Identifying key data to establish how timing of NGS testing can affect survival outcomes in meningitis patients

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Motivation

Meningitis and encephalitis are important causes of morbidity and mortality worldwide, with bacterial meningitis affecting approximately 1 in 6 patients fatally and causing severe complications in 1 in 5 survivors. In critical care settings treating these life-threatening infections, timing is everything—antibiotic treatment should be started as soon as possible when bacterial meningitis is suspected, and the timely identification of causative agents is critical for the administration of effective treatment. However, determining the optimal timing for next-generation sequencing (NGS) testing within the complex diagnostic workflow remained unclear for healthcare providers seeking to maximize patient survival outcomes.

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Solution

Plinth developed a comprehensive Health Economics and Outcomes Research (HEOR) analysis to evaluate how the timing of metagenomic NGS testing impacts patient survival and healthcare utilization in meningitis cases. Working with a dataset of patient records linked to thousands of genetic test results, Plinth created an interactive analytics portal that systematically examined the relationship between test timing, diagnostic workflow, and critical outcomes including time-to-discharge and overall survival. Additionally, Plinth advised on strategic data collection priorities to strengthen future efficacy assessments and optimize evidence generation for regulatory and clinical decision-making.

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Impact

Plinth's HEOR analysis provided evidence-based insights into optimal NGS testing protocols that can improve patient survival while reducing healthcare costs. The comprehensive evaluation of diagnostic timing, test sequencing, and resource utilization—combined with strategic guidance on data collection for ongoing efficacy monitoring—enables healthcare systems to implement data-driven protocols that maximize the life-saving potential of advanced diagnostic testing while optimizing cost-effectiveness. This analysis framework can be readily applied to other infectious disease contexts where rapid, accurate diagnosis is critical for patient outcomes.

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