Objective To identify volatile organic compounds (VOCs) associated with postoperative hypoxemia in patients undergoing abdominal surgery. Methods We prospectively enrolled 76 patients who were admitted to the intensive care unit (ICU) after undergoing abdominal surgery at Peking University People's Hospital between December 10, 2022 and June 30, 2023. Exhaled air samples were collected within 24 h after surgery. Thermal desorption gas chromatography mass spectrometry (TD-GC-MS) was used for identification and quantification of VOCs components in the exhaled air samples. Occurrence of hypoxemia with 24 h after surgery was evaluated and the patients were assigned into hypoxemia group and non-hypoxemia group. The profiles of exhaled VOCs were compared between the two groups with Lasso-Logistic regression. Result Among the 76 patients, 27 had hypoxemia and 49 had no hypoxemia. After Lasso-Logistic regression, the results showed that increased levels of allyl methyl sulfide (OR:1.000, p = 0.03), benzothiazole (OR:1.000, p = 0.041) and a decreased level of 2,3,6-trimethyldecane (OR:1.000, p = 0.009) in exhaled breath were associated with the occurrence of postoperative hypoxemia. The significance of the three VOCs conbined in predicting postoperative hypoxemia was evaluated and the area under the ROC curve was 0.785 (95% CI: 0.675, 0.895). Conclusion The findings demonstrate that an increase in allyl methyl sulfide and benzothiazole levels and a decrease in 2,3,6-trimethyldecane in exhaled breath are associated with the occurrence of postoperative hypoxemia. A combination of the three VOCs exhibited a good predictive value for postoperative hypoxemia.

Purpose This study explored risk factors and gut microbiome characteristics associated with postoperative bowel functional recovery in patients undergoing colorectal surgery. Methods Patients undergoing colorectal surgery between January 2023 and October 2023 were stratified into two cohorts based on the timing of their first postoperative defecation (≤ 5 days vs. >5 days). Clinical data were systematically recorded to identify independent risk factors for delayed bowel recovery. Fresh postoperative stool specimens were collected and analyzed using metagenomic sequencing to examine the relationship between gut microbiota composition and bowel functional outcomes. Results Thirty-five patients were enrolled. Multivariate analysis identified the timing of first postoperative enteral feeding ( p < 0.01) as an independent risk factor for delayed defecation. Alpha diversity indices showed no significant differences between groups in microbial species richness. However, patients with delayed defecation (> 5 days) exhibited a lower relative abundance of probiotic taxa (e.g., Bifidobacterium, Lactobacillus) and an increased abundance of pathogenic bacteria compared to the ≤ 5-day cohort. Metagenomic profiling further demonstrated impaired microbial metabolic pathways in the delayed recovery group, including reduced carbohydrate metabolism (e.g., glycolysis/gluconeogenesis) and amino acid metabolism (e.g., selenocysteine and taurine biosynthesis). Conclusions Early postoperative resumption of enteral nutrition and probiotics may enhance bowel functional recovery. The observed reductions in microbial-driven gluconeogenesis/glycolysis, selenocysteine, and taurine synthesis suggest dysregulation of these metabolic pathways may compromise intestinal mucosal repair and homeostasis, contributing to delayed postoperative recovery.

Acute respiratory distress syndrome (ARDS) is a common respiratory disorder in surgical intensive care units, and its unique pathological features and pathogenesis pose significant health risks to patients. Although circular RNAs have been implicated in various disease processes, their biological functions in ARDS remain largely unexplored. We aimed to investigate the role of circSRSF1 in lipopolysaccharide (LPS)-induced lung injury, thereby providing a basis for biomarker screening and the identification of potential therapeutic targets for ARDS. In this study, we observed a marked increase in circSRSF1 expression in peripheral blood mononuclear cells from patients with ARDS, which correlated with elevated inflammatory markers, including white blood cell counts, interleukin-6 and interleukin-8, and procalcitonin. Using an LPS-induced cell injury model, we demonstrated that inhibiting circSRSF1 in RAW 264.7 cells significantly reduced apoptosis, attenuated inflammatory responses and macrophage M1 polarization, lowered oxidative stress levels, and decreased LPS-induced reactive oxygen species production. These findings suggest that circSRSF1 may play a crucial role in ARDS pathogenesis, with its elevated expression potentially serving as a biomarker for the condition. Overall, CircSRSF1 may offer new insights into ARDS development and provide a reference for future clinical research and therapeutic strategies.

