Prediction Of Sepsis Research Articles

Time to positivity of Coagulase Negative Staphylococcus In Neonatal Blood Cultures as an adjunct tool to help discriminate between sepsis and contamination.

To assess the usefulness of time to positivity (TTP) to distinguish between sepsis and contamination in coagulase-negative staphylococci (CoNS) isolates. Unicentric retrospective observational. Medical records of 168 patients with suspected sepsis and positive blood culture for CoNS were reviewed. Patients were subdivided into sepsis (29%) and probable contamination (71%). Logistic regression analyses were performed to evaluate different risk factors and clinical signs and symptoms associated with sepsis. TTP cut-off value that best discriminated sepsis from contamination was found to be 18 h. Regression analysis revealed that TTP ≤ 18 h, gestational age ≤32 weeks, taquycardia/bradycardia and hypoactivity/lethargy were independent predictors of sepsis. TTP is useful in distinguishing sepsis from contamination, especially in neonates with lower gestational age (<32 weeks). The clinical signs that most increase the discriminatory power of TTP are the presence of tachycardia/bradycardia or hypoactivity.

NEWS, SIRS and qSOFA criteria for predicting sepsis and sepsis with high risk of death in emergency room: A comparison study and improved predictive models based on local data from CETAT and MIMIC-IV databases

Early identification of sepsis in emergency room patients is critical for initiating timely interventions, highlighting the need for effective predictive scoring systems. A retrospective observational study was conducted using data from the CETAT database collected between December 2019 and October 2021. The study evaluated how well the SIRS, qSOFA, and NEWS scoring systems, along with logistic regression models, predict sepsis and high-risk sepsis in emergency room patients. The logistic regression models were further optimized by incorporating additional features based on local data.A total of 12,799 patients were analyzed, including 1,360 sepsis cases, of which 373 were classified as high-risk sepsis. The NEWS score demonstrated superior predictive performance compared to qSOFA and SIRS, with an AUC-ROC of 0.737 (95% CI 0.72–0.75) for sepsis and 0.653 (95% CI 0.62–0.69) for high-risk sepsis. After optimization, the NEWS-based model improved to an AUC-ROC of 0.756 (95% CI 0.74–0.77) for sepsis and 0.718 (95% CI 0.69–0.75) for high-risk sepsis. Further enhancement was observed with the inclusion of additional clinical variables, resulting in AUC-ROC values of 0.834 (95% CI 0.82–0.85) for sepsis and 0.756 (95% CI 0.73–0.78) for high-risk sepsis. Data from the MIMIC-IV database, which included sepsis status and relevant variables for SIRS, qSOFA, and NEWS score calculations, confirmed that the optimized NEWS-based model improved the sepsis prediction AUC-ROC from 0.690 (95% CI 0.68–0.70) to 0.708 (95% CI 0.70–0.72), and consistently outperformed qSOFA and SIRS in sepsis prediction.

Early prediction of sepsis in emergency department patients using various methods and scoring systems.

Early recognition of sepsis, a common life-threatening condition in intensive care units (ICUs), is beneficial for improving patient outcomes. However, most sepsis prediction models were trained and assessed in the ICU, which might not apply to emergency department (ED) settings. To establish an early predictive model based on basic but essential information collected upon ED presentation for the follow-up diagnosis of sepsis observed in the ICU. This study developed and validated a reliable model of sepsis prediction among ED patients by comparing 10 different methods based on retrospective electronic health record data from the MIMIC-IV database. In-ICU sepsis was identified as the primary outcome. The potential predictors encompassed baseline demographics, vital signs, pain scale, chief complaints and Emergency Severity Index (ESI). 80% and 20% of the total of 425 737 ED visit records were randomly selected for the train set and the test set for model development and validation, respectively. Among the methods evaluated, XGBoost demonstrated an optimal predictive performance with an area under the curve (AUC) of 0.90 (95% CI: 0.90-0.91). Logistic regression exhibited a comparable predictive ability to XGBoost, with an AUC of 0.89 (95% CI: 0.89-0.90), along with a sensitivity and specificity of 85% (95% CI: 0.83-0.86) and 78% (95% CI: 0.77-0.80), respectively. Neither of the five commonly used severity scoring systems demonstrated satisfactory performance for sepsis prediction. The predictive ability of using ESI as the sole predictor (AUC: 0.79, 95% CI: 0.78-0.80) was also inferior to the model integrating ESI and other basic information. The use of ESI combined with basic clinical information upon ED presentation accurately predicted sepsis among ED patients, strengthening its application in ED. The proposed model may assist nurses in risk stratification management and prioritize interventions for potential sepsis patients, even in low-resource settings.

