Critically ill patients often experience significant skeletal muscle wasting due to prolonged bed rest, metabolic disorders, inflammatory responses and malnutrition, which affects the patient's mobility and may also lead to increased mortality. Timely and accurate assessment of muscle status is important for optimizing treatment strategies and improving patient prognosis. There are various limitations in the current methods of assessing muscle mass, and muscle ultrasound, as a noninvasive, convenient, low-cost and suitable technique for bedside monitoring, has received increasing attention for its application in muscle assessment of critically ill patients. However, there are still a number of challenges in its practical application, such as the lack of uniform standards for the measurement method, the high dependence on the operation, and the reproducibility of the data that needs to be optimized, and so on. The aim of this article is to systematize the research progress of muscle ultrasound in muscle assessment of critically ill patients, and to discuss the advantages and limitations of its clinical application, in order to provide a scientific basis for future research and clinical practice.
Humans ; Critical Illness ; Ultrasonography ; Muscle, Skeletal/diagnostic imaging*

Nutritional support therapy is one of the extremely important treatment methods for patients in the intensive care unit. Timely and effective nutritional support regimens can improve patients' immune function, reduce complications, and optimize clinical outcomes. Energy expenditure is influenced by multiple factors, including patients' baseline characteristics (such as physical condition, gender, age) and dynamic changes in indicators (such as body temperature, nutritional support regimens, and therapeutic interventions). The currently recognized "gold standard" for accurately assessing energy metabolism in clinical practice is the indirect calorimetry system, also known as the metabolic cart. This device monitors carbon dioxide production and oxygen consumption in real time and uses specific algorithms to estimate the metabolic proportions of the three major nutrients (carbohydrates, fats, and proteins) in energy expenditure. An appropriate nutrient ratio helps maintain the balance between supply and demand in the body's nutritional metabolism. In the management of critically ill patients, the application of the metabolic cart enables personalized nutritional therapy, avoiding over- or under-supply of energy and optimizing the use of medical resources. Furthermore, with real-time, quantitative data support from the energy metabolism monitoring system, clinicians can develop more precise nutritional intervention strategies, thereby improving patient prognosis. This article provides a systematic review of the technical features of the metabolic cart and its application value in various critical care scenarios, aiming to offer a reference for indirect calorimetry in clinical practice.
Humans ; Critical Illness/therapy* ; Nutritional Support ; Energy Metabolism ; Calorimetry, Indirect

