To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis. A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort. A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO2; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ 2 = 6.729, P = 0.566), the validation group (χ 2 = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good. OASIS, 24-hour SpO2, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.

Acute pancreatitis (AP) is a severe inflammatory disease characterized by self-digestion of pancreatic tissue and inflammatory responses. Recent studies have revealed a close connection between gut microbiota and AP. The gut microbiota community, a complex ecosystem composed of trillions of microorganisms, is closely associated with various physiological activities of the host, including metabolic processes, immune system regulation, and intestinal structure maintenance. However, in patients with AP, dysbiosis of the gut microbiota are believed to play a key role in the occurrence and progression of the disease. This dysbiosis not only impairs the integrity of the intestinal barrier, but may also exacerbate inflammatory responses through multiple mechanisms, thereby affecting the severity of the disease and patient' clinical prognosis. This article reviews the mechanisms of action of gut microbiota in AP, explores how gut microbiota dysbiosis affects disease progression, and evaluates current clinical treatment methods to regulate intestinal flora, including probiotic supplementation, fecal microbiota transplantation, antibiotic therapy, and early enteral nutrition. In addition, this article discusses the efficacy and safety of the aforementioned therapeutic approaches, and outlines future research directions, aiming to provide novel perspectives and strategies for the diagnosis, treatment and prognostic evaluation of AP. Through in-depth understanding the interaction between gut microbiota and AP, it is expected that more precise and personalized therapeutic regimens will be developed to improve patients' quality of life and clinical outcomes.

To construct a longitudinal trajectory model of arterial oxygen tension (PaO2) within 24 hours after cardiac arrest (CA). A retrospective cohort study was conducted. CA patients admitted to the ICU from 2014 to 2015 were selected from the eICU Collaborative Research Database (eICU-CRD). Data about patients' demographic characteristics, history of comorbidities, laboratory test indicators within 24 hours of intensive care unit (ICU) admission [including all PaO2 data and arterial carbon dioxide tension (PaCO2)], vasopressor use, and clinical outcomes were extracted from the database. The primary outcome variable was all-cause in-hospital mortality. Group-based trajectory model (GBTM) were built based on the changes in PaO2 within 24 hours of ICU admission, and patients were grouped according to their initial static PaO2 values upon ICU admission. Multivariable adjusted Poisson regression analysis was used to compare the in-hospital mortality risk among patients in different PaO2 dynamic trajectory groups. Sensitivity analyses were performed using multivariable logistic regression and multivariable adjusted Poisson regression without imputation of missing values. A total of 3 866 CA patients were included. Three GBTM trajectory groups were identified based on PaO2 changes within 24 hours of ICU admission: Group-1 (low level first increased then decreased, 148 cases), Group-2 (sustained low level, 3 040 cases), and Group-3 (first high level then decreased, 678 cases). Significant differences were found among the three groups in age, body weight, maximum serum potassium, maximum PaCO2, minimum hemoglobin (Hb), vasopressor use, total hospitalization time, ICU stay, and hospital mortality. After incorporating variables with significant differences into the multivariable adjusted Poisson regression model, results showed that compared to Group-2 patients, patients in Group-1 and Group-3 had an increased risk of all-cause in-hospital mortality [Group-1 adjusted relative risk (aRR) = 1.20, 95% confidence interval (95%CI) was 1.02-1.41; Group-3 aRR = 1.11, 95%CI was 1.01-1.24]. Based on initial static PaO2 values at ICU admission, patients were divided into four groups: PaO2 < 100 mmHg (1 mmHg = 0.133 kPa; 1 217 cases), PaO2 100-200 mmHg (569 cases), PaO2 201-300 mmHg (547 cases), and PaO2 > 300 mmHg (1 082 cases). Multivariable adjusted Poisson regression analysis indicated a significant upward trend in aRR for the latter three groups compared to the PaO2 < 100 mmHg group. Sensitivity analyses revealed that compared to Group-2, patients in Group-1 and Group-3 had a significantly increased risk of all-cause in-hospital mortality (both P < 0.05). Within 24 hours after return of spontaneous circulation in CA patients, PaO2 exhibits different dynamic trajectories, and patients with hyperoxia have an increased risk of in-hospital mortality.

