Sepsis In Intensive Care Unit Research Articles

Predicting High-Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation.

To develop and validate a prediction model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF). AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non-invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction. Multicentre observational study. This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)-based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models. The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis. This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF. Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC. TRIPOD checklist for prediction model studies was followed in this study. Patients were involved in the sample of the study.

Mortality of patients with sepsis in intensive care units at tertiary hospitals in Jordan: Prospective cohort study.

The aim of this study was to describe the 30-day mortality rate of adult patients with sepsis and septic shock in 6 intensive care units of 2 tertiary hospitals in Jordan. A prospective cohort design was used. Patients with sepsis and septic shock admitted to the medical and surgical intensive care units at 2 tertiary hospitals were followed up during the period between February 2022 and June 2022 (N = 148). Data were analyzed using SPSS, version 23. Moreover, descriptive statistics, chi-square, and binary logistic regression were used. Notably, 52.7% of patients with sepsis and septic shock died within 30 days of diagnosis of sepsis and septic shock. Sequential Organ Failure Assessment score and the history of having solid tumors significantly predicted the 30-day mortality rate. Moreover, 43 (29.0%) patients with sepsis and septic shock had positive blood cultures, and 46 (31.0%) had positive urine cultures. Patients with sepsis and septic shock have a notable mortality rate that can be predicted from total Sequential Organ Failure Assessment scores and from the history of having solid tumors. Early assessment and initiation of treatment for sepsis essentially would reduce the likelihood of progression of sepsis to septic shock and would reduce associated patients' mortality.

Correlation of Serum Albumin Levels With the Severity of Sepsis Among Intensive Care Unit Patients.

Background Sepsis is a critical and potentially fatal medical condition characterized by significant illness and death rates. Early recognition and assessment of sepsis severity are vital for its optimal management. Determination of its severity by Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II, is quite a complex process as these score systems require complex and costly investigations. Therefore, this study was designed to determine the predictive capacity of serum albumin levels for the severity of sepsis in intensive care unit (ICU) patients. Methods This cross-sectional study was carried out on 201 ICU-admitted patients with diagnosed sepsis at Benazir Bhutto Hospital (BBH), Rawalpindi, Pakistan from March 2022 to April 2023. Recruitment of patients was performed through consecutive sampling and predefined inclusion and exclusion criteria. Prior to the data collection, ethical approval and informed consent were obtained. Data was gathered via a self-designed proforma. SOFA score was applied for the determination of the severity of sepsis. Patients were categorized into three groups based on sepsis severity (SOFA score). Data analysis was done in the Statistical Package for the Social Sciences (SPSS) version 25. Descriptive and inferential statistics compared study variables. Pearson's correlation and a simple linear regression model were used to assessthe relationship between serum albumin levels and sepsis severity and the predictive capacity of serum albumin levels for sepsis severity respectively. The statistical significance of the p-value was set at less than 0.05. Results Among the 201 patients, 64 (31.84%) had sepsis, 98 (48.75%) had severe sepsis, and 39 (19.41%) had septic shock. Hypoalbuminemia was present among 119 (59.20%) patients while 82 (40.80%) patients had normal albumin levels. Significant differences were found in the total bilirubin, serum creatinine, platelet count, PaO2/FiO2 ratio, mean arterial pressure, Glasgow Coma Scale score, SOFA score, serum albumin level, and the prevalence of normal and low albumin levels across three study groups (p < 0.05). Pearson's correlation analysis showed a strong negative correlation between serum albumin level and SOFA score (correlation coefficient (r) = -0.78, p = 0.001). Linear regression analysis confirmed an inverse relationship between serum albumin levels and SOFA scores (beta coefficient = -2.70, p = 0.002). Conclusions In the present study, serum albumin level was noted as a reliable predictor of sepsis severity in ICU patients. Lower serum albumin levels were associated with higher SOFA scores, indicating more severe sepsis. This study supports the use of serum albumin as a simple and cost-effective biomarker for early identification of sepsis severity, particularly in resource-limited settings.