OBJECTIVE: To analyze the efficacy and access safety of extracorporeal membrane oxygenation (ECMO) in series with continuous renal replacement therapy (CRRT) access for critically ill patients using propensity score matching analysis, and to explore the potential influencing factors of infection. METHODS: A total of 200 critically ill patients who received both ECMO and CRRT treatment in the intensive care unit (ICU) of Huai'an Second People's Hospital from December 2020 to December 2024 were retrospectively selected as the research subjects. They were divided into the independent operation group (72 cases) and the series system group (128 cases) according to the access connection mode of ECMO and CRRT. Propensity score matching analysis was used to perform 1 : 1 matching for patients of the two groups. The general data [age, gender, body mass index (BMI), clinical diagnosis, underlying disease, intubation method, intubation position, disease severity, ECMO support duration, catheter indwelling duration, oxygenation index (PaO₂/FiO₂) at 48 hours after ECMO initiation, serum creatinine (SCr), procalcitonin (PCT), hemoglobin (Hb), white blood cell count (WBC), platelet count (PLT)], treatment status [ECMO initiation duration, ECMO operation duration, ECMO flow, left ventricular ejection fraction (LVEF), CRRT initiation duration, CRRT catheter indwelling duration, inflow and outflow volume of replacement fluid], clinical outcome indicators (28-day survival rate, length of ICU stay, renal function recovery, fluid balance compliance rate), and access safety indicators (incidence of ECMO access thrombosis, incidence of infection, and incidence of bleeding events) of all the patients were collected. Subgroup analysis was conducted based on the occurrence of infection, and multivariate Logistic regression analysis was used to screen the potential risk factors for infection in critically ill patients receiving both ECMO and CRRT treatment. RESULTS: Finally, a total of 120 patients were successfully matched, with 60 patients in both the independent operation group and the series system group. No statistically significant differences were observed in the general data between the two groups, indicating comparability. Compared with the independent operation group, the ECMO flow at 48 hours after ECMO initiation, SCr, and alanine transaminase (ALT) of the patients in the series system group were significantly decreased, while the LVEF at 48 hours after ECMO initiation was significantly increased, additionally, the CRRT initiation duration, CRRT catheter indwelling duration, and the length of ICU stay were significantly shortened, and the inflow and outflow volume of replacement fluid were significantly increased. The incidence of infection and bleeding events in the series system group was significantly lower than that in the independent operation group [infection incidence: 11.67% (7/60) vs. 36.67% (22/60), bleeding event incidence: 8.33% (5/60) vs. 48.33% (29/60), both P < 0.05]. No significant difference was found in the other general data, treatment status, clinical outcome indicators, or access safety indicators between the two groups. Among the 120 patients, 29 cases developed infection (accounting for 24.17%), and 91 cases had no infection (accounting for 75.83%). Compared with the non-infection group, the catheter indwelling duration was significantly prolonged and PCT was significantly increased in the infection group, while the PLT and the proportion of patients with ECMO and CRRT access connected via the series system were significantly decreased. Multivariate Logistic regression analysis showed that catheter indwelling duration [odds ratio (OR) = 1.277, 95% confidence interval (95%CI) was 1.001-1.629, P = 0.049], PCT (OR = 1.529, 95%CI was 1.222-1.914, P < 0.001], PLT (OR = 0.953, 95%CI was 0.926-0.981, P = 0.001), and access connection mode (OR = 0.289, 95%CI was 0.090-0.930, P = 0.037) were potential risk factors for infection in critically ill patients. CONCLUSIONS The ECMO-in-series CRRT access can accelerate the initiation of CRRT, avoid local bleeding, stabilize patients' cardiac, hepatic and renal functions, reduce potential infection risks, and improve the prognosis of patients.
Humans ; Extracorporeal Membrane Oxygenation/adverse effects* ; Critical Illness/therapy* ; Retrospective Studies ; Continuous Renal Replacement Therapy ; Male ; Female ; Intensive Care Units ; Propensity Score ; Middle Aged ; Renal Replacement Therapy ; Adult ; Aged ; Risk Factors

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

OBJECTIVES: To explore the risk factors of feeding intolerance (FI) in critically ill children receiving enteral nutrition (EN) and to construct a prediction nomogram model for FI. METHODS: A retrospective study was conducted to collect data from critically ill children admitted to the Pediatric Intensive Care Unit of Xiangya Hospital, Central South University, between January 2015 and October 2020. The children were randomly divided into a training set (346 cases) and a validation set (147 cases). The training set was further divided into a tolerance group (216 cases) and an intolerance group (130 cases). Multivariate logistic regression analysis was used to screen for risk factors for FI in critically ill children receiving EN. A nomogram was constructed using R language, which was then validated on the validation set. The model's discrimination, calibration, and clinical net benefit were evaluated using receiver operating characteristic curves, calibration curves, and decision curves. RESULTS: Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition were identified as independent risk factors for FI in critically ill children receiving EN (P<0.05). Based on these factors, a nomogram prediction model for FI in critically ill children receiving EN was developed. The area under the receiver operating characteristic curve for the training set and validation set was 0.934 (95%CI: 0.906-0.963) and 0.852 (95%CI: 0.787-0.917), respectively, indicating good discrimination of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the model had a good fit (χ 2=12.559, P=0.128). Calibration curve and decision curve analyses suggested that the model has high predictive efficacy and clinical application value. CONCLUSIONS Duration of bed rest, shock, gastrointestinal decompression, use of non-steroidal anti-inflammatory drugs, and combined parenteral nutrition are independent risk factors for FI in critically ill children receiving EN. The nomogram model developed based on these factors exhibits high predictive efficacy and clinical application value.
Humans ; Critical Illness ; Enteral Nutrition/adverse effects* ; Male ; Risk Factors ; Female ; Child, Preschool ; Infant ; Nomograms ; Retrospective Studies ; Child ; Logistic Models

