Clinical evaluation of droplet digital PCR in the early identification of suspected sepsis patients in the emergency department: a prospective observational study.

Background: Rapid and accurate diagnosis of the causative agents is essential for clinical management of bloodstream infections (BSIs) that might induce sepsis/septic shock. A considerable number of suspected sepsis patients initially enter the health-care system through an emergency department (ED), hence it is vital to establish an early strategy to recognize sepsis and initiate prompt care in ED. This study aimed to evaluate the diagnostic performance and clinical value of droplet digital PCR (ddPCR) assay in suspected sepsis patients in the ED. Methods: This was a prospective single-centered observational study including patients admitted to the ED from 25 October 2022 to 3 June 2023 with suspected BSIs screened by Modified Shapiro Score (MSS) score. The comparison between ddPCR and blood culture (BC) was performed to evaluate the diagnostic performance of ddPCR for BSIs. Meanwhile, correlative analysis between ddPCR and the inflammatory and prognostic-related biomarkers were conducted to explore the relevance. Further, the health economic evaluation of the ddPCR was analyzed. Results: 258 samples from 228 patients, with BC and ddPCR performed simultaneously, were included in this study. We found that ddPCR results were positive in 48.13% (103 of 214) of episodes, with identification of 132 pathogens. In contrast, BC only detected 18 positives, 88.89% of which were identified by ddPCR. When considering culture-proven BSIs, ddPCR shows an overall sensitivity of 88.89% and specificity of 55.61%, the optimal diagnostic power for quantifying BSI through ddPCR is achieved with a copy cutoff of 155.5. We further found that ddPCR exhibited a high accuracy especially in liver abscess patients. Among all the identified virus by ddPCR, EBV has a substantially higher positive rate with a link to immunosuppression. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity as well as prognosis. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs. Conclusions: The multiplexed ddPCR delivers precise and quantitative load data on the causal pathogen, offers the ability to monitor the patient's condition and may serve as early warning of sepsis in time-urgent clinical situations as ED. Importance: Early detection and effective administration of antibiotics are essential to improve clinical outcomes for those with life-threatening infection in the emergency department. ddPCR, an emerging tool for rapid and sensitive pathogen identification used as a precise bedside test, has developed to address the current challenges of BSI diagnosis and precise treatment. It characterizes sensitivity, specificity, reproducibility, and absolute quantifications without a standard curve. ddPCR can detect causative pathogens and related resistance genes in patients with suspected BSIs within a span of three hours. In addition, it can identify polymicrobial BSIs and dynamically monitor changes in pathogenic microorganisms in the blood and can be used to evaluate antibiotic efficacy and survival prognosis. Moreover, the copies of pathogens in ddPCR were positively correlated with various markers of inflammation, coagulation, immunity. With high sensitivity and specificity, ddPCR facilitates precision antimicrobial stewardship and reduces health care costs.

Core sepsis-related competencies for medical students: an international consensus by Delphi technique.

BACKGROUND: Sepsis is a life-threatening condition which may arise from infection in any organ system and requires early recognition and management. Healthcare professionals working in any specialty may need to manage patients with sepsis. Educating medical students about this condition may be an effective way to ensure all future doctors have sufficient ability to diagnose and treat septic patients. However, there is currently no consensus on what competencies medical students should achieve regarding sepsis recognition and treatment. This study aims to outline what sepsis-related competencies medical students should achieve by the end of their medical student training in both high or upper-middle incomes countries/regions and in low or lower-middle income countries/regions. METHODS: Two separate panels from high or upper-middle income and low or lower-middle income countries/regions participated in a Delphi method to suggest and rank sepsis competencies for medical students. Each panel consisted of 13-18 key stakeholders of medical education and doctors in specialties where sepsis is a common problem (both specialists and trainees). Panelists came from all continents, except Antarctica. RESULTS: The panels reached consensus on 38 essential sepsis competencies in low or lower-middle income countries/regions and 33 in high or upper-middle incomes countries/regions. These include competencies such as definition of sepsis and septic shock and urgency of antibiotic treatment. In the low or lower-middle income countries/regions group, consensus was also achieved for competencies ranked as very important, and was achieved in 4/5 competencies rated as moderately important. In the high or upper-middle incomes countries/regions group, consensus was achieved in 41/57 competencies rated as very important but only 6/11 competencies rated as moderately important. CONCLUSION: Medical schools should consider developing curricula to address essential competencies, as a minimum, but also consider addressing competencies rated as very or moderately important.

