The association between enteral nutrition with survival of critical patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) results in several complications and mortality in intensive care unit (ICU) patients. Limited studies have investigated the effect of enteral nutrition (EN) on the survival of COVID-19 patients in the ICU. The aim of this study was to investigate the association of EN with biochemical and pathological indices associated with mortality in ICU patients with COVID-19. METHODS: This case-control study was conducted on 240 patients with COVID-19 hospitalized in the ICU including 120 eventual nonsurvived as the cases and 120 survived patients as the controls. All of the patients received EN as a high protein high volume or standard formula. Data on general information, anthropometric measurements, and the results of lab tests were collected. RESULTS: The recovered patients received significantly more high protein (60.8% vs. 39.6%, p = .004) and high volume (61.6% vs. 42.3%, p = .005) formula compared to the nonsurvived group. Mortality was inversely associated with high volume (odds ratio [OR]: 0.45 confidence interval [CI]95%, p = .008) and high protein (OR: 0.42 CI95%, p = .003) formula. The results remained significant after adjusting for age and sex. Further adjustment for underlying diseases, smoking, body mass index, and the acute physiology and chronic health evaluation II (APACHE II) score did not change the results. CONCLUSION: The findings of the study showed that there was a significant inverse association between mortality and high volume and high protein formula in patients with COVID-19. Further investigation is warranted.

Triglyceride-glucose index as a potential predictor for in-hospital mortality in critically ill patients with intracerebral hemorrhage: a multicenter, case-control study.

BACKGROUND: The correlation between the triglyceride-glucose index (TyG) and the prognosis of ischemic stroke has been well established. This study aims to assess the influence of the TyG index on the clinical outcomes of critically ill individuals suffering from intracerebral hemorrhage (ICH). METHODS: Patients diagnosed with ICH were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Various statistical methods, including restricted cubic spline (RCS) regression, multivariable logistic regression, subgroup analysis, and sensitivity analysis, were employed to examine the relationship between the TyG index and the primary outcomes of ICH. RESULTS: A total of 791 patients from MIMIC-IV and 1,113 ones from eICU-CRD were analyzed. In MIMIC-IV, the in-hospital and ICU mortality rates were 14% and 10%, respectively, while in eICU-CRD, they were 16% and 8%. Results of the RCS regression revealed a consistent linear relationship between the TyG index and the risk of in-hospital and ICU mortality across the entire study population of both databases. Logistic regression analysis revealed a significant positive association between the TyG index and the likelihood of in-hospital and ICU death among ICH patients in both databases. Subgroup and sensitivity analysis further revealed an interaction between patients' age and the TyG index in relation to in-hospital and ICU mortality among ICH patients. Notably, for patients over 60 years old, the association between the TyG index and the risk of in-hospital and ICU mortality was more pronounced compared to the overall study population in both MIMIC-IV and eICU-CRD databases, suggesting a synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of patients with ICH. CONCLUSIONS: This study established a positive correlation between the TyG index and the risk of in-hospital and ICU mortality in patients over 60 years who diagnosed with ICH, suggesting that the TyG index holds promise as an indicator for risk stratification in this patient population.

Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.

BACKGROUND: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. OBJECTIVE: We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. METHODS: Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. RESULTS: For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2). CONCLUSIONS: We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

Dynamic Prediction of Mortality Using Longitudinally Measured Pediatric Sequential Organ Failure Assessment Scores: A Joint Modeling Approach.

OBJECTIVES: The pediatric Sequential Organ Failure Assessment (pSOFA) score was designed to track illness severity and predict mortality in critically ill children. Most commonly, pSOFA at a point in time is used to assess a static patient condition. However, this approach has a significant drawback because it fails to consider any changes in a patients' condition during their PICU stay and, especially, their response to initial critical care treatment. We aimed to evaluate the performance of longitudinal pSOFA scores for predicting mortality. DESIGN: Single-center, retrospective cohort study. SETTING: Quaternary 40-bed PICU. PATIENTS: All patients admitted to the PICU between 2015 and 2021 with at least 24 hours of ICU stay. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We calculated daily pSOFA scores up to 30 days, or until death or discharge from the PICU, if earlier. We used the joint longitudinal and time-to-event data model for the dynamic prediction of 30-day in-hospital mortality. The dataset, which included 9146 patients with a 30-day in-hospital mortality of 2.6%, was divided randomly into training (75%) and validation (25%) subsets, and subjected to 40 repeated stratified cross-validations. We used dynamic area under the curve (AUC) to evaluate the discriminative performance of the model. Compared with the admission-day pSOFA score, AUC for predicting mortality between days 5 and 30 was improved on average by 6.4% (95% CI, 6.3-6.6%) using longitudinal pSOFA scores from the first 3 days and 9.2% (95% CI, 9.0-9.5%) using scores from the first 5 days. CONCLUSIONS: Compared with admission-day pSOFA score, longitudinal pSOFA scores improved the accuracy of mortality prediction in PICU patients at a single center. The pSOFA score has the potential to be used dynamically for the evaluation of patient conditions.

