 10 years), sex, Fisher grade (+/- 1), location, smoking (at least 3 first parameters). Gene expression profiles of blood cells were determined using 25,000~gene microarray. Blood sample: 2.5 mL harvested in PAXgene® Blood RNA tubes (PreAnalytix) RNA extraction: PAXgene® Blood RNA kit (Qiagen). We used a Universel Reference RNA (Stratagene). RNA amplification and labelling: kit Amino Allyl MessageAmp II (Ambion). We hybridized 4 microarrays per patient using pangenomic microarrays from the "Réseau National des Génopôles" (Illkirch, France). 2 slides were hybridized with reference RNA labelled Cy3 and patient RNA labelled Cy5, and 2 slides were hybridized with reference RNA labelled Cy5 and patient RNA labelled Cy3. Hybridation : Agilent protocol with few modifications : 750 ng of each labelled RNA were hubriddized at 60°C during 17 hours in an Aglient hybridization oven. After washings, Slides were scanned with a GenePix 4000B scanner (Molecular Devices). Image intensity data were extracted with GenePix Pro 6.0 analysis software. Quantification of Cy3 and Cy5 and selection of good spots were performed using the MAIA software (Novikov E and Barillot E. Software package for automatic microarray image analysis (MAIA). The ACUITY software was then used to normalize log ratios Cy3/Cy5 with Lowess non linear normalization, to filter out genes not present in at least 3 slides out of 4, to evaluate the reproducibility of the 4 microarrays of each patient (hierarchical clustering, Self Organizing Maps). Statistical analyses to insure reproducibility was performed using Excel (correlation coefficients, ANOVA). Only slides that passed all reprocubility tests were validated.

Project description:Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved.

Dataset Information

Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

Publications

Subarachnoid Hemorrhage Trials: Cutting, Sliding, or Keeping mRS Scores and WFNS Grades.

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