Effectiveness of automated alerting system compared to usual care for the management of sepsis

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.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

BACKGROUND

The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).

METHODS

After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.

RESULTS

This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.

CONCLUSIONS

In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

The application of metagenomics, radiomics and machine learning for diagnosis of sepsis

Introduction

Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis.

Methods

Blood samples from sepsis patients were sequenced. α-diversity and β-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms.

Results

The results of α-diversity and β-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group (p < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, Mycobacterium canettii, Solanum pennellii, Ralstonia insidiosa, Ananas comosus, Moraxella osloensis, Escherichia coli, Staphylococcus hominis, Camelina sativa, and Cutibacterium acnes. The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation (p < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation (p < 0.05) with Cutibacterium acnes, Ralstonia insidiosa, Moraxella osloensis, and Staphylococcus hominis. Ananas comosus showed a significant positive correlation (p < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation (p < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction.

Discussion

This study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.

Sepsis Alert Systems, Mortality, and Adherence in Emergency Departments: A Systematic Review and Meta-Analysis

Importance

Early detection and management of sepsis are crucial for patient survival. Emergency departments (EDs) play a key role in sepsis management but face challenges in timely response due to high patient volumes. Sepsis alert systems are proposed to expedite diagnosis and treatment initiation per the Surviving Sepsis Campaign guidelines.

Objective

To review and analyze the association of sepsis alert systems in EDs with patient outcomes.

Data Sources

A thorough search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library from January 1, 2004, to November 19, 2023.

Study Selection

Studies that evaluated sepsis alert systems specifically designed for adult ED patients were evaluated. Inclusion criteria focused on peer-reviewed, full-text articles in English that reported on mortality, ICU admissions, hospital stay duration, and sepsis management adherence. Exclusion criteria included studies that lacked a control group or quantitative reports.

Data Extraction and Synthesis

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Two independent reviewers conducted the data extraction using a standardized form. Any disagreements were resolved through discussion. The data were synthesized using a random-effects model due to the expected heterogeneity among the included studies.

Main Outcomes and Measures

Key outcomes included mortality, intensive care unit admissions, hospital stay duration, and adherence to the sepsis bundle.

Results

Of 3281 initially identified studies, 22 (0.67%) met inclusion criteria, encompassing 19 580 patients. Sepsis alert systems were associated with reduced mortality risk (risk ratio [RR], 0.81; 95% CI, 0.71 to 0.91) and length of hospital stay (standardized mean difference [SMD], -0.15; 95% CI, -0.20 to -0.11). These systems were also associated with better adherence to sepsis bundle elements, notably in terms of shorter time to fluid administration (SMD, -0.42; 95% CI, -0.52 to -0.32), blood culture (SMD, -0.31; 95% CI, -0.40 to -0.21), antibiotic administration (SMD, -0.34; 95% CI, -0.39 to -0.29), and lactate measurement (SMD, -0.15; 95% CI, -0.22 to -0.08). Electronic alerts were particularly associated with reduced mortality (RR, 0.78; 95% CI, 0.67 to 0.92) and adherence with blood culture guidelines (RR, 1.14; 95% CI, 1.03 to 1.27).

Conclusions and Relevance

These findings suggest that sepsis alert systems in EDs were associated with better patient outcomes along with better adherence to sepsis management protocols. These systems hold promise for enhancing ED responses to sepsis, potentially leading to better patient outcomes.

Hospital-Onset Sepsis Warrants Expanded Investigation and Consideration as a Unique Clinical Entity

Sepsis causes more than a quarter million deaths among hospitalized adults in the United States each year. Although most cases of sepsis are present on admission, up to one-quarter of patients with sepsis develop this highly morbid and mortal condition while hospitalized. Compared with patients with community-onset sepsis (COS), patients with hospital-onset sepsis (HOS) are twice as likely to require mechanical ventilation and ICU admission, have more than two times longer ICU and hospital length of stay, accrue five times higher hospital costs, and are twice as likely to die. Patients with HOS differ from those with COS with respect to underlying comorbidities, admitting diagnosis, clinical manifestations of infection, and severity of illness. Despite the differences between these patient populations, patients with HOS sepsis are understudied and warrant expanded investigation. Here, we outline important knowledge gaps in the recognition and management of HOS in adults and propose associated research priorities for investigators. Of particular importance are questions regarding standardization of research and clinical case identification, understanding of clinical heterogeneity among patients with HOS, development of tailored management recommendations, identification of impactful prevention strategies, optimization of care delivery and quality metrics, identification and correction of disparities in care and outcomes, and how to ensure goal-concordant care for patients with HOS.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Continuous monitoring of physiological data using the patient vital status fusion score in septic critical care patients