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation.

Background: Sepsis, causing serious organ and tissue damage and even death, has not been fully elucidated. Therefore, understanding the key mechanisms underlying sepsis-associated immune responses would lead to more potential therapeutic strategies. Methods: Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal trajectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods were used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative reverse transcription-polymerase chain reaction was to validate mRNA expression of hub genes in peripheral blood neutrophils of two mice sepsis models. Results: We discovered two novel neutrophil subtypes with a significant increase under sepsis. These two neutrophil subtypes were enriched in the late state during neutrophils differentiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy and robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). The SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were significant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutrophils of CLP and LPS-induced sepsis mice models. Conclusions: Our research discovered new clusters of neutrophils in sepsis. These five hub genes provide novel biomarkers targeting neutrophils for the treatment of sepsis.

Direct Microbial Identification using An Automated Microbial Identification System to Facilitate the EUCAST RAST Method Without Mass Spectrometry.

Gram-negative (GN) sepsis is a medical emergency where management in resource-limited settings relies on conventional microbiological culture techniques providing results in 3-4 days. Recognizing this delay in turnaround time (TAT), both EUCAST and CLSI have developed protocols for determining AST results directly from positively flagged automated blood culture bottles (+aBCs). EUCAST rapid AST (RAST) protocol was first introduced in 2018, where zone diameter breakpoints for four common etiological agents of GN sepsis, i.e., Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii complex can be reported. However, those clinical laboratories that have implemented this method in their routine workflow rely on mass spectrometry-based microbial identification, which is not easily available, thus precluding its implementation in resource-limited settings. To circumvent it, we evaluated a direct inoculum protocol (DIP) using a commercial automated microbial identification and antimicrobial susceptibility testing system (aMIAST) to enable early microbial identification within 8 h of positive flagging of aBC. We evaluated this protocol from January to October 2023 to identify the four RAST reportable GN (RR-GN) in the positively flagged aBC. The microbial identification results in DIP were compared with the standard inoculum preparation protocol (SIP) in aMIAST. Of 204 +aBCs with monomorphic GN (+naBC), one of the 4 RR-GN was identified in 105 +naBCs by SIP (E. coli: 50, K. pneumoniae: 20, P. aeruginosa: 9 and A. baumannii complex: 26). Of these, 94% (98/105) were correctly identified by DIP whereas major error and very major error rates were 6% (7/105) and 1.7% (4/240), respectively. When DIP for microbial identification is done using the EUCAST RAST method, provisional clinical reports can be provided within 24 h of receiving the sample. This approach has the potential to significantly reduce the TAT, enabling early institution of appropriate antimicrobial therapy.

Short-term culture for rapid identification by mass spectrometry and automated antimicrobial susceptibility testing from positive bottles.

