Early Detection Of Sepsis Research Articles

Validity of eosinophil count and monocyte-lymphocyte ratio for early detection of neonatal sepsis

Validity of eosinophil count and monocyte-lymphocyte ratio for early detection of neonatal sepsis

MATERNAL C-REACTIVE PROTEIN IN PREGNANCY AND ITS RELATION WITH EARLY ONSET NEONATAL SEPSIS

Objective:To determine association of early-onset neonatal sepsis (EONS) in newborns of serum C-reactive protein positive mothers.
 Study Design: Cross sectional study
 Place and duration of study: Department of Pediatrics, Combined Military Hospital Quetta, Pakistan from September 2016 to March 2018.
 Material and Methods: 105 neonates (both gender) of all mothers who were C - reactive protein positive were enrolled in this study using inclusion and exclusion criteria.Neonatal sepsis was diagnosed by clinical examination, which was carried out by senior pediatrician, blood complete picture and c reactive protein. Neonates were treated as per departmental protocol. Stratification was done in regard to gestational age, birth weight and age of neonates in hours. Post stratification chi square test was applied and p-value less than 0.05 was considered significant.
 Results:Out of total 105 patients, 46 (44%) were male and 59 (56%)were female neonates. Amongst the babies of 105 CRP positive mothers, 79 babies were having signs and symptoms of neonatal sepsis and 26 babies were having no signs and symptoms of neonatal sepsis with statistically significant relation between maternal CRP and neonatal sepsis of p value <0.05.
 Conclusion:Increased maternal CRP level is associated with increased risk of neonatal sepsis. Maternal CRP may be used as a good screening tool for early detection of neonatal sepsis.

Diagnostic Markers for Early Detection of Neonatal Sepsis

Background: Neonatal sepsis (NS) is a major health problem throughout the world. The diagnosis of sepsis is challenging due to the non-specific nature of the clinical presentation, the variety of other neonatal disorders with the differential diagnostic workup, lack of sensitivity and specificity of available diagnostic procedures, and the delay in the results of blood cultures in addition to high negative results reported. The diagnosis of suspected sepsis has to be based on clinical symptoms together with biochemical parameters. A diagnostic marker with high diagnostic sensitivity and specificity would be a valuable tool for decreasing the burden of neonatal sepsis Purpose: Evaluate the validity of interleukin-6 (IL-6) in the early diagnosis of neonatal sepsis or the use of a combination of diagnostic markers, C-reactive protein (CRP), and IL-6. Methods: The study included 30 patients with NS (Group I) and 30 healthy newborns as control (Group II) were admitted to Neonatal Intensive Care Unit (NICU) from January 2017 to June 2017. All neonates were subjected to history taking, clinical examination, and laboratory investigations including complete blood count (CBC), blood culture and sensitivity testing, CRP, and IL-6. Results: the most causative organism of neonatal sepsis in NICU was Klebsiella spp. followed by CONS. IL-6 results with cut-off value 50pg/ml, the sensitivity was 100%, the specificity was 90.32%, the positive predictive value of 90.63%, negative predictive value, and the diagnostic accuracy was 95.16. Moreover, IL-6 levels are significantly higher statistically in NS patients than controls. Conclusion: This study validated the diagnostic capability of IL-6 and showed that the combination of CRP and IL-6 as a panel for the early diagnosis of NS could enhance the sensitivity in the diagnosis of NS and may provide a new diagnostic strategy for NS patients Objective: Evaluate the validity of interleukin-6 (IL-6) in early diagnosis of neonatal sepsis or the use of combination of diagnostic markers, C-reactive protein (CRP) and IL-6. Patients and methods: The study included 30 patients with NS (Group I) and 30 apparently healthy newborns as control (Group II) were admitted to Neonatal Intensive Care Unit (NICU) from January 2017 to June 2017. All neonates were subjected to history taking, clinical examination, and laboratory investigations including: complete blood count (CBC), blood culture and sensitivity testing, CRP, and IL-6. Results: the most causative organism of neonatal sepsis in NICU was klebsiella spp. followed by CONS. IL-6 results with cut-off value 50pg/ml, the sensitivity was 100%, the specificity was 90.32%, positive predictive value of 90.63%, negative predictive value, and the diagnostic accuracy was 95.16. Moreover, IL-6 levels are significant higher statistically in NS patients than controls. Conclusion: This study validated the diagnostic capability of IL-6 and showed that the combination of CRP and IL-6 as a panel for the early diagnosis of NS could enhance the sensitivity in the diagnosis of NS and may provide a new diagnostic strategy for NS patients.

