Tackling Sepsis: The Innovation Landscape and Opportunities for Precision Medicine

Sepsis and the Cost of Care

Sepsis remains one of the most significant challenges hospitals and health systems face today. As a leading cause of morbidity and mortality globally, sepsis imposes staggering costs on healthcare systems, accounting for over $60 billion in annual healthcare expenditures in the United States alone. Beyond the direct costs, sepsis carries profound human burden, including long-term patient complications and high mortality rates. Infections caused by Clostridioides difficile (C. diff), Methicillin-resistant Staphylococcus aureus (MRSA), and multidrug-resistant gram-negative bacteria—common hospital-acquired pathogens often resulting from prolonged hospital stays—further compound these challenges. They increase patient morbidity, drive up healthcare costs, and add to the complexity of treatment by introducing additional clinical complications.

Healthcare organizations are under increasing pressure to address these outcomes due to financial incentives and penalties tied to patient outcomes. The Centers for Medicare & Medicaid Services (CMS) has implemented programs like the Hospital-Acquired Condition Reduction Program and the Sepsis Core Measure (SEP-1), which penalize hospitals for poor performance in managing sepsis and related conditions. While these measures have driven awareness and improvements, they have also exposed gaps in existing tools and strategies.

The Evolution of Sepsis Programs and Incentives

Sepsis management programs have evolved significantly over the years. Early efforts focused on improving the timeliness of interventions such as antibiotic administration and fluid resuscitation. Today, the focus is broader, encompassing early detection, risk stratification, and personalized treatment plans. Despite advancements, hospitals continue to face significant challenges, including variability in sepsis definitions, clinical practice guidelines, and predictive tool accuracy.

Programs like CMS’s SEP-1 have incentivized hospitals to adopt standardized care protocols. However, achieving compliance often involves extensive data collection and reporting, which can burden clinical teams and strain resources. Moreover, the variability in patient populations, hospital infrastructure, and available technologies has highlighted the need for more tailored approaches.

The Innovation Landscape: Tools and Challenges

Innovation in sepsis management has led to the development of a wide range of tools, from predictive algorithms to advanced laboratory tests. Yet, the effectiveness of these solutions varies widely, raising questions about their accuracy, sensitivity, and specificity.

Predictive Models: Tools like the Epic Sepsis Model and Prenosis’ Sepsis ImmunoScore exemplify the spectrum of innovation. The Epic Sepsis Model, while widely implemented, has faced criticism for its high false-positive rates and reliance on electronic health record (EHR) data, which can introduce bias. On the other hand, newer entrants like Prenosis leverage machine learning to enhance predictive accuracy. However, adopting such tools often requires significant investment and integration efforts, driven by the need for seamless integration into existing hospital workflows. Additionally, missing or incomplete data may preclude a result, meaning if certain data is unavailable, the utility of these tools drops off significantly. The measured sensitivity and specificity of these tools also vary widely, influencing their reliability and clinical utility. According to a JAMIA Open article, within a 6-hour time window for sepsis, the Epic Sepsis Model version 1 (ESPMv1) demonstrated a sensitivity of 14.7% and specificity of 95.3%, highlighting significant limitations in its predictive performance (1). Additionally, a study published in JAMA Internal Medicine notably found that the Epic Sepsis Model did not identify 67% of patients with sepsis despite generating alerts for an ESM score of 6 or higher for 6971 of all 38?455 hospitalized patients (18%), thus creating a large burden of alert fatigue (2).

Laboratory Diagnostics: Laboratory tests play a critical role in sepsis diagnosis. Innovations such as procalcitonin assays (sensitivity of approximately 75% and specificity of 80%) and host response biomarkers offer promising avenues for early detection. However, individual laboratory tests may not be sufficient and often must be combined with more advanced diagnostics to achieve optimal accuracy and decision-making. Additionally, these tests must be ordered rapidly to treat sepsis adequately, meaning physicians must rely on clinical judgement to determine if these tests are appropriate. Balancing the benefits of these diagnostics with their financial and logistical implications remains a critical challenge. Furthermore, the problem of blood culture contamination—a common issue—can significantly worsen the accuracy of sepsis detection. Tools like Inflammatix, Inc. ’s Triverity, which leverages novel sepsis-specific biomarkers without EHR integration, offer an alternative approach with potentially higher specificity and sensitivity for certain patient populations.

Unmet Needs and the Path Forward

Despite advancements, managing sepsis continues to be challenged by slow detection and inconsistent accuracy due to fragmented data sources and the absence of a unified platform to capture and assess critical patient information. Current approaches often overlook essential biomarkers or depend on hospital-specific lab tests, restricting real-time applicability. Meanwhile, the volume and accessibility of clinical data are expanding rapidly—from EHRs and lab results to genomic sequencing and continuous patient monitoring. However, the potential benefits remain unrealized without systems that can quickly and accurately organize, assess, and analyze this information. The need is clear: a dynamic system that integrates diverse data sources and evolves alongside advancements in diagnostics and patient care.

Speed and accuracy are paramount. Research shows that each hour of delay in diagnosing and treating sepsis increases mortality risk by approximately 8%. These delays worsen patient outcomes and undermine antibiotic stewardship. As information grows, so does the complexity of decision-making. A robust, adaptable platform capable of structuring vast, heterogeneous data into actionable real-time insights could transform sepsis management. By incorporating novel diagnostic methods, emerging biomarkers, and expanding EHR data fields, such a system would ensure clinicians receive precise, timely guidance to improve prevention and treatment strategies.

Conclusion

The fight against sepsis is far from over, but the innovation landscape offers hope. By leveraging advances in AI, biotechnology, and precision medicine, healthcare organizations can improve the speed and accuracy of sepsis detection and intervention. Achieving this requires cutting-edge tools and robust data infrastructure to integrate and analyze complex data sets efficiently.

At Decode Health , we are committed to addressing these challenges head-on. By developing solutions that bridge the gap between innovation and practical application, we aim to empower healthcare providers with the insights they need to improve outcomes and reduce the burden of sepsis on patients and systems alike. Together, we can turn the tide against one of healthcare’s most formidable foes.

Works Cited

1. Ostermayer DG, Braunheim B, Mehta AM, Ward J, Andrabi S, Anwar Mohammad Sirajuddin. External validation of the Epic sepsis predictive model in 2 county emergency departments. JAMIA Open. 2024;7(4). doi:https://doi.org/10.1093/jamiaopen/ooae133
2. Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, DeTroyer-Cooley O, Pestrue J, Phillips M, Konye J, Penoza C, Ghous M, Singh K. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Internal Medicine. 2021;181(8). doi:https://doi.org/10.1001/jamainternmed.2021.2626