Detection Of Sepsis Research Articles

Sepsis: key insights, future directions, and immediate goals. A review and expert opinion.

This review explores the current landscape and evolving understanding of sepsis, highlighting both challenges and future directions. Sepsis remains a major global health burden, with diverse clinical presentations complicating timely diagnosis and management. Existing definitions, including the Sepsis-3 criteria, emphasize the importance of organ dysfunction, yet early sepsis detection remains limited by available tools. The Sequential Organ Failure Assessment (SOFA) score, though widely used, may not fully capture early stages of infection or account for complex presentations like noncatecholamine-resistant shock. Additionally, the review underscores the pressing need for standardized terminology across sepsis and shock characterization to ensure consistency in diagnosis and treatment strategies. Accessible, resource-adapted solutions are particularly crucial in low- and middle-income countries where sepsis-related mortality rates are higher due to limited resources. Future research should focus on developing and validating integrated, multi-parameter tools that combine clinical, biochemical, and microbiological data to improve sepsis outcomes globally. Advancing sepsis care will require both technological innovation and collaborative, globally consistent guidelines to bridge disparities in healthcare delivery.

Adult Code Sepsis: A Narrative Review of its Implementation and Impact.

This narrative review explores the implementation and impact of sepsis code protocols, an urgent intervention strategy designed to improve clinical outcomes in patients with sepsis. We examined the degree of implementation, activation criteria, areas of implementation, personnel involved, responses after activation, goals and targets, impact on clinical indicators, and challenges in implementation. The reviewed evidence suggests that sepsis codes can significantly reduce sepsis-related mortality and enhance early administration of treatments. However, variability in activation criteria and inconsistent application present ongoing challenges. The review considers the incorporation of newer scoring systems, such as NEWS and MEWS, and the potential integration of machine learning tools for early sepsis detection. It highlights the importance of tailoring implementation to specific healthcare contexts and the value of ongoing training to optimize sepsis response. Limitations include the ongoing controversy surrounding sepsis definitions and the need for standardized, feasible quality indicators. Future research should focus on standardizing activation criteria, improving protocol adherence, and exploring emerging technologies to enhance early sepsis detection and management. Despite challenges, sepsis codes show promise in improving patient outcomes when implemented thoughtfully and consistently across healthcare settings.

Early detection of neonatal sepsis and reduction of overall antibiotic exposure: Towards precision medicine

Infections claim the lives of over half a million newborns annually and expose survivors to the risk of lifelong disability. The challenge to clinicians is to identify newborns with invasive infections rapidly, promptly initiate antimicrobial treatment, and take measures to prevent and treat organ dysfunction. Moreover, excessive antibiotic use is a global public health problem. Despite considerable research on clinical and laboratory markers of neonatal sepsis, the effective translation into clinical practice remains limited. There is no single clinical or laboratory marker, nor any combination of markers that definitively confirms or rules out neonatal sepsis. The interpretation of these markers should take into account their diagnostic value for a given patient, along with their added value to the clinical decision-making process. The digitalization of health care systems, combined with increased computational power and advances in machine learning, offers the possibility of developing accurate predictive algorithms for early detection of neonatal sepsis.

A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

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<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 < 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).

Damage-associated molecular patterns in bacteraemic infection, including a comparative analysis with bacterial DNA, a pathogen-associated molecular pattern

Damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) are key triggers of inflammation in sepsis. However, they have rarely been studied simultaneously. Thus, in the present study of patients with bacteraemic infection, we aimed to study how DAMP dynamics are linked to disease severity and outcome and to compare diagnostic and prognostic properties of a DAMP and a previously analysed PAMP (16S rDNA). In a prospective study of adult patients hospitalized with culture-proven community-onset bacteraemic infection, caused by Streptococcus pneumonia (n = 30), Staphylococcus aureus (n = 27), or Escherichia coli (n = 26), dynamics of a PAMP, i.e. 16S rDNA, have previously been presented. For the present study, blood samples obtained on hospital days 1–2 (when blood culture was positive), 3–4, 7 ± 1, 14 ± 2, and 28 ± 4 were analysed for four different DAMPs, i.e., nuclear DNA (nDNA), mitochondrial DNA (mtDNA), heat shock protein 90 alpha (HSP90α), and extracellular high mobility group box 1 (HMGB1). Sepsis was defined according to the Sepsis-3 criteria. The study outcomes were sepsis at admission and negative outcome, defined as intensive care unit (ICU) admission and/or death within 60 days. Of 83 study patients, sepsis was noted in 41 patients (49%) and a negative outcome was noted in 17 patients (20%). nDNA had areas under the receiver operating characteristic (ROC) curves of 0.78 for sepsis and 0.76 for negative outcome, which were higher than those of the other DAMPs and additional biomarkers (CRP, IL-6, IL-8, and IL-10). The nDNA and positive 16S rDNA results on day 1–2 were correlated with each other (r = 0.68, p < 0.001). Multivariate analyses showed that high day 1–2 concentrations of both nDNA and 16S rDNA were independently associated with sepsis. In addition, high day 1–2 concentration of nDNA was independently associated with negative outcomes. While 16S rDNA dissipated from the circulation within days, nDNA concentrations remained elevated throughout the follow-up period in patients with negative outcome. In conclusion, nDNA outperformed the other DAMPs regarding sepsis detection and outcome prediction. Both nDNA (a DAMP) and 16S rDNA (a PAMP) were independently linked to sepsis; nDNA was also associated with negative outcomes and persisted elevated in such cases. This highlights nDNA as an interesting marker within sepsis pathogenesis and as a promising clinical biomarker, warranting further studies.

