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

OBJECTIVE: To explore the relationship between serum chloride levels and prognosis in patients with hepatic coma in the intensive care unit (ICU). METHODS: We analyzed 545 patients with hepatic coma in the ICU from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Associations between serum chloride levels and 28-day and 1-year mortality rates were assessed using restricted cubic splines (RCSs), Kaplan-Meier (KM) curves, and Cox regression. Subgroup analyses, external validation, and mechanistic studies were also performed. RESULTS: A total of 545 patients were included in the study. RCS analysis revealed a U-shaped association between serum chloride levels and mortality in patients with hepatic coma. The KM curves indicated lower survival rates among patients with low chloride levels (< 103 mmol/L). Low chloride levels were independently linked to increased 28-day and 1-year all-cause mortality rates. In the multivariate models, the hazard ratio ( HR) for 28-day mortality in the low-chloride group was 1.424 (95% confidence interval [ CI]: 1.041-1.949), while the adjusted hazard ratio for 1-year mortality was 1.313 (95% CI: 1.026-1.679). Subgroup analyses and external validation supported these findings. Cytological experiments suggested that low chloride levels may activate the phosphorylation of the NF-κB signaling pathway, promote the expression of pro-inflammatory cytokines, and reduce neuronal cell viability. CONCLUSION Low serum chloride levels are independently associated with increased mortality in patients with hepatic coma.
Humans ; Male ; Female ; Middle Aged ; Intensive Care Units ; Prognosis ; Chlorides/blood* ; Aged ; Coma/blood* ; Adult

The establishment and development of neonatal intensive care unit(NICU)have significantly increased the survival rate of premature infants.However,the diagnosis,treatment,and surgeries performed in NICU may expose neonates to more noxious stimuli.As the neonatal period is crucial for brain development,these noxious stimuli may cause irreversible damage to the neonatal nervous system.Existing clinical studies have shown that repetitive noxious stimuli during the neonatal period can lead to poor brain development,persistent hyperalgesia,and various sequelae.However,the underlying mechanisms remain unclear,and effective treatment methods are lacking.This article summarizes the effects of repetitive noxious stimuli during the neonatal period on neural development and the complications,aiming to provide a basis for the neonatal analgesia management and the prevention and treatment of related sequelae.
Humans ; Infant, Newborn ; Brain/growth & development* ; Infant, Premature ; Intensive Care Units, Neonatal ; Hyperalgesia ; Pain

Objective To investigate the effect of health failure mode and effect analysis(HFMEA)in optimizing the management process of postoperative diabetes insipidus in children undergoing neurosurgery.Methods Based on HFMEA,a management flowchart for postoperative diabetes insipidus in children undergoing neurosurgery was created.Brainstorming was adopted to identify failure modes in the workflow,analyze risk factors,and develop improvement measures,thereby refining the management flowchart.The amelioration and prognosis of diabetes insipidus in these children before(October 2022 to November 2023)and after(January 2024 to February 2025)implementation of the management flowchart were compared.Results The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery alleviated the symptoms of diabetes insipidus regarding the number of diabetes insipidus in the pediatric intensive care unit(P=0.006),the average daily urine output in the pediatric intensive care unit(P=0.001),the proportion of electrolyte abnormalities at discharge/transfer(P=0.037),the duration of mechanical ventilation(P=0.007),and the length of stay in the intensive care unit(P=0.001).Conclusion The HFMEA-based management process for postoperative diabetes insipidus in children undergoing neurosurgery is beneficial to the optimization of the management process,the alleviation of postoperative diabetes insipidus,and the improvement of prognosis in these children.
Humans ; Diabetes Insipidus/etiology* ; Neurosurgical Procedures/adverse effects* ; Child ; Postoperative Complications/therapy* ; Healthcare Failure Mode and Effect Analysis ; Intensive Care Units, Pediatric ; Risk Factors

Human ; Intensive Care Units ; Critical Care ; Philippines

Humans ; Milk, Human ; Milk Banks/standards* ; Intensive Care Units, Neonatal ; Infant, Newborn ; China ; Consensus

