BACKGROUND: Hospitalization in neonatal intensive care units (NICU) exposes preterm infants to adverse stimuli, including continuous 24-hour lighting. There is currently no standardized NICU layout advised for the best development of preterm neonates. This meta-analysis aimed to assess the impact of cycled light (CL) exposure on clinical outcomes in premature infants admitted to NICU as synthesized in previous studies. MATERIALS AND METHODS: This meta-analysis protocol was developed following the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement. A search was performed in PubMed/MEDLINE, EMBASE, Scopus, and Cochrane databases using the MeSH/key words: ―light exposure‖ AND pre-term AND cycled AND (RCT OR trials OR ―randomized controlled trial). The pooled Mean Difference with corresponding 95% CI was computed for weight gain, duration until start of enteral feeding, and duration of ICU stay using the Mantel–Haenszel random-effect model. RESULTS: Nine studies were included. The pooled mean difference showed that among preterm infants who had cycled light exposure, average daily weight gain (MD=6.24 grams, 95%CI=1.36 to 11.13, p=0.01) was significantly higher than those with continuous light exposure. The average time to start enteral feeding (MD=-3.84 days, 95%CI=-7.56 to -0.13, p=0.04) and average ICU stay (MD=-8.43 days, 95%CI=-12.54 to -4.31, p<0.0001) among neonates who had cycled light exposure were significantly shorter. CONCLUSION Benefits were seen in preterm infants when exposed to cycled light as opposed to continuous light. CL exposed infants showed a daily weight gain that was 6.24 grams higher, on average, and began enteral feeding nearly 4 days sooner. It led to a decrease in the duration of ICU stay by around 8 to 9 days on average. Further trials to determine the impact of cycled light exposure on morbidity and mortality among preterm neonates is recommended.
Human ; Male,Female ; Systematic review ; Meta-analysis ; Infant, Premature ; Intensive care units, Neonatal ; Intensive care, Neonatal ; Light ; Lighting ; Critical care

Simiao Yong'an Decoction is derived from the New Compilation of Proved Prescriptions(Yan Fang Xin Bian). This formula has the effects of clearing heat and detoxifying as well as activating blood and relieving pain. It is mainly used to treat gangrene caused by excessive heat toxin and with the clinical manifestations including dark red and slightly swollen limbs, scorching skin, ulceration and odor, severe pain, occasional fever, thirst, red tongue, and rapid pulse. Nowadays, Simiao Yong'an Decoction is mostly used in the treatment of thromboangitis obliterans, ulcers in arteriosclerosis obliterans of lower limbs, stent restenosis after angioplasty of lower limbs, ecthyma, deep venous thrombosis, diabetic arteriosclerosis obliterans of lower limbs, diabetic foot ulcer, acute knee arthritis, varicose veins of lower limbs, coronary heart disease, post percutaneous coronary intervention(PCI), sepsis, gout, tumor, chronic tonsillitis in children, and other diseases. It has been identified that diabetic foot with infection, sepsis, arteriosclerosis obliterans, and thromboangitis obliterans belong to the category of gangrene in traditional Chinese medicine(TCM), and Simiao Yong'an Decoction is an ancient specialized prescription for treating this disease. The diseases that can be treated by Simiao Yong'an Decoction include arteriosclerosis obliterans, thromboangitis obliterans, diabetic foot, and ecthyma. The symptoms that can be treated by Simiao Yong'an Decoction include dark red, blackened, slightly swollen, burning, ulceration, and odor in the fingers and toes, and toes falling off, hands and feet decaying and collapsing, severe and unbearable pain in some cases. Furthermore, this formula is effective for skin ulceration spreading, pus dripping, swollen and proliferating lymph nodes. These symptoms are always accompanied by dry mouth, thirst, irritability, yellow urine, and dry stool. The TCM symptoms include red tongue, thin and white tongue coating, and wiry and rapid pulse. In the case with the complication of refractory hypotension, large dosage of Astragali Radix is used to replenish Qi, reinforce healthy Qi, and expressing toxin, which can often achieve blood pressure-elevating and anti-inflammatory effects. Simiao Yong'an Decoction is often combined with Simiao Pills and Guizhi Fuling Pills. High-dose medication is the key to the effectiveness of this formula. Integrated traditional Chinese and western medicine plays an important role in the treatment of diabetic foot with infection, sepsis, septic shock, arteriosclerosis obliterans, and thromboangitis obliterans.
Humans ; Diabetic Foot/physiopathology* ; Drugs, Chinese Herbal/administration & dosage* ; Arteriosclerosis Obliterans/physiopathology* ; Sepsis/physiopathology* ; Critical Care ; Male ; Infections/physiopathology*