Biomimetic Nanomodulator Regulates Oxidative and Inflammatory Stresses to Treat Sepsis-Associated Encephalopathy.

Sepsis-associated encephalopathy (SAE) is a devastating complication of sepsis, affecting approximately 70% of patients with sepsis in intensive care units (ICU). Although the pathophysiological mechanisms remain elusive, sepsis is typically accompanied by systemic inflammatory response syndrome (SIRS) and hyper-oxidative conditions. Here, we introduce a biomimetic nanomodulator (mAOI NP) that specifically targets inflammation site and simultaneously regulates oxidative and inflammatory stresses. mAOI NPs are constructed using metal-coordinated polyphenolic antioxidants (tannic acid) and flavonoid quercetin, which are then coated with macrophage membrane to enhance pharmacokinetics and enable SAE targeting. In a cecal ligation and puncture (CLP)-induced severe sepsis model, mAOI NPs effectively mitigate oxidative stress by purging reactive oxygen species, repairing mitochondrial damage and activating the Nrf2/HO-1 signaling pathway; while polarizing M1 macrophages or microglia toward anti-inflammatory M2 subtype. mAOI NPs potently inhibit sepsis progress, prolong overall survival from 25 to 66% and enhance learning and memory capabilities in SAE mice. Further proteomics analysis reveals that mAOI NPs modulate neurodevelopment processes related to learning and memory formation while also exerting anti-inflammatory and antioxidative effects on brain tissue responses associated with SAE pathology. This study offers significant potential for improving patient outcomes and revolutionizing the treatment landscape for this devastating complication of sepsis.

Predictive Value of Combined Detection of Serum LGALS3BP and GDF-15 for the Prognosis of ICU Sepsis Patients.

This study aims to investigate the effectiveness of combining serum lectin galactoside-binding soluble 3 binding protein (LGALS3BP) with growth differentiation factor 15 (GDF-15) for predicting outcomes in sepsis patients in an intensive care unit (ICU) setting. The study involved 208 sepsis patients from the ICU of our hospital. These patients were categorized based on their 28-day survival outcomes into two groups: 166 in the survival group and 42 in the mortality group. The serum levels of LGALS3BP and GDF-15 were measured using the ELISA technique. Pearson and Spearman methods were utilized for correlation analysis. Factors affecting mortality in ICU sepsis patients were evaluated through multivariate logistic regression analysis. The efficacy of these biomarkers in prognosis prediction was assessed using receiver operating characteristic (ROC) curve analysis. The proportion of septic shock, APACHE II score, SOFA score, and serum LGALS3BP and GDF-15 levels in ICU sepsis patients in the death group were obviously higher than those in the survival group (P<0.05). The severity of ICU sepsis patients, APACHE II score, and SOFA score were obviously positively correlated with serum LGALS3BP and GDF-15 levels (P<0.05). LGALS3BP (OR: 95% CI=2.745:1.583~4.761) and GDF-15 (OR: 95% CI=2.639:1.423~4.893) were independent risk factors for death in ICU sepsis patients (P<0.05). The AUC of serum LGALS3BP and GDF-15 levels alone in predicting death in ICU sepsis patients was 0.859 and 0.854, obviously lower than the AUC of the combination, 0.943 (Z=2.704, 2.287, P<0.05). The AUC for predicting mortality in ICU sepsis patients using the APACHE II and SOFA scores were 0.832 and 0.842, respectively. The differences in comparison to the AUCs of LGALS3BP and GDF-15 were not statistically significant (P > 0.05). Serum levels of LGALS3BP and GDF-15 can both be used as predictive indicators for death in ICU sepsis patients, and their combined predictive efficacy is better.

Comparison of Trends of Procalcitonin and Neutrophil to Lymphocyte Ratio in Patients of Sepsis in Intensive Care Unit.