BACKGROUND: Early and appropriate antibiotic treatment improves the clinical outcome of patients with sepsis. There is an urgent need for rapid identification (ID) and antimicrobial susceptibility testing (AST) of bacteria that cause bloodstream infection (BSI). Rapid ID and AST can be achieved by short-term incubation on solid medium of positive blood cultures using MALDI-TOF mass spectrometry (MS) and the BD M50 system. The purpose of this study is to evaluate the performance of rapid method compared to traditional method. METHODS: A total of 124 mono-microbial samples were collected. Positive blood culture samples were short-term incubated on blood agar plates and chocolate agar plates for 5 ∼ 7 h, and the rapid ID and AST were achieved through Zybio EXS2000 MS and BD M50 System, respectively. RESULTS: Compared with the traditional 24 h culture for ID, this rapid method can shorten the cultivation time to 5 ∼ 7 h. Accurate organism ID was achieved in 90.6% of Gram-positive bacteria (GP), 98.5% of Gram-negative bacteria (GN), and 100% of fungi. The AST resulted in the 98.5% essential agreement (EA) and 97.1% category agreements (CA) in NMIC-413, 99.4% EA and 98.9% CA in PMIC-92, 100% both EA and CA in SMIC-2. Besides, this method can be used for 67.2% (264/393) of culture bottles during routine work. The mean turn-around time (TAT) for obtaining final results by conventional method is approximately 72.6 ± 10.5 h, which is nearly 24 h longer than the rapid method. CONCLUSIONS: The newly described method is expected to provide faster and reliable ID and AST results, making it an important tool for rapid management of blood cultures (BCs). In addition, this rapid method can be used to process most positive blood cultures, enabling patients to receive rapid and effective treatment.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Diagnostic and Prognostic Ability of Pancreatic Stone Protein: A Scoping Review.

Pancreatic stone protein (PSP) is an acute-phase reactant mainly produced in response to stress. Its diagnostic and prognostic accuracy for several types of infection has been studied in several clinical settings. The aim of the current review was to assess all studies examining a possible connection of pancreatic stone protein levels with the severity and possible complications of patients diagnosed with infection. We performed a systematic search in PubMed, Scopus, the Cochrane Library and Clinicaltrials.gov to identify original clinical studies assessing the role of pancreatic stone protein in the diagnosis and prognosis of infectious diseases. We identified 22 eligible studies. Ten of them provided diagnostic aspects, ten studies provided prognostic aspects, and another two studies provided both diagnostic and prognostic information. The majority of the studies were performed in an intensive care unit (ICU) setting, five studies were on patients who visited the emergency department (ED), and three studies were on burn-injury patients. According to the literature, pancreatic stone protein has been utilized in patients with different sites of infection, including pneumonia, soft tissue infections, intra-abdominal infections, urinary tract infections, and sepsis. In conclusion, PSP appears to be a useful point-of-care biomarker for the ED and ICU due to its ability to recognize bacterial infections and sepsis early. Further studies are required to examine PSP's kinetics and utility in specific populations and conditions.

Heparin-binding protein as a biomarker for the diagnosis of sepsis in the intensive care unit: a retrospective cross-sectional study in China.

OBJECTIVES: This study aims to investigate the diagnostic value of heparin-binding protein (HBP) in sepsis and develop a sepsis diagnostic model incorporating HBP with key biomarkers and disease-related scores for rapid, and accurate diagnosis of sepsis in the intensive care unit (ICU). DESIGN: Clinical retrospective cross-sectional study. SETTING: A comprehensive teaching tertiary hospital in China. PARTICIPANTS: Adult patients (aged ≥18 years) who underwent HBP testing or whose blood samples were collected when admitted to the ICU. MAIN OUTCOME MEASURES: HBP, C reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), interleukin-6 (IL-6), lactate (LAC), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) score were recorded. RESULTS: Between March 2019 and December 2021, 326 patients were enrolled in this study. The patients were categorised into a non-infection group (control group), infection group, sepsis group and septic shock group based on the final diagnosis. The HBP levels in the sepsis group and septic shock group were 45.7 and 69.0 ng/mL, respectively, which were significantly higher than those in the control group (18.0 ng/mL) and infection group (24.0 ng/mL) (p<0.001). The area under the curve (AUC) value of HBP for diagnosing sepsis was 0.733, which was lower than those corresponding to PCT, CRP and SOFA but higher than those of IL-6, LAC and APACHE II. Multivariate logistic regression analysis identified HBP, PCT, CRP, IL-6 and SOFA as valuable indicators for diagnosing sepsis. A sepsis diagnostic model was constructed based on these indicators, with an AUC of 0.901, a sensitivity of 79.7% and a specificity of 86.9%. CONCLUSIONS: HBP could serve as a biomarker for the diagnosis of sepsis in the ICU. Compared with single indicators, the sepsis diagnostic model constructed using HBP, PCT, CRP, IL-6 and SOFA further enhanced the diagnostic performance of sepsis.