Extubation failure in ICU patients is associated with poor outcomes. Existing prediction models often rely on static data, missing dynamic disease fluctuations. This study introduces TrAcE, a deep learning-based model integrating static and temporal data for improved extubation failure prediction with explainable results. The model was trained and validated using MIMIC-III (Medical Information Mart for Intensive Care-III) data and tested on the LOCAL-Ext (a local database for extubation) dataset. A Transformer-based neural network with temporal fusion was used to screen extubation records (Patients planned for post-extubation non-invasive ventilation or tracheostomy were excluded). Model performance was assessed using AUROC (area under the receiver operating curve) and AUPRC (area under the precision recall curve). Explainability was ensured via Captum’s occlusion method, identifying feature attributions at both population and individual levels. TrAcE’s extubation timing was compared to the spontaneous breathing test (SBT) in LOCAL-Ext. From MIMIC-III, 5,895 patients (4,126 for training, 1,729 for validation) were selected. LOCAL-Ext included 6,765 test patients. TrAcE outperformed other models, achieving AUROCs of 0.823 (95

BACKGROUND:This study aims to evaluate the association between SII and the prevalence of MetS in the female population of the United States, utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2013-2018. METHODS:The study analyzed data from 6140 participants. The association between SII and MetS was investigated using both univariate and multivariable analyses. Additionally, the relationship between SII and all-cause as well as cardiovascular mortality was assessed through Cox proportional hazards regression models. RESULTS:MetS was prevalent in 37.52% of the study population. Participants diagnosed with MetS showed higher SII scores compared to those without MetS. There was a nonlinear "J"-shaped dose-response relationship between increased SII levels and increased all-cause mortality in the MetS population. The same trend of relationship between SII levels and cardiovascular mortality was observed in people aged 60-years and older in the MetS population. CONCLUSION:The results of this study suggest that there is a significant association between elevated SII and the presence of MetS, and that systemic inflammation indicated by SII may contribute to the development and progression of MetS and its associated complications. Further studies are warranted to elucidate the underlying mechanisms between SII and MetS and to evaluate the clinical utility of SII in risk stratification and management strategies.

OBJECTIVE:To analyze the clinical characteristics, microbiological analysis, and drug resistance patterns of intensive care unit (ICU) bloodstream infection. METHODS:A prospective cohort study method was employed to collect clinical data from patients suspected of bloodstream infection (BSI) during their stay in ICUs across 67 hospitals in 16 provinces and cities nationwide, from July 1, 2021, to December 31, 2022. Electronic data collection technology was used to gather general information on ICU patients, including gender, age, length of hospital stay, as well as diagnostic results, laboratory tests, imaging studies, microbiological results (including smear, culture results, and pathogen high-throughput testing), and prognosis. Patients were divided into a BSI group and a non-BSI group based on the presence or absence of BSI; further, patients with BSI were categorized into a drug-resistant group and a non-drug-resistant group based on the presence or absence of drug resistance. Differences in the aforementioned indicators between groups were analyzed and compared; variables with P < 0.10 in the univariate analysis were included in a multivariate Logistic regression analysis to identify risk factors for mortality and drug resistance in ICU patients with BSI. RESULTS:A total of 2 962 ICU patients suspected of BSI participated in the study, including 790 in the BSI group and 2 172 in the non-BSI group. Patients in the BSI group were mainly from East China and Southwest China, with significantly higher age and mortality rates than those in the non-BSI group. Among ICU patients with BSI, Staphylococcus had the highest detection rate (8.10%), followed by Klebsiella pneumoniae (7.47%); there were 169 cases in the drug-resistant group and 621 cases in the non-drug-resistant group; 666 cases survived, and 124 cases died (mortality was 15.70%). There were statistically significant differences between the death group and the survival group in terms of age, regional distribution, and bloodstream infections caused by Gram negative (G-) bacilli, Enterococcus faecium, Aspergillus, and Klebsiella pneumoniae; multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.01, 95% confidence interval (95%CI) was 1.00-1.03], regional distribution (OR = 4.07, 95%CI was 1.02-1.34), Enterococcus faecium infection (OR = 3.64, 95%CI was 1.16-11.45), and Klebsiella pneumoniae infection (OR = 2.64,95%CI was 1.45-4.80) were independent risk factors for death in ICU patients with BSI (all P < 0.05). There were statistically significant differences between the drug-resistant group and the non-drug-resistant group in terms of age and bloodstream infections caused by Gram positive (G+) cocci and G- bacilli; multivariate Logistic regression analysis showed that age (OR = 1.01,95%CI was 1.00-1.03), G- bacilli infection (OR = 2.18, 95%CI was 1.33-3.59), Escherichia coli infection (OR = 0.28,95%CI was 0.09-0.84), and Enterococcus faecium infection (OR = 3.35, 95%CI was 1.06-10.58) were independent risk factors for drug resistance in ICU patients with BSI (all P < 0.05). CONCLUSIONS:Bloodstream infections may increase the mortality of ICU patients. Older age, regional distribution, Enterococcus faecium infection and Klebsiella pneumoniae infection can increase the mortality rate of ICU patients with BSI; bloodstream infections caused by G- bacilli are prone to drug resistance, but have no significant impact on the mortality of ICU patients with BSI.

