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

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.

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.

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.