Accurate and standardized methods for assessing the vital status of patients are crucial for patient care and scientific research. This study introduces the Patient Vital Status (PVS), which quantifies and contextualizes a patient's physical status based on continuous variables such as vital signs and deviations from age-dependent normative values. The vital signs, heart rate, oxygen saturation, respiratory rate, mean arterial blood pressure, and temperature were selected as input to the PVS pipeline. The method was applied to 70 pediatric patients in the intensive care unit (ICU), and its efficacy was evaluated by matching high values with septic events at different time points in patient care. Septic events included systemic inflammatory response syndrome (SIRS) and suspected or proven sepsis. The comparison of maximum PVS values between the presence and absence of a septic event showed significant differences (SIRS/No SIRS: p < 0.0001, η2 = 0.54; Suspected Sepsis/No Suspected Sepsis: p = 0.00047, η2 = 0.43; Proven Sepsis/No Proven Sepsis: p = 0.0055, η2 = 0.34). A further comparison between the most severe PVS in septic patients with the PVS at ICU discharge showed even higher effect sizes (SIRS: p < 0.0001, η2 = 0.8; Suspected Sepsis: p < 0.0001, η2 = 0.8; Proven Sepsis: p = 0.002, η2 = 0.84). The PVS is emerging as a data-driven tool with the potential to assess a patient's vital status in the ICU objectively. Despite real-world data challenges and potential annotation biases, it shows promise for monitoring disease progression and treatment responses. Its adaptability to different disease markers and reliance on age-dependent reference values further broaden its application possibilities. Real-time implementation of PVS in personalized patient monitoring may be a promising way to improve critical care. However, PVS requires further research and external validation to realize its true potential.

Trend of mortality and length of stay in the emergency department following implementation of a centralized sepsis alert system

Introduction

Sepsis alerts based on laboratory and vital sign criteria were found insufficient to improve patient outcomes. While most early sepsis alerts were implemented into smaller scale operating systems, a centralized new approach may provide more benefits, overcoming alert fatigue, improving deployment of staff and resources, and optimizing the overall management of sepsis. The objective of the study was to assess mortality and length of stay (LOS) trends in emergency department (ED) patients, following the implementation of a centralized and automated sepsis alert system.

Methods

The automated sepsis alert system was implemented in 2021 as part of a hospital-wide command and control center. Administrative data from the years 2018 to 2021 were collected. Data included ED visits, in-hospital mortality, triage levels, LOS, and the Canadian Triage and Acuity Scale (CTAS).

Results

Mortality rate for patients classified as CTAS I triage level was the lowest in 2021, after the implementation of the automated sepsis alert system, compared to 2020, 2019, and 2018 (p < 0.001). The Kaplan-Meier survival curve revealed that for patients classified as CTAS I triage level, the probability of survival was the highest in 2021, after implementation of the sepsis alert algorithm, compared to previous years (Log Rank, Mantel-Cox, χ²=29.742, p < 0.001). No significant differences in survival rate were observed for other triage levels.

Conclusion

Implementing an automated sepsis alert system as part of a command center operation significantly improves mortality rate associated with LOS in the ED for patients in the highest triage level. These findings suggest that a centralized early sepsis alert system has the potential to improve patient outcomes.

Exploiting Electronic Data to Advance Knowledge and Management of Severe Infections

Purpose of Review

To identify opportunities and recent advances in the use of multicentric digital data sources and networks to investigate the epidemiology and management of patients with infections admitted to intensive care units (ICUs).

Recent Findings

Electronic surveillance systems for a range of serious infections have been reported from large cohorts with evident improvements in efficiency, objectivity, and comprehensiveness of coverage as compared to traditional methods. Electronic data, most notably from electronic health records, has been used to define the epidemiology and outcomes of severe infections in several settings and conditions and has facilitated population-based evaluation. Automated alerts and notifications hold promise to identify patients at risk for sepsis and bloodstream infection although demonstration of efficacy in interventional trials is needed.

Summary

Exploitation of electronic data in ICUs has led to a better understanding of the epidemiology of severe infections and holds promise for future interventional clinical trials.

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.

Management of Sepsis and Septic Shock: What Have We Learned in the Last Two Decades?

Sepsis is a clinical syndrome encompassing physiologic and biological abnormalities caused by a dysregulated host response to infection. Sepsis progression into septic shock is associated with a dramatic increase in mortality, hence the importance of early identification and treatment. Over the last two decades, the definition of sepsis has evolved to improve early sepsis recognition and screening, standardize the terms used to describe sepsis and highlight its association with organ dysfunction and higher mortality. The early 2000s witnessed the birth of early goal-directed therapy (EGDT), which showed a dramatic reduction in mortality leading to its wide adoption, and the surviving sepsis campaign (SSC), which has been instrumental in developing and updating sepsis guidelines over the last 20 years. Outside of early fluid resuscitation and antibiotic therapy, sepsis management has transitioned to a less aggressive approach over the last few years, shying away from routine mixed venous oxygen saturation and central venous pressure monitoring and excessive fluids resuscitation, inotropes use, and red blood cell transfusions. Peripheral vasopressor use was deemed safe and is rising, and resuscitation with balanced crystalloids and a restrictive fluid strategy was explored. This review will address some of sepsis management's most important yet controversial components and summarize the available evidence from the last two decades.

