OBJECTIVE

To compare the sedation practices of adult intubated patients with COVID-19-related Acute Respiratory Distress Syndrome (C-ARDS) and ARDS from other causes, and their impact on clinical outcomes in a tertiary hospital.

We performed a retrospective cohort on the sedation practices of adult intubated patients with C-ARDS and non-C-ARDS admitted to the intensive care unit of a tertiary hospital from January 2021 to December 2021. Electronic medical records were reviewed to obtain sedative use, sedative dosages, clinical outcomes, and complications.

Among the 150 included patients, 112 had C-ARDS, and 38 had non-C-ARDS. The C-ARDS group showed a significant difference with the non-C-ARDS group in terms of BMI (24.11 vs. 21.09 kg/m2, p < 0.001), use of higher PEEP (16 vs. 10, p < 0.001), and prone positioning (40.18% vs 2.63%, p < 0.01). In terms of sedation practice, C-ARDS patients targeted deeper RASS scores (p=0.038), with a significantly higher proportion receiving more than one sedative (82.14% vs. 18.42, p < 0.001) than non-C-ARDS patients. Sedation doses for midazolam (78 mg/d vs. 36 mg/d; p=0.01) and propofol (mean 2626±1312.97 mg/d vs. 1742±380.99 mg/d; p=0.007), were significantly higher among C-ARDS versus non-C-ARDS group. Duration of hospitalization (9 vs. 20 days; p < 0.001) and ventilator use (7 vs. 14.50 days; p < 0.001) were significantly shorter in the C-ARDS group, albeit with a high mortality (100% vs. 89.47%; p=0.004). Shock-requiring pressor was significantly associated with multiple sedation use [OR=15.11 (1.52-2032.89); p=0.017] and combination use of benzodiazepine and non-benzodiazepines [OR=11.51 (1.17-1541.91); p=0.034] in the C-ARDS but not the C-ARDS group.

Patients with C-ARDS had higher sedation requirements in terms of dosage and number of sedatives. The use of multiple sedatives was significantly associated with shock-requiring pressor. We recommend the development of a sedation protocol to guide sedation practices and monitoring of complications in the critically ill.

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

OBJECTIVE: To evaluate the effect of palliative care on drug use, medical service utilization and medical expenditure of patients with advanced cancer. METHODS: A cohort of patients including pal-liative care and standard care was constructed using the medical records of the patients in Peking University Cancer Hospital from 2018 to 2020, and coarsened exact matching was used to match the two groups of patients. The average monthly opioid consumption, hospitalization rate, intensive care unit (ICU) rate and operation rate, and the average monthly total cost were selected to evaluate drug use, medical service utilization and medical expenditure. Chi-square test and Wilcoxon signed rank test were used to compare the differences between the two groups before and after exposure and the change in the palliative care group. The net impact of palliative care on the patients was calculated using the difference-in-differences analysis. RESULTS: In this study, 180 patients in the palliative care group and 3 101 patients in the stan-dard care group were finally included in the matching, and the matching effect of the two groups was good (L₁ < 0.1). Before and after exposure, the average monthly opioid consumption in the palliative care group was significantly higher than that in the standard care group (Before exposure: 0.3 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; After exposure: 0.7 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; DDD refers to defined daily dose), palliative care significantly increased the average monthly opioid consumption in the patients (0.3 DDD/person-month, P < 0.01). The hospitalization rate (48.9% vs. 74.3%, P < 0.01) and operation rate (3.9% vs. 8.8%, P < 0.01) of the patients in palliative care group were significantly lower than those in standard care group, and the ICU rate became similar between the two groups (1.1% vs. 1.6%, P=0.634). Palliative care significantly reduced the patients ' hospitalization rate (-25.6%, P < 0.01), ICU rate (-4.9%, P < 0.01) and operation rate (-14.5%, P < 0.01). Before and after exposure, the average monthly total costs of pal-liative care group were slightly higher than those of standard care group (Before exposure: 20 092.3 yuan vs. 19 132.8 yuan, P=0.725; After exposure: 9 719.8 yuan vs. 8 818.8 yuan, P=0.165). Palliative care increased the average monthly total cost by 2 208.8 yuan, but it was not statistically significant (P=0.316). CONCLUSION Palliative care can increase the opioid consumption in advanced cancer patients, reduce the rates of hospitalization, ICU and surgery, but has no significant effect on medical expenditure.
Humans ; Palliative Care/economics* ; Neoplasms/drug therapy* ; Analgesics, Opioid/economics* ; Male ; Female ; Middle Aged ; Aged ; Hospitalization/economics* ; Intensive Care Units/statistics & numerical data* ; Health Expenditures/statistics & numerical data* ; Adult ; Drug Utilization/statistics & numerical data* ; Patient Acceptance of Health Care/statistics & numerical data*