This study examines the trends of procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR), and sequential organ failure assessment (SOFA) scores in intensive care unit (ICU) sepsis patients from different infection sources. Elevations in PCT and NLR reflect infection severity and predict sepsis prognosis. Combining them may enhance diagnostic accuracy and prognostic capabilities, despite variations in cut-off values. The study emphasizes the significance of these biomarkers in improving sepsis management and patient outcomes. This was a prospective observation study of ICU sepsis patients from different infection sources. Procalcitonin and NLR levels were measured on days 0, 2, and 4 of admission. Sequential organ failure assessment scores on these days were also analyzed. The cut-off values were obtained for predicting the prognosis of sepsis ICU patients. The study included 100 sepsis patients with an equal distribution of males and females and a mean age of 72 years. Procalcitonin showed a significant decrease over time, while NLR initially increased before decreasing on day 4, and SOFA scores showed no significant changes. Deceased patients had significantly higher PCT and SOFA scores on days 2 and 4. Receiver operating characteristic curve analysis showed promising predictive results for PCT on day 4 and SOFA scores on days 2 and 4. Understanding the trends of PCT and NLR concerning the infection source can provide deeper insights into their diagnostic and prognostic capabilities. This comparative analysis of PCT, NLR, and SOFA score trends contributes to the improvement of patient outcomes through accurate assessment of sepsis severity and progression, early diagnosis, and timely intervention. Jayara A, Mascarenhas J, Gandhi B, Nimbolkar J. Comparison of Trends of Procalcitonin and Neutrophil to Lymphocyte Ratio in Patients of Sepsis in Intensive Care Unit. Indian J Crit Care Med 2024;28(10):942-951.

The clinical application value of multi-site mNGS detection of patients with sepsis in intensive care units

BackgroundSepsis remains a leading cause of mortality in intensive care units, and rapid and accurate pathogen detection is crucial for effective treatment. This study evaluated the clinical application of multi-site metagenomic next-generation sequencing (mNGS) for the diagnosis of sepsis, comparing its performance against conventional methods.MethodsA retrospective analysis was conducted on 69 patients with sepsis consecutively admitted to the Department of Intensive Care Medicine, Meizhou People’s Hospital. Samples of peripheral blood and infection sites were collected for mNGS and conventional method tests to compare the positive rate of mNGS and traditional pathogen detection methods and the distribution of pathogens. The methods used in this study included a comprehensive analysis of pathogen consistency between peripheral blood and infection site samples. Additionally, the correlation between the pathogens detected and clinical outcomes was investigated.ResultsOf the patients with sepsis, 57.97% experienced dyspnea, and 65.2% had underlying diseases, with hypertension being the most common. mNGS demonstrated a significantly higher pathogen detection rate (88%) compared to the conventional method tests (26%). The pathogen consistency rate was 60% between plasma and bronchoalveolar lavage fluid samples, and that of plasma and local body fluid samples was 63%. The most frequently detected pathogens were gram-negative bacteria, and Klebsiella pneumonia. There were no significant differences in the clinical features between the pathogens.ConclusionmNGS is significantly superior to conventional methods in pathogen detection. There was a notable high pathogen consistency detection between blood and local body fluid samples, supporting the clinical relevance of mNGS. This study highlights the superiority of mNGS in detecting a broad spectrum of pathogens quickly and accurately.Trial registrationNot applicable.

Comorbid burden at ICU admission in COVID-19 compared to sepsis and acute respiratory distress syndrome.