Monocyte Distribution Width, Neutrophil-to-Lymphocyte Ratio, and Platelet-to-Lymphocyte Ratio Improves Early Prediction for Sepsis at the Emergency.

(1) Background: Sepsis is a life-threatening condition, and most patients with sepsis first present to the emergency department (ED) where early identification of sepsis is challenging due to the unavailability of an effective diagnostic model. (2) Methods: In this retrospective study, patients aged ≥20 years who presented to the ED of an academic hospital with systemic inflammatory response syndrome (SIRS) were included. The SIRS, sequential organ failure assessment (SOFA), and quick SOFA (qSOFA) scores were obtained for all patients. Routine complete blood cell testing in conjugation with the examination of new inflammatory biomarkers, namely monocyte distribution width (MDW), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), was performed at the ED. Propensity score matching was performed between patients with and without sepsis. Logistic regression was used for constructing models for early sepsis prediction. (3) Results: We included 296 patients with sepsis and 1184 without sepsis. A SIRS score of >2, a SOFA score of >2, and a qSOFA score of >1 showed low sensitivity, moderate specificity, and limited diagnostic accuracy for predicting early sepsis infection (c-statistics of 0.660, 0.576, and 0.536, respectively). MDW > 20, PLR > 9, and PLR > 210 showed higher sensitivity and moderate specificity. When we combined these biomarkers and scoring systems, we observed a significant improvement in diagnostic performance (c-statistics of 0.796 for a SIRS score of >2, 0.761 for a SOFA score of >2, and 0.757 for a qSOFA score of >1); (4) Conclusions: The new biomarkers MDW, NLR, and PLR can be used for the early detection of sepsis in the current sepsis scoring systems.

Study the role of c-reactive protein and thrombocytopenia in diagnosing early neonatal sepsis

Background: In clinical practice, early detection of neonatal sepsis remains difficult. Neonatal sepsis diagnosis is a challenge because of its non-specific presentation and the low sensitivity of the time-consuming bacterial cultures. So, many sepsis markers, like C-reactive protein (CRP) and platelet count, are emerging to improve its diagnosis. The aim of the study was to evaluate the role of CRP and platelet count in early neonatal sepsisMethods: This study was conducted in the department of SNCU in Virudhunagar Government Headquarters hospital, 50 neonates with culture-confirmed sepsis were included. Before antibiotic treatment, neonates CRP and platelet count, and other inflammatory markers were assessed.Results: In 50 newborns, culture was positive in 22 cases, CRP was positive in 24 cases, and thrombocytopenia in 18 cases. In 22 culture-positive cases, 19 cases are CRP positive, and 17 cases had thrombocytopenia. In 17 culture-positive thrombocytopenia cases, 16 cases are Gram-negative stain, 1 case is Gram-positive stain.Conclusions: CRP and Platelet can be helpful in the future diagnosis of neonatal sepsis, despite being promising and convenient markers for neonatal sepsis.

Characterization of the blood and neutrophil-specific microbiomes and exploration of potential bacterial biomarkers for sepsis in surgical patients.

IntroductionRecent studies have demonstrated the presence of a circulating microbiome in the blood of healthy subjects and chronic inflammatory patients. However, our knowledge regarding the blood microbiome and its potential roles in surgical patients remains very limited. The objective of this study was to determine the blood microbial landscape in surgical patients and to explore its potential associations with postoperative sepsis.Materials and Methods2825 patients who underwent surgical treatments were screened for enrollment and 204 cases were recruited in this study. The patients were sub‐grouped into noninfected, infected, sepsis, and septic shock according to postoperative clinical manifestations. A total of 222 blood samples were obtained for neutrophil isolation, DNA extraction and high‐throughput sequencing, quantitative proteomics analysis, and flow cytometric analyses.ResultsBlood and neutrophils in surgical patients and healthy controls contained highly diverse microbiomes, mainly comprising Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. The majority (80.7%–91.5%) of the microbiomes were composed of gut‐associated bacteria. The microbiomes in septic patients were significantly distinct from those of healthy controls, and marked differences in microbiome composition were observed between sepsis and septic shock groups. Several specific bacterial genera, including Flavobacterium, Agrococcus, Polynucleobacter, and Acidovorax, could distinguish patients with septic shock from those with sepsis, with higher area under curve values. Moreover, Agrococcus, Polynucleobacter, and Acidovorax were positively associated with the sequential (sepsis‐related) organ failure assessment scores and/or acute physiology and chronic health examination scores in septic shock patients. The proteins involved in bactericidal activities of neutrophils were downregulated in septic patients.ConclusionsWe present evidence identifying significant changes of blood and neutrophil‐specific microbiomes across various stages of sepsis, which might be associated with the progression of sepsis after surgical treatments. Several certain bacterial genera in blood microbiome could have potential as microbial markers for early detection of sepsis.