Evaluation of evolving sepsis screening criteria in discriminating suspected sepsis and mortality among adult patients admitted to the intensive care unit

BACKGROUND

Institutions struggle with successful use of sepsis alerts within electronic health records.

OBJECTIVE

Test the association of sepsis screening measurement criteria in discrimination of mortality and detection of sepsis in a large dataset.

DESIGN

Retrospective, cohort study using a large United States (U.S.) intensive care database. The Institutional Review Board exempt status was obtained from Kansas University Medical Center Human Research Protection Program (10-1-2015).

SETTING

334 U.S. hospitals participating in the eICU Research Institute.

PARTICIPANTS

Nine hundred twelve thousand five hundred and nine adult intensive care admissions from 183 hospitals.

METHODS

Exposures included: systemic inflammatory response syndrome criteria ≥ 2 (Sepsis-1); systemic inflammatory response syndrome criteria with organ failure criteria ≥ 3.5 points (Sepsis-2); and sepsis-related organ failure assessment score ≥ 2 and quick score ≥ 2 (Sepsis-3). Discrimination of outcomes was determined with/without (adjusted/unadjusted) baseline risk exposure to a model. The receiver operating characteristic curve (AUROC) and odds ratios (ORs) for each decile of baseline risk of sepsis or death were assessed.

RESULTS

Within the eligible cohort of 912,509, a total of 86,219 (9.4 %) patients did not survive their hospital stay and 186,870 (20.5 %) met the definition of suspected sepsis. For suspected sepsis discrimination, Sepsis-2 (unadjusted AUROC 0.67, 99 % CI: 0.66-0.67 and adjusted AUROC 0.77, 99 % CI: 0.77-0.77) outperformed Sepsis-3 (SOFA unadjusted AUROC 0.61, 99 % CI: 0.61-0.61 and adjusted AUROC 0.74, 99 % CI: 0.74-0.74) (qSOFA unadjusted AUROC 0.59, 99 % CI: 0.59-0.60 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). Sepsis-2 also outperformed Sepsis-1 (unadjusted AUROC 0.58, 99 % CI: 0.58-0.58 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). In between differences of AUROCs were statistically significantly different. Sepsis-2 ORs were higher for the outcome of suspected sepsis when considering deciles of risk than the other measurement systems.

CONCLUSIONS AND RELEVANCE

Sepsis-2 outperformed other systems in suspected sepsis detection and was comparable to SOFA in prognostic accuracy of mortality in adult intensive care patients.

Effects of Albumin Supplements on In-Hospital Mortality in Patients with Sepsis or Septic Shock: A Systemic Review and Meta-Analysis

Objective

To explore the clinical effects of albumin supplements on the basis of crystalloid solution in patients with sepsis or septic shock.

Methods

The online databases including PubMed, Web of Science, Cochrane Library, and EMBASE were comprehensively searched from inception to June 28, 2021, with the keywords including “albumin,” “sepsis,” or “septic shock.” Retrospective cohort (RC) and randomized controlled trials (RCT) were included for analysis. Two authors independently searched and analyzed the literature. The in-hospital mortality at 7 days and 28 days, duration of mechanical ventilation, renal replacement therapy, length of ICU stay, and length of hospital stay were compared between patients with albumin supplements and crystalloid solution and those with crystalloid alone.

Results

A total of 10 studies with 6463 patients were eventually included for meta-analysis. The in-hospital mortality of patients at 7 days (OR = 1.00, 95% CI: 0.81–1.23) and 28 days (OR = 1.02, 95% CI: 0.91–1.13) did not show a significant difference between the two groups of patients. Also, the pooled results demonstrated no significant differences in duration of mechanical ventilation (OR = 0.29, 95% CI: −0.05–0.63), renal replacement therapy (WMD = 1.15, 95% CI: 0.98–1.35), length of ICU stay (WMD = −0.07, 95% CI: −0.62–0.48), and length of hospital stay (WMD = −0.09, 95% CI: −0.70–0.52) between patients receiving albumin plus crystalloid solution and those with crystalloid solution alone.

Conclusion

Albumin supplements on the basis of crystalloid solution did not improve the 7-day and 28-dayin-hospital mortality in patients with sepsis or septic shock compared with those with crystalloid solution alone.