Sarcopenia, characterized by loss of muscle mass and strength, particularly affects older adults and is linked to increased morbidity and mortality. The study aimed to investigate the relationship between biomarkers, including hemoglobin (Hb), lactate dehydrogenase (LDH), and Systemic Immune-Inflammation Index (SII), and sarcopenia in the US population. Utilizing NHANES data from 2003 to 2018, the study analyzed 5,615 participants, categorizing them based on quartiles of Hb, SII, and LDH levels. It employed logistic regression models to assess the relationship between these biomarkers and sarcopenia risk, adjusting for various confounders. High levels of LDH, Hb and SII were significantly associated with sarcopenia, with higher risk in the highest quartile. The AUC for all indicators in predicting sarcopenia was 0.925 (sensitivity 0.925; specificity 0.743). The study concludes that elevated Hb, LDH, and SII levels are significant biomarkers associated with sarcopenia, emphasizing the role of inflammation in its development and the potential for these markers in early detection and intervention.

Objective The identification of myocardial injury in the intensive care unit (ICU) has received little attention from researchers. Therefore, this retrospective cohort study aimed to develop a machine-learning model to predict the occurrence of myocardial injury in the ICU.Methods Based on the Clinical Research Data Platform of Peking University People's Hospital, we enrolled adult, non-cardiac surgical, and non-obstetric patients who were admitted to the ICU between 2012 and 2022. Logistic regression, random forest, LASSO regression, support vector machine and extreme gradient boosting (XGBoost) models were developed to predict myocardial injury.Results Data from 7453 non-cardiac surgery adult patients in ICU were collected in the derivation cohort (myocardial injury group: 2161 [29%], non-myocardial injury group: 5292 [71%]). Among the five models, the XGBoost model (area under the curve = 0.779; accuracy = 0.781) exhibited the best predictive performance for myocardial injury and the results were explained by the SHapley Additive exPlanations analysis. The top six features of the XGBoost model were maximal heart rate, respiratory rate, temperature, minimal heart rate, age and plasma transfusion.Conclusion This machine-learning model, developed using the XGBoost algorithm, could be a valuable tool for clinical decision-making and detecting myocardial injury in the ICU.

BACKGROUND: Acute kidney injury (AKI) is a common and significant complication in the Intensive Care Unit (ICU), affecting more than half of all patients admitted. This condition is associated with increased morbidity and mortality, underscoring the urgent need for accurate and specific biomarkers to enable early diagnosis and intervention. Dickkopf-3 (DKK3) has emerged as a promising candidate biomarker for renal injury.METHODS: We conducted a single-center, prospective cohort study from March 1 to July 1, 2023, enrolling 166 non-cardiac postoperative patients admitted to the ICU. Serum and urinary DKK3 levels were quantified using enzyme-linked immunosorbent assay (ELISA) kits. A multifactorial logistic regression model was constructed, incorporating changes in serum creatinine (ΔScr), cystatin C (CysC), serum DKK3 levels, and the serum DKK3 to urine DKK3 ratio.RESULTS: Elevated serum DKK3 levels were significantly associated with an increased incidence of AKI and a composite outcome of adverse events (AKI or death). The multifactorial logistic regression model exhibited excellent performance, with an area under the receiver operating characteristic curve (AUC) of 0.98. Decision curve analysis (DCA) demonstrated a net clinical benefit of utilizing serum DKK3 levels to guide treatment decisions, particularly at higher risk thresholds.CONCLUSIONS: Serum DKK3 is a robust diagnostic biomarker for AKI, effectively stratifying patients based on protein levels. The predictive model that incorporates DKK3 provides a valuable tool for clinical decision-making in the ICU setting. Further validation in larger and more diverse populations is warranted.