AI Algorithms for Modeling the Risk, Progression, and Treatment of Sepsis, Including Early-Onset Sepsis—A Systematic Review

Sepsis remains a significant contributor to neonatal mortality worldwide. However, the nonspecific nature of sepsis symptoms in neonates often leads to the necessity of empirical treatment, placing a burden of ineffective treatment on patients. Furthermore, the global challenge of antimicrobial resistance is exacerbating the situation. Artificial intelligence (AI) is transforming medical practice and in hospital settings. AI shows great potential for assessing sepsis risk and devising optimal treatment strategies. Background/Objectives: This review aims to investigate the application of AI in the detection and management of neonatal sepsis. Methods: A systematic literature review (SLR) evaluating AI methods in modeling and classifying sepsis between 1 January 2014, and 1 January 2024, was conducted. PubMed, Scopus, Cochrane, and Web of Science were systematically searched for English-language studies focusing on neonatal sepsis. Results: The analyzed studies predominantly utilized retrospective electronic medical record (EMR) data to develop, validate, and test AI models to predict sepsis occurrence and relevant parameters. Key predictors included low gestational age, low birth weight, high results of C-reactive protein and white blood cell counts, and tachycardia and respiratory failure. Machine learning models such as logistic regression, random forest, K-nearest neighbor (KNN), support vector machine (SVM), and XGBoost demonstrated effectiveness in this context. Conclusions: The summarized results of this review highlight the great promise of AI as a clinical decision support system for diagnostics, risk assessment, and personalized therapy selection in managing neonatal sepsis.

Affinity-based 3D-printed microfluidic chip for clinical sepsis detection with CD69, CD64, and CD25

Sepsis is a life-threatening immune response to infection in the body, eventually resulting in fatal organ failure. Current methods utilize blood cultures and quick-Sequential-Organ-Failure-Assessment (qSOFA), but there is a need for more accurate and time-sensitive diagnostic methods to improve survival rates. We present a 3D-printed microfluidic chip that bioconjugates antibodies CD69, CD64, and CD25 to channel surfaces to capture sepsis cells in blood samples and validate it with clinical samples (n = 125 septic, n = 10 healthy). Other variables were taken such as healthy volunteer blood samples and patient demographics to validate and confirm our device’s diagnostic ability. Statistical differences were found between healthy volunteer and sepsis patient antigen cell counts (CD69 p-value < 0.001, CD64 p-value < 0.004, CD25 p-value < 0.0009), and were confirmed using principal component analysis. Demographics such as length of stay, age, culture results, and need for surgery also factored into sepsis detection on a smaller scale than the antigen cell counts. The receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.989, 0.988, and 0.992 for CD69, CD64, and CD25, respectively, and a combined biomarker panel of 0.997. Overall, the device performed within a shorter time frame of 4 h compared to standard blood culture tests and was validated for use in detecting sepsis in patients.