Comorbidities are similarly associated with short-term mortality for COVID-19, acute respiratory distress syndrome (ARDS) and sepsis in intensive care unit (ICU) patients, but their adjusted frequencies at admission are unknown. Thus, we aimed to evaluate the adjusted distribution, reported as odds ratios, of known risk factors (i.e., age, sex and comorbidities) for ICU admission between COVID-19, sepsis and ARDS patients in this nationwide registry-based study. In this cohort study, we included adult patients admitted to Swedish ICUs with COVID-19 (n = 7382) during the pandemic and compared them to patients admitted to ICU with sepsis (n = 22,354) or ARDS (n = 2776) during a pre-COVID-19 period. The main outcomes were the adjusted odds for comorbidities, sex, and age in multivariable logistic regression on diagnostic categories in patients admitted to ICU, COVID-19 or sepsis and COVID-19 or ARDS. We found that most comorbidities, as well as age, had a stronger association with sepsis admission than COVID-19 admission with the exception of male sex, type 2 diabetes mellitus, and asthma that were more strongly associated with COVID-19 admission, while no difference was seen for chronic renal failure and obesity. For COVID-19 and ARDS admission most risk factors were more strongly associated with ARDS admission except for male sex, type 2 diabetes mellitus, chronic renal failure, and obesity which were more strongly associated with COVID-19 admission, whereas hypertension, chronic obstructive pulmonary disease and asthma were not different. Patients admitted to ICU with sepsis or ARDS carry a heavier burden of comorbidity and high age than patients admitted with COVID-19. This is likely caused by a combination of: (1) respiratory failure in COVID-19 being less dependent on comorbidities than in other forms of ARDS, and the cause of critical illness in other infections causing sepsis and (2) COVID-19 patients being deferred admission in situations where patients with the other syndromes were admitted.

Clinical and Microbiological Study of Intra-Abdominal Infections in a Tertiary Care Hospital

Intra-abdominal infections (IAIs) are one of the important contributors to sepsis in intensive care units. The emergence of antibiotic resistance and the diversification of etiological agents make it challenging to determine the optimal empirical therapy. This study attempts to know the etiological agents, their antibiotic susceptibility patterns, and the risk factors associated with IAIs in different settings. This prospective cross-sectional study was conducted in a tertiary care facility from January 2023 to June 2023. Adult and paediatric patients having primary IAI or developed infections during their hospital stay were included in this study. Specimen like peritoneal swabs or fluid from intra-abdominal drains placed for more than 24 hours were excluded. Matrix-assisted Laser Desorption/Ionization Time-Of-Flight was used to identify the etiological agents. Vitek®2 system was used to perform the antimicrobial susceptibility. Associated risk factors were documented. A total of 86 cases were analysed. The majority of the patients had complicated IAIs (95.3%), and 65.12 % acquired the infection in the community (CA-IAI). The vast number of cases presented with intra-abdominal abscesses (46.5%). Diabetes and hepatic disorders were the frequent underlying comorbid conditions associated with CA-IAIs. Prolonged hospital stay and the presence of concomitant conditions like malignancy and chronic renal failure significantly influenced the occurrence of hospital-acquired infections (HA-IAIs). E. coli was the frequently isolated Gram-negative pathogen both in the community and hospital settings. Whereas among Gram-positives, Enterococcus predominated and was commonly isolated from HA-IAIs. Enterobacterales were highly susceptible to meropenem and piperacillin-tazobactam. E. coli and Klebsiella were the frequent extended-spectrum beta-lactamase producers and showed the least susceptibility towards cephalosporins and fluoroquinolones. Multidrug-resistant organisms (MDROs) (p=.013), including carbapenem-resistant strains (p=.048), were significantly isolated from hospital-acquired IAIs. The high prevalence of IAIs with MDROs in hospital settings emphasizes the importance of developing hospital-based antibiotic policy, infection control measures, and judicious use of antibiotics.