Predictive Value of Red Blood Cell Distribution Width-to-Platelet Ratio for Severity in Pyogenic Liver Abscess: A Retrospective Observational Study.

Background: The ratio of red blood cell distribution width (RDW) to platelet ratio (RPR) may have prognostic value in several inflammation-related diseases. However, few studies have been conducted on the value of RPR for predicting the severity of pyogenic liver abscess (PLA). Methods: Patients receiving the diagnosis of PLA from February 2013 to December 2022 were enrolled in this retrospective study. We collected data related to baseline characteristics and laboratory results within the first 24 hours the of admission. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate the predictive ability of different indicators for severity in PLA. Results: A total of 278 patients were enrolled. For the prediction of sepsis in PLA, RPR had the highest AUC (0.83; 95% confidence interval [CI], 0.78-0.89) with a sensitivity of 0.78 and specificity of 0.82. For the prediction of septic shock, RPR also had the highest AUC (0.74; 95% CI, 0.60-0.88) with a sensitivity of 0.67 and specificity of 0.79. The best cutoff value for RPR to predict sepsis was 0.08 and to predict septic shock was 0.11. Conclusions: An increase in RPR level serves as a useful indicator with a predictive capacity for severity in PLA.

Evaluation of a Risk Model for Sepsis Early Prediction in Infants with Epidermolysis Bullosa

Abstract Objective Epidermolysis bullosa (EB) is a severe hereditary condition characterized by fragile skin that can lead to complications, including severe infections such as sepsis. Current research on sepsis in children with EB is limited, and there is a need for specific biomarkers that can aid in early detection and management. Methods This study analyzed blood samples from 92 children diagnosed with EB, 42 of whom developed sepsis. We investigated various inflammatory proteins and clinical parameters as potential biomarkers. Multivariate analysis was used to determine predictors of sepsis occurrence. Results Elevated levels of C-reactive protein (CRP), interleukin-6 (IL-6), lactate, and decreased oxygen saturation were significantly associated with sepsis in children with EB. The predictive model displayed an area under the receiver operating characteristic curve of 0.80 in the training set and 0.77 in the validation set, indicating good predictive accuracy. Conclusion Our findings suggest that CRP, IL-6, lactate, and oxygen saturation are reliable predictors of sepsis in children with EB. These biomarkers should be monitored closely to facilitate early diagnosis and improve outcomes in this vulnerable population. The study underscores the need for tailored research and diagnostic strategies for children with EB at risk of sepsis.

Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.

With machine learning (ML) carving a niche in diverse medical disciplines, its role in sepsis prediction, a condition where the 'golden hour' is critical, is of paramount interest. This study assesses the factors influencing the efficacy of ML models in sepsis prediction, aiming to optimize their use in clinical practice. We searched Medline, PubMed, Google Scholar, and CENTRAL for studies published from inception to October 2023. We focused on studies predicting sepsis in real-time settings in adult patients in any hospital settings without language limits. The primary outcome was area under the curve (AUC) of the receiver operating characteristic. This meta-analysis was conducted according to PRISMA-NMA guidelines and Cochrane Handbook recommendations. A Network Meta-Analysis using the CINeMA approach compared ML models against traditional scoring systems, with meta-regression identifying factors affecting model quality. From 3,953 studies, 73 articles encompassing 457,932 septic patients and 256 models were analyzed. The pooled AUC for ML models was 0.825 and it significantly outperformed traditional scoring systems. Neural Network and Decision Tree models demonstrated the highest AUC metrics. Significant factors influencing AUC included ML model type, dataset type, and prediction window. This study establishes the superiority of ML models, especially Neural Network and Decision Tree types, in sepsis prediction. It highlights the importance of model type and dataset characteristics for prediction accuracy, emphasizing the necessity for standardized reporting and validation in ML healthcare applications. These findings call for broader clinical implementation to evaluate the effectiveness of these models in diverse patient groups. https://inplasy.com/inplasy-2023-12-0062/, identifier, INPLASY2023120062.