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

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

PURPOSE: To compare the efficacy of continuous renal replacement therapy (CRRT) using either oXiris or conventional hemopurification filters in the treatment of intra-abdominal sepsis. METHODS: We conducted a retrospective analysis of septic patients with severe intra-abdominal infections admitted to our hospital from October 2019 to August 2023. Patients who meet the criteria for intra-abdominal sepsis based on medical history, symptoms, physical examination, and laboratory/imaging findings were included. EXCLUSION CRITERIA: pregnancy, terminal malignancy, prior CRRT before intensive care unit admission, pre-existing liver or renal failure. Heart rate (HR), mean arterial pressure, oxygenation index, lactic acid level (Lac), platelet count (PLT), neutrophil percentage, serum levels of procalcitonin, C-reactive protein, interleukin (IL)-6, norepinephrine dosage, acute physiology and chronic health evaluation II (APACHE II), and sequential organ failure assessment (SOFA) scores before and after 24 h and 72 h of treatment, as well as ventilator use time, hemopurification treatment time, intensive care unit and hospital lengths of stay, and 14-day and 28-day mortality were compared between patients receiving CRRT using either oXiris or conventional hemofiltration. Statistical analysis was performed using SPSS Statistics 26.0 software, including the construction of predictive models via logistic regression equations and repeated measures ANOVA. RESULTS: Baseline values including time to antibiotic administration, time to source control, and time to initiation of CRRT were similar between the 2 groups (all p>0.05). Patients receiving conventional CRRT exhibited significant changes in HR but of none of the other indexes at the 24 h and 72 h time points (p=0.041, p=0.026, respectively). The oXiris group showed significant improvements in HR, Lac, IL-6, and APACHE II score 24 h after treatment (p<0.05); after 72 h, all indexes were improved except PLT (all p<0.05). Intergroup comparison disclosed significant differences in HR, Lac, norepinephrine dose, APACHE II, SOFA, neutrophil percentage, and IL-6 after 24 h of treatment (p<0.05). Mean arterial pressure, serum levels of procalcitonin, C-reactive protein, SOFA score, and norepinephrine dosage were similar between the 2 groups at 24 h (p>0.05). Except for HR, oxygenation index, and PLT, post-treatment change rates of △ (%) were significantly greater in the oXiris group (p < 0.05). Duration of ventilator use, CRRT time, and intensive care unit and hospital lengths of stay were similar between the 2 groups (p>0.05). The 14-day mortality rates of the 2 groups were similar (p=0.091). After excluding patients whose CRRT was interrupted, 28-day mortality was significantly lower in the oXiris than in the conventional group (25.0% vs. 54.2%; p=0.050). The 28-day mortality rate increased by 9.6% for each additional hour required for source control and by 21.3% for each 1-point increase in APACHE II score. CONCLUSIONS In severe abdominal infections, the oXiris filter may have advantages over conventional CRRT, which may provide an alternative to clinical treatment. Meanwhile, early active infection source control may reduce the case mortality rate of patients with severe abdominal infections.
Humans ; Retrospective Studies ; Female ; Male ; Middle Aged ; Sepsis/mortality* ; Aged ; Adult ; Continuous Renal Replacement Therapy/methods* ; Intraabdominal Infections/mortality* ; APACHE ; Organ Dysfunction Scores ; Intensive Care Units ; Treatment Outcome

The intelligent oxygen management system is a software designed with various algorithms to automatically titrate inhaled oxygen concentration according to specific patterns. This system can be integrated into various ventilator devices and used during assisted ventilation processes, aiming to maintain the patient's blood oxygen saturation within a target range. This paper employs a scoping review methodology, focusing on research related to intelligent oxygen management systems in neonatal intensive care units. It reviews the fundamental principles, application platforms, and clinical outcomes of these systems, providing a theoretical basis for clinical implementation.
Humans ; Intensive Care Units, Neonatal ; Infant, Newborn ; Oxygen/administration & dosage* ; Oxygen Inhalation Therapy/methods* ; Respiration, Artificial