Development and validation of an interpretable model for predicting sepsis mortality across care settings.

There are numerous prognostic predictive models for evaluating mortality risk, but current scoring models might not fully cater to sepsis patients' needs. This study developed and validated a new model for sepsis patients that is suitable for any care setting and accurately forecasts 28-day mortality. The derivation dataset, gathered from 20 hospitals between September 2019 and December 2021, contrasted with the validation dataset, collected from 15 hospitals from January 2022 to December 2022. In this study, 7436 patients were classified as members of the derivation dataset, and 2284 patients were classified as members of the validation dataset. The point system model emerged as the optimal model among the tested predictive models for foreseeing sepsis mortality. For community-acquired sepsis, the model's performance was satisfactory (derivation dataset AUC: 0.779, 95% CI 0.765-0.792; validation dataset AUC: 0.787, 95% CI 0.765-0.810). Similarly, for hospital-acquired sepsis, it performed well (derivation dataset AUC: 0.768, 95% CI 0.748-0.788; validation dataset AUC: 0.729, 95% CI 0.687-0.770). The calculator, accessible at https://avonlea76.shinyapps.io/shiny_app_up/ , is user-friendly and compatible. The new predictive model of sepsis mortality is user-friendly and satisfactorily forecasts 28-day mortality. Its versatility lies in its applicability to all patients, encompassing both community-acquired and hospital-acquired sepsis.

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.

This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, particularly distinguishing between Escherichia coli (E. coli) and non-E. coli infections. Utilizing machine learning, we conducted a retrospective analysis of 119 elderly sepsis patients, employing a random forest model to evaluate clinical biomarkers and infection sites. The model demonstrated high diagnostic accuracy, with an overall accuracy of 87.5%, and impressive precision and recall rates of 93.3% and 87.5%, respectively. It identified infection sites, platelet distribution width, reduced platelet count, and procalcitonin levels as key predictors. The model achieved an F1 Score of 90.3% and an area under the receiver operating characteristic curve of 88.0%, effectively differentiating between sepsis subtypes. Similarly, logistic regression and least absolute shrinkage and selection operator analysis underscored the significance of infectious sites. This methodology shows promise for enhancing elderly sepsis diagnosis and contributing to the advancement of precision medicine in the field of infectious diseases.

Neutrophil-to-lymphocyte Ratio as a Marker for Diagnosis and Prognostication of Sepsis.

PURPOSE: Exploring the ideal marker for early diagnosis and prognosis of sepsis is crucial due to limitations of available sepsis indicators. Hence, we aimed to evaluate the neutrophil-to-lymphocyte ratio (NLR) as a diagnostic and prognostic marker of sepsis. MATERIALS AND METHODS: This prospective case-control study was conducted at a tertiary care teaching public hospital. NLR values among cases and controls were compared for diagnosis. Among cases, serial trends in NLR values, outcome (survival or death), and various parameters [such as Sequential Organ Failure Assessment (SOFA) score, duration of intensive care unit (ICU) stay, etc.] were compared between survivors and nonsurvivors for prognosis. Analysis was performed using MS Excel and PSPP version 1.0.1. RESULTS: A total of 120 patients (60 cases and 60 controls) were analyzed. The NLR among cases was significantly higher (p = 1.31 × 10-16) than in controls. Using binary logistic regression, a high NLR was found to be a statistically significant predictor of sepsis category (p = 2.25 × 10-5). The association of various variables among survivors and nonsurvivors of cases showed statistically significant differences: NLR (p = 5.29 × 10-5), mean = 13.27, interquartile range (IQR) = 5.90, z-value = -4.042), C-reactive protein (CRP) (p = 4.80 × 10-7), mean = 74.40, IQR = 21.30, z-value = -5.034), D-dimer (p = 4.32 × 10-8), mean = 7.09, IQR = 0.88, z-value = -5.477), SOFA score (p = 0.00118, mean = 8.50, IQR = 3.00, z-value = -3.244), and duration of hospital stay (p = 0.03578, mean = 13.45, IQR = 8.00, z-value = -2.099). CONCLUSION: The NLR emerges as a valuable marker for both diagnosis and prognosis in sepsis. Elevated NLR levels aid in diagnosing sepsis at very early stages, and the trend of NLR demonstrates a dynamic course throughout the disease process. Persistently elevated NLR and high NLR values correlate with poor outcomes in sepsis. Additionally, NLR can be correlated with other prognostic markers of sepsis and mortality. Therefore, we recommend the utilization of NLR as a quick, easy, and cost-effective marker for both early diagnosis and regular prognostication of sepsis.