Clinical Utility of Recently FDA Approved IntelliSep test (Sepsis biomarker) for early Diagnosis of Sepsis: Comparison with other Biomarkers and von Willebrand Factor/ADAMTS13 Ration

Abstract Background Sepsis, a relatively common presentation in emergency departments, is a life-threatening disease. Recently, IntelliSep test by Cytovale (San Francisco, CA) received FDA approval as sepsis biomarker. We investigated clinical utility of this test using 44 patients (19 disease group and 25 non-disease group). Materials and Methods IntelliSep assesses cellular host response via deformability cytometry of leukocyte biophysical properties and intended for use in conjunction with clinical assessments and laboratory findings for early detection of sepsis. Test results are available within 10 minutes. Results consists of IntelliSep Band 1 (low risk, score: 0.1-4.9) to Band 3 (high risk, score: 6.3-10; total scale: 0.1-10). We measured IntelliSep score in 44 patients using EDTA blood. Left over plasma was used for measuring both plasma von Willebrand factor (vWF) antigen and ADAMTS13 antigen by ELISA assays. Monocyte distribution width (MDW) were obtained during routine CBC analysis using Beckman hematology analyzer. Lactate and high sensitivity troponin I were measured using Beckman analyzer. Results The median IntelliSep score was two-fold higher in the disease group (mean score 7.1, SD: 1.5, median: 7.4) compared to non-disease group (mean score 4.0, SD: 1.4, Median: 3.7) indicating that this new test can identify patients with high risk of developing sepsis and also rule out low risk patients (p&amp;lt;0.01 by both Mann-Whitney U test two tailed and t-test two tailed). The median MDW was also 34.8 % higher in the disease group (mean:25.6, SD: 5.0, Median: 24.4) compared to non-disease group (mean: 18.9, SD: 2.4, Median: 18.1) and difference was also statistically significant (p &amp;lt; 0.01 by both Mann-Whitney U test and t-test, two tailed). However, correlation between IntelliSep scores and MDW was only modest (overall, r= 0.66). Both lactate and procalcitonin concentrations were significantly higher in the disease group compared to the non-disease group. Interestingly, high-sensitivity troponin I concentrations were also significantly higher in the disease group. In addition, we observed significantly higher plasma vWF antigen in the disease group compared to the non-disease group (mean: 67.4 ug/mL in disease group vs 34.1 ug/mL in the non-disease group. However, in some patients in the disease group plasma vWF values were 6.8-fold higher than normal. In addition, vWF/ADAMTS13 ratio was also significantly elevated in the disease group compared to non-disease group. Discussion New IntelliSep test is useful in early prediction of sepsis in patients admitted to emergency department and may be superior to MDW and lactate for early diagnosis. Significant increase in plasma vWF in these patients indicate that vWF may also be a surrogate marker for endotheliopathy in sepsis. Lack of correlation between plasma VWF and Intellisep results indicates that these two markers probably measure different pathways for sepsis (endothelial activation vs neutrophil activation).

