Rationale: Early evidence for the benefit of mechanical thrombectomy (MT) in pediatric patients with intracranial large vessel occlusion has been shown in previous retrospective cohorts. Higher-level evidence is needed to overcome the limitations of these studies such as the lack of a control group and the retrospective design. Randomized trials will very likely not be feasible, and several open questions remain, for example, the impact of arteriopathic etiologies or a possible lower age limit for MT. Save ChildS Pro therefore aims to demonstrate the safety and effectiveness of MT in pediatric patients compared to the best medical management and intravenous thrombolysis. Design: Save ChildS Pro is designed as a worldwide multicenter prospective registry comparing the safety and effectiveness of MT to the best medical care alone in the treatment of pediatric arterial ischemic stroke (AIS). It will include pediatric patients (<18 years) with symptomatic acute intracranial arterial occlusion who underwent either MT or best medical treatment including intravenous thrombolysis. Outcomes: The primary endpoint of Save ChildS Pro is the modified Rankin Scale score at 90 days post-stroke. Secondary endpoints will comprise the decrease of the Pediatric National Institutes of Health Stroke Scale score from admission to discharge and rate of complications. Discussion: Save ChildS Pro aims to provide high-level evidence for MT for pediatric patients with AIS, thereby improving functional outcome and quality of life and reducing the individual, societal, and economic burden of death and disability resulting from pediatric stroke. Clinical Trial Registration: Save ChildS Pro is registered at the German Clinical Trials Registry (DRKS; identifier: DRKS00018960).

ABSTRACT

Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 – 0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI = 0.75 – 1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.

SIGNIFICANCE STATEMENT

Lower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, airway microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86-1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63-0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75-1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.