Independent Validation of Sepsis Index for Sepsis Screening in the Emergency Department.

(1) Background: The early detection of sepsis is still challenging, and there is an urgent need for biomarkers that could identify patients at a high risk of developing it. We recently developed an index, namely the Sepsis Index (SI), based on the combination of two CBC parameters: monocyte distribution width (MDW) and mean monocyte volume (MMV). In this study, we sought to independently validate the performance of SI as a tool for the early detection of patients at a high risk of sepsis in the Emergency Department (ED). (2) Methods: We enrolled all consecutive patients attending the ED with a request of the CBC. MDW and MMV were measured on samples collected in K3-EDTA tubes on the UniCel DxH 900 haematology analyser. SI was calculated based on the MDW and MMV. (3) Results: We enrolled a total of 703 patients stratified into four subgroups according to the Sepsis-2 criteria: control (498), infection (105), SIRS (52) and sepsis (48). The sepsis subgroup displayed the highest MDW (median 27.5, IQR 24.6–32.9) and SI (median 1.15, IQR 1.05–1.29) values. The ROC curve analysis for the prediction of sepsis showed a good and comparable diagnostic accuracy of the MDW and SI. However, the SI displayed an increased specificity, positive predictive value and positive likelihood ratio in comparison to MDW alone. (4) Conclusions: SI improves the diagnostic accuracy of MDW for sepsis screening.

Activación del Código Sepsis en urgencias: rendimiento de la escala q-SOFA respecto los criterios SIRS

IntroductionThe early diagnosis and treatment of sepsis remains a challenge in hospital emergency departments. ObjectiveThe objective was to evaluate the performance of the qSOFA (quick Sequential related Organ Failure Assessment) for activating a Sepsis Code in the emergency department, compared to applying previous criteria based on SIRS (Systemic Inflammatory Response Syndrome). MethodsPatients ≥18years were included who consulted an emergency department of a tertiary hospital over two periods of time (May-October 2015), using SIRS criteria (SIRS Group) and over the same months in 2017 using qSOFA criteria (qSOFA Group). ResultsOne hundred and five episodes were analysed: 70 corresponded to the SIRS Group and 55 to the qSOFA Group. The groups were comparable in terms of history, with a greater presence of neoplastic disease in the qSOFA Group. No differences were observed in the form of presentation, the action taken in the emergency department, admissions to critical care units, use of vasopressors or mean hospital stay between the two groups. A higher 30-day mortality rate was observed in the qSOFA group (21.81% vs. 4.3%; P=.003). In the multivariate study, neoplastic disease was noteworthy as an independent factor of poor prognosis (OR 5.36; P=.01), and activation using the SIRS criteria as a factor of good prognosis (OR .15; P=.021). ConclusionsThe activation of the Sepsis Code using qSOFA criteria results in a reduction in detection, with greater associated mortality. Other complementary tools need to be sought for the early detection of sepsis in the emergency department.