Avian influenza H10N3 is a type of avian influenza virus that can occasionally infect humans and cause severe pneumonia and acute respiratory distress syndrome (ARDS). On December 25, 2024, a 23-year-old obese female patient with H10N3 avian influenza complicated with severe ARDS was admitted to the Fourth People's Hospital of Nanning. The patient was transferred to our department due to "fever, cough, and shortness of breath for 13 days". Physical examination revealed moist rales in bilateral lungs. Chest imaging showed large areas of ground-glass opacity and consolidation in both lungs. Based on the patient's medical history, clinical manifestations, and laboratory findings, she was diagnosed with human infection of H10N3 avian influenza, severe pneumonia, and severe ARDS. Supported by mechanical ventilation and extracorporeal membrane oxygenation (ECMO), daily monitoring of airway peak pressure, plateau pressure (Pplat), driving pressure (ΔP), and lung compliance was performed to guide the adjustment of tidal volume (VT) and positive end-expiratory pressure (PEEP) during invasive mechanical ventilation. Medications including anti-avian influenza virus agents, antibacterial drugs, and antifungals were administered. Eventually, the patient's condition improved gradually, and she was successfully weaned from ECMO. No ventilator-induced lung injury (VILI) or multiple organ dysfunction syndrome (MODS) related to ARDS occurred during ECMO support. However, during the final stage of ventilator weaning after the restoration of spontaneous breathing, a right pneumothorax occurred. Closed thoracic drainage was performed, after which the ventilator was successfully discontinued. The patient was successfully transferred out of the intensive care unit (ICU), recovered fully, and was discharged from the hospital. In the invasive mechanical ventilation management of patients infected with H10N3 avian influenza complicated by ARDS, monitoring airway peak pressure, Pplat, ΔP, and assessing pulmonary compliance may facilitate more standardized management of such ARDS patients and help reduce VILI.

To explore the timing of weaning extracorporeal membrane oxygenation (ECMO) and analyze the risk factors that affect survival outcomes before weaning. A retrospective case-control study was conducted. Patients who received ECMO treatment and were weaned according to physicians' orders at the Second Xiangya Hospital of Central South University from January 2020 to June 2024 were enrolled as the study subjects. The general information, underlying diseases, indications and processes of ECMO, vital signs and arterial blood gas analysis 1 hour before weaning test, and biochemical indicators 24 hours before weaning test were collected through the hospital electronic medical record system. The primary outcome measure was the hospital mortality. The variables with P < 0.1 in univariate analysis and correlation analysis were included into binary Logistic regression analysis to identify risk factors. A nomogram model was constructed to predict the risk of weaning death in patients with ECMO, and receiver operator characteristic curve (ROC curve) and calibration curve were drawn to evaluate the model. Decision curve analysis (DCA) was used to evaluate the clinical net benefit rate of the model. A total of 32 ECMO patients were included, among whom 10 received veno-arterial ECMO (VA-ECMO) and 22 received veno-venous ECMO (VV-ECMO). During the hospitalization period, 23 patients survived, while 9 died. The time from mechanical ventilation to ECMO activation in the death group was significantly longer than that in the survival group, and the time from ECMO cessation to discharge was significantly shorter than that in the survival group. The levels of diastolic blood pressure (DBP) and albumin (Alb) before weaning were significantly lower than those in the survival group, and the level of procalcitonin (PCT) was significantly higher than that in the survival group (all P < 0.05). Spearman correlation analysis showed that DBP, PCT, Alb, and thrombin time (TT) were correlated with the weaning outcomes of ECMO patients (r values were -0.450, 0.373, -0.376, -0.346, all P < 0.1). Binary Logistic regression analysis showed that the final indicators entering the regression equation included DBP [odds ratio (OR) = 0.864, 95% confidence interval (95%CI) was 0.756-0.982], PCT (OR = 1.157, 95%CI was 0.679-1.973), and TT (OR = 0.852, 95%CI was 0.693-1.049), and a nomogram model was constructed to predict the weaning outcomes of ECMO patients. ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the weaning outcome of ECMO patients was 0.831, with a sensitivity of 77.8% and a specificity of 65.2%. Its predictive value was better than that of single indicators DBP, PCT, and TT (AUC of 0.787, 0.739, and 0.722, respectively). The calibration curve showed that the prediction probability of the model was in good consistency with the actual observed results, the Hosmer-Lemeshow goodness of fit test showed that, χ 2 = 8.3521, P = 0.400, indicating that the model fits well. DCA showed that across risk threshold of 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of single indicator. The nomogram model constructed with DBP, PCT, and TT has certain predictive value for the weaning outcomes of ECMO patients and can be used as a screening indicator for ECMO weaning timing.