Clinical biomarker profiles reveals gender differences and mortality factors in sepsis.

Background: Sepsis is a major contributor to global morbidity and mortality, affecting millions each year. Notwithstanding the decline in sepsis incidence and mortality over decades, gender disparities in sepsis outcomes persist, with research suggesting higher mortality rates in males. Methods: This retrospective study aims to delineate gender-specific clinical biomarker profiles impacting sepsis progression and mortality by examining sepsis cases and related clinical data from the past three years. Propensity score matching was used to select age-matched healthy controls for comparison. Results: Among 265 sepsis patients, a significantly higher proportion were male (60.8%, P<0.001). While mortality did not significantly differ by gender, deceased patients were significantly older (mean 69 vs 43 years, P=0.003), more likely to have hypertension (54% vs 25%, P=0.019), and had higher SOFA scores (mean ~10 vs 4, P<0.01) compared to survivors. Principal Component Analysis (PCA) showed clear separation between sepsis patients and healthy controls. 48 serum biomarkers were significantly altered in sepsis, with Triiodothyronine, Apolipoprotein A, and Serum cystatin C having the highest diagnostic value by ROC analysis. Gender-stratified comparisons identified male-specific (e.g. AFP, HDLC) and female-specific (e.g. Rheumatoid factor, Interleukin-6) diagnostic biomarkers. Deceased patients significantly differed from survivors, with 22 differentially expressed markers; Antithrombin, Prealbumin, HDL cholesterol, Urea nitrogen and Hydroxybutyrate had the highest diagnostic efficiency for mortality. Conclusion: These findings enhance our understanding of gender disparities in sepsis and may guide future therapeutic strategies. Further research is warranted to validate these biomarker profiles and investigate the molecular mechanisms underlying these gender differences in sepsis outcomes.

[Development and validation of a nomogram for predicting 3-month mortality risk in patients with sepsis-associated acute kidney injury].

OBJECTIVE: To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI). METHODS: Based on the American Medical Information Mart for Intensive Care- IV (MIMIC- IV), clinical data of S-AKI patients from 2008 to 2021 were collected. Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7 : 3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds. RESULTS: Based on the survival status at 3 months after diagnosis, patients were divided into 7 768 (68.54%) survivors and 3 566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95%CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95%CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95%CI was 0.56-0.58) and SIRS score's 0.56 (95%CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. CONCLUSIONS: A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC- IV and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

[Construction and validation of a predictive model for early occurrence of lower extremity deep venous thrombosis in ICU patients with sepsis].

OBJECTIVE: To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction. METHODS: 726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA). RESULTS: The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95%CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score (OR = 1.20, 95%CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95%CI was 0.97 to 0.99, P = 0.009), D-dimer (OR = 1.03, 95%CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection (OR = 2.53, 95%CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation (OR = 3.72, 95%CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95%CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95%CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. CONCLUSIONS: The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.

[Analysis of 28 day-mortality risk factors in sepsis patients and construction and validation of predictive model].