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: an observational study

BackgroundSepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits from identifying the syndrome early and treating it promptly and effectively. Prompt and effective detection, diagnosis, and treatment of sepsis requires frequent monitoring and assessment of patient vital signs and other relevant data present in the electronic health record. MethodsThis study explored the development of machine learning-based models to generate a novel sepsis risk index (SRI) which is an intuitive 0–100 marker that reflects the risk of a patient acquiring sepsis or septic shock and assists in timely diagnosis. Machine learning models were developed and validated using openly accessible critical care databases. The model was developed using a single database (from one institution) and validated on a separate database consisting of patient data collected across multiple ICUs. ResultsThe developed model achieved an area under the receiver operating characteristic curve of 0.82 and 0.84 for the diagnosis of sepsis and septic shock, respectively, with a sensitivity and specificity of 79.1% [75.1, 82.7] and 73.3% [72.8, 73.8] for a sepsis diagnosis and 83.8% [80.8, 86.5] and 73.3% [72.8, 73.8] for a septic shock diagnosis. ConclusionThe SRI provides critical care HCPs with an intuitive quantitative measure related to the risk of a patient having or acquiring a life-threatening infection. Evaluation of the SRI over time may provide HCPs the ability to initiate protective interventions (e.g., targeted antibiotic therapy).

Epidemiology, antimicrobial resistance profile and management of carbapenem-resistant Klebsiella pneumoniae among mothers with suspected sepsis in Ethiopia

BackgroundEarly detection and proper management of maternal sepsis caused by carbapenem-resistant Klebsiella pneumoniae (CRKP) can significantly reduce severe complications and maternal mortality. This study aimed to describe the epidemiology, antimicrobial resistance profile, and management of carbapenem-resistant K. pneumoniae among sepsis-suspected maternal cases in Ethiopia.MethodsA prospective cross-sectional study was conducted in five tertiary hospitals from June 2021 to December 2023. Isolation, identification, and antimicrobial susceptibility testing of the isolates were carried out following standard microbiological procedures as stated in the CLSI guidelines. Data on socio-demographics, risk factors, and management strategies were collected with structured questionnaires. Associations between variables were determined using logistic regression analysis in STATA-21. A p-value of less than 0.05 was statistically significant.ResultsOf the 5613 total women suspected of having maternal sepsis, 609 (10.8%) of them were infected with K. pneumoniae. The prevalence rates of MDR, XDR, and PDR K. pneumoniae strains were 93.9%, 24.3%, and 10.9%, respectively. The resistance rates for the last-resort antibiotics; amikacin, tigecycline, carbapenem, and third-generation cephalosporin were 16.4%, 29.1%, 31.9%, and 93.0%, respectively. The combination of carbapenem with tigecycline or amikacin therapy was used to manage maternal sepsis caused by cephalosporin-and carbapenem-resistant strains. Sepsis associated risk factors, including septic abortion [AOR = 5.3; 95%CI:2.2–14.4]; extended hospitalization [AOR = 3.7; 95%CI: 1.6–19.4]; dilatation and curettage [AOR = 2.2; 95%CI:1.3–13.4]; cesarean wound infection [AOR = 4.1; 95%CI:2.0–9.2]; indwelling catheterization [AOR = 2.1;95%CI: 1.4–6.2]; ICU admission [AOR = 4.3; 95%CI:2.4–11.2]; post abortion [AOR = 9.8; 95%CI:5.7–16.3], and recurrent UTI [AOR = 3.3; 95%CI: 1.6–13.2] were significantly associated with maternal sepsis caused by K. pneumoniae.ConclusionsThe prevalence of maternal sepsis caused by carbapenem- resistant K. pneumoniae is high and serious attention needs to be given to combat transmission. Therefore, improving awareness, early diagnosis, IPC, integrated maternal surveillance, improved sanitation and efficient antimicrobial stewardship are crucial to combating bacterial maternal sepsis.

The role of microbial cell free DNA sequencing in sepsis detection in the neonate.

The role of microbial cell free DNA sequencing in sepsis detection in the neonate.

A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples

BackgroundNeonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, but they are limited by their invasiveness, high costs, centralized testing, frequent delays, inaccuracies in results, and the need for sophisticated laboratory equipment. MethodsWe introduced a novel, non-invasive, cost-efficient, and easy-to-use technology that can provide rapid results at a point-of-care. The technology utilized a lab-built metal oxide semiconductor-based electronic nose (cNose) combined with volatile organic compound (VOC) biomarkers identified through gas chromatography-mass spectrometry (GC–MS) analysis. The system was evaluated using fecal profiling tests involving a total of 32 samples, including 17 positive and 15 negative sepsis, confirmed by blood culture. To assess the performance in discriminating patients from healthy controls, four machine learning algorithms were implemented. ResultsBased on the cross-validation results, the MLPNN model provided the best results in distinguishing between neonates with positive and negative sepsis, achieving high-performance results of 90.63 % accuracy, 88.24 % sensitivity, and 93.33 % specificity at a 95 % confidence interval. Specific VOCs associated with neonatal sepsis, such as alcohols, acids, and esters, were successfully identified through GC–MS analysis, further validating the diagnostic capability of the cNose device. ConclusionThe overall observations show the feasibility of using cNose system as a promising tool for real-time and bedside sepsis detection, potentially improving patient outcomes.