Suspected infection can progress to sepsis or septic shock, contributing to a high mortality rate among patients admitted to ICU. However, the characteristics of suspected infection remain incompletely defined. We aimed to develop and validate predictive models to identify independent risk factors for mortality and multidrug-resistant infection in patients with suspected infection upon ICU admission in mainland China. We prospectively collected medical data from patients with suspected infection admitted to ICUs across mainland China between July 2021 and December 2022. Patients were randomly allocated to a training cohort and a validation cohort at a 7:3 ratio. Using machine learning algorithms, we identified risk factors and constructed predictive models for mortality and multidrug-resistant infection. The performance of models developed by logistic regression, random forest, extreme gradient boosting, and gradient boosting machine was evaluated using the area under the curve (AUC), Brier score, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), the Youden index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). A total of 2963 patients from 67 hospitals in mainland China were enrolled. The most common infection sites were the lung (79.28

This review delves into the role of Dickkopf-3 (DKK3), a secreted glycoprotein and member of the Dickkopf family, in non-malignant diseases. DKK3 is particularly known for its regulatory effects on the Wnt signaling pathway, a critical mediator in various biological processes including cell proliferation, differentiation, and migration. Our review highlights DKK3’s influence in disorders of the cardiovascular, respiratory, renal, and muscular systems, where it contributes to disease progression by modulating these key biological processes. As an emerging biomarker, DKK3’s levels have been found to correlate with various disease states, underscoring its potential diagnostic and therapeutic implications.

While mortality for critically ill patients has decreased, many survivors face persistent physical, cognitive, and psychological impairments, collectively known as post-intensive care syndrome, which significantly reduce health-related quality of life (HRQoL). Nutrition is a crucial component of recovery, yet evidence-based strategies for post-intensive care unit (ICU) nutritional management remain underdeveloped. The Global Research Initiative on Post-ICU Nutrition (GRIP) was established to address this gap by advancing research, education, and clinical practice in post-ICU nutrition. International experts in the field of critical care nutrition were invited to a diagnostic matrix meeting, to develop a definition of post-ICU patients relevant to GRIP, discuss emerging evidence regarding post-ICU nutritional management, and identify core research domains to guide future research. The consortium consensus was achieved. A post-ICU patient is defined as any adult patient who has been admitted to an ICU for more than 48 h and is in the post-ICU recovery phase, which begins after the first ICU discharge and continues for up to one year, regardless of hospital length of stay, readmissions, or discharge destination. Ten core nutrition research domains were identified, including: (1) pathophysiology of post-ICU recovery, (2) phenotyping and personalised nutrition strategies, (3) timing and delivery of nutrition, (4) nutritional intake monitoring and optimisation, (5) nutrition interventions and effectiveness, (6) long-term functional and health-related quality of life outcomes, (7) digital tools and remote monitoring, (8) education and healthcare professional engagement, (9) implementation science and system integration, and (10) patient and family involvement. GRIP envisions a future in which patients post-ICU receive personalised, timely, and effective nutritional care to enhance recovery, reduce complications, and improve long-term HRQoL. By identifying knowledge gaps, initiating targeted research projects, and supporting global educational efforts, GRIP aims to generate robust evidence, foster international collaboration, and strengthen clinical capacity to improve global post-ICU nutritional care.