Myocardial strain is regulated by cardiac preload in the early stage of sepsis

BackgroundOwing to a lack of data, this study aimed to explore the effect of cardiac preload on myocardial strain in patients with sepsis.MethodsA total of 70 patients with sepsis in intensive care unit (ICU) of a tertiary teaching hospital in China from January 2018 to July 2019 and underwent transthoracic echocardiography were enrolled. Echocardiographic data were recorded at ICU admission and 24 h later. Patients were assigned to low left ventricular end-diastolic volume index (LVEDVI) and normal LVEDVI groups. We assessed the impact of preload on myocardial strain between the groups and analyzed the correlation of echocardiographic parameters under different preload conditions.ResultsThirty-seven patients (53%) had a low LVEDVI and 33 (47%) a normal LVEDVI. Those in the low LVEDVI group had a faster heart rate (121.7 vs. 95.3, p < 0.001) and required a greater degree of fluid infusion (3.67 L vs. 2.62 L, P = 0.019). The left ventricular global strain (LVGLS) (-8.60% vs. -10.80%, p = 0.001), left ventricular global circumferential strain (LVGCS) (-13.83% vs. -18.26%, p = 0.006), and right ventricular global longitudinal strain (RVGLS) (-6.9% vs. -10.60%, p = 0.001) showed significant improvements in the low LVEDVI group after fluid resuscitation. However, fluid resuscitation resulted in a significantly increased cardiac afterload value (1172.00 vs. 1487.00, p = 0.009) only in the normal LVEDVI group. Multivariate backward linear regression showed that LVEDVI changes were independently associated with myocardial strain-related improvements during fluid resuscitation. The baseline LVEDVI was significantly negatively correlated with the LVGLS and RVGLS (r = -0.44 and − 0.39, respectively) but not LVGCS. LVEDVI increases during fluid resuscitation were associated with improvements in the myocardial strain degree.ConclusionsMyocardial strain alterations were significantly influenced by the cardiac preload during fluid resuscitation in sepsis.

Role and mechanisms of autophagy, ferroptosis, and pyroptosis in sepsis-induced acute lung injury.

Sepsis-induced acute lung injury (ALI) is a major cause of death among patients with sepsis in intensive care units. By analyzing a model of sepsis-induced ALI using lipopolysaccharide (LPS) and cecal ligation and puncture (CLP), treatment methods and strategies to protect against ALI were discussed, which could provide an experimental basis for the clinical treatment of sepsis-induced ALI. Recent studies have found that an imbalance in autophagy, ferroptosis, and pyroptosis is a key mechanism that triggers sepsis-induced ALI, and regulating these death mechanisms can improve lung injuries caused by LPS or CLP. This article summarized and reviewed the mechanisms and regulatory networks of autophagy, ferroptosis, and pyroptosis and their important roles in the process of LPS/CLP-induced ALI in sepsis, discusses the possible targeted drugs of the above mechanisms and their effects, describes their dilemma and prospects, and provides new perspectives for the future treatment of sepsis-induced ALI.

Correlation Between Red Cell Distribution Width (RDW) and Sequential Organ Failure Assessment (SOFA) Score in Sepsis Patients

Background: There are an estimated 31.5 million cases of sepsis per year, and approximately 61% end up in sepsis shock, with a potential mortality rate of 5.3 million per year. In addition to the sequential organ failure assessment (SOFA) score, several biomarkers have been used to assess severity and predict mortality, including red cell distribution width (RDW). RDW is increased in conditions of ineffective red blood cell production or destruction that occur in inflammation or infection. RDW above 15.5% is associated with rigid and indestructible red blood cells, which can impede microcirculation and contribute to organ dysfunction, a part of the SOFA score.Objective: The aim of this study was to determine the correlation between RDW and SOFA score in sepsis patients admitted to the intensive care unit (ICU) of Rumah Sakit Umum Pusat Haji Adam Malik Medan.Methods: This study used a cohort-prospective study design with a consecutive sampling technique of the population that met the inclusion criteria. The samples were sepsis patients who were admitted to the ICU of Rumah Sakit Umum Pusat Haji Adam Malik Medan in April–June 2023. The data was analyzed by a Pearson or Spearman analysis test.Result: There was a significant correlation between RDW and SOFA score (r = 0.471; p&lt;0.05) in ICU sepsis patients. There was a significant correlation (p = 0.003) between RDW values and SOFA scores on the third day, where there was a moderate degree of correlation (r = 0.471), but there was no significant correlation (p = 0.103) between RDW values and SOFA scores on the first day. The sensitivity, specificity, and cut-off values of RDW were found to be 76.5%, 75%, and 14.75%, which compared to SOFA were 76.5%, 75%, and 10.5.Conclusion: There is a statistically significant relationship between RDW and SOFA, with a moderate correlation.

