Intensive Care Unit Patients Research Articles

Nutritional support in critical care patients: Challenges, strategies, and recent advances

Nutritional support is a critical component of managing patients in intensive care units (ICUs). Critical illness triggers a hypermetabolic state, leading to significant nutritional demands and muscle wasting. Proper nutritional interventions can positively impact clinical outcomes, reduce the duration of mechanical ventilation, and improve overall recovery. However, delivering adequate nutrition to critically ill patients present several challenges, including the patient's unstable condition, varying metabolic needs, gastrointestinal dysfunction, and difficulties in achieving nutritional goals. Recent advances in understanding the nutritional requirements of ICU patients, the role of early enteral nutrition, and the development of specialized formulas have led to improved patient care. Strategies such as personalized nutrition, immunonutrition, and monitoring tools like indirect calorimetry have become essential components of ICU nutrition management. Additionally, managing critically ill patients with comorbidities, such as sepsis or multi-organ failure, requires tailored approaches to prevent malnutrition and overfeeding.This review highlights the key challenges associated with nutritional support in critical care, current strategies employed to optimize nutrition, and the recent advances in the field. Evidence-based practices with individualized care, nutritional support can enhance patient recovery, reduce ICU stay, and lower morbidity and mortality rates.

High level of systemic immune inflammation index elevates delirium risk among patients in intensive care unit

Evidence regarding the effect of systemic immune-inflammation index on delirium occurrence is limited. This study aimed to investigate the association between SII and delirium in intensive care unit (ICU) patients. Methods: Information was extracted from Medical Information Mart for Intensive Care-IV. Four logistic regression model was established and incorporated with subgroup analysis and restricted cubic spline (RCS). The cutoff value of SII was acquired from receiver operator characteristic curve (ROC), and propensity score matching (PSM) was utilized to attenuate the confounding effect. Survival analysis was utilized to evaluate the relationship between SII and 30-day or 90-day all-cause mortality. Results: Among the 7,518 participants, 1,685 cases of delirium occurred. Individuals in the highest quartile of SII exhibited a heightened delirium risk, with a significant multivariable-adjusted odds ratio (OR) of 3.12(2.24,4.33). Tendency analysis, subgroup analysis and PSM together confirmed the positive relationship. Results of Cox regression displayed the risk of both 30-day and 90-day mortality increased about 50% in the higher-SII group. Conclusion: Higher levels of SII is positively associated with the occurrence of delirium and increased all-cause mortality risk.

Trends in Bacterial Distribution and Antimicrobial Resistance in Intensive Care Units of Hubei Province, China: A Four-year Surveillance Study (2020-2023).

To investigate the distribution characteristics of common bacteria and changes in antimicrobial resistance in intensive care unit (ICU) patients in 58 hospitals in Hubei Province from 2020-2023. The antimicrobial agents for antimicrobial susceptibility tests was selected based on the 2022 China Antimicrobial Resistance surveillance system (CARSS) technical scheme, and the specific experimental operation was based on the requirements of the CLSI M02 and M07 documents. The commercial instruments were used following the manufacturer's instructions. The interpretation of antimicrobial susceptibility test results was based on the 2023 CLSI M100 standard. There were 15 585, 19 258, 23 423 and 22 395 clinical isolates in the ICU from 2020 to 2023, respectively. Among them, gram-positive bacteria accounted for 20.5% (3190/15 585), 21.2% (4089/19 258), 21.6% (5067/23 423) and 21.6% (4 831/22 395), respectively. Gram-negative bacteria accounted for 79.5% (12 395/15 585), 78.8% (15 169/19 258), 78.4% (18 356/23 423) and 78.4% (17 564/22 395) of the bacteria, respectively. The top 5 isolates of gram-positive bacteria were Staphylococcus aureus, Enterococcus faecium, Streptococcus pneumoniae, Enterococcus faecalis, Staphylococcus epidermidis and gram-negative bacteria were Klebsiella pneumoniae, Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa and Stenotrophomonas maltophil, respectively, but the proportions and rankings of the isolates in different years slightly differed. The detection rate of methicillin-resistant S. aureus (MRSA) decreased from 44.4% in 2020 to 36% in 2023, and that of methicillin-resistant coagulase-negative Staphylococcus (MRCNS) decreased from 79.8% in 2020 to 73.8% in 2022 and increased to 78.4% in 2023. The detection rates of both vancomycin-resistant E. faecium and E. faecalis were lower than 1%. The detection rate of carbapenem-resistant P. aeruginosa (CRPA) decreased from 25% in 2020 to 19.7% in 2022 and increased slightly to 20.6% in 2023. The detection rate of carbapenem-resistant A. baumannii (CRAB) decreased from 81.9% in 2020 to 79.7% in 2022 and increased to 82.9% in 2023. The detection rate of third-generation cephalosporin-resistant E. coli decreased from 59.8% in 2020 to 53.1% in 2022 and increased to 52.5% in 2023. The detection rate of fluoroquinolone-resistant E. coli decreased from 62.7% in 2020 to 50.2% in 2022 and increased slightly to 51.0% in 2023. The detection rate of carbapenem-resistant E. coli (CRECO) decreased from 3.3% in 2020 to 1.8% in 2022 and slightly increased to 2.1% in 2023. The detection rate of third-generation cephalosporin-resistant K. pneumoniae decreased from 34.3% in 2020 to 26.3% in 2022 and then increased to 32.4% in 2023. The detection rate of carbapenem-resistant K. pneumoniae (CRKPN) increased from 17.9% to 19.4% in 2020, decreased to 13.2% in 2022, and rose sharply to 20.4% in 2023. MRSA showed a continuous downwards trend from 2020 to 2023, while the detection rates of MRCNS and most multidrug-resistant gram-negative bacteria continuously decreased from 2020 to 2022 but tended to increase in 2023. Therefore, it is still necessary to strengthen the monitoring of bacterial resistance and rational application of antibiotics and actively and effectively control nosocomial infections.

