Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

OBJECTIVES: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. DESIGN: We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. SETTING: Twenty-five PICUs across the United States. PATIENTS: EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. CONCLUSIONS: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort.

Background: Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions. Methods: We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme. Findings: Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes. Interpretation: Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock.

Objective: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata. Design: We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata. Setting: 25 pediatric intensive care units (PICU) across the U.S. Patients: EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data. Interventions: None. Measurements and Main Results: The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS. Conclusions: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.

First-in-Human Assessment of 11C-LSN3172176, an M1 Muscarinic Acetylcholine Receptor PET Radiotracer.

This was a first-in-human study of the PET radiotracer 11C-LSN3172176 for the muscarinic acetylcholine receptor subtype M1. The objectives of the study were to determine the appropriate kinetic model to quantify binding of the tracer to M1 receptors, and the reliability of the chosen quantification method. Methods: Six healthy subjects completed the test-retest protocol, and 5 healthy subjects completed the baseline-scopolamine blocking protocol. Multiple modeling methods were applied to calculate total distribution volume (VT) and nondisplaceable binding potential (BPND) in various brain regions. The reference region was selected from the blocking study. The occupancy plot was applied to compute receptor occupancy by scopolamine and nondisplaceable distribution volume. Results: Tracer uptake was highest in the striatum, followed by neocortical regions and white matter, and lowest in the cerebellum. Regional time-activity curves were fitted well by all models. The 2-tissue-compartment (2TC) model fits were good, but the 2TC parameters often could not be reliably estimated. Because VT correlated well between the 2TC and 1-tissue-compartment (1TC) models after exclusion of unreliable estimates, the 1TC model was chosen as the most appropriate. The cerebellum showed the lowest VT, consistent with preclinical studies showing little to no specific binding in the region. Further, cerebellar VT did not change between baseline and blocking scans, indicating that the cerebellum is a suitable reference region. The simplified reference tissue model (SRTM) slightly underestimated 1TC BPND, and the simplified reference tissue model 2 (SRTM2) improved BPND estimation. An 80-min scan was sufficient to quantify VT and BPND The test-retest study showed excellent absolute test-retest variability for 1TC VT (≤5%) and BPND (≤10%). In the baseline and blocking studies, occupancy values were lower in the striatum than in nonstriatal regions, as may be attributed to differences in regional acetylcholine concentrations. Conclusion: The 1TC and SRTM2 models are appropriate for quantitative analysis of 11C-LSN3172176 imaging data. 11C-LSN3172176 displayed excellent test-retest reproducibility and is a highly promising ligand to quantify M1 receptors in the human brain.

Defining the brain circuits involved in psychiatric disorders: IMI-NEWMEDS.

Despite the vast amount of research on schizophrenia and depression in the past two decades, there have been few innovative drugs to treat these disorders. Precompetitive research collaborations between companies and academic groups can help tackle this innovation deficit, as illustrated by the achievements of the IMI-NEWMEDS consortium.

Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration.

OBJECTIVE: To investigate the effect of enriching mild cognitive impairment (MCI) clinical trials using combined markers of amyloid pathology and neurodegeneration. METHODS: We evaluate an implementation of the recent National Institute for Aging-Alzheimer's Association (NIA-AA) diagnostic criteria for MCI due to Alzheimer disease (AD) as inclusion criteria in clinical trials and assess the effect of enrichment with amyloid (A+), neurodegeneration (N+), and their combination (A+N+) on the rate of clinical progression, required sample sizes, and estimates of trial time and cost. RESULTS: Enrichment based on an individual marker (A+ or N+) substantially improves all assessed trial characteristics. Combined enrichment (A+N+) further improves these results with a reduction in required sample sizes by 45% to 60%, depending on the endpoint. CONCLUSIONS: Operationalizing the NIA-AA diagnostic criteria for clinical trial screening has the potential to substantially improve the statistical power of trials in MCI due to AD by identifying a more rapidly progressing patient population.

Perspective: The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partner Scientific Board (PPSB).

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partner Scientific Board (PPSB) is comprised of representatives of private, for-profit entities (including pharmaceutical, biotechnology, diagnostics, imaging companies, and imaging contract research organizations), and nonprofit organizations that provide financial and scientific support to ADNI through the Foundation for the National Institutes of Health. The PPSB serves as an independent, open, and precompetitive forum in which all private sector and not-for-profit partners in ADNI can collaborate, share information, and offer scientific and private-sector perspectives and expertise on issues relating to the ADNI project. In this article, we review and highlight the role, activities, and contributions of the PPSB within the ADNI project, and provide a perspective on remaining unmet needs and future directions.

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Operationalizing hippocampal volume as an enrichment biomarker for amnestic mild cognitive impairment trials: effect of algorithm, test-retest variability, and cut point on trial cost, duration, and sample size.

The objective of this study was to evaluate the effect of computational algorithm, measurement variability, and cut point on hippocampal volume (HCV)-based patient selection for clinical trials in mild cognitive impairment (MCI). We used normal control and amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) as normative reference and screening cohorts. We evaluated the enrichment performance of 4 widely used hippocampal segmentation algorithms (FreeSurfer, Hippocampus Multi-Atlas Propagation and Segmentation (HMAPS), Learning Embeddings Atlas Propagation (LEAP), and NeuroQuant) in terms of 2-year changes in Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and Clinical Dementia Rating Sum of Boxes (CDR-SB). We modeled the implications for sample size, screen fail rates, and trial cost and duration. HCV based patient selection yielded reduced sample sizes (by ∼40%-60%) and lower trial costs (by ∼30%-40%) across a wide range of cut points. These results provide a guide to the choice of HCV cut point for amnestic MCI clinical trials, allowing an informed tradeoff between statistical and practical considerations.

A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #1: processes and requirements.

There is increasing interest in the application of quantitative magnetic resonance imaging (MRI) methods to drug development, but as yet little standardization or best practice guidelines for its use in this context. Pharmaceutical trials are subject to regulatory constraints and sponsor company processes, including site qualification and expectations around study oversight, blinding, quality assurance and quality control (QA/QC), analysis and reporting of results. In this article, we review the processes on the sponsor side and also the procedures involved in data acquisition at the imaging site. We then propose summary recommendations to help guide appropriate imaging site qualification, as part of a framework of 'good imaging practice' for functional (f)MRI studies applied to drug development.

A procedural framework for good imaging practice in pharmacological fMRI studies applied to drug development #2: protocol optimization and best practices.

Functional magnetic resonance imaging (fMRI) experiments are more complex compared with standard radiological imaging, involving additional data streams and hardware along with complex analysis methods. Here, we propose guidelines based around mitigating risks associated with the complexities of the technique at the level of the individual imaging protocol, including workable and effective quality assurance/quality control procedures and rigorous, predefined, analysis pipelines. Our aim is to provide a framework for 'good imaging practice' (GIP), enabling these requirements to be addressed at an appropriate level of detail. The development of a procedural framework for GIP in pharmaceutical fMRI studies could lead to greater acceptance of the method within industry and facilitate validation and, eventually, qualification of the technique as an imaging biomarker.