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i) faithfulness to the prediction, i.e., its correctness about the reasons for prediction, and (ii) usefulness to the user. While there are metrics to evaluate faithfulness, to our knowledge, there are no automated metrics to evaluate the usefulness of explanations in the clinical context. Our objective is to develop a new metric to evaluate usefulness of AI explanations to clinicians. Usefulness evaluation needs to consider both (a) how humans generally process explanations and (b) clinicians' specific requirements from explanations presented by clinical decision support systems (CDSS). Our new scoring method can evaluate the usefulness of explanations generated by any XAI method that provides importance values for the input features of the prediction model. Our method draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature. We evaluate our method in a case study on predicting onset of sepsis in intensive care units. Our analysis shows that the scores obtained using our method corroborate with independent evidence from clinical literature and have the required qualities expected from such a metric. Thus, our method can be used to evaluate and select useful explanations from a diverse set of XAI techniques in clinical contexts, making it a fundamental tool for future research in the design of AI-driven CDSS.

ICU care through NGS – Based identification of infectious agents: A comparative study

BackgroundSepsis claims 1 in 5 lives annually as per global statistics. Sepsis incidence in recent studies represents at least 35 % of all ICU admissions and has a high mortality rate, especially in the presence of co-existing morbidities. The challenge has been to accurately diagnose the causative organism, considering factors such as possible polymicrobial infections, commensals and environmental contaminants. Legacy techniques such as culture, automated culture systems or even newer species-specific PCR or film array these challenges difficult to overcome. The Bactfast® and Fungifast® assays along with the integrated workflow is based on next generation sequencing and have the ability to demarcate infecting pathogen from contamination and commensal. The unique ability to pinpoint the exact pathogen, considering the commensal and contamination in a variety of samples, with an extremely high sensitivity could lead it to be a tool of diagnostic choice for non-resolving ICU sepsis due to its comprehensive coverage and speed. The aim of this study was to evaluate the use of Bactfast® and Fungifast® as a last mile diagnostic tool in a ICU setting. MethodThis study was carried out considering access to four intensive care units (ICU). Legacy testing, mostly done on culture, was conducted at the various integrated microbiology facilities of the hospitals where the ICUs were located, in Chennai, India. NABL accredited laboratory Micro Genomics (India) Pvt Ltd, was established as the central processing facility for next generation sequencing to run the Bactfast® and Fungifast® assay. Co-relation of results for 490 samples was done retrospectively by a multi-disciplinary team of consultants which comprised of microbiologists, and infectious disease physicians. ResultThe diagnostic workflow established with the Bactfast® assay provided a sensitivity of 94.1 % and specificity of 86.6 %. Identification of pathogens in Bactfast® was better when compared to the data published in 2017, as reflected by positive co-relation with clinical confirmation. Although the Fungifast® specificity was high, at 99.4 %, only 12 samples were positive on fungal culture out of 490 samples. Therefore, it was concluded a further study for fungi based on multiple technologies with more true positive samples is required to evaluate the test. ConclusionBactfast® can identify pathogens in a sample without any bias. Its introduction as diagnostic modality in life threatening ICU sepsis could reduce mortality and morbidity. Although the initial results of Fungifast® are encouraging a further research is required for more information on test sensitivity.

Neutrophil CD64 – A Reliable Predictive Marker of Sepsis in Adult Intensive Care Unit

Abstract BACKGROUND: Sepsis poses a significant threat to adult patients admitted to intensive care units (ICUs), contributing to substantial morbidity and mortality. Neutrophil CD64 (nCD64) expression has been linked to inflammatory responses during infection or tissue injury, suggesting its potential as a predictive marker for sepsis. MATERIALS AND METHODS: We conducted a prospective analytical study over 2 years at a tertiary care hospital, enrolling 91 sepsis cases from the adult ICU. Alongside routine laboratory parameters, including complete blood count, prothrombin time, activated partial thromboplastin time, fibrinogen, C-reactive protein, and procalcitonin, the expression of nCD64 was analyzed using a Beckman Coulter Navios flow cytometer. Median fluorescence intensity (MFI) was calculated for these cases. Statistical analysis was performed using SPSS version 25.0 software, with a significance threshold of P &lt; 0.05. RESULTS: MFI scores demonstrated a notable increase in diagnosed sepsis cases, as determined by clinical and biochemical parameters. Moreover, changes in MFI scores on Day 4 exhibited a correlation with other clinical and biochemical parameters, strengthening the association between nCD64 expression and sepsis severity. CONCLUSION: Our findings suggest that nCD64 serves as an independent prognostic factor in adult ICU sepsis patients. It offers a promising alternative to traditional sepsis markers for predicting patient outcomes, emphasizing its potential clinical utility in guiding therapeutic interventions and improving patient care in ICU settings.

