Sepsis is a critical medical condition that results from an abnormal immune response to infection, often causing organ dysfunction and high rates of morbidity and mortality. Prompt treatment, especially with antibiotics, can significantly improve outcomes. However, the varied clinical presentation of sepsis makes early detection difficult, contributing to higher mortality rates. This underscores the urgent need for reliable risk assessment tools to help clinicians identify high-risk patients quickly and accurately. Although numerous tools have been proposed, such as clinical methods, laboratory tests, and biomarkers, none have been universally adopted. Notably, no ai-based model for sepsis detection has received FDA approval for commercial use.
Researchers at NEJM ai, a division of the Massachusetts Medical Society, developed and validated the Sepsis ImmunoScore, the first ai-based tool cleared by the FDA to identify patients at risk for sepsis. Designed for integration with electronic medical records (EMR), this machine learning-based tool predicts the likelihood of sepsis onset or progression within 24 hours of patient evaluation. In April 2024, it received marketing authorization from the FDA through the de novo route. The study aimed to evaluate the performance of Sepsis ImmunoScore in detecting sepsis (based on Sepsis-3 criteria) and its secondary outcomes, including in-hospital mortality, ICU admission, length of hospital stay, ventilation mechanics and the use of vasopressors.
The study conducted a prospective multicenter observational study to develop and evaluate a machine learning algorithm, Sepsis ImmunoScore, designed to identify sepsis within 24 hours and evaluate critical illness outcomes, such as mortality and ICU admission. The study enrolled adult patients hospitalized at five US hospitals between April 2017 and July 2022. Participants included those with suspected infections and lithium heparin plasma samples collected within six hours of orders blood culture. The study population was divided into three cohorts: derivation (n=2366), internal validation (n=393), and external validation (n=698). The primary outcome was the diagnosis of sepsis within 24 hours using Sepsis-3 criteria, while secondary outcomes included metrics such as in-hospital mortality, length of hospital stay, transfer to ICU, mechanical ventilation, and vasopressor use.
The Sepsis ImmunoScore, developed using a calibrated random forest model, used 22 patient-specific characteristics, including vital signs and laboratory results, to predict sepsis risk. Missing data for specific parameters were handled by imputation. The accuracy of the algorithm was tested using AUROC, likelihood ratios, and predictive values across all risk categories, with confidence intervals reported. Sensitivity analyzes distinguished between the initial diagnosis of sepsis and cases that developed within 24 hours.
The study evaluated 3,457 patient encounters with valid Sepsis ImmunoScore results distributed across derivation (2,366 encounters), internal validation (393), and external validation (698) cohorts. Participants reflected typical demographic and clinical characteristics of sepsis patients in the U.S. Sepsis rates varied by cohort: 32% in referral, 28% in internal validation, and 22% in external validation. Patients diagnosed with sepsis had higher rates of severe illness and mortality compared to those without sepsis. The Sepsis ImmunoScore algorithm used 22 input parameters, including demographics, vital signs, metabolic panel results, complete blood count, and sepsis biomarkers such as PCT and CRP, to generate a stratified risk score. SHAP analysis highlighted PCT, respiratory rate, and systolic blood pressure as the most influential factors, and the derivation set achieved an AUC of 0.85, demonstrating strong diagnostic accuracy.
The algorithm's risk categories effectively predicted sepsis and its secondary outcomes, such as in-hospital mortality, ICU admissions, mechanical ventilation, and vasopressor use within 24 hours. Risk stratification showed a gradual increase in the severity of outcomes in all categories, validated by external data sets. Diagnostic and prognostic analyzes indicated strong performance, with an AUC of 0.84 for diagnosing sepsis at presentation and 0.76 for predicting its development within 24 hours. These results underscore the ability of Sepsis ImmunoScore to integrate multidimensional data for comprehensive sepsis risk assessment. Compared to existing FDA-approved diagnostic tools, which often focus on single biomarkers such as PCT or biophysical properties of leukocytes, Sepsis ImmunoScore offers a broader, more predictive approach.
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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, he brings a new perspective to the intersection of ai and real-life solutions.
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