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

Human ; Intensive Care Units ; Critical Care ; Philippines

Objective: This study described clinicodemographic profiles of children with prolonged length of stay admitted at the regular service ward of Philippine Children’s Medical Center (January 2023 – December 2023).

Materials and Methods: A descriptive and retrospective clinical profiling of patients with prolonged length of stay at PCMC was done (January 2023 to December 2023). Included were patients aged 1-18 years old admitted at the regular service ward and discussed during the overstaying audit. Excluded were those admitted at Neonatal Intensive Care Unit and Newborn Service Ward. Patients with prolonged length of stay admitted under the Hematology and Oncology Service ward and those with incomplete chart information were also excluded. Data collected were age, sex, area of residence, presence of comorbidities, diagnosis, reason for prolonged length of stay, and clinical outcomes.

Results: 153 patients were included in this study. Majority of the patients with prolonged length of stay were adolescents (43.79%). Most of the patients identified in this study were female (78%). Those who overstayed were predominantly from Quezon City (27.45%). Comorbidities were present in 93.46% of patients. Neurologic conditions accounted for majority of the admissions (35.29%). Most common reason for overstaying of patients was due to a medical reason (91.5%). Furthermore, 93.46% of patients were discharged while 6.54% died.

Conclusion: This retrospective study presented the clinical profile of patients with prolonged length of stay who were mostly adolescents, with female predominance. Neurologic disease was the most common diagnosis identified among patients. Those patients who have prolonged length of stay were generally because of medical problems mostly due to the complexity and chronicity of their disease. Strengthening of ongoing service delivery network and prompt subspecialty referrals and involvement may be recommended to address discharge delays and maximize hospital resources.

Human ; Male ; Female ; Infant: 1-23 Months ; Child Preschool: 2-5 Yrs Old ; Child: 6-12 Yrs Old ; Adolescent: 13-18 Yrs Old ; Length Of Stay ; Critical Care ; Chart

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

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

OBJECTIVES: Prolonged invasive mechanical ventilation is associated with increased risks of severe complications such as retinopathy of prematurity and bronchopulmonary dysplasia. Although neonatal intensive care unit (NICU) follow the principle of early extubation, extubation failure rates remain high, and reintubation may further increase the risk of adverse outcomes. This study aims to identify risk factors and short-term prognosis associated with first extubation failure in neonates, to provide evidence for effective clinical intervention strategies. METHODS: Clinical data of neonates who received invasive ventilation in the NICU of Children's Hospital of Nanjing Medical University from January 1, 2019, to December 31, 2021, were retrospectively collected. Neonates were divided into a successful extubation group and a failed extubation group based on whether reintubation occurred within 72 hours after the first extubation. Risk factors and short-term outcomes related to extubation failure were analyzed. RESULTS: A total of 337 infants were included, with 218 males (64.69%). Initial extubation failed in 34 (10.09%) infants. Compared with the successful extubation group, the failed extubation group had significantly lower gestational age [(31.37±5.14) weeks vs (34.44±4.07) weeks], age [2.5 (1.00, 8.25) h vs 5 (1.00, 22.00) h], birth weight [(1 818.97±1128.80) g vs (2 432.18±928.94) g], 1-minute Apgar score (6.91±1.90 vs 7.68±2.03), and the proportion of using mask oxygenation after extubation (21% vs 46%) (all P<0.05). Conversely, compared with the successful extubation group, the failed extubation group had significantly higher rates of vaginal delivery (59% vs 32%), caffeine use during mechanical ventilation (71% vs 38%), dexamethasone use at extubation (44% vs 17%), the highest positive end-expiratory pressure level within 72 hours post-extubation [6(5.00, 6.00) cmH₂O vs 5 (0.00, 6.00) cmH₂O] (1 cmH₂O=0.098 kPa), the highest FiO₂ within 72 hours post-extubation [(34.35±5.95)% vs (30.22±3.58)%], and duration of noninvasive intermittent positive pressure ventilation after extubation [0.5 (0.00, 42.00) hours vs 0 (0, 0) hours] (all P<0.05). Multivariate analysis identified gestational age <28 weeks (OR=5.570, 95% CI 1.866 to 16.430), age at NICU admission (OR=0.959, 95% CI 0.918 to 0.989), and a maximum FiO₂≥35% within 72 hours post-extubation (OR=4.541, 95% CI 1.849 to 10.980) as independent risk factors for extubation failure (all P<0.05). Additionally, the failed extubation group exhibited significantly higher incidences of necrotizing enterocolitis grade II or above, moderate-to-severe bronchopulmonary dysplasia, severe bronchopulmonary dysplasia, retinopathy of prematurity, treatment abandonment due to poor prognosis, and discharge on home oxygen therapy (all P<0.05). Total hospital length of stay and total hospitalization costs were also significantly increased in the failed extubation group (all P<0.05). CONCLUSIONS Gestational age <28 weeks, younger age at NICU admission, and FiO₂≥35% after extubation are high-risk factors for first extubation failure in neonates. Extubation failure markedly increases the risk of adverse clinical outcomes.
Humans ; Infant, Newborn ; Male ; Female ; Airway Extubation/adverse effects* ; Risk Factors ; Retrospective Studies ; Respiration, Artificial/methods* ; Intensive Care Units, Neonatal ; Prognosis ; Gestational Age ; Bronchopulmonary Dysplasia ; Infant, Premature ; Treatment Failure ; Intubation, Intratracheal

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