To develop and validate a predictive model for the risk of bloodstream infection (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (CRKP). A literature search was conducted in PubMed, Cochrane Library, and Embase databases from inception to July 2022 to identify studies reporting statistically significant risk factors for CRKP-BSI. Relative risks (RR) were extracted and pooled. Based on factor weights, a risk-scoring model was established. For external validation, hospitalized CRKP-infected patients from January 2016 to January 2022 at Shanghai First People's Hospital were included. Clinical data were used to calculate individual risk scores. The predictive accuracy was assessed using receiver operator characteristic curve (ROC curve). Patients were stratified into low-to-intermediate-risk and high-risk groups based on the optimal cut-off, and CRKP BSI incidence was compared between groups. The literatures related to the risk factors of CRKP-BSI published from database inception to July 2022 was retrieved and screened from PubMed, Cochrane Library, and Embase. Fourteen risk factors were included in the scoring model: cardiovascular disease, severe neutropenia or immunosuppression, intensive care unit (ICU) stay history, prior hospitalization, carbapenem exposure, aminoglycoside exposure, antifungal exposure, endotracheal intubation or tracheostomy, mechanical ventilation, hemodialysis, central venous catheter, indwelling urinary catheter, CRKP colonization, and Klebsiella pneumoniae positivity at non infection sites. The total score ranged from 0 to 173.5 points. In the validation cohort of 230 CRKP-infected patients, 41 developed CRKP BSI. The model yielded an area under the curve (AUC) of 0.783 (95%CI was 0.689-0.876). The optimal cut off was 81.25 points, with sensitivity of 75.6% and specificity of 81.0%. Based on this cut off, 163 patients were categorized as low-to-intermediate risk and 67 patients as high risk. The incidence of CRKP BSI in the high-risk group was significantly higher than in the low-to-intermediate-risk group [64.2% (43/67) vs. 4.9% (8/163); RR = 13.175 (95%CI was 5.920-29.319), P < 0.001]. The model, based on 14 routinely available clinical parameters, demonstrated good performance in predicting CRKP BSI risk and may assist clinicians in early identification of high risk patients.

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, referring to a diffuse brain dysfunction caused by sepsis in the absence of direct central nervous system (CNS) infection. SAE occurs in up to 70% of patients with sepsis. Globally, the annual incidence of sepsis ranges from 30.0 to 48.9 million cases, resulting in approximately 11 million deaths per year, which accounts for 20% of all global mortalities. SAE is identified as an independent risk factor contributing to the increased mortality rate among these patients. Early diagnosis of SAE and related cerebral protection interventions hold significant clinical importance. Currently, the main indicators of brain function for sepsis patients include Glasgow coma score (GCS), confusion assessment method for the intensive care unit (CAM-ICU), electroencephalogram (EEG), brain CT or magnetic resonance imaging (MRI) and other related imaging changes, which have the problems of low sensitivity, poor specificity, and non-objective evaluation of the results of the diagnosis of SAE. This article focuses on the latest progress in the pathogenesis of SAE and systematically reviews potential biomarkers related to the onset of SAE from multiple aspects, including inflammatory markers, endothelial and neuronal injury markers, and metabolic markers. This will provide new insights for the clinical diagnosis and treatment of SAE.

To investigate the current status of early pain and agitation management in critically ill patients in Guizhou Province. A retrospective study was performed using data collected from a quality control activity conducted between April and June 2021 in non-provincial public hospitals with general intensive care unit (ICU) in Guizhou Province. Hospital-level data included hospital name and grade, ICU staffing, and number of ICU beds. Patient-level data included characteristics of patients treated in the general ICU on the day of the survey (e.g., age, sex, primary diagnosis), as well as pain and agitation assessments and the types of analgesic and sedative medications administered within 24 hours of ICU admission. A total of 947 critically ill ICU patients from 145 hospitals were included, among which 104 were secondary-level hospitals and 41 were tertiary-level hospitals. Within 24 hours of ICU admission, 312 (32.9%) critically ill patients received pain assessments, and 277 (29.3%) received agitation assessments. Among the pain assessment tools, the critical care pain observation tool (CPOT) was used in 44.2% (138/312) of critically ill ICU patients, with a significantly higher usage rate in tertiary hospitals compared to secondary hospitals [52.3% (69/132) vs. 38.3% (69/180), P < 0.05]. The Richmond agitation-sedation scale (RASS) was used in 93.8% (260/277) of critically ill ICU patients for agitation assessment, with no significant difference between hospital levels. Among the 947 critically ill patients, 592 (62.5%) received intravenous analgesics within 24 hours, with remifentanil being the most commonly used [42.9% (254/592)]; 510 (53.9%) received intravenous sedatives, with midazolam being the most frequently used [60.8% (310/510)]. Mechanical ventilation data were available for 932 critically ill patients, of whom 579 (62.1%) received mechanical ventilation and 353 (37.9%) did not. Compared with non-ventilated patients, ventilated patients had significantly higher rates of analgesic and sedative use [analgesics: 77.9% (451/579) vs. 38.8% (137/353); sedatives: 71.8% (416/579) vs. 25.8% (91/353); both P < 0.05]. In terms of analgesic selection, ventilated patients were more likely to receive strong opioids than non-ventilated patients [85.8% (95/137) vs. 69.3% (387/451), P < 0.05]. For sedatives, ventilated patients preferred midazolam [66.6% (277/416)], whereas non-ventilated patients more often received dexmedetomidine [45.1 (41/91)]. Blood pressure within 24 hours of ICU admission were available for 822 critically ill patients, of whom 245 (29.8%) had hypotension and 577 (70.2%) did not. Compared with non-hypotensive patients, hypotensive patients had significantly higher rates of analgesic and sedative use [analgesics: 74.7% (183/245) vs. 59.8% (345/577); sedatives: 65.7% (161/245) vs. 51.3% (296/577); both P < 0.05], but there was no significant difference in the choice of analgesic or sedative agents between the two groups. The proportion of critically ill ICU patients in Guizhou Province who received standardized pain and agitation assessments was relatively low. The most commonly used assessment tools were CPOT and RASS, while remifentanil and midazolam were the most frequently used analgesic and sedative agents, respectively. Secondary-level hospitals had a lower rate of using standardized pain assessment tools compared to tertiary-level hospitals. Mechanical ventilation and hypotension were associated with the use of analgesic and sedative medications.