Cortisol Levels During First Admission Day Are Associated With Clinical Outcomes in Surgical Critically Ill Patients.

IMPORTANCE: To explore the correlation between cortisol levels during first admission day and clinical outcomes. OBJECTIVES: Although most patients exhibit a surge in cortisol levels in response to stress, some suffer from critical illness-related corticosteroid insufficiency (CIRCI). Literature remains inconclusive as to which of these patients are at greater risk of poor outcomes. DESIGN: A retrospective study. SETTING: A surgical ICU (SICU) in a tertiary medical center. PARTICIPANTS: Critically ill patients admitted to the SICU who were not treated with steroids. MAIN OUTCOMES AND MEASURES: Levels of cortisol taken within 24 hours of admission (day 1 [D1] cortisol) in 1412 eligible patients were collected and analyzed. Results were categorized into four groups: low (0-10 µg/dL), normal (10-25 µg/dL), high (25-50 µg/dL), and very high (above 50 µg/dL) cortisol levels. Primary endpoint was 90-day mortality. Secondary endpoints were the need for organ support (use of vasopressors and mechanical ventilation [MV]), ICU length of stay (LOS), and duration of MV. RESULTS: The majority of patients (63%) had high or very high D1 cortisol levels, whereas 7.6% had low levels and thus could be diagnosed with CIRCI. There were statistically significant differences in 90-day mortality between the four groups and very high levels were found to be an independent risk factor for mortality, primarily in patients with Sequential Organ Failure Assessment (SOFA) less than or equal to 3 or SOFA greater than or equal to 7. Higher cortisol levels were associated with all secondary endpoints. CIRCI was associated with favorable outcomes. CONCLUSIONS AND RELEVANCE: In critically ill surgical patients D1 cortisol levels above 50 mcg/dL were associated with mortality, need for organ support, longer ICU LOS, and duration of MV, whereas low levels correlated with good clinical outcomes even though untreated. D1 cortisol level greater than 50 mcg/dL can help discriminate nonsurvivors from survivors when SOFA less than or equal to 3 or SOFA greater than or equal to 7.

Association between arterial oxygen and mortality across critically ill patients with hematologic malignancies: results from an international collaborative network.

PURPOSE: Patients with hematological malignancies are at high risk for life-threatening complications. To date, little attention has been paid to the impact of hyperoxemia and excess oxygen use on mortality. The aim of this study was to investigate the association between partial pressure of arterial oxygen (PaO2) and 28-day mortality in critically ill patients with hematologic malignancies. METHODS: Data from three international cohorts (Europe, Canada, Oceania) of patients who received respiratory support (noninvasive ventilation, high-flow nasal cannula, invasive mechanical ventilation) were obtained. We used mixed-effect Cox models to investigate the association between day one PaO2 or excess oxygen use (inspired fraction of oxygen ≥ 0.6 with PaO2 > 100 mmHg) on day-28 mortality. RESULTS: 11,249 patients were included. On day one, 5716 patients (50.8%) had normoxemia (60 ≤ PaO2 ≤ 100 mmHg), 1454 (12.9%) hypoxemia (PaO2 < 60 mmHg), and 4079 patients (36.3%) hyperoxemia (PaO2 > 100 mmHg). Excess oxygen was used in 2201 patients (20%). Crude day-28 mortality rate was 40.6%. There was a significant association between PaO2 and day-28 mortality with a U-shaped relationship (p < 0.001). Higher PaO2 levels (> 100 mmHg) were associated with day-28 mortality with a dose-effect relationship. Subgroup analyses showed an association between hyperoxemia and mortality in patients admitted with neurological disorders; however, the opposite relationship was seen across those admitted with sepsis and neutropenia. Excess oxygen use was also associated with subsequent day-28 mortality (adjusted hazard ratio (aHR) [95% confidence interval (CI)]: 1.11[1.04-1.19]). This result persisted after propensity score analysis (matched HR associated with excess oxygen:1.31 [1.20-1.1.44]). CONCLUSION: In critically-ill patients with hematological malignancies, exposure to hyperoxemia and excess oxygen use