Background. Bloodstream infection is amongst the leading causes of mortality for critical postoperative patients. However, data, especially from developing countries, are scary. Clinical decision-making tools for predicting postoperative bloodstream infection-related mortality are important but still lacking. Objective. To analyze the distribution of pathogens and develop a nomogram for predicting mortality in patients with postoperative bloodstream infection in the surgical intensive care unit. Methods. The clinical data, infection and pathogen-related data, and prognosis of patients with PBSI in the SICU from January 2017 to January 2022 were retrospectively collected. The distribution of pathogens and clinical characteristics of patients with PBSI were analyzed. The patients were assigned to a died group and a survived group according to their survival status. Independent predictors for mortality were identified by univariate and multivariate analyses. A nomogram for predicting PBSI-related death was developed based on these independent predictors. Calibration and decision-curve analysis were established to evaluate the nomogram. We collected postoperative patients admitted to our center from February 2022 to June 2023 as external validation sets to verify the nomogram. We also add the Brier score to further validate the model. Results. In the training set, 7128 patients admitted to the SICU after different types of surgery were collected. A total of 198 patients and 308 pathogens were finally enrolled. The mean age of patients with PBSI was 64.38 ± 16.22 (range 18–90) years, and 56.1% were male. Forty-five patients (22.7%) died in the hospital. Five independent predictors including BMI, APACHE II score, estimated glomerular filtration rate (eGFR), urine volume in the first 24 hours after surgery, and peak temperature before positive blood cultures were selected to establish the nomogram. The area under the receiver operating characteristic curve for the prediction model was 0.922. Calibration curve and decision curve analysis showed good performance of the nomogram. Seventy patients with PBSI were collected as an external validation set, and thirteen patients died in this set. The external validation set was used to validate the nomogram, and the results showed that the AUC was 0.930 which was higher than that in the training set indicating that the nomogram had a good discrimination. The brier score was 0.087 for training set and 0.050 for validation set. Conclusions. PBSI was one of the key issues that clinicians were concerned and could be assessed with a good predictive model using simple clinical factors.

Purpose:The recognition of sepsis as a heterogeneous syndrome necessitates identifying distinct subphenotypes to select targeted treatment. Methods:Patients with sepsis from the MIMIC-IV database (2008-2019) were randomly divided into a development cohort (80%) and an internal validation cohort (20%). Patients with sepsis from the ICU database of Peking University People's Hospital (2008-2022) were included in the external validation cohort. Time-series k-means clustering analysis and dynamic time warping was performed to develop and validate sepsis subphenotypes by analyzing the trends of 21 vital signs and laboratory indicators within 24 h after sepsis onset. Inflammatory biomarkers were compared in the ICU database of Peking University People's Hospital, whereas treatment heterogeneity was compared in the MIMIC-IV database. Findings:Three sub-phenotypes were identified in the development cohort. Type A patients (N = 2525, 47%) exhibited stable vital signs and fair organ function, type B (N = 1552, 29%) was exhibited an obvious inflammatory response and stable organ function, and type C (N = 1251, 24%) exhibited severely impaired organ function with a deteriorating tendency. Type C demonstrated the highest mortality rate (33%) and levels of inflammatory biomarkers, followed by type B (24%), whereas type A exhibited the lowest mortality rate (11%) and levels of inflammatory biomarkers. These subphenotypes were confirmed in both the internal and external cohorts, demonstrating similar features and comparable mortality rates. In type C patients, survivors had significantly lower fluid intake within 24 h after sepsis onset (median 2891 mL, interquartile range (IQR) 1530-5470 mL) than that in non-survivors (median 4342 mL, IQR 2189-7305 mL). For types B and C, survivors showed a higher proportion of indwelling central venous catheters (p < 0.05). Conclusion:Three novel phenotypes of patients with sepsis were identified and validated using time-series data, revealing significant heterogeneity in inflammatory biomarkers, treatments, and consistency across cohorts.

Abstract Background Ultrasound has widely used in various medical fields related to critical care. While online and offline ultrasound trainings are faced by certain challenges, remote ultrasound based on the 5G cloud platform has been gradually adopted in many clinics. However, no study has used the 5G remote ultrasound cloud platform operating system for standardized critical care ultrasound training. This study aimed to evaluate the feasibility and effectiveness of 5G-based remote interactive ultrasound training for standardized diagnosis and treatment in critical care settings. Methods A 5G-based remote interactive ultrasound training system was constructed, and the course was piloted among critical care physicians. From July 2022 to July 2023, 90 critical care physicians from multiple off-site locations were enrolled and randomly divided into experimental and control groups. The 45 physicians in the experimental group were trained using the 5G-based remote interactive ultrasound training system, while the other 45 in the control group were taught using theoretical online videos. The theoretical and practical ultrasonic capabilities of both groups were evaluated before and after the training sessions, and their levels of satisfaction with the training were assessed as well. Results The total assessment scores for all of the physicians were markedly higher following the training (80.7 ± 11.9) compared to before (42.1 ± 13.4) by a statistically significant margin (P < 0.001). Before participating in the training, the experimental group scored 42.2 ± 12.5 in the critical care ultrasound competency, and the control group scored 41.9 ± 14.3—indicating no significant differences in their assessment scores (P = 0.907). After participating in the training, the experimental group’s assessment scores were 88.4 ± 6.7, which were significantly higher than those of the control group (72.9 ± 10.8; P < 0.001). The satisfaction score of the experimental group was 42.6 ± 2.3, which was also significantly higher than that of the control group (34.7 ± 3.1, P < 0.001). Conclusion The 5G-based remote interactive ultrasound training system was well-received and effective for critical care. These findings warrant its further promotion and application.

