Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever

Phenotypic Landscape of Immune Cells in Sepsis: Insights from High-Dimensional Mass Cytometry

Understanding the sepsis-induced immunological response can be facilitated by identifying phenotypic changes in immune cells at the single-cell level. Mass cytometry, a novel multiparametric single-cell analysis technique, offers considerable benefits in characterizing sepsis-induced phenotypic changes in peripheral blood mononuclear cells. Here, we analyzed peripheral blood mononuclear cells from 20 sepsis patients and 10 healthy donors using mass cytometry and employing 23 markers. Both manual gating and automated clustering approaches (PhenoGraph) were used for cell identification, complemented by uniform manifold approximation and projection (UMAP) for dimensionality reduction and visualization. Our study revealed that patients with sepsis exhibited a unique immune cell profile, marked by an increased presence of monocytes, B cells, and dendritic cells, alongside a reduction in natural killer (NK) cells and CD4/CD8 T cells. Notably, significant changes in the distributions of monocytes and B and CD4 T cells were observed. Clustering with PhenoGraph unveiled the subsets of each cell type and identified elevated CCR6 expression in sepsis patients' monocyte subset (PG#5), while further PhenoGraph clustering on manually gated T and B cells discovered sepsis-specific CD4 T cell subsets (CCR4low CD20low CD38low) and B cell subsets (HLA-DRlow CCR7low CCR6high), which could potentially serve as novel diagnostic markers for sepsis.

Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission

BACKGROUND

Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics.

METHODS

We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature.

RESULTS

After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857-0.887). External validations show AUROCs of 0.805 (95% CI, 0.787-0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837-0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836-0.877) than PCT alone (0.790; 95% CI, 0.759-0.821; P < 0.001).

CONCLUSIONS

Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

Machine learning algorithms in sepsis

Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.

Feature augmentation and semi-supervised conditional transfer learning for early detection of sepsis

Early detection of Sepsis is crucial for improving patient outcomes, as it is a significant public health concern that results in substantial morbidity and mortality. However, despite the widespread use of the Sequential Organ Failure Assessment (SOFA) in clinical settings to identify sepsis, obtaining sufficient physiological data before onset remains challenging, limiting early detection of sepsis. To address this challenge, we propose an interpretable machine learning model, ITFG (Interpretable Tree-based Feature Generation), that leverages potential correlations between features based on existing knowledge to identify sepsis within six hours of onset using valuable and continuous physiological measures. Furthermore, we introduce a Semi-supervised Attention-based Conditional Transfer Learning (SAC-TL) framework to enhance the model's generality and enable it to be used for early warning of sepsis in the target domain with less information from the source domain. Our proposed approaches effectively address the problem of systematic feature sparsity and missing data, while also being practical for different degrees of generalizability. We evaluated our proposed approaches on open datasets, MIMIC and PhysioNet, obtaining AUC of 97.98% and 86.21%, respectively, demonstrating their effectiveness in different data environments and achieving the best early detection results.

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review

BACKGROUND

Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention.

METHODS

PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality.

RESULTS

This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation.

CONCLUSIONS

This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.

Better prognostic ability of NEWS2, SOFA and SAPS-II in septic patients

BACKGROUND AND OBJECTIVES

To compare the ability of qSOFA, NEWS2, SOFA, LODS, SIRS, APACHE-II and SAPS-II scores.

MATERIAL AND METHODS

Analysis of in-hospital mortality of 203 patients admitted to the ICU because of sepsis. The scores were compared according to their application. Discrimination was evaluated with AUC-ROC curve and performance with the Akaike's (AIC) and Bayesian information criterion (BIC).

RESULTS

In-hospital mortality was 31.53%. NEWS2 showed better mortality discrimination ability and better performance considering the AIC/BIC criterion for mortality tan qSOFA (AUC-ROC=.615 and .536; P=.039). SOFA presented higher performance and AUC-ROC tan LODS (.776 vs .693; P=.01) and both showed higher discrimination ability than SIRS (AUC-ROC=.521; P<.003). Finally, SAPS-II was able to predict mortality with better performance than APACHE-II and presented higher discrimination capacity but without statistical significance compared (AUROC=.738 for SAPS-II and AUROC=.673 for APACHE-II; P=.08).

CONCLUSION

NEWS2 is a better predictor of mortality than qSOFA and its implementation for the early recognition of the septic patient or the patient with higher risk in the emergency and hospitalization wards should be addressed. In addition, given that SOFA and SAPS-II showed better performance and are simpler than LODS and APACHE-II, respectively, both should be considered the scores of choice in this setting.

Impact of satellite blood culture on early diagnosis of sepsis

Background

The aim of this study was to assess whether satellite blood culture (SBC) can improve turnaround times, antibiotic switching, and patient prognosis, relative to laboratory blood culture (LBC).  .

Methods

Patients with sepsis treated in the intensive care units (ICUs) of Henan Provincial People's Hospital from February 5, 2018 to January 19, 2019 who met the inclusion criteria were recruited to the study and divided into the SBC group and LBC group according to different blood culture methods. Patient demographics, blood culture, antibiotic adjustment, and prognosis data were collected and compared between the two groups.  .

Results

A total of 204 blood culture sets from 52 ICU patients, including 100 from the medical microbiology LBC group and 104 from the SBC group, were analyzed in this study. There was no significant difference in the positive rates between the two groups. Time from specimen collection to incubation was significantly shorter in the SBC group than that in the LBC group (1.65 h vs. 3.51 h, z=-4.09, P<0.001). The median time from specimen collection to notification of blood culture positivity was 24.83 h in the SBC group and 27.83 h in the LBC group. Median times from adjustment of antibiotics according to the first report were 26.05 h and 51.71 h in the SBC and LBC groups, respectively, while those according to the final report were 97.17 h and 111.45 h, respectively. Median ICU lengths of stay were 15.00 days and 17.00 days in the SBC and LBC groups, respectively, and median ICU lengths of stay were 18.00 days and 23.50 days, respectively. Mean hospitalization costs were 157.99 and 186.73 thousand yuan in the SBC and LBC groups, respectively.  .

Conclusion

SBC can significantly reduce blood culture turnaround times; however, there were no significant differences between the two blood culture methods in initial reporting of positive cultures, time to adjustment of antibiotic therapy, or medical costs, despite a trend toward improvement.

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation

Purpose

Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, we developed a machine learning model with a high rate of missing and erroneous data to enable prediction under missing, noisy, and erroneous inputs, as in the actual clinical situation.

Materials and methods

The proposed artificial neural network model was implemented using the MATLAB ANN toolbox, based on stochastic gradient descent. The dataset was collected over the past decade with approval from the appropriate institutional review boards, and the sepsis status was identified and labeled using Sepsis-3 clinical criteria. The imputation method was built by last observation carried forward and mean value, aimed to simulate clinical situation.

Results

The mean area under the receiver operating characteristic (ROC) curve (AUC) of classifying sepsis and nonsepsis patients was 0.82 and 0.786 at 0 h and 40 h prior to onset, respectively. The highest model performance was found for one-hourly data, demonstrating that our ANN model can perform adequately with limited hourly data provided.

Conclusions

Our model has the moderate ability to predict sepsis up to 40 h in advance under simulated clinical situation with real-world data.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.