Predictive value of the dynamics of absolute lymphocyte counts for 90-day mortality in ICU sepsis patients: a retrospective big data study

ObjectivesThe objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care...

Development and validation of a nomogram to predict risk of septic cardiomyopathy in the intensive care unit

The aim of this study was to develop a simple but effective nomogram to predict risk of septic cardiomyopathy (SCM) in the intensive care unit (ICU). We analyzed data from patients who were first admitted to the ICU for sepsis between 2008 and 2019 in the MIMIC-IV database, with no history of heart disease, and divided them into a training cohort and an internal validation cohort at a 7:3 ratio. SCM is defined as sepsis diagnosed in the absence of other cardiac diseases, with echocardiographic evidence of left (or right) ventricular systolic or diastolic dysfunction and a left ventricular ejection fraction (LVEF) of less than 50%. Variables were selected from the training cohort using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop an early predictive model for septic cardiomyopathy. A nomogram was constructed using logistic regression analysis and its receiver operating characteristic (ROC) and calibration were evaluated in two cohorts. A total of 1562 patients participated in this study, with 1094 in the training cohort and 468 in the internal validation cohort. SCM occurred in 13.4% (147 individuals) in the training cohort, 16.0% (75 individuals) in the internal validation cohort. After adjusting for various confounding factors, we constructed a nomogram that includes SAPS II, Troponin T, CK-MB index, white blood cell count, and presence of atrial fibrillation. The area under the curve (AUC) for the training cohort was 0.804 (95% CI 0.764–0.844), and the Hosmer–Lemeshow test showed good calibration of the nomogram (P = 0.288). Our nomogram also exhibited good discriminative ability and calibration in the internal validation cohort. Our nomogram demonstrated good potential in identifying patients at increased risk of SCM in the ICU.

Clinical predictive value of the CRP-albumin-lymphocyte index for prognosis of critically ill patients with sepsis in intensive care unit: a retrospective single-center observational study.

Sepsis is a complex syndrome characterized by physiological, pathological, and biochemical abnormalities caused by infection. Its development is influenced by factors such as inflammation, nutrition, and immune status. Therefore, we combined C-reactive protein (CRP), albumin, and lymphocyte, which could reflect above status, to be the CRP-albumin-lymphocyte (CALLY) index, and investigated its association with clinical prognosis of critically ill patients with sepsis. This retrospective observational study enrolled critically ill patients with sepsis who had an initial CRP, albumin, and lymphocyte data on the first day of ICU admission. All data were obtained from the Affiliated Hospital of Jiangsu University. The patients were divided into quartiles (Q1-Q4) based on their CALLY index. The outcomes included 30-/60-day mortality and acute kidney injury (AKI) occurrence. The association between the CALLY index and these clinical outcomes in critically ill patients with sepsis was evaluated using Cox proportional hazards and logistic regression analysis. A total of 1,123 patients (63.0% male) were included in the study. The 30-day and 60-day mortality rates were found to be 28.1 and 33.4%, respectively, while the incidence of AKI was 45.6%. Kaplan-Meier analysis revealed a significant association between higher CALLY index and lower risk of 30-day and 60-day mortality (log-rank p < 0.001). Multivariate Cox proportional hazards analysis indicated that the CALLY index was independently associated with 30-day mortality [HR (95%CI): 0.965 (0.935-0.997); p = 0.030] and 60-day mortality [HR (95%CI): 0.969 (0.941-0.997); p = 0.032]. Additionally, the multivariate logistic regression model showed that the CALLY index served as an independent risk predictor for AKI occurrence [OR (95%CI): 0.982 (0.962-0.998); p = 0.033]. The findings of this study indicated a significant association between the CALLY index and both 30-day and 60-day mortality, as well as the occurrence of AKI, in critically ill patients with sepsis. These findings suggested that the CALLY index may be a valuable tool in identifying sepsis patients who were at high risk for unfavorable outcomes.