Vitamin D supplementation may be beneficial in improving the prognosis of patients with sepsis-associated acute kidney injury in the intensive care unit: a retrospective study

BackgroundVitamin D, an essential fat-soluble micronutrient, exerts diverse physiological effects including the regulation of calcium ion homeostasis, modulation of immune response, and enhancement of resistance against infectious pathogens. Empirical investigations have elucidated an association between inadequate levels of vitamin D and adverse clinical outcomes in critically ill cohorts, with a noteworthy prevalence of vitamin D deficiency observed among patients afflicted with acute kidney injury (AKI). In the context of this retrospective inquiry, our aim was to assess the potential correlation between vitamin D supplementation administered during admission to the intensive care unit (ICU) and the improvement of outcomes specifically in cases of severe AKI.MethodsThis study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV), a repository of ICU patient records from Beth Israel Deaconess Medical Center (BIDMC) in the United States. We focused on patients diagnosed with epsis-associated acute kidney injury (SA-AKI), dividing them into those who received vitamin D supplementation during their ICU admission and those who did not. Our primary analysis evaluated in-hospital mortality using various statistical methods, such as Kaplan–Meier survival curves, Cox proportional hazards regression models, and subgroup analyses. To enhance the robustness of our findings, we used propensity score matching (PSM) to reduce potential biases. Secondary outcomes included 28-day, 90-day mortality rates and norepinephrine-free days at 28 days.ResultsIn this investigation, a cohort of 11,896 individuals diagnosed with SA-AKI was studied. Among them, 2,724 patients received vitamin D supplementation (the vitamin D group) while 9,172 did not (the no-vitamin D group). Kaplan–Meier survival analysis indicated a significant difference in survival probabilities between the two cohorts. Upon adjusting for potential confounders using Cox regression modeling, a notably decreased risk of hospitalization and ICU mortality was observed in the vitamin D group compared to the no-vitamin D group, with an adjusted risk ratio for in-hospital mortality of 0.56 (95% CI: 0.5–0.63). These findings were consistent following PSM and subsequent adjustments for propensity score, pairwise algorithm (PA), and overlapping weights (OW) analyses, yielding hazard ratios ranging from 0.53 to 0.59, all with p-values <0.001. Notably, E-value analyses underscored the robustness of these results against potential unmeasured confounders.ConclusionThis study suggests that vitamin D supplementation may be associated with a reduced in-hospital mortality rate among SA-AKI patients in the ICU. Furthermore, the 28-day, 90-day mortality rates and norepinephrine days were significantly reduced in the group receiving vitamin D supplementation.

Use of Sedative and Analgesic Agents in Pediatric Intensive Care Unit Patients: Pediatric Health Information System Database

Use of Sedative and Analgesic Agents in Pediatric Intensive Care Unit Patients: Pediatric Health Information System Database

Sedation Strategies in Pediatric Intensive Care Unit Patients: Challenges in Management

Sedation Strategies in Pediatric Intensive Care Unit Patients: Challenges in Management

Interleukin-10: Genetic and biochemical prediction of sepsis-induced acute kidney injury in critically ill patients in intensive care unit: A cross-sectional study.