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units

OBJECTIVES: Global epidemiological data indicate that 20% to 30% of intensive care unit (ICU) sepsis patients progress to deep vein thrombosis (DVT) due to coagulopathy, with an associated mortality rate of 25% to 40%. Existing prognostic tools have limitations. This study aims to develop and validate nomogram and machine learning models to predict in-hospital mortality in sepsis patients with DVT and assess their clinical applicability. METHODS: This multicenter retrospective study drew on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV; n=2 235), the eICU Collaborative Research Database (eICU-CRD; n=1 274), and the Patient Admission Dataset from the ICU of Third Xiangya Hospital, Central South University (CSU-XYS-ICU; n=107). MIMIC-IV was split into a training set (n=1 584) and internal validation set (n=651), with the remaining datasets used for external validation. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and Bayesian Information Criterion (BIC), and a nomogram model was constructed. An extreme gradient boosting (XGBoost) algorithm was used to build the machine learning model. Model performance was assessed by the concordance index (C-index), calibration curves, Brier score, decision curve analysis (DCA), and net reclassification improvement index (NRI). RESULTS: Five key predictors, age [odds ratio (OR)=1.02, 95% CI 1.01 to 1.03, P<0.001], minimum activated partial thromboplastin (APTT; OR=1.09, 95% CI 1.08 to 1.11, P<0.001), maximum APTT (OR=1.01, 95% CI 1.00 to 1.01, P<0.001), maximum lactate (OR=1.56, 95% CI 1.39 to 1.75, P<0.001), and maximum serum creatinine (OR=2.03, 95% CI 1.79 to 2.30, P<0.001), were included in the nomogram. The model showed robust performance in internal validation (C-index=0.845, 95% CI 0.811 to 0.879) and external validation (eICU-CRD: C-index=0.827, 95% CI 0.800 to 0.854; CSU-XYS-ICU: C-index=0.779, 95% CI 0.687 to 0.871). Calibration curves indicated good agreement between predicted and observed outcomes (Brier score<0.25), and DCA confirmed clinical benefit. The XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.982 (95% CI 0.969 to 0.985) in the training set, but performance declined in external validation (eICU-CRD, AUC=0.825, 95% CI 0.817 to 0.861; CSU-XYS-ICU, AUC=0.766, 95% CI 0.700 to 0.873), though it remained above clinical thresholds. Net reclassification improvement was slightly lower for XGBoost compared with the nomogram (NRI=0.58). CONCLUSIONS Both the nomogram and XGBoost models effectively predict in-hospital mortality in sepsis patients with DVT. However, the nomogram offers superior generalizability and clinical usability. Its visual scoring system provides a quantitative tool for identifying high-risk patients and implementing individualized interventions.
Humans ; Sepsis/complications* ; Machine Learning ; Nomograms ; Venous Thrombosis/complications* ; Retrospective Studies ; Hospital Mortality ; Male ; Female ; Middle Aged ; Aged ; Intensive Care Units ; Prognosis ; Bayes Theorem

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

OBJECTIVES: To evaluate the correlation of mean arterial oxygen tension (PaO₂) during the first 24 h following intensive care unit (ICU) admission with mortality in critically ill patients with acute kidney injury (AKI) and determine the optimal PaO₂ threshold for devising oxygen therapy strategies for these patients. METHODS: We collected the clinical data of ICU patients with AKI from the MIMIC-IV database. Based on the optimal first 24-h PaO₂ threshold determined by receiver operating characteristic (ROC) curve analysis and the Youden index maximization principle, we classified the patients into hyperoxia group (with PaO₂ ≥137.029 mmHg) and hypoxemia group (PaO₂<137.029 mm Hg). Multivariable logistic regression and propensity score matching were used to evaluate the correlation of first 24-h PaO₂ levels with in-hospital mortality of the patients. RESULTS: Among the 18 335 patients, 46.7% were in the hyperoxia group, who had an overall mortality rate of 16.9%. The optimal PaO₂ threshold (137.029 mm Hg) had a sensitivity of 78.3%, a specificity of 63.7%, and an AUC of 0.76 (95% CI: 0.74=0.78). Hyperoxia within the first 24 h after ICU admission was associated with a significantly lower in-hospital mortality (OR=0.78) and 90-day mortality (OR=0.77), particularly in stage 1 AKI patients. A non-linear relationship was identified between PaO₂ and mortality of the patients (P<0.001). Kaplan-Meier survival curves indicated a significantly increased 90-day survival rate in the patients in hyperoxia group (P<0.001), who also had shorter durations of mechanical ventilation, less vasopressor use, and shorter lengths of hospital/ICU stay. CONCLUSIONS Maintenance of a PaO₂ level ≥137.029 mmHg within 24 h after ICU admission may improve clinical outcomes of critically ill AKI patients, which underscores the importance of targeted oxygen delivery in ICU care.
Humans ; Acute Kidney Injury/blood* ; Male ; Female ; Middle Aged ; Intensive Care Units ; Aged ; Oxygen/blood* ; Hospital Mortality ; Partial Pressure ; Adult ; Databases, Factual