A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data

Early detection of sepsis can assist in clinical triage and decision-making, resulting in early intervention with improved outcomes. This study aims to develop a machine learning framework to predict the onset of sepsis through EHR data by applying tensor decomposition on correlation matrices of clinical covariates for every record, arranged on an hourly basis for the length of stay (LOS) in intensive care unit.A third-order tensor θx,y,z representing a clinical correlation among selected 24 covariates for a considered time frame of sepsis onset duration of 6 h, with a stride of 1 h is formed for each record. Such a fused tensor with dimensions θ∈R24×24×LOS for every record undergoes Tucker decomposition with an optimal choice of rank. The factor matrices U1,U2,U3 thus obtained after decomposition are excluded and only the core tensor σ with a dimension σ∈R10×10×LOS has been retained, and used to provide latent features for prediction of sepsis onset. A five-fold cross-validation scheme is employed wherein the obtained 100 latent features from the reshaped core tensor, are fed to Light Gradient Boosting Machine Learning models (LightGBM) for binary classification, further alleviating the involved class imbalance. The machine-learning framework is designed via Bayesian optimization, yielding an average normalized utility score of 0.4314 on publicly available PhysioNet/Computing in Cardiology Challenge 2019 training data.The proposed tensor decomposition deciphers the higher-order interrelations among the considered clinical covariates for early prediction of sepsis and the results obtained are on par with existing state-of-the-art performances.

PMD12 Using Real-World Evidence (RWE) and a MULTI-Stakeholder Approach to Assess the MULTI-Dimensional IMPACT of a New Diagnostic Device for the EARLY Identification of Sepsis in the Italian National Health System

Sepsis is a life-threatening medical emergency affecting 48.9 millions of people worldwide, accounting for 20% of global deaths in 2017. Early identification able to reduce Time To Antibiotics (TTA) may significantly reduce disease severity and mortality. However, serious delays still affect the management of septic patients and make urgent the need for a diagnostic test. Monocyte Distribution Width (MDW) is a new biomarker recently developed for the early detection of sepsis. This study aims to conduct a multidimensional and multi-stakeholder assessment of the MDW technology being at the stage-gate between clinical validation and translation into the Italian National Health System.

Implementing Explainable AI in Healthcare: Techniques for Interpretable Machine Learning Models in Clinical Decision-Making

The integration of explainable artificial intelligence (XAI) in healthcare is revolutionizing clinical decision-making by providing clarity around complex machine learning (ML) models. As AI becomes increasingly critical in medical fields—ranging from diagnostics to treatment personalization—the interpretability of these models is crucial for fostering trust, transparency, and accountability among healthcare providers and patients. Traditional "black-box" models, such as deep neural networks, often achieve high accuracy but lack transparency, creating challenges in highly regulated, high-stakes settings like healthcare. Explainable AI addresses this issue by employing methods that make model decisions understandable and justifiable, ensuring that clinicians can interpret, trust, and apply AI recommendations safely and effectively. This paper presents a comprehensive analysis of explainable AI techniques specifically tailored for healthcare applications, focusing on two primary approaches: intrinsic interpretability and post-hoc interpretability. Intrinsic techniques, which design models to be naturally interpretable (e.g., decision trees, logistic regression), enable clinicians to directly trace and understand the rationale behind model predictions. Post-hoc techniques, on the other hand, provide interpretability for complex models after they have been trained. Examples include SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and saliency maps in medical imaging, each of which provides insights into how and why specific predictions are made. This study also examines the unique challenges of implementing explainable AI in healthcare, such as balancing accuracy with interpretability, addressing the diversity of stakeholder needs, and ensuring data privacy. Through real-world case studies—such as early sepsis detection in intensive care units and the use of saliency maps in radiology—the paper demonstrates how explainable AI improves clinical workflows, enhances patient outcomes, and fosters regulatory compliance by enabling transparency in automated decision-making. Ultimately, this work underscores the transformative potential of explainable AI to make machine learning models not only powerful but also trustworthy, actionable, and ethical in the context of healthcare.

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

BackgroundSepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input.MethodsIn our study, we analyzed patients’ conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets “near” to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers.ResultsWe demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models.ConclusionApplying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.

Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis

Early detection of sepsis can facilitate early clinical intervention with effective treatment and may reduce sepsis mortality rates. In view of this, machine learning-based automated diagnosis of sepsis using easily recordable physiological data can be more promising as compared to the gold standard rule-based clinical criteria in current practice. This study aims to develop such a machine learning framework that demonstrates the quantification of heterogeneity within the tabular electronic health records (EHR) data of clinical covariates to capture both linear relationships and nonlinear correlation for the early prediction of sepsis. Here, the statistics of pairwise association for each hour-covariate pair within the EHR data for every 6-hours window-duration with selected 24 covariates is described using pointwise mutual information (PMI) matrix. This matrix gives the heterogeneity of data as a two-dimensional map. Such matrices are fused horizontally along the z-axis as vertical slices in the xy plane to form a 3-way tensor for each record with the corresponding Length of Stay (L). Tensor factorization of such fused tensor for every record is performed using Tucker decomposition, and only the core tensors are retained later, excluding the 3 unitary matrices to provide the latent feature set for the prediction of sepsis onset. A five-fold cross-validation scheme is employed wherein the obtained 120 latent features from the reshaped core tensor, are fed to Light Gradient Boosting Machine Learning models (LightGBM) for binary classification, further alleviating the involved class imbalance. The machine-learning framework is designed via Bayesian optimization, yielding an average normalized utility score of 0.4519 as defined by challenge organizers and area under the receiver operating characteristic curve (AUROC) of 0.8621 on publicly available PhysioNet/Computing in Cardiology Challenge 2019 training data.The proposed tensor decomposition of 3-way fused tensor formulated using PMI matrices leverages higher-order temporal interactions between the pairwise associations among the clinical values for early prediction of sepsis. This is validated with improved risk prediction power for every hour of admission to the ICU in terms of utility score, AUROC, and F1 score. The results obtained show a significant improvement particularly in terms of utility score of ~1.5−2% under a 5-fold cross-validation scheme on entire training data as compared to a top entrant research study that participated in the challenge.

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis.

With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty. For instance, when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an area under the PR curve of 0.483, whereas the (less interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC and 0.359 AUPR.

A implantação de um protocolo de sepse no atendimento do pronto socorro e o impacto nos custos em um hospital privado na Região Amazônica

Objective: To analyze the economic impact of implementing an early sepsis detection protocol in the emergency department of a private hospital in the city of Belém, Pará, Brazil. Methods: Experimental, cross-sectional, retrospective study, carried out from March 2015 to December 2019 from a highly complex referral Center. The implementation of the protocol took place in March 2015. Results: A total of 257 patients were studied, 128 from the group before the protocol (AP group) and 129 belonging to the group after the protocol (DP group), with similar proportions. in relation to sex. Regarding the mean age and standard deviation, in the AP group it was 73.5 ± 23.3 and in the DP group 76.0 ± 17.0. After the implantation of the protocol, there was a reduction in the time of organ dysfunction, mortality rate, ICU stay and hospital stay, both with statistical difference of p = 0.0015, p = 0.04, p = 0.012 and p = 0.0401, respectively. In the evaluation of hospital costs, a reduction in the average spent with each patient was identified both in the total cost of hospitalization and in the total cost referring only to the length of stay in the ICU. Conclusion: It is noticeable that sepsis costs are a public health problem both nationally and internationally. However, such expenses can be reduced with the implementation of protocols that aim to standardize the care provided to patients and thereby positively influence the reduction of hospital stay, hospitalization costs and mortality.

Comparative study of diagnostic value of Apo lipoprotein AI in neonatal sepsis in Relation to hematological score

Objective The aim of this study is to study serum level of Apolipoprotein A1 as a diagnostic marker in neonatal sepsis in relation to haematological score. Background: Neonatal sepsis has become a global health problem due to its high morbidity and mortality. Blood culture continues to be the main method to diagnose neonatal sepsis. The present work aimed to study of serum level of Apolipoprotein A1 as a diagnostic marker in neonatal sepsis in Relation to Hematological score. Methods: This study is a case control study, which was done on 60 neonates who admitted to neonatal ICU (NICU) of Benha University Hospitals and Benha Teaching Hospitals of both sexes after obtaining an informed consent from the children's caregiver. Group I: 40 neonates with a positive clinical sepsis score (CSS) and hematological sepsis score (HSS). Group I were subdivided into two subgroups A , B: Subgroup A: 20 Neonates with early Sepsis within the first 7 days of life. Subgroup B: 20 Neonates with late Sepsis after 7 days of life. Group II (Controls) included 20 healthy neonates with no evidence of sepsis. This study was carried out in Al-Qalubyia Governorate, from June 2019 to May 2020. Results: There was significant increase in HSS in sepsis group than control. Klebsilla was the common organism. there were statistically significant decrease in serum level of Apolipoprotein A1 at diagnosis among cases than control.There was no statistically significant difference between Early onset neonatal sepsis group and Late onset neonatal sepsis group regarding HSS at diagnosis.There were no statistically significant difference between Early onset neonatal sepsis group and Late onset neonatal sepsis group regarding serum level of Apolipoprotein A1 at diagnosis. Serum level of Apolipoprotein A1 at diagnosis was statistically significant lower among Early onset neonatal sepsis group than Control group. Serum level of Apolipoprotein A1 at diagnosis was statistically significant lower among late onset neonatal sepsis group than Control group. There were statistically significant positive correlation between serum level of Apolipoprotein A1 at diagnosis and Platelet count, and there was no statistically significant difference between serum level of Apolipoprotein A1 at diagnosis and other numerical data.Serum level of Apolipoprotein A1 is more accurate in the diagnosis of neonatal sepsis than HSS. Conclusion: Apo A1 appeared to be a useful marker for the early detection and diagnosis of neonatal sepsis than HSS.