Hematology scoring model to predict sepsis in preterm neonates

Background Neonatal sepsis is a major cause of neonatal morbidity and mortality, especially in developing countries. Atypical clinical symptoms lead to delays in diagnosis and treatment. Scoring a combination of routine hematology parameters may be able to predict the occurrence of sepsis in preterm neonates. Objective To formulate a new model for neonatal sepsis scoring from various complete blood count parameters to predict sepsis in preterm neonates. Methods This analytical cross sectional study using secondary data from the Registry of the Neonatology Division was conducted at the RSUP Dr. Hasan Sadikin, Bandung, West Java. Subjects were neonates diagnosed with sepsis, of gestational age 28–36 weeks, who were born at the RSUP Dr. Hasan Sadikin from January to December 2021. Laboratory results of patients who met the inclusion criteria were recorded. Subjects were divided into either proven sepsis and probable sepsis groups, based on blood culture results. Results Of 112 subjects, 35.7% had proven sepsis and 64.3% probable sepsis. In the proven sepsis group, 52.5% of subjects were male, median birth weight was 1,490 grams, median gestational age was 32 weeks, 90% were small for gestational age, and 60% were delivered normally. Multivariable analysis by multiple logistic regression revealed that the parameters associated with the incidence of neonatal sepsis were c-reactive protein (CRP) &gt; 0.18 mg/dL (score 6), hematocrit &lt;40% (score 4), platelet-to-lymphocyte ratio (PLR) &lt; 19.623 (score 4); monocyte-lymphocyte ratio (MLR) &lt; 0.461 (score 2); and mean platelet volume (MPV) value &gt;10 (score 2). Score &gt;8 had a sensitivity of 85% and specificity of 70.8%, with area under the ROC curve of 0.865 (P&lt;0.001). Scoring accuracy was 75.8%, with a positive predictive value of 61.8%, a negative predictive value of 89.5%, and Kappa index of 51.5% with moderate agreement. Conclusion A hematological score &gt;8 can be used as a predictor of sepsis in preterm neonates.

Burden and mortality of congenital gastrointestinal anomalies: insights from a nationwide cohort study.

Improved perioperative care has enhanced survival in children with congenital gastrointestinal conditions and abdominal wall defects (AWD). However, epidemiological and surgical outcomes in developing nations are still scarce. Our aim was to assess the burden and mortality of common congenital gastrointestinal anomalies and AWD in Malaysia, and their influencing factors. Using the Global PaedSurg study protocol with permission, we performed a prospective cohort study on children presenting for the first time between October 2021 and April 2022 with these conditions: Esophageal atresia (EA), congenital diaphragmatic hernia (CDH), intestinal atresia, gastroschisis, exomphalos, anorectal malformation (ARM) and Hirschsprung's disease. We compared mortality and 30-day outcome data across different geographical regions in Malaysia. There were 228 patients with 242 study conditions (EA n = 28, CDH n = 36, intestinal atresia n = 49, gastroschisis n = 12, exomphalos n = 8, ARM n = 77, Hirschsprung's disease n = 32). Our mortality rate was 8.8%; 60% of these were CDH patients. Factors significantly associated with mortality were CDH diagnosis, central venous access requirement, higher American Society of Anesthesiologists (ASA) score, blood transfusion and ventilation requirement. Diagnosis of CDH is the most important predictor for sepsis on arrival and mortality, therefore measures should be taken for early recognition and aggressive management.

Predictive value of IL-8 for mortality risk in elderly sepsis patients of emergency department

BackgroundSepsis significantly impacts morbidity and mortality, particularly among older adults. Despite extensive research, early recognition and prognosis prediction of sepsis remain challenging. IL-8, a chemokine produced by inflammatory cells like monocytes and endothelial cells, has shown potential in predicting mortality in sepsis patients, though its role in elderly sepsis remains unexplored. ObjectivesThe present study aimed to explore the predictive ability of interleukin-8 (IL-8) for mortality risk in elderly septic patients. Methods220 elderly sepsis patients were included in the present study. Serum samples were obtained within 1 h of admission to assess serum IL-8, white blood cell (WBC), procalcitonin (PCT), C-reactive protein (CRP), and lactic acid (LAC) levels. The Sequential Organ Failure Score (SOFA) and Acute Physiological and Chronic Health Assessment II (APACHE II) were recorded. Logistic regression analysis was employed to identify independent predictors of mortality within 28 days for elderly patients diagnosed with sepsis. Further, the capacity of these factors to predict 28-day mortality within this patient cohort was evaluated. ResultsSOFA score, APACHE II score, LAC, and IL-8 were all significant independent predictors for 28-day mortality in elderly sepsis patients (P < 0.05). The AUC of the ROC curve for IL-8 was calculated to be 0.701, indicating a moderately predictive performance. In comparison, the AUC for LAC was marginally higher at 0.708. Nevertheless, the results of the statistical analysis revealed no significant difference in the predictive value between IL-8 and LAC. Moreover, the present findings indicate that the combined assessment of IL-8 and SOFA score demonstrated superior predictive value for mortality compared to using IL-8 alone. ConclusionsIL-8 LAC, APACHE II, and SOFA can be considered independent predictors factors for mortality of elderly sepsis patients. Utilizing the combination of IL-8 and SOFA demonstrates a heightened predictive capability compared to using any single index alone.