Role of C-reactive Protein and Procalcitonin in Early Diagnostic Accuracy and Their Prognostic Significance in Sepsis.

Sepsis is a serious condition that often results in high fatality rates, particularly in intensive care units (ICUs). Its nonspecific clinical characteristics makes early diagnosis and therapy difficult, despite how critical they are. The use of biomarkers like procalcitonin (PCT) and C-reactive protein (CRP) in the diagnosis and prognosis of sepsis has demonstrated encouraging results. In contrast to PCT, which is highly selective for bacterial infections, CRP is an acute-phase protein that reflects systemic inflammation. This study aimed to assess the diagnostic accuracy and prognostic significance of CRP and PCT in early sepsis detection and outcome prediction. This study was a retrospective cohort study that involved 90 patients in the ICU who met the criteria for sepsis-3. CRP and PCT levels, clinical data, and outcomes were obtained from electronic medical records. The diagnostic accuracy was tested using receiver operating characteristic (ROC) curves, while the prognostic relevance was analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. The mean CRP level was 102.3 mg/L and PCT level was 5.4 ng/mL. ROC analysis revealed an area under the curve (AUC) of 0.78 for CRP and 0.82 for PCT, indicating better diagnostic performance for PCT. High levels of CRP and PCT were associated with poorer survival, with median survival times of 18 and 15 days, respectively, for high-level groups. Cox regression identified CRP and PCT as significant predictors of mortality, with hazard ratios of 1.50 and 1.68, respectively. Both CRP and PCT are valuable biomarkers for diagnosing and prognosticating sepsis. PCT, with its higher specificity for bacterial infections, demonstrates superior diagnostic accuracy compared to CRP. Elevated levels of both biomarkers are associated with increased mortality risk, highlighting their potential role in early sepsis management and outcome prediction.

Presepsin Levels in Infection-Free Subjects with Diabetes Mellitus: An Exploratory Study.

Systemic inflammation has been recognized as the cause and consequence of metabolic dysregulation in diabetes mellitus (DM). Presepsin has recently emerged as a promising biomarker for the detection of bacterial infections and sepsis. There is evidence that gut dysbiosis results in the increased circulating concentrations of Gram-negative bacteria lipopolysaccharide, the linkage of presepsin, which in turn promotes insulin resistance and correlates with the risk of diabetic complications. Thus, we hypothesized that presepsin could reflect the magnitude of systemic inflammation and metabolic decompensation in patients with DM even in the absence of infection. In this cross-sectional pilot study, we included 75 infection-free individuals with well-controlled (n = 19) and uncontrolled (n = 23) type 2 diabetes (T2D), well-controlled (n = 10) and uncontrolled (n = 10) type 1 diabetes (T1D), and normoglycemic controls (n = 13). Presepsin levels were compared between the groups and potential associations with demographic, clinical, and laboratory parameters were explored. We observed that the duration of DM was associated with presepsin values (p = 0.008). When the participants were classified into the type of DM groups, the presepsin levels were found to be lower in the patients with T2D compared to those with T1D (p = 0.008). However, significance in that case was driven by the difference between the well-controlled groups. After adjusting for the effects of DM duration, presepsin was significantly lower in the well-controlled T2D group compared to the well-controlled T1D group [1.34 (2.02) vs. 2.22 (4.20) ng/mL, p = 0.01]. Furthermore, we adjusted our findings for various confounders, including age, body mass index, and waist circumference, and found that the difference in the presepsin values between the adequately controlled groups remained significant (p = 0.048). In conclusion, our findings suggest that presepsin could potentially serve as a surrogate marker of inflammation and metabolic control in people with DM.

Neonatal screening for critical congenital heart diseases in Peru: an urgent call

Congenital heart diseases are the most common congenital malformations worldwide and represent one of the leading causes of neonatal death, in addition to the significant use of human and financial resources by health systems. The purpose of this document is to support the implementation of neonatal screening for critical congenital heart diseases using pulse oximetry according to the different geographical altitudes of Peru. This technology is widely used worldwide and has high sensitivity, specificity, and cost-effectiveness. At many latitudes, it has led to better survival in this group of patients and in the neonatal population in general since its use in the early detection of sepsis, pneumonia, and other conditions that affect the oxygenation of the newborn. Neonatal screening for critical congenital heart disease is applicable at all levels of healthcare at a national level, and its implementation must be a priority to improve neonatal health.