Comparative efficacy of antioxidant therapies for sepsis and septic shock in the intensive care unit: A frequentist network meta-analysis

BackgroundAntioxidant therapy is gaining traction in managing sepsis and septic shock, owing to its perceived positive impact on patient outcomes. This study sought to compare the efficacy of five antioxidant therapies (melatonin, vitamin C, vitamin E, selenium, and N-acetylcysteine, both individually and in combination with other compounds such as vitamin B1, hydrocortisone, propolis, and glutamine) in treating sepsis or septic shock in the intensive care unit (ICU). MethodsThe study involved randomized and multi-arm trials with sepsis or septic shock patients using melatonin, vitamin C, vitamin E, selenium, or N-acetylcysteine. Studies were sourced from PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and WHO - Clinical Trials Registry Platform for the frequentist network meta-analysis on 28-day mortality and Sequential Organ Failure Assessment (SOFA) scores. The risk of bias was assessed using the Physiotherapy Evidence Database scale. Therapies were compared directly and indirectly using R software. ResultsThe study of 56 trials involving 9,366 patients was included. Bias assessment revealed that 89.3 % of trials achieved excellent or good quality. Based on treatment ranking and pairwise comparisons, melatonin with propolis (SUCRA = 93.29 %) is effective in improving SOFA scores, statistically significant, with no publication bias (p= 0.73). High-dose vitamin C (SUCRA = 83.97 %), vitamin C with vitamin B1 (SUCRA = 78.72 %), and melatonin (SUCRA = 67.03 %) are potential therapies for organ dysfunction. Melatonin (SUCRA = 88.22 %) and high-dose vitamin C (SUCRA = 80.75 %) were the most effective in reducing 28-day mortality rates. However, analysis indicated that the results for 28-day mortality rates were not statistically significant. Also, these results contained publication bias (p= 0.02). ConclusionThe study offers fresh perspectives on antioxidant therapy treatments for sepsis or septic shock in ICU, emphasizing the combination of melatonin and propolis notably reduces SOFA scores for those patients.

Evaluation of an Intensive Care Unit Sepsis Alert in Critically Ill Medical Patients.

Sepsis alerts commonly used for intensive care unit (ICU) patients can lead to alert fatigue because these patients generally meet 1 or more of the criteria for systemic inflammatory response syndrome. To identify ICU patients at greatest risk for sepsis-related consequences, an ICU-specific sepsis alert was implemented. To evaluate an ICU sepsis alert based on modified criteria for systemic inflammatory response syndrome among critically ill medical patients. This retrospective evaluation was conducted at a comprehensive tertiary referral center. Patients included were at least 18 years old, were admitted to the critical care medicine service, and had at least 1 sepsis alert between January 1 and February 29, 2020. The sepsis alert identified patients meeting at least 2 modified systemic inflammatory response syndrome criteria (white blood cell count, <4000/μL or >12 000/μL; body temperature, <36 °C or >38.3 °C; heart rate, >110/min; and respiratory rate, >21/min), with at least 1 of the 2 criteria being white blood cell count or body temperature. For 128 alerts evaluated, the positive predictive value was 72%. Of 713 patients who were admitted to the critical care medicine service and did not have a sepsis alert, 7 received a sepsis diagnosis. The ICU sepsis alert had a negative predictive value of 99%, sensitivity of 92.9%, and specificity of 95.1%. An ICU sepsis alert using modified systemic inflammatory response syndrome criteria was effective for identifying sepsis in critically ill medical patients.