FedDSS: A data-similarity approach for client selection in horizontal federated learning

Background and objectiveFederated learning (FL) is an emerging distributed learning framework allowing multiple clients (hospitals, institutions, smart devices, etc.) to collaboratively train a centralized machine learning model without disclosing personal data. It has the potential to address several healthcare challenges, including a lack of training data, data privacy, and security concerns. However, model learning under FL is affected by non-i.i.d. data, leading to severe model divergence and reduced performance due to the varying client's data distributions. To address this problem, we propose FedDSS, Federated Data Similarity Selection, a framework that uses a data-similarity approach to select clients, without compromising client data privacy. MethodsFedDSS comprises a statistical-based data similarity metric, a N-similar-neighbor network, and a network-based selection strategy. We assessed FedDSS' performance against FedAvg's in i.i.d. and non-i.i.d. settings with two public pediatric sepsis datasets (PICD and MIMICIII). Selection fairness was measured using entropy. Simulations were repeated five times to evaluate average loss, true positive rate (TPR), and entropy. ResultsIn i.i.d setting on PICD, FedDSS achieved a higher TPR starting from the 9th round and surpassing 0.6 three rounds earlier than FedAvg. On MIMICIII, FedDSS's loss decreases significantly from the 13th round, with TPR > 0.8 by the 2nd round, two rounds ahead of FedAvg (at the 4th round). In the non-i.i.d. setting, FedDSS achieved TPR > 0.7 by the 4th and > 0.8 by the 7th round, earlier than FedAvg (at the 5th and 11th rounds). In both settings, FedDSS showed reasonable fairness (entropy of 2.2 and 2.1). ConclusionWe demonstrated that FedDSS contributes to improved learning in FL by achieving faster convergence, reaching the desired TPR with fewer communication rounds, and potentially enhancing sepsis prediction (TPR) over FedAvg.

437 Use of a Sepsis Prediction Model to Augment Acuity Assignment and Improve Triage Process

437 Use of a Sepsis Prediction Model to Augment Acuity Assignment and Improve Triage Process

Clinical Characteristics and Risk Factors of Sepsis in Patients with Liver Abscess.

Aims/Background Liver abscess (LA) is a serious medical condition that predisposes patients to sepsis. However, predicting sepsis in LA patients has rarely been explored. This study employed univariate and multivariate logistic regression analyses to identify independent risk factors for sepsis, which would provide guidance for clinical diagnosis and treatment. Methods A total of 122 patients with LA treated in Peking University People's Hospital from 1 January 2016 to 31 October 2022 were recruited. Among the cases, 35 patients had sepsis (sepsis group) while the remaining 87 did not have sepsis (non-sepsis group). Clinical data were collected for all enrolled cases. Univariate analysis was performed to identify potential predictors, which were tested in multivariable logistic analysis to pinpoint the independent risk factors for sepsis in LA patients; these findings were utilized to develop a prediction model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the prediction model. Informed consent to participate was obtained from the patients or their relatives. Results The incidence of shivering in the sepsis group was significantly higher than that in the non-sepsis group (p < 0.05). Through the univariate analysis, it was found that the reduction in platelet count and prothrombin time activity and the elevation of glycosylated hemoglobin (HbAlc) and procalcitonin (PCT) were more significant in the sepsis group than in the non-sepsis group (p < 0.05). Multivariate logistic regression analysis revealed that PCT and HbAlc were independent risk predictors of sepsis in LA patients within the derivation cohort (p < 0.05). Conclusion Elevated levels of HbAlc and PCT were independent risk factors for sepsis associated with LA. Patients with LA exhibiting elevated PCT levels demonstrated a 21% increased susceptibility to sepsis, and those with elevated HbAlc levels showed a 38% heightened risk for sepsis.