Sepsis is a potentially life-threatening condition that eventually causes multiorgan dysfunction in critically ill patients. Acute kidney injury (AKI) is a severe life-threatening complication of sepsis, a condition termed sepsis-induced AKI (S-AKI), with poor clinical outcomes and high mortality rates. Inflammatory and immunological responses are important variables in S-AKI. This study aimed to examine the relationship of rs1518111 polymorphism in the interleukin-10 ( IL-10 ) gene and serum/urine IL-10 levels with sepsis-induced AKI in critically ill patients in the intensive care unit (ICU). In this cross-sectional study, 310 critically ill adult patients were recruited, of whom, 197 developed S-AKI. Real-time polymerase chain reaction was performed to detect the rs1518111 polymorphism. Circulating blood and urine IL-10 levels of IL-10 were measured. For rs1518111 SNP, the presence of at least one T allele increased the risk of occurrence of S-AKI (odds ratio [OR]: 1.34, 95% CI: 1.07-3.17; p < 0.001), regardless of the type of infection and severity of sepsis. Blood and urine IL-10 levels were an excellent prediction of S-AKI (area under the receiver operating characteristic curve [AUC]: 0.881 and 0.953 and sensitivity: 90.2% and 97.6% at cutoff of 133.5 and 5.67 pg/mL, respectively). Regression analysis showed that white blood cell count and increased blood and urine IL-10 levels, in addition to the presence of TT genotype, are independent risk factors for S-AKI. rs1518111 polymorphism in the IL-10 gene is a risk factor for sepsis-induced AKI in the ICU. Serum/urine IL-10 levels may be used as predictors of S-AKI in critically ill patients with sepsis, thereby improving early management.

Distorted Memory among the Patients in Intensive Care Unit and its Related Factors.

Distorted Memory among the Patients in Intensive Care Unit and its Related Factors.

Characteristics and outcomes of adults with acute brain injuries admitted to intensive care units in Australia and New Zealand from 2013 to 2022

BackgroundThe characteristics and outcomes of patients with acute brain injuries admitted to the intensive care unit (ICU) in Australia and New Zealand (ANZ) are insufficiently described. ObjectiveThis study aimed to describe the epidemiology of acute brain injury in ICU patients in ANZ. MethodsA binational retrospective cohort study was conducted using the ANZ Intensive Care Society Adult Patient Database. Adult unplanned admissions from 2013 to 2022 were eligible unless the presence of acute brain injury could not be determined or the admission was for end-of-life care. In cases where a patient had multiple admissions, only the first was included. The population was divided into two cohorts: acute brain injury diagnoses and other diagnoses. The primary outcome was in-hospital mortality. Secondary outcomes included 90- and 180-day mortality, ICU and hospital lengths of stay, duration of invasive ventilation, and the proportion discharged home. ResultsAcute brain injuries accounted for 92,948 of 684,981 unplanned ICU admissions (14%). Hypoxic ischaemic encephalopathy, traumatic brain injury, and seizures were the most common diagnoses. A total of 24,568 of 92,948 (26%) and 62,603 of 592,033 (10%) patients with acute brain injuries and other diagnoses, respectively, died in hospital. Among the brain-injured patients, the highest hospital mortality was in hypoxic ischaemic encephalopathy (53%), intracerebral haemorrhage (36%), subarachnoid haemorrhage (22%), and ischaemic stroke (22%); the lowest mortality was in traumatic brain injury (14%), central nervous system infection (10%), and seizures (4%). Acute brain injury patients were more likely to receive invasive mechanical ventilation, had longer ICU and hospital lengths of stay, had higher 90- and 180-day mortality, and were more likely to be discharged to chronic care than other patients. ConclusionsAcute brain injuries accounted for a disproportionally high number of in-hospital deaths occurring in our cohort of adults who received unplanned ICU care; however, the mortality rates varied, and patients with central nervous system infections and seizures had similar or lower mortality compared to patients without brain injury.

Early identification of non-occlusive acute mesenteric ischemia

Acute mesenteric ischemia (AMI) is insidious in the early stage of the disease, and once ischemia reaches the late stage, irreversible intestinal necrosis and even multiple system organ failure occur, ultimately leading to death. Early diagnosis and intervention are the key to improving the outcome of AMI. Intensive care unit (ICU) patients mostly present with non-occlusive mesenteric ischemia (NOMI) due to hypoperfusion. This article reviews the high risk factors, clinical manifestations, biomarkers, and imaging examinations of AMI in order to provide ideas for clinicians in the early identification of this disease.