Prognostic and diagnostic utility of pancreatic stone protein in pediatric sepsis and mortality.

Early detection and prognosis of sepsis in critically ill children is crucial. The aim of this research was to investigate the prognostic ability of pancreatic stone protein (PSP) in validating sepsis and predicting mortality in a prospective observational study. In a single-center study, pediatric intensive care unit patients were divided into cohorts of confirmed and suspected sepsis, as well as survivors and nonsurvivors. Patients with positive blood culture growth were considered to have confirmed sepsis, while their negative counterparts were considered to have suspected sepsis. Comparisons were made between complete blood counts, laboratory parameters, mortality indices, and C-reactive protein (CRP), procalcitonin (PCT), and PSP levels. The correlations between PSP and alternative inflammatory markers and mortality indices were then analyzed. The diagnostic and prognostic applicability of PSP for sepsis confirmation and mortality prediction was assessed using receiver operating characteristic curve analysis. PSP levels were significantly elevated in patients with confirmed sepsis and within the nonsurvivor segment. In confirming sepsis and predicting mortality, PSP outperformed CRP and PCT in terms of sensitivity. It had sensitivity of 95% in diagnosing sepsis at a cut-off level of 50 ng/L, with an area under the curve (AUC) of 0.67 (95% CI: 0.52-0.81), and sensitivity of 92% in predicting mortality, with an AUC of 0.71 (95% CI: 0.56-0.83). In addition, PSP showed significant correlations with CRP, PCT, and mortality scores. PSP is emerging as a highly sensitive marker for confirming sepsis and predicting mortality in critically ill pediatric patients. Incorporating the PSP biomarker into routine clinical practice could potentially improve the management of pediatric sepsis.

Nursing Interventions and Biochemical Markers in Early Detection of Neonatal Sepsis

Nursing Interventions and Biochemical Markers in Early Detection of Neonatal Sepsis

Sepsis Biomarkers: Advancements and Clinical Applications-A Narrative Review.

Sepsis is now defined as a life-threatening syndrome of organ dysfunction triggered by a dysregulated host response to infection, posing significant challenges in critical care. The main objective of this review is to evaluate the potential of emerging biomarkers for early diagnosis and accurate prognosis in sepsis management, which are pivotal for enhancing patient outcomes. Despite advances in supportive care, traditional biomarkers like C-reactive protein and procalcitonin have limitations, and recent studies have identified novel biomarkers with increased sensitivity and specificity, including circular RNAs, HOXA distal transcript antisense RNA, microRNA-486-5p, protein C, triiodothyronine, and prokineticin 2. These emerging biomarkers hold promising potential for the early detection and prognostication of sepsis. They play a crucial role not only in diagnosis but also in guiding antibiotic therapy and evaluating treatment effectiveness. The introduction of point-of-care testing technologies has brought about a paradigm shift in biomarker application, enabling swift and real-time patient evaluation. Despite these advancements, challenges persist, notably concerning biomarker variability and the lack of standardized thresholds. This review summarizes the latest advancements in sepsis biomarker research, spotlighting the progress and clinical implications. It emphasizes the significance of multi-biomarker strategies and the feasibility of personalized medicine in sepsis management. Further verification of biomarkers on a large scale and their integration into clinical practice are advocated to maximize their efficacy in future sepsis treatment.

Strategies for early identification and management of sepsis in children

Pediatric sepsis continues to be a major public health concern, leading to considerable morbidity and mortality despite progress in medical treatments. This review explores methods for the prompt detection and management of sepsis in children, highlighting the importance of early recognition and intervention. Diagnosing sepsis in children is difficult because of its non-specific symptoms and the diverse ways it presents in different age groups. Key strategies include enhancing parental education, improving pre-hospital recognition by emergency medical services (EMS), and leveraging the pivotal role of nurses in emergency settings. Effective management involves prompt administration of antimicrobials, appropriate use of vasoactive agents, and comprehensive post-sepsis care to improve long-term outcomes. Advanced diagnostic tools and rapid, reliable biomarkers are crucial for early detection. Additionally, antimicrobial stewardship programs and public health campaigns play a vital role in reducing sepsis incidence. By focusing on these strategies, healthcare providers can significantly improve the early identification and management of sepsis in children, enhancing survival rates and long-term quality of life for pediatric sepsis survivors.