The Role of Monocyte Distribution Width in the Early Prediction of Sepsis in Patients Undergoing Cardiovascular Surgery: A Cross-Sectional Study.

Background and Objectives: This is the first study to examine the role of monocyte distribution width (MDW) in predicting sepsis after cardiovascular surgery. Methods: This study included 43 consecutive patients who had undergone cardiovascular surgery between July 2021 and July 2022. All patients were examined at the following three time points (TPs): preoperative period (TP1), postoperative at 24 h (TP2), and discharge (TP3). SOFA score, leukocyte count, neutrophil-to-lymphocyte ratio (NLR), MDW, C-reactive protein (CRP), and procalcitonin (PCT) levels were tested at each TPs. The Sepsis-3 criteria were used to diagnose patients with sepsis. Results: The mean values of all variables (leukocyte count, NLR, MDW, CRP, and PCT levels) were significantly higher at TP2 and TP3 than at TP1 (p < 0.05). All these values were significantly higher at TP2 than at TP3 (p < 0.05). Patients with sepsis had significantly higher mean values for leukocyte count, NLR, MDW, CRP, and PCT levels than those without sepsis (p < 0.05). There was a significant correlation between MDW and inflammatory markers (CRP, PCT, and NLR) during the three time periods (p < 0.05). According to the ROC analysis, the optimal MDW cutoff value with the highest sensitivity and specificity for predicting sepsis in the postoperative period was 20.5. Conclusions: Our findings indicate that elevated MDW levels may be a valuable predictor of sepsis in patients following cardiovascular surgery.

Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants

Abstract Background A pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants. Methods We determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike). We selected an equal number of control times, matched for gestational and chronologic age. We reviewed records for infection and non-infection events around POWS spikes and control times. We calculated the frequencies and PPV of a POWS spike for infection or another significant event. Results We reviewed 111 POWS spike times and 111 control times. Days near POWS spikes were more likely to have clinical events than control days (77% vs 50%). A POWS spike had 52% PPV for suspected or confirmed infection and 77% for any clinically significant event. Respiratory deterioration occurred near more POWS spike times than control times (34% vs 18%). Conclusions In a retrospective cohort, infection and respiratory deterioration were common clinical correlations of a POWS spike. POWS had a high PPV for significant clinical events with or without infection. Impact There are significant gaps in understanding the best approach to implementing continuous sepsis prediction models so that clinicians can best respond to early signals of deterioration. Infection and respiratory deterioration were common clinical events identified at the time of a high predictive model score. Understanding the clinical correlates of a high-risk early warning score will inform future implementation efforts.

Predicting and Avoiding Complications in Percutaneous Nephrolithotomy in the Era of Personalized Medicine: A Scoping Review.

Percutaneous nephrolithotomy (PCNL) is associated with a wide range of complications. This review aims to explore how recent technological advancements and personalized medicine can help prevent or predict these complications. A scoping review was conducted according to the PRISMA-SCR guidelines and registered on the Open Science Framework in April 2024. A literature search was performed on PUBMED, Web of Science, and Scopus databases. This review focused on predictive AI models, 3D surgical models, intrasurgical image guidance, and biomarkers. Articles meeting the following criteria were included: publication between 2019 and 2024, written in English, involving human participants, and discussing technological advancements or personalized medicine in the context of complications in PCNL. Of the 11,098 articles searched, 35 new studies were included. We identified a few articles on predictive AI models. Several studies demonstrated that 3D presurgical models and virtual models could enhance surgical planning and reduce complications. New intrasurgical image and guidance systems showed the potential in reducing bleeding and radiation exposure. Finally, several biomarkers were identified as predictors of sepsis and other complications. This scoping review highlights the potential of emerging technologies in reducing and predicting PCNL complications. However, larger prospective studies are required for validation.