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Critically ill children often present with anemia and have a higher demand for transfusions compared to other pediatric patients. This guideline provides guidance and recommendations for blood transfusions in cases of general critical illness, septic shock, acute brain injury, extracorporeal membrane oxygenation, non-life-threatening bleeding, and hemorrhagic shock. This article interprets the background and evidence of the blood transfusion provisions for critically ill and severely bleeding children in the "Guideline for pediatric transfusion", aiming to enhance understanding and implementation of this aspect of the guidelines. Citation:Chinese Journal of Contemporary Pediatrics, 2025, 27(4): 395-403.
Humans ; Critical Illness ; Blood Transfusion/standards* ; Child ; Hemorrhage/therapy* ; Practice Guidelines as Topic

OBJECTIVES: To investigate the association of serum albumin level after human albumin infusion with 28-day mortality in critically ill patients with acute kidney injury (AKI) and its impact on 90-day outcomes of the patients. METHODS: We conducted a retrospective cohort study based on the MIMIC IV database (2008-2019), including 5918 AKI patients treated with albumin in the ICU. Based on serum albumin levels within 72 h after albumin infusion, the patients were divided into low (<30 g/L), medium (30-35 g/L), and high albumin (>35 g/L) groups. Restricted cubic spline regression and multivariate logistic regression were used to analyze the association of albumin levels with patient mortality, and the results were verified in a external validation cohort consisting of 110 sepsis-induced AKI patients treated in Nanfang Hospital between 2017 and 2022 using survival analysis and multivariate adjustment. RESULTS: In the MIMIC training cohort, multivariate logistic regression showed no significant differences in 28-day mortality of the patients with different albumin levels (P>0.05). However, restricted cubic spline analysis indicated a non-linear dose-response relationship between albumin levels and 28-day mortality (threshold effect: risk increased when albumin levels >3.6 g/dL). Secondary endpoint analysis revealed that the patients with high albumin levels had a shorter duration of mechanical ventilation (P<0.001) but a longer ICU stay (P<0.001). In the validation cohort, albumin levels ≥30 g/L were significantly associated with a reduced 28-day mortality rate (P<0.05). CONCLUSIONS The association between increased serum albumin levels following albumin infusion and 28-day mortality of critically ill patients with AKI exhibits a cohort dependency and can be influenced by multiple factors including disease type and severity, infusion strategies, and statistical methods.
Humans ; Acute Kidney Injury/therapy* ; Critical Illness/mortality* ; Retrospective Studies ; Serum Albumin/analysis* ; Male ; Female ; Intensive Care Units ; Middle Aged ; Logistic Models ; Aged