OBJECTIVE: To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients. METHODS: A retrospective cohort study was conducted. 281 sepsis patients admitted to the department of intensive care unit (ICU) of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects. The patients were divided into a training set (197 cases) and a validation set (84 cases) according to a 7 : 3 ratio. The general information, clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected. Patients were divided into survival group and death group based on 28-day outcomes. The differences in various data were compared between the two groups. The optimal predictive variables were selected using Lasso regression, and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model. Receiver operator characteristic curve (ROC curve), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the nomogram model. RESULTS: Out of 281 cases of sepsis, 82 cases died with a mortality of 29.18%. The number of patients who died in the training and validation sets was 54 and 28, with a mortality of 27.41% and 33.33% respectively. Lasso regression, univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality. There were use of vasoactive drugs [odds ratio (OR) = 5.924, 95% confidence interval (95%CI) was 1.244-44.571, P = 0.043], acute physiology and chronic health evaluation II (APACHE II: OR = 1.051, 95%CI was 1.000-1.107, P = 0.050), combined with multiple organ dysfunction syndrome (MODS: OR = 17.298, 95%CI was 5.517-76.985, P < 0.001), neutrophil count (NEU: OR = 0.934, 95%CI was 0.879-0.988, P = 0.022) and oxygenation index (PaO2/FiO2: OR = 0.994, 95%CI was 0.988-0.998, P = 0.017). A nomogram model was constructed using the independent predictive factors mentioned above, ROC curve analysis showed that the AUC of the nomogram model was 0.899 (95%CI was 0.856-0.943) and 0.909 (95%CI was 0.845-0.972) for the training and validation sets respectively. The C-index was 0.900 and 0.920 for the training and validation sets respectively, with good discrimination. The Hosmer-Lemeshoe tests both showed P > 0.05, indicating good calibration. Both DCA and CIC plots demonstrate the model's good clinical utility. CONCLUSIONS: The use of vasoactive, APACHE II score, comorbid MODS, NEU and PaO2/FiO2 are independent risk factors for 28-day mortality in patients with sepsis. The nomogram model based on these 5 indicators has a good predictive ability for the occurrence of mortality in sepsis patients.

The Value of Systemic Immune-Inflammation Index and T Cell Subsets in the Severity and Prognosis of Sepsis.

Systemic immune-inflammation index (SII) and T cell subsets show involvement in mortality risk in septic patients, and we explored their predictive value in sepsis. Subjects were categorized into the Sepsis (SP)/Septic Shock (SSP)/Septic Shock (SPS) groups. T cell subsets [T-helper (Th)1, Th2, regulatory T cells (Treg), Th17]/platelets (PLT)/neutrophils (NEU)/lymphocytes (LYM)/C-reactive protein (CRP)/procalcitonin (PCT)/interleukin (IL)-4/IL-10/fibrinogen (FIB) were measured by an automatic blood biochemical analyzer/flow cytometry/Countess II FL automatic blood cell analyzer, with SII calculated. The correlations between SII/T cell subsets with Acute Physiology and Chronic Health Evaluation (APACH) II/Sequential Organ Failure Assessment (SOFA) scores and the predictive value of SII/Th1/Th2 for septic diagnosis/prognosis were analyzed using Spearman/ROC curve/Kaplan-Meier. The three groups varied in PLT/NEU/LYM/CRP/PCT/IL-4/IL-10/FIB levels and APACH II/SOFA scores. Compared with the SP group, the other two groups showed elevated APACH II/SOFA scores and SII/Th1/Th2/Th17/Treg levels. SII/Th1/Th2 levels significantly positively correlated with APACH II/SOFA scores. SII/Th1/Th2 levels had high predictive value for septic diagnosis/prognosis, with their combination exhibiting higher predictive value. Septic patients with high SII/Th1/Th2 levels exhibited lower survival rates. Altogether, SII, Th1, and Th2 had good predictive value for the diagnosis and prognosis of patients with varying severity of sepsis, with their high levels increasing mortality in septic patients.