To the Editor: Sepsis is a clinical syndrome characterized by life-threatening organ dysfunction caused by a dysregulated host response to infection. Early intervention with antibiotics, intravenous fluids, and other supportive measures can significantly improve the chances of recovery. For every hour of delay in diagnosing and treating patients with septic shock, there is a 7.6% increase in the mortality rate.[1] Despite advances in diagnostic technology, clinicians are still unable to detect the origin of sepsis in approximately one-quarter (28%) of patients with septic shock by the end of their intensive care unit (ICU) stay.[2] Therefore, a more rapid test to detect a broad spectrum of pathogens is essential for the diagnosis and treatment of sepsis. Although metagenomic next-generation sequencing (mNGS) is a novel solution for pathogen detection, it is labor-intensive and time-consuming. A typical mNGS experiment takes ~24 h, which is significantly longer than that of serological and polymerase chain reaction (PCR)-based tests. Rapid mNGS is required by clinicians to obtain accurate results within a rapid timeframe. A user-friendly and rapid procedure would aid clinicians in decision-making, which may eventually benefit patients. Therefore, we designed an mNGS workflow based on the Illumina platform with a theoretical turnaround time (TAT) of 7 h. This study was approved by the Research Ethics Board of the Peking University People's Hospital (No. 2021PHB410-001). Informed consent was obtained from all patients that were enrolled in the study. To expedite a standard mNGS procedure with a ~24 h turnaround [Figure 1A], we modified a previously validated experimental protocol[3] and designed an ultra-rapid mNGS workflow according to the following: (1) automation in nucleic acid extraction and library preparation through the use of a cartridge-based point-of-care device.[3] The device comprised four chambers, each of which was equipped with liquid handling, temperature control, and magnetic separator modules to facilitate DNA extraction, enzymatic fragmentation, end repair, dA-tailing, adaptor ligation, and library purification; (2) PCR-free library preparation in which only one nucleic acid purification step was needed[3]; (3) Miniseq rapid reagent kit was used (~25 million reads, 3 pmol/L of pooled library input) and 50 base pairs were sequenced instead of 100; (4) one plasma and one negative control (NC) were sequenced in each run, simplifying the pooling processes; and (5) the bioinformatics pipeline was optimized to reduce runtime. The theoretical TAT for the ultra-rapid mNGS was 7 h, representing one of the fastest mNGS tests performed on the Illumina platform [Figure 1B]. Microbial reads identified from a library were reported if: (1) the sequencing data passed quality control filters (library concentration >50 pmol/L, Q20 >85%, Q30 >80%); (2) the NC in the same sequencing run does not contain the species or reads per million (RPM) (sample)/RPM (NC) is ≥5.Figure 1: Experimental steps for ultra-rapid (A) and standard mNGS (B). Contingency tables for the ultra-rapid mNGS results compared to the initial culture, CMTs, and clinical adjudications (C). Sankey diagram showing the clinical actions in response to mNGS results and patient outcomes (D). CMTs: Conventional microbiological tests; mNGS: Metagenomic next-generation sequencing; PPA: Positive percentage agreement. PPV: Positive percentage; QC: Quality control; qPCR: Quantitative polymerase chain reactionTo explore whether the ultra-rapid mNGS using blood samples has real-world benefits, particularly in the ICU where patients with sepsis have exhibited an increase in mortality with a delay in effective antimicrobial initiation, 36 patients were enrolled from the ICU department at Peking University People's Hospital, Beijing, China according to the following criteria: (1) 18 years and older, suspected of sepsis (body temperature >38°C or <36°C with elevated serum C-reactive protein [CRP] or procalcitonin [PCT] levels); (2) sequential organ failure assessment (SOFA) score of +2 or higher; (3) expected survival time of ≥8 h; and (4) providing informed consent [Supplementary Figure 1, https://links.lww.com/CM9/C38]. Bilateral double bottles (aerobic and anaerobic) were collected for blood cultures. In some cases, specimens other than peripheral blood were sent for culture. Positive culture results were recorded within 3 days before or after mNGS for analytical performance evaluation. Additional microbiological tests were ordered by clinicians when deemed necessary, including the interferon-gamma release assay (IGRA), acid-fast stain, Gram stain, cytomegalovirus (CMV)/Epstein–Barr virus (EBV) quantitative real-time PCR (qPCR), β-D-glucan test (G test), galactomannan test (GM test), cryptococcal capsular antigen (CrAg) test, and influenza A/B antigen test. Moreover, an in-house standard mNGS test that utilized Nextseq 550Dx (Illumina, California, San Diego, USA) with a 24-h TAT was performed in five cases (designated as routine mNGS in the manuscript). The details and results of these tests are presented in Supplementary Table 1, https://links.lww.com/CM9/C38. The clinical characteristics of the enrolled patients are summarized in Supplementary Tables 2 and 3, https://links.lww.com/CM9/C38. All patients were administered empirical antibiotics prior to microbiological testing. Three different reference standards were used to evaluate the diagnostic accuracy of the ultra-rapid mNGS: (1) blood cultures resulted in a positive percentage agreement (PPA) and negative percentage agreement (NPA) of 72.73% and 12.00%, respectively; (2) a composite standard that included all conventional microbiological tests (CMTs) that generated a PPA and NPA of 44.83% and 0%, respectively; and (3) clinical adjudications based on the examination of medical records, imaging scans, microbiological findings, and responses to antibiotics (whether symptoms improved or exacerbated), which resulted in a PPA and NPA of 82.86% and 100.00%, respectively [Figure 1C]. Combined with laboratory and clinical data, the pathogen results were classified as clinically relevant (definite, probable, and possible) or clinically irrelevant (unlikely) according to the composite microbiological and clinical criteria outlined in the Karius test.