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

OBJECTIVE: To observe the effects of enhanced rehabilitation on the prognosis of critically ill patients in the intensive care unit (ICU). METHODS: A single-center retrospective historical controlled study was conducted, patients admitted to the ICU of Peking University First Hospital from May 1, 2020, to April 30, 2021, and from October 1, 2021, to September 30, 2022 were enrolled. According to the different rehabilitation treatment strategies during different periods, patients were divided into the conventional rehabilitation group (patients receiving conventional rehabilitation treatment from May 1, 2020, to April 30, 2021) and the enhanced rehabilitation group (patients receiving the therapy of multidisciplinary team, ie medical care-rehabilitation-nursing care from October 1, 2021, to September 30, 2022). General data, acute physiology and chronic health evaluation II (APACHE II), and study endpoints were collected. Primary endpoints included rehabilitation-therapy rate, intervention time for rehabilitation, rehabilitation-related adverse events, and prognostic indicators such as (length of stay in hospital, length of stay in the ICU, and duration of mechanical ventilation). Secondary endpoints included incidence of deep vein thrombosis and hospital mortality. Kaplan-Meier curves were used to analyze cumulative discharge rates within 50 days. RESULTS: A total of 539 ICU patients were enrolled, with 245 in the conventional rehabilitation group and 294 in the enhanced rehabilitation group; 322 patients had an APACHE II score ≤ 15, while 217 patients had an APACHE II score > 15. Compared to the conventional rehabilitation group, the enhanced rehabilitation group demonstrated significantly higher rehabilitation-therapy rate [51.70% (152/294) vs. 11.43% (28/245)], earlier intervention time for rehabilitation [days: 2.00 (1.00, 3.00) vs. 4.00 (3.00, 7.00)]; shorter length of stay in hospital [days: 18.00 (12.00, 30.00) vs. 21.00 (13.00, 36.00)] and lower incidence of DVT [17.01% (50/294) vs. 24.08% (59/245)]. The differences were all statistically significant (all P < 0.05). There were no rehabilitation-related adverse events occurred in either group. Kaplan-Meier analysis demonstrated a significantly higher cumulative discharge rate within 50 days in the enhanced rehabilitation group compared to the conventional rehabilitation group [86.7% (255/294) vs. 82.9% (203/245); Log-Rank test: χ² = 4.262, P = 0.039]. Subgroup analysis showed that for patients with APACHE II score ≤ 15, the enhanced rehabilitation subgroup had higher rehabilitation-therapy rate [44.32% (78/176) vs. 6.16% (9/146), P < 0.05]. For patients with APACHE II score > 15, compared to the conventional rehabilitation group, the enhanced subgroup demonstrated higher rehabilitation-therapy rate [62.71% (74/118) vs. 19.19% (19/99), P < 0.05] and shorter length of stay in hospital [days: 20.50 (12.00, 31.25) vs. 26.00 (16.00, 43.00), P < 0.05]. CONCLUSIONS Enhanced rehabilitation therapy with medical care, rehabilitation and nursing care, improved rehabilitation-therapy rate, advanced time of rehabilitation treatment, reduced length of stay in hospital and incidence of deep vein thrombosis in critically ill patients, particularly benefited those with APACHE II score > 15. The enhanced rehabilitation was beneficial to the patient in the intensive care unit with safety and worth more investigation.
Humans ; Retrospective Studies ; Critical Illness/rehabilitation* ; Intensive Care Units ; Prognosis ; Length of Stay ; APACHE ; Historically Controlled Study ; Male ; Female ; Middle Aged ; Aged

Intra-abdominal candidiasis (IAC) is the most common invasive candidiasis, with a high incidence among critically ill patients, which can significantly increase medical costs and affect prognosis. In order to standardize the diagnosis and treatment of IAC in critically ill patients, experts in related fields were organized by the Peking University Critical Care Medicine (PKUCCM), Committee of Critical Care Medicine and Organ Support, China Association for Promotion of Health Science and Technology organized experts in related fields to initiate and form a working group. Expert writers drafted the consensus based on evidence-based medical evidence. A committee composed of critical care physicians, infectious disease physicians, surgeons, dermatologists specializing in antifungal fields, and clinical pharmacists discussed and revised the consensus draft through a standardized process, and finally formulated this consensus. This consensus contains a total of 20 core recommendations, mainly focusing on the epidemiology, high-risk factors, diagnostic techniques and methods (including traditional microbiological culture techniques, clinical risk prediction tools, serological tests, molecular biological tests, and histopathological examinations) of IAC, diagnostic criteria, stratified treatment strategies, antifungal drug selection, control the sources of infection, combined treatment, de-escalation strategies, drug treatment courses, prognosis, and special types of IAC. The aim is to provide expert guidance for the standardized clinical diagnosis and treatment of IAC in critically ill patients, with a view to improving prognosis of patients.
Humans ; Critical Illness ; Intraabdominal Infections/therapy* ; Antifungal Agents/therapeutic use* ; Consensus ; Candidiasis/drug therapy* ; Critical Care ; Candidiasis, Invasive/diagnosis*