To compare the efficacy and safety of intravenous colistin sulfate combined with nebulized inhalation versus intravenous monotherapy for pulmonary infections caused by carbapenem-resistant organism (CRO). A multicenter retrospective cohort study was conducted. Clinical data were collected from patients admitted to the intensive care unit (ICU) of 10 tertiary class-A hospitals in Henan Province between July 2021 and May 2023, who received colistin sulfate for CRO pulmonary infections. Data included baseline characteristics, inflammatory markers [white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), C-reactive protein (CRP)], renal function indicators [serum creatinine (SCr), blood urea nitrogen (BUN)], life support measures, anti-infection regimens, clinical efficacy, microbiological clearance rate, and prognostic outcomes. Patients were divided into two groups: intravenous group (colistin sulfate monotherapy via intravenous infusion) and combination group ((intravenous infusion combined with nebulized inhalation of colistin sulfate). Changes in parameters before and after treatment were analyzed. A total of 137 patients with CRO pulmonary infections were enrolled, including 89 in the intravenous group and 48 in the combination group. Baseline characteristics, life support measures, daily colistin dose, and combination regimens (most commonly colistin sulfate plus carbapenems in both groups) showed no significant differences between two groups. The combination group exhibited higher clinical efficacy [77.1% (37/48) vs. 59.6% (52/89)] and microbiological clearance rate [60.4% (29/48) vs. 39.3% (35/89)], both P < 0.05. Pre-treatment inflammatory and renal parameters showed no significant differences between two groups. Post-treatment, the combination group showed significantly lower WBC and CRP [WBC (×109/L): 8.2±0.5 vs. 10.9±0.6, CRP (mg/L): 14.0 (5.7, 26.6) vs. 52.1 (24.4, 109.6), both P < 0.05], whereas NEU, PCT, SCr, and BUN levels showed no significant between two groups. ICU length of stay was shorter in the combination group [days: 16 (10, 25) vs. 21 (14, 29), P < 0.05], although mechanical ventilation duration and total hospitalization showed no significant differences between two groups. Intravenous colistin sulfate combined with nebulized inhalation improved clinical efficacy and microbiological clearance in CRO pulmonary infections with an acceptable safety profile.

Acute kidney injury (AKI) is one of the most common complications in critically ill patients, and sepsis is the main cause of AKI in the intensive care unit (ICU), which can lead to a poor prognosis in severe cases. For patients with sepsis-associated acute kidney injury (SA-AKI) for whom urgent dialysis is indicated, it is now clear that renal replacement therapy (RRT) can be initiated immediately to control disease progression. However, the optimal timing to initiate RRT in patients whose disease is not severe enough to warrant urgent dialysis remains controversial. Some previous studies were small and heterogeneous, and there was a lack of effective reference indicators for guiding RRT in SA-AKI patients. Therefore, this article reviews the relevant experimental studies on the treatment of critically ill patients with AKI in recent years, and reviews the latest research progress on the optimal timing of RRT initiation, in order to provide an effective reference for clinical practice.