Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threatening condition and a major cause of cancer-related mortality. Patients with neutropenic sepsis are generally treated with Broad Spectrum Antibiotics (BSA) as a first-line medication that destroys common causative organisms but may either miss the true pathogen or be overly broad leading to an increased risk of development of Antimicrobial Resistance (AMR). Physicians resort to using BSA due to a typical delay of 2-5 days for specific organism identification by blood cultures. We report the development and validation of an explainable AI powered system to predict bacterial growth in blood cultures (N=110) using readily available hematological parameters, enabling predictions 2-5 days ahead of actual culture results. Our best performing models yielded an accuracy and F1 score of 78%. In predicting gram-negative bacteria (GNB), the models demonstrated an accuracy and F1 score of 63%. To our knowledge, this is the first study to explore AI-powered early prediction of bacteremia in neutropenic sepsis patients in a South Asian population.

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients

BACKGROUND

Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has encouraged the exploration of rapid prediction methodologies. Cellular Population Data (CPD), which provides detailed insights into white blood cell morphology and functionality, is a promising technique for the early detection of bacteremia.

METHODS

This study applied machine learning models to analyze laboratory data from hospitalized patients at risk of bacteremia from three hospitals. Using complete blood count (CBC), differential count (DC), and CPD, collected at various time intervals, we trained two sets of artificial intelligence models: one trained using data from patients in the Emergency Department (ED) and another specifically designed for and trained using data from a hospitalized cohort. We evaluated the performance of both models by applying them to the same hospitalized population and comparing their outcomes.

RESULTS

The study encompassed analysis of over 66,000 CBC samples. The model tailored for hospitalized patients exhibited superior performance in bacteremia prediction across all cohorts compared with the ED-model, achieving an area under the receiver operating characteristic curve (AUROC) of 0.772 in the validation cohort from China Medical University Hospital and 0.808 and 0.843 in two other hospital cohorts. Notably, nearly half of the top fifteen important features identified by shapely additive explanations values were CPD parameters, underscoring the pivotal role of CPD in predictive models for bacteremia.

CONCLUSIONS

Artificial intelligence models incorporating CPD data can accurately predict bacteremia in hospitalized patients. Models specifically trained on hospitalized patient data demonstrate enhanced performance over those based on ED data in predicting bacteremia occurrences. Future research must explore the clinical effects of these models, focusing on their potential to assist physicians in managing antibiotic use and patient health.

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI's transformative potential in sepsis care.

Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact

Machine learning (ML) is currently being widely studied and applied in data analysis and prediction in various fields, including laboratory medicine. To comprehensively evaluate the application of ML in laboratory medicine, we reviewed the literature on ML applications in laboratory medicine published between February 2014 and March 2024. A PubMed search using a search string yielded 779 articles on the topic, among which 144 articles were selected for this review. These articles were analyzed to extract and categorize related fields within laboratory medicine, research objectives, specimen types, data types, ML models, evaluation metrics, and sample sizes. Sankey diagrams and pie charts were used to illustrate the relationships between categories and the proportions within each category. We found that most studies involving the application of ML in laboratory medicine were designed to improve efficiency through automation or expand the roles of clinical laboratories. The most common ML models used are convolutional neural networks, multilayer perceptrons, and tree-based models, which are primarily selected based on the type of input data. Our findings suggest that, as the technology evolves, ML will rise in prominence in laboratory medicine as a tool for expanding research activities. Nonetheless, expertise in ML applications should be improved to effectively utilize this technology.

Assessing sepsis-induced immunosuppression to predict positive blood cultures

Introduction

Bacteremia is a life-threatening condition that can progress to sepsis and septic shock, leading to significant mortality in the emergency department (ED). The standard diagnostic method, blood culture, is time-consuming and prone to false positives and false negatives. Although not widely accepted, several clinical and artificial intelligence-based algorithms have been recently developed to predict bacteremia. However, these strategies require further identification of new variables to improve their diagnostic accuracy. This study proposes a novel strategy to predict positive blood cultures by assessing sepsis-induced immunosuppression status through endotoxin tolerance assessment.

Methods

Optimal assay conditions have been explored and tested in sepsis-suspected patients meeting the Sepsis-3 criteria. Blood samples were collected at ED admission, and endotoxin (lipopolysaccharide, LPS) challenge was performed to evaluate the innate immune response through cytokine profiling.

Results

Clinical variables, immune cell population biomarkers, and cytokine levels (tumor necrosis factor [TNFα], IL-1β, IL-6, IL-8, and IL-10) were measured. Patients with positive blood cultures exhibited significantly lower TNFα production after LPS challenge than did those with negative blood cultures. The study also included a validation cohort to confirm that the response was consistent.

Discussion

The results of this study highlight the innate immune system immunosuppression state as a critical parameter for sepsis diagnosis. Notably, the present study identified a reduction in monocyte populations and specific cytokine profiles as potential predictive markers. This study showed that the LPS challenge can be used to effectively distinguish between patients with bloodstream infection leading to sepsis and those whose blood cultures are negative, providinga rapid and reliable diagnostic tool to predict positive blood cultures. The potential applicability of these findings could enhance clinical practice in terms of the accuracy and promptness of sepsis diagnosis in the ED, improving patient outcomes through timely and appropriate treatment.

Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records

Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates demographic, laboratory, and comprehensive medical history data to classify patients into high-risk and low-risk BSI groups. By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. The XAI framework significantly outperformed traditional models, particularly with tree-based algorithms, demonstrating superior specificity and sensitivity in BSI prediction. This approach promises to optimize resource allocation and potentially reduce healthcare costs while providing interpretability for clinical decision-making, making it a valuable tool in hospital systems for early intervention and improved patient outcomes.

The application of artificial intelligence in the management of sepsis

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.