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

Acute Care Surgery (ACS) is an emerging discipline of general surgery that integrates trauma, emergency surgery, critical care medicine, and surgical rescue. It is a modern model for the diagnosis and treatment of patients with acute and critical abdominal conditions. Compared to the traditional model, ACS integrates the theories and techniques of trauma, critical care, and surgery. It consolidates surgical wards and intensive care units into a single department for operational management. The care of acute care patients is led by physicians who are qualified in both critical care medicine and surgery. This model improves efficiency and significantly reduces morbidity and mortality of patients. Both international and domestic hospitals have had multiple surgical rescue teams that have embarked on exploratory work in the development of ACS, accumulating a certain amount of experience. The Expert Working Group of Acute Care Surgery, Chinese Medical Doctor Association Division of Surgeons has formed a preliminary guideline for the development of the Department of Acute Care Surgery based on the current experience and accomplishment in China for the reference of hospitals at all levels.
Critical Care ; Humans ; General Surgery ; Acute Care Surgery

The accurate and timely detection of biochemical coagulation indicators is pivotal in pulmonary and critical care medicine. Despite their reliability, traditional laboratories often lag in terms of rapid diagnosis. Point-of-care testing (POCT) has emerged as a promising alternative, which is awaiting rigorous validation. We assessed 226 samples from patients at the First Affiliated Hospital of Guangzhou Medical University using a Beckman Coulter AU5821 and a PUSHKANG POCT Biochemistry Analyzer MS100. Furthermore, 350 samples were evaluated with a Stago coagulation analyzer STAR MAX and a PUSHKANG POCT Coagulation Analyzer MC100. Metrics included thirteen biochemical indexes, such as albumin, and five coagulation indices, such as prothrombin time. Comparisons were drawn against the PUSHKANG POCT analyzer. Bland-Altman plots (MS100: 0.8206‒0.9995; MC100: 0.8318‒0.9911) evinced significant consistency between methodologies. Spearman correlation pinpointed a potent linear association between conventional devices and the PUSHKANG POCT analyzer, further underscored by a robust correlation coefficient (MS100: 0.713‒0.949; MC100: 0.593‒0.950). The PUSHKANG POCT was validated as a dependable tool for serum and whole blood biochemical and coagulation diagnostics. This emphasizes its prospective clinical efficacy, offering clinicians a swift diagnostic tool and heralding a new era of enhanced patient care outcomes.
Humans ; Point-of-Care Testing ; Critical Care ; Blood Coagulation Tests/methods* ; Male ; Blood Coagulation ; Female ; Middle Aged ; Reproducibility of Results ; Prothrombin Time ; Aged ; Adult ; Point-of-Care Systems

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 systematically summarize and analyze the project applications and funding in the field of Emergency and Critical Care Medicine by the Medical Science Department of the National Natural Science Foundation of China (NSFC) from 2010 to 2024, and to identify research hotspots and developmental trends, providing scientific references for the high-quality development of the Emergency and Critical Care Medicine in China. METHODS: Data on all project applications and funding in the field of Emergency and Critical Care Medicine (application code H16) from 2010 to 2024 were collected from the NSFC Grants System, including project application numbers, funding numbers and amounts, project categories, regional and affiliated institutions distributions. Keyword co-occurrence analysis was conducted using VOSviewer software to identify research hotspots, and results were presented using bar charts, pie charts, and Sankey diagrams. RESULTS: Over the past 15 years, the Emergency and Critical Care Medicine field of NSFC received 13 747 project applications and funded 1 781 projects, with a cumulative funding amount of 8.064 99 billion RMB. The annual number of applications increased from 296 in 2010 to 1 971 in 2024, representing an average annual growth rate of 40.42%. Similarly, the number of funded projects grew from 45 in 2010 to 175 in 2024, with an average annual growth rate of 20.63%, while annual funding rose from 20.01 million RMB in 2010 to 74.20 million RMB in 2024, reflecting an average annual growth rate of 19.34%. The majority of funded projects belonged to the General Program (774 projects), Young Scientists Fund (754 projects), and Regional Science Fund (163 projects), collectively accounting for 94.95% of total funded projects (1 691/1 781). Funding was concentrated in two primary research areas: Organ Dysfunction and Support (H1602, 751 projects) and Sepsis (H1601, 612 projects), together comprising 76.53% of total funded projects (1 363/1 781). The total number of funded projects (1 781 projects) in Emergency and Critical Care Medicine was fewer than the average across the subfields of Medical Science Department (4 181 projects). Shanghai (305 projects, 17.1%), Guangdong (222 projects, 12.5%), Jiangsu (154 projects, 8.6%), Zhejiang (149 projects, 8.4%), and Beijing (134 projects, 7.5%) ranked as the top five regions in terms of funded projects. Keyword co-occurrence analysis revealed that sepsis, organ injury, pulmonary injury and poisoning, and cardiopulmonary resuscitation were the main research hotspots in the field of Emergency and Critical Care Medicine over the past 15 years. CONCLUSION From 2010 to 2024, the NSFC funding for the field of Emergency and Critical Care Medicine has shown a significant upward trajectory, providing vital support for the rapid advancement of basic and applied research. This growth has played a crucial role in facilitating the high-quality development of Emergency and Critical Care Medicine in China.
China ; Critical Care/economics* ; Emergency Medicine/economics* ; Humans ; Foundations