[4] The microorganisms detected using different methods in each patient are shown in Supplementary Figure 2A, https://links.lww.com/CM9/C38. Compared with a routine mNGS, the ultra-rapid mNGS exhibited the same results in 3/5 cases and detected more bacteria in blood or sputum by culture in 2/5 cases. The time from sample collection to the results of all microbiological tests was recorded [Supplementary Figures 1 and 2B and Supplementary Table 1, https://links.lww.com/CM9/C38]. The average TAT for the ultra-rapid mNGS was 10.53 h (minimum 7.4 h), which was ostensibly faster than other microbiological methods, especially culture (average TAT 97.72 h). In a real clinical setting, qPCR is performed no quicker than a G-test (average TAT 26.66 h vs. 19.87 h). The delay in TAT is caused by experimental scheduling; upon sample arrival in a clinical laboratory, technicians need to wait for more samples to arrive to start batch processing, which is also the case for routine mNGS in which 10–20 samples are handled simultaneously (average TAT, 55.4 h). We also investigated whether faster mNGS reporting could lead to better antibiotic management. To this end, we analyzed all cases and categorized the clinicians' actions into (1) escalation of antibiotics, (2) de-escalation of antibiotics, (3) increase in the types of antibiotics, (4) reduction in the types of antibiotics (in cases of combination antibiotic therapy), (5) validation/confirmation of the empirical therapy and no change in antibiotics, and (6) irrelevant results and no change in antibiotics. As shown in Supplementary Figure 3A, https://links.lww.com/CM9/C38, the impact of mNGS was the validation of empirical therapy (n = 14), followed by additional antibiotics (n = 10), and fewer antibiotics (n = 9), antibiotic escalation (n = 2), and no change (n = 1). Next, we evaluated whether these clinical managements affected patient outcomes. Among the 36 mNGS reports, 30 (83%) were deemed clinically relevant based on a retrospective review of medical records. On day 30 following the mNGS test, 17 of the 30 patients survived and 13 (43%) died, compared to four and two (33%) of six patients whose mNGS results were considered irrelevant, respectively [Figure 1D and Supplementary Figure 3B, https://links.lww.com/CM9/C38]. However, 9/10 of the patients whose empirical antibiotics were validated by mNGS survived, which was higher than that of other types of clinical actions [Figure 1D]. Lastly, we analyzed the monetary expenditure associated with antibiotic use during the 24-h time window before and after ultra-rapid mNGS testing. A change in antibiotic costs occurred in 20 of the 36 patients. A total reduction of 10,909.52 Chinese Yuan (~1558.5 US dollars) was observed in 15 cases [Supplementary Figure 4, https://links.lww.com/CM9/C38]. In five cases, an increase in antibiotic charge was seen (1413.12 Chinese Yuan, ~201.9 US dollars), largely due to the use of additional antibiotics targeting pathogens identified by mNGS that were not covered by empirical treatment [Supplementary Table 1, https://links.lww.com/CM9/C38]. To date, the fastest TAT of mNGS was 6 h, which was performed on a Nanopore sequencer.[4] The cost of Nanopore-based mNGS was approximately $300/sample, as compared to $100/sample for the Illumina platform.[5] Moreover, the sequencing output of the Nanopore was lower than that of the Illumina with a higher error rate in base calling. Therefore, it would be cost-effective if an Illumina-based mNGS could be expedited to a level comparable to that of the Nanopore. Our work demonstrated that 7 h-mNGS was plausible using an Illumina sequencer. The clinical implementation of this workflow yielded an average sample-to-result time of 10.6 h, with a minimum of 7.4 h [Supplementary Table 3, https://links.lww.com/CM9/C38]. We employed three different standards to evaluate the diagnostic performance of ultra-rapid mNGS [Figure 1C] and observed high PPA and NPA when using clinical adjudication as a reference, in which clinicians reached a definitive microbiological diagnosis based on a more systematic, thorough, albeit subjective review of clinical cases, which reflected a more accurate account of clinical situations. Our study has several limitations. First, no control group was included. Second, the sample size was small and biased towards elderly Han Chinese males, which prevented a more comprehensive evaluation of the technique in broader ethnic and age groups. Third, the study was conducted in the ICU of a single tertiary hospital; therefore, we could not fully assess the cost-benefit from a wider perspective, especially in low-resource settings. The ultra-rapid mNGS workflow can be easily implemented in a clinical setting with the help of a point-of-care automation device; thus, only one person is required to complete the procedure. Currently, the protocol can only handle one plasma sample using the MiniSeq sequencer owing to the limited data output. Other platforms with higher throughputs and faster sequencing time can accommodate more samples in a single run. Acknowledgement The Miniseq rapid reagent kits in this study were provided by Illumina (China) Scientific Co., Ltd. This study was supported by the National Natural Science Foundation of China (No. 82241048) and Beijing Major Epidemic Prevention and Control Key Specialty Project-Medical Laboratory Excellence Project (2022). Conflicts of interest Z. Du, J. Wang, and C. Liu are employees of Hangzhou Matridx Biotechnology Co., Ltd. The rest of the authors declare no conflict of interest.