Comparative analysis of deep learning models for various nonalcoholic fatty liver disease datasets

Fatty liver disease is caused by increased liver buildup or weight above 5-10%. This disorder is widespread in people with diabetes, overweight persons, and metabolic syndrome patients. Clinical decision support systems can improve liver failure diagnosis and prediction to reduce this situation. Many liver failure models have drawbacks, and liver failure prediction is still a problem. This work uses four large open-access critical care patient datasets to create and verify liver failure risk prediction models. This study aims to construct a clinically applicable diagnostic and predictive model that evaluates the probability or risk of liver failure in intensive care unit (ICU) patients using extreme gradient boosting (XGBoost), artificial neural networks (ANN), multi-layer perceptron (MLP), Modular Neural Network (MNN), and generalized feed forward (GFF). We evaluated performance metrics using these models: accuracy, sensitivity, specificity, and predictive accuracy.

Assistive Communication Device Used During Pediatric Noninvasive Ventilation.

Noninvasive ventilation therapy is a common approach in acute respiratory failure as an alternative to invasive mechanical ventilation. Assistive communication strategies are necessary to overcome communication impairments and anxiety caused by oronasal obstruction in pediatric intensive care unit patients undergoing noninvasive ventilation. A 7-year-old girl was admitted with a history of pulmonary arterial hypertension and recurrent respiratory failure requiring bilevel positive airway pressure intervention. The patient experienced initial lack of bilevel positive airway pressure mask compliance due to oronasal mask discomfort. The trial of a novel communication device (SPEAX, Ataia Medical) involving a microphone that attaches to the patient's bilevel positive airway pressure mask for enhanced communication. Use of the assistive communication device was associated with increased intelligibility to familiar listeners, mask compliance, enhanced mobility, and reduced patient anxiety levels throughout hospitalization and in later episodes of readmission. The implementation of assistive communication devices can alleviate communication barriers in pediatric bilevel positive airway pressure users, contributing to increased compliance and diminished anxiety for patients and caregivers. Providing a pathway for effective communication may reduce sedation use, minimizing risks of sedation-related delirium and improving overall quality of life in these patients.

Deep-Learning Model for Mortality Prediction of ICU Patients with Paralytic Ileus

Paralytic Ileus (PI) patients in the Intensive Care Unit (ICU) face a significant risk of death. Current predictive models for PI are often complex and rely on many variables, resulting in unreliable outcomes for such a serious health condition. Predicting mortality in ICU patients with PI is particularly challenging due to the vast amount of data and numerous features involved. To address this issue, a deep-learning predictive framework was developed using the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset, which includes data from 1017 ICU patients with PI. By employing SHAP (SHapley Additive exPlanations) analysis, we were able to narrow down the features to six distinct clinical lab items. The proposed framework, called DLMP (Deep Learning Model for Mortality Prediction of ICU Patients with PI), utilizes these six unique clinical lab items: Anion gap, Platelet, PTT, BUN, Total Bilirubin, and Bicarbonate, along with one demographic variable as inputs to a neural network consisting of only two neuron layers. DLMP achieved an outstanding prediction performance with an AUC score of 0.887, outperforming existing predictive models for ICU patients with PI. The DLMP framework significantly enhances the prediction of mortality for PI patients compared to traditional process mining and machine learning models. This model holds considerable potential for prognosis, enabling families to be better informed about the severity of a patient’s condition and to prepare accordingly. Furthermore, the model is valuable for research purposes and clinical trials.

Rol del personal de psicología y enfermería en el cuidado humanizado al paciente de la Unidad de Cuidados Intensivos

The comprehensive care of critical patient in Intensive Care Units places the dignity of the person at the center, not only of the patient in critical condition, but also of their families, highlighting the comprehensive care of the impact that the permanence in these units generates, care that requires a multidisciplinary approach, in which health personnel are involved, doctors and nurses, as well as experts in psychological treatments, among other professionals who provide quality to the passage through the ICU. Within this humanizing dynamic of ICUs, a topic of study is that has the general objective of analyzing the content and scope of humanized health care of patients in ICUs, with special reference to the psychological and emotional needs of patients and their families during their stay in the ICU and in the subsequent period. A detailed search and study of the literature concerning the objective of the research was carried out. The main conclusion reached after the analysis is the identification of the most relevant aspects of the role of psychology and nursing personnel in the care of the emotional and psychological needs of critically ill patients.