A knowledge-transfer-based approach for combining ordinal regression and medical scoring system in the early prediction of sepsis with electronic health records

Objective:The prediction of sepsis, especially early diagnosis, has received a significant attention in biomedical research. In order to improve current medical scoring system and overcome the limitations of class imbalance and sample size of local EHR (electronic health records), we propose a novel knowledge-transfer-based approach, which combines a medical scoring system and an ordinal logistic regression model. Materials and Methods:Medical scoring systems (i.e. NEWS, SIRS and QSOFA) are generally robust and useful for sepsis diagnosis. With local EHR, machine-learning-based methods have been widely used for building prediction models/methods, but they are often impacted by class imbalance and sample size. Knowledge distillation and knowledge transfer have recently been proposed as a combination approach for improving the prediction performance and model generalization. In this study, we developed a novel knowledge-transfer-based method for combining a medical scoring system (after a proposed score transformation) and an ordinal logistic regression model. We mathematically confirmed that it was equivalent to a specific form of the weighted regression. Furthermore, we theoretically explored its effectiveness in the scenario of class imbalance. Results:For the local dataset and the MIMIC-IV dataset, the VUS (the volume under the multi-dimensional ROC surface, a generalization measure of AUC-ROC for ordinal categories) of the knowledge-transfer-based model (ORNEWS) based on the NEWS scoring system were 0.384 and 0.339, respectively, while the VUS of the traditional ordinal regression model (OR) were 0.352 and 0.322, respectively. Consistent analysis results were also observed for the knowledge-transfer-based models based on the SIRS/QSOFA scoring systems in the ordinal scenarios. Additionally, the predicted probabilities and the binary classification ROC curves of the knowledge-transfer-based models indicated that this approach enhanced the predicted probabilities for the minority classes while reducing the predicted probabilities for the majority classes, which improved AUCs/VUSs on imbalanced data. Discussion:Knowledge transfer, which combines a medical scoring system and a machine-learning-based model, improves the prediction performance for early diagnosis of sepsis, especially in the scenarios of class imbalance and limited sample size.

Predictors of Sepsis and Sepsis-related Mortality in Critically Ill Burn Patients: A Single Tertiary Care Center Experience.

Clinical diagnosis of sepsis is challenging, emphasizing the importance of regular bacterial surveillance, and tailored antimicrobial therapy. This study aims to elucidate the predictors of sepsis in critically ill burn patients. A retrospective analysis was conducted on patients admitted to the burn intensive care unit between 2016 and 2022. Demographics, type of burn, total body surface area (TBSA), presence of inhalation injury, mortality, sepsis, deep vein thrombosis, pulmonary embolism, pneumonia, cultures, and laboratory findings were collected. Descriptive statistics and survival analysis were used to analyze trends during the 7-year period. The study encompassed 196 participants. Among patient factors, men constituted 73.4% (n = 102) of those without sepsis and 86.0% (n = 49) with sepsis, with an association between sepsis and lower age (34 versus 41 years) as well as larger TBSA (41.1% versus 17.3%). Inhalation injury was a significant predictor of sepsis [35.1% (n = 20) versus 11.6% (n = 16)]. Mortality was higher in sepsis cases [17.5% (n = 10) versus 2.9% (n = 4)], as well as positive blood cultures [47.4% (n = 27) versus 2.2% (n = 3)], positive wound cultures [71.9% (n = 41) versus 12.2% (n = 17)], and positive fungal cultures [12.3% (n = 7) versus 0% (n = 0)]. Multivariable analysis identified age and TBSA as significant predictors of sepsis (P = 0.025, P < 0.001). Age, TBSA affected emerge as a strong risk factor for sepsis among critically ill burn patients. It underscores the need for vigilant monitoring to improve outcomes and reduce sepsis-related mortality.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach.

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes.

Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms.

Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.