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

OBJECTIVE: To evaluate the impact of critical care warning platform (CWP) on clinical outcomes of patients transferred from internal medical ward to intensive care unit (ICU) based on real-world data. METHODS: A retrospective cohort study was conducted. The patients transferred from internal medical ward to ICU of Zhongda Hospital, Southeast University, between January 2022 and October 2024, were enrolled. They were divided into critical care warning group and conventional treatment group based on whether they were connected to the CWP. The patients in the critical care warning group were connected to the CWP, which collected real-time vital signs and treatment data. The platform automatically calculated severity scores, generated individualized risk assessments, and triggered warning alerts, allowing clinicians to adjust treatment plans accordingly. The patients in the conventional treatment group were not connected to the CWP and relied on conventional clinical judgment and nursing measures for treatment management. Baseline characteristics [gender, age, body mass index (BMI), admission type, severity score of illness, underlying diseases, and disease type at ICU admission], primary clinical outcome (in-hospital mortality), and secondary clinical outcomes [ICU mortality, length of ICU stay, total length of hospital stay, and mechanical ventilation and continuous renal replacement therapy (CRRT) status] were collected. Multivariate Logistic regression was used to analyze the impact of CWP on in-hospital death, and subgroup analyses were performed based on different patient characteristics. RESULTS: A total of 1 281 patients were enrolled, with 768 in the critical care warning group and 513 in the conventional treatment group. Compared with the conventional treatment group, the proportion of patients in the critical care warning group with underlying diseases of diabetes and malignancy and transferred to ICU due to sepsis was lowered, however, there were no statistically significant differences in other baseline characteristics between the two groups. Regarding the primary clinical outcome, the in-hospital mortality in the critical care warning group was significantly lower than that in the conventional treatment group [17.6% (135/768) vs. 25.7% (132/513), P < 0.01]. For secondary clinical outcomes, compared with the conventional treatment group, the patients in the critical care warning group had significantly fewer days of mechanical ventilation within 28 days [days: 2 (1, 6) vs. 2 (1, 8), P < 0.05], significantly shorter length of ICU stay [days: 3 (2, 8) vs. 4 (2, 10), P < 0.01], and significantly lower ICU mortality [15.1% (116/768) vs. 21.4% (110/513), P < 0.01]. Multivariate Logistic regression analysis showed that, after adjusting for age and underlying diseases, the use of CWP was significantly associated with a reduction of in-hospital mortality among patients transferred from internal medical ward to ICU [odds ratio (OR) = 0.670, 95% confidence interval (95%CI) was 0.502-0.894, P = 0.006]. Further subgroup analysis revealed that, among patients transferred to ICU due to sepsis, the use of CWP significantly reduced in-hospital mortality (OR = 0.514, 95%CI was 0.367-0.722, P < 0.001). In patients aged ≥ 70 years old (OR = 0.587, 95%CI was 0.415-0.831, P = 0.003) and those with underlying diseases of malignancy (OR = 0.124, 95%CI was 0.046-0.330, P < 0.001), CWP also showed significant protective effects on in-hospital prognosis. CONCLUSION The use of CWP is significantly associated with a reduction in in-hospital mortality among patients transferred from internal medical ward to ICU, demonstrating its potential in assessing the deterioration of hospitalized patients.
Humans ; Intensive Care Units ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Critical Care ; Male ; Female ; Patient Transfer ; Middle Aged ; Aged ; Cohort Studies