Disseminated intravascular coagulation (DIC) poses a high mortality risk, yet its exact impact remains contentious. This study investigates DIC's association with mortality in individuals with sepsis, emphasizing multiple organ function. Using data from the Peking University People's Hospital Investigation on Sepsis-Induced Coagulopathy database, we categorized patients into DIC and non-DIC groups based on DIC scores within 24 h of ICU admission (< 5 cutoff). ICU mortality was the main outcome. Initial data comparison preceded logistic regression analysis of mortality factors post-propensity score matching (PSM). Employing mediation analysis estimated direct and indirect associations. Of 549 participants, 131 were in the DIC group, with the remaining 418 in the non-DIC group. Following baseline characteristic presentation, PSM was conducted, revealing significantly higher nonplatelet sequential organ failure assessment (nonplt-SOFA) scores (6.3 ± 2.7 vs 5.0 ± 2.5, P < 0.001) and in-hospital mortality rates (47.3% vs 29.5%, P = 0.003) in the DIC group. A significant correlation between DIC and in-hospital mortality persisted (OR 2.15, 95% CI 1.29–3.59, P = 0.003), with nonplt-SOFA scores (OR 1.16, 95% CI 1.05-1.28, P = 0.004) and hemorrhage (OR 2.33, 95% CI 1.08-5.03, P = 0.032) as predictors. The overall effect size was 0.1786 (95% CI 0.0542-0.2886), comprising a direct effect size of 0.1423 (95% CI 0.0153-0.2551) and an indirect effect size of 0.0363 (95% CI 0.0034-0.0739), with approximately 20.3% of effects mediated. These findings underscore DIC's association with increased mortality risk in patients with sepsis, urging anticoagulation focus over bleeding management, with organ dysfunction assessment recommended for anticoagulant treatment efficacy.