Short-Term Wearable Sensors for In-Hospital Medical and Surgical Patients: Mixed Methods Analysis of Patient Perspectives.

BackgroundContinuous vital sign monitoring using wearable sensors may enable early detection of patient deterioration and sepsis.ObjectiveThis study aimed to explore patient experiences with wearable sensor technology and carry out continuous monitoring through questionnaire and interview studies in an acute hospital setting.MethodsPatients were recruited for a wearable sensor study and were asked to complete a 9-item questionnaire. Patients responses were evaluated using a Likert scale and with continuous variables. A subgroup of surgical patients wearing a Sensium Vital Sign Sensor was invited to participate in semistructured interviews. The Sensium wearable sensor measures the vital signs: heart rate, respiratory rate, and temperature. All interview data were subjected to thematic analysis.ResultsOut of a total of 500 patients, 453 (90.6%) completed the questionnaire. Furthermore, 427 (85.4%) patients agreed that the wearable sensor was comfortable, 429 (85.8%) patients agreed to wear the patch again when in hospital, and 398 (79.6%) patients agreed to wear the patch at home. Overall, 12 surgical patients consented to the interviews. Five main themes of interest to patients emerged from the interviews: (1) centralized monitoring, (2) enhanced feelings of patient safety, (3) impact on nursing staff, (4) comfort and usability, and (5) future use and views on technology.ConclusionsOverall, the feedback from patients using wearable monitoring sensors was strongly positive with relatively few concerns raised. Patients felt that the wearable sensors would improve their sense of safety, relieve pressure on health care staff, and serve as a favorable aspect of future health care technology.

Serial measurement of pancreatic stone protein for the early detection of sepsis in intensive care unit patients: a prospective multicentric study

BackgroundThe early recognition and management of sepsis improves outcomes. Biomarkers may help in identifying earlier sub-clinical signs of sepsis. We explored the potential of serial measurements of C-reactive protein (CRP), procalcitonin (PCT) and pancreatic stone protein (PSP) for the early recognition of sepsis in patients hospitalized in the intensive care unit (ICU).MethodsThis was a multicentric international prospective observational clinical study conducted in 14 ICUs in France, Switzerland, Italy, and the United Kingdom. Adult ICU patients at risk of nosocomial sepsis were included. A biomarker-blinded adjudication committee identified sepsis events and the days on which they began. The association of clinical sepsis diagnoses with the trajectories of PSP, CRP, and PCT in the 3 days preceding these diagnoses of sepsis were tested for markers of early sepsis detection. The performance of the biomarkers in sepsis diagnosis was assessed by receiver operating characteristic (ROC) analysis.ResultsOf the 243 patients included, 53 developed nosocomial sepsis after a median of 6 days (interquartile range, 3–8 days). Clinical sepsis diagnosis was associated with an increase in biomarkers value over the 3 days preceding this diagnosis [PSP (p = 0.003), PCT (p = 0.025) and CRP (p = 0.009)]. PSP started to increase 5 days before the clinical diagnosis of sepsis, PCT 3 and CRP 2 days, respectively. The area under the ROC curve at the time of clinical sepsis was similar for all markers (PSP, 0.75; CRP, 0.77; PCT, 0.75).ConclusionsWhile the diagnostic accuracy of PSP, CRP and PCT for sepsis were similar in this cohort, serial PSP measurement demonstrated an increase of this marker the days preceding the onset of signs necessary to clinical diagnose sepsis. This observation justifies further evaluation of the potential clinical benefit of serial PSP measurement in the management of critically ill patients developing nosocomial sepsis.Trial registration The study has been registered at ClinicalTrials.gov (no. NCT03474809), on March 16, 2018. https://www.clinicaltrials.gov/ct2/show/NCT03474809?term=NCT03474809&draw=2&rank=1.