Clinical trial to verify the safety of an automatic electric airway suction system: a multicenter prospective randomized study

BackgroundAcute lung injury and respiratory failure in patients in the intensive care unit (ICU) present challenges owing to varied factors. Manual suctioning, which is the current standard, burdens healthcare professionals and poses risks. The Lmeca.A1000 automatic suction device offers an innovative solution to this problem. This study aimed to evaluate the safety of this device.MethodsThis prospective, multicenter, randomized trial compared automatic and manual suction in ICU patients. Mucosal injury and pneumonia were assessed using bronchoscopy and monitoring. Mechanical stability and adverse events were also evaluated.ResultsIn total, 117 critically ill adults were screened: 56 in the experimental group and 53 in the control group completing the study. No significant difference in mucosal injury was found at 3 or 7 days after treatment (p = 0.871, and 0.750). The incidence of VAP over 2 weeks was 5.36% in the experimental group and 1.89% in the control group, not significantly different (p = 0.582). Adverse events occurred in 14.3 and 13.2%, respectively, with no significant difference (p = 0.870). The incidence of clinical trial device malfunction within 3 days of application was 0.03% and within 2 weeks of follow-up was 0.02%.ConclusionsThis study evidenced the non-inferiority of the automatic suction device to the manual method in terms of safety. The adoption of an automated system could alleviate the workload of healthcare professionals while maintaining effective airway management.

Nutritional status as a predictor of 30-day mortality among intensive care unit patients in sub-Saharan Africa: a prospective cohort study

BackgroundMalnutrition is a major public health issue, causing significant mortality and morbidity, especially in developing nations. However, the magnitude and its impact on clinical outcomes in Intensive Care Unit (ICU) patients need to be investigated better in Sub-Saharan Africa, and this study was intended to address these issues.ObjectiveThe objective of this study was to assess the effect of malnutrition on clinical outcomes among ICU patients.MethodA prospective cohort study was conducted among 436 ICU patients, 218 of whom were malnourished. After obtaining ethical approval, malnourished and well-nourished patients were followed for thirty days to examine the effects of nutritional status on clinical outcomes and its determinants. At admission, nutritional screening and evaluation were performed with Subjective Global Assessment (SGA) and Malnutrition Universal Screening Tool (MUST), and during the next thirty days, it was evaluated every seven days with SGA, MUST, and modified Nutrition Risk in Critically Ill (NUTRIC).ResultThis study demonstrated that the incidence of 30-day mortality was 47.9%( 95% CI: 43.2 to 52.6). The hazards of death in patients with malnutrition increased by 40% as compared to well-nourished patients (aHR = 1.4, 95% CI: 1.33 to 2.56), and patients with diabetes mellitus had 4 times the hazards of death (aHR = 4.2, 95% CI: 2.12 to 8.28).ConclusionMalnutrition is prevalent in adult ICU patients and has been linked to a higher 30-day mortality and a more extended ICU stay. MUST, SGA, and NUTRIC, well-validated, practical, cost-effective, and non-invasive techniques for routinely evaluating nutritional status in critically ill patients, were good predictors of mortality.

Noise-sensitivity during sleep in non-sedated mechanically ventilated patients is associated with weaning duration: a polysomnographic study.

Poor sleep is a major concern in intensive care units (ICUs), particularly in mechanically ventilated patients, because it is associated with longer duration of the weaning phase and higher mortality. High noise levels in ICUs are frequently reported by patients as one of the most disturbing sleep-disrupting factors but would be responsible for less than 20% of arousals. This suggests major inter-individual variability in noise sensitivity. Our objectives were to define and assess noise sensitivity in mechanically ventilated patients and to explore its association with sleep duration, sleep quality and weaning duration. We retrospectively re-analyzed polysomnographies (PSGs) recorded in 29 non-sedated patients, mechanically ventilated for at least 24h and difficult to wean (i.e. ≥ 1 spontaneous breathing trial failure). All the arousals were identified on all the PSGs. We calculated mean noise level and identified all noise peaks (an abrupt increase of noise intensity of more than 10 decibels (dBA)) preceding each arousal. Each 21-second period preceding each arousal was divided into seven 3-second bins. We built a pre-event time histogram for each PSG by counting the total number of noise peaks in each bin. If the total number of noise peaks in one bin exceeded the average of the seven bins plus 2 SD, we considered that there was a significant relationship between the number of noise peaks in this bin and the arousal. The patient was then considered as noise-sensitive. Presence of atypical sleep, proportion of sleep stages, and weaning duration were assessed. Nineteen out of 29 patients (66%) were noise-sensitive. Duration of weaning from ventilator was significantly longer in noise-insensitive patients (median [interquartile range] 2 [1-2] versus 5 [2-8] days; p < 0.01). Proportion of N1, N2, N3 sleep stages and rapid eye movement sleep were similar in noise-sensitive and noise-insensitive patients. In contrast, the proportion of patients displaying atypical sleep was higher in noise-insensitive patients. Our results report for the first time that most ICU patients were noise-sensitive. Lower noise sensitivity was associated with atypical sleep and could reflect lower brain reactivity to environment.