Monocyte distribution width compared with C-reactive protein and procalcitonin for early sepsis detection in the emergency department.

PurposeMonocyte distribution width (MDW) has been suggested as an early biomarker of sepsis, but few studies have compared MDW with conventional biomarkers, including C-reactive protein (CRP) and procalcitonin (PCT). This study evaluated MDW as a biomarker for sepsis and compared it with CRP and PCT.Materials and methodsPatients aged 18–80 years who visited the emergency department were screened and prospectively enrolled in a tertiary medical center. Complete blood count, MDW, CRP, and PCT were examined. Diagnostic performance for sepsis was tested using the area under the curve (AUC) of receiver operating characteristic (ROC) curves, sensitivity, and specificity.ResultsIn total, 665 patients were screened, and 549 patients with valid laboratory test results were included in the analysis. The patients were categorized into three groups according to the Sepsis-3 criteria: non-infection, infection, and sepsis. MDW showed the highest value in the sepsis group (median [interquartile range], 24.0 [20.8–27.8]). The AUC values for MDW, CRP, PCT, and white blood cells for predicting sepsis were 0.71 (95% confidence interval [CI], 0.67–0.75), 0.75 (95% CI, 0.71–0.78], 0.76 (95% CI, 0.72–0.79, and 0.61 (95% CI, 0.57–0.65), respectively. With the optimal cutoff value of the cohort, the sensitivity was 83.0% for MDW (cutoff, 19.8), 69.7% for CRP (cutoff, 4.0), and 76.6% for PCT (cutoff, 0.05). The combination of quick Sequential Organ Failure Assessment (qSOFA) with MDW improved the AUC (0.76; 95% CI, 0.72–0.80) to a greater extent than qSOFA alone (0.67; 95% CI, 0.62–0.72).ConclusionsMDW reflected a diagnostic performance comparable to that of conventional diagnostic markers, implying that MDW is an alternative biomarker. The combination of MDW and qSOFA improves the diagnostic performance for early sepsis.

13 Utilization of a Burn Sepsis Algorithm: A Five-Year Analysis

Abstract Introduction After the first 24 hours, the major cause of death in burn patients is multiple organ dysfunction/failure syndrome. It is preceded by an infection in 83% of burn patients, with reported septic mortality up to 65%. Since the early recognition and treatment of infection has been shown to decrease mortality from sepsis, we implemented a multidisciplinary algorithm designed to rapidly identify septic adult burn patients. Methods Adult (≥18 y) admissions between 7/1/2014 – 6/30/2019 were identified from our registry, and all initial sepsis screens were evaluated in the EMR. Patients were screened clinically at least twice daily and were considered a “positive” screen if MAP < 65 mmHg (SBP< 90 mmHg) or if 2 of the 3 occurred: Temp >102.2F; HR >120; RR >28 (or RR >10% of ventilator set rate if set rate is >24 bpm). A positive screen prompted lab work to include CBC, BMP, procalcitonin (PCT), and lactic acid (LA), per protocol. If PCT >3.0 ng/ml or LA >2.0 mmol/L, or both thrombocytopenia and hyperglycemia were present, a “Burn Code Sepsis” was initiated and included cultures, a CXR, and empiric antibiotics. A patient was then formally considered “septic” (i.e. infected) if the cultures were positive or the CXR demonstrated an infectious process. Results There were 1,523 admissions during the 5-year period, and 228 initial positive screens. Of the 228 patients with positive screens, 159 (70%) were infected. There was a significant difference in PCT level between patients with and without infections, while no difference was noted for LA, WBC, platelets, temperature, glucose, age, %TBSA burned, or time to triggering a positive screen between the groups (Table). Defining PCT ≥3.0 ng/mL as being positive for sepsis demonstrated a 76% PPV and a 36% NPV for PCT alone. Our initial sepsis screening algorithm had a sensitivity of 64.8% and a specificity of 40.6% over the five-year period. Conclusions Recognition of sepsis remains difficult in burn patients. PCT and the use of a sepsis screening algorithm may have a role in the early detection of sepsis. Further research is warranted.