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general population, and delays in diagnosis can lead to irreversible neurological damage. Early diagnosis of stroke is critical to protect brain tissue and treat neurological defects. Therefore, we developed a machine learning model to diagnose stroke in patients with acute neurological manifestations in the ICU. We retrospectively collected data on patients’ underlying diseases, blood coagulation tests, procedures, and medications before neurological symptom onset from 206 patients at the Chungbuk National University Hospital ICU (July 2020–July 2022) and 45 patients at Chungnam National University Hospital between (July 2020–March 2023). Using the Categorical Boosting (CatBoost) algorithm with Bayesian optimization for hyperparameter selection and k-fold cross-validation to mitigate overfitting, we analyzed model-feature relationships with SHapley Additive exPlanations (SHAP) values. Internal model validation yielded an average accuracy of 0.7560, sensitivity of 0.8959, specificity of 0.7000, and area under the receiver operating characteristic curve (AUROC) of 0.8201. External validation yielded an accuracy of 0.7778, sensitivity of 0.7500, specificity of 0.7931, and an AUROC of 0.7328. These results demonstrated the model’s effectiveness in diagnosing stroke in non-neurological ICU patients with acute neurological manifestations using their electronic health records, making it valuable for the early detection of stroke in ICU patients.

Predicting AKI in critical patients: An interpretable model based on albumin and fluid balance.

Acute kidney injury (AKI) is a clinically complex syndrome with a high incidence and mortality rate in the intensive care unit (ICU). Early identification of high-risk patients and timely intervention are crucial. A local database was used to construct a model that predicts the incidence of AKI in ICU patients within 48 hours. We conducted a study involving 9,628 critically ill patients at Zhejiang Provincial People's Hospital and divided the cohort into derivation and validation groups. We collected and analyzed demographic data, vital signs, laboratory tests, medications, clinical interventions, and other information for all patients, resulting in a total of 232 variables. Six different machine learning algorithms were employed to construct models, and the optimal model was selected and validated. A total of 2,441 patients were included, of whom 1,138 (46.62%) met the AKI criteria. A model was derived that included 16 variables such as albumin transfusion, fluid balance, diastolic blood pressure (DBP), partial pressure of oxygen (PO2), blood glucose (GLU), platelet (PLT), baseline serum creatinine (bSCr), serum sodium, age, epinephrine, proton pump inhibitor (PPI), intra-abdominal infection, anemia, diabetes, glycerin fructose, and nutritional pathway. The area under the receiver operating characteristic curve (AUC) was 0.822. Subgroup analysis revealed the impact of blood pressure fluctuations on AKI. Additionally, the study demonstrated a bidirectional effect of albumin and fluid balance on AKI. This model is highly accurate and may facilitate the early diagnosis of and interventions for AKI.

"Nursing after the care of COVID-19 patients in intensive care units: systematic review"

Analizar los impactos físicos y emocionales que generó la atención de pacientes en unidades de cuidado intensivo por covid-19 en el personal de enfermería mediante una revisión sistemática de la literatura.Revisión sistemática de artículos originales, revisiones de literatura y meta-análisis que aborden los impactos físicos y/o mentales en profesionales de enfermería por la atención de pacientes con covid-19 en UCI en las bases de datos PubMed, Science Direct y Scielo.Se seleccionaron 24 artículos. Entre los impactos emocionales se identificaron ansiedad, depresión, angustia, estrés, culpa, desolación. En relación con los impactos físicos se identificaron insomnio, agotamiento, lesiones.La atención de pacientes Covid-19 en UCI impactó en el bienestar emocional y físico del personal de enfermería a causas de diferentes estresores generando el desarrollo de estrategias de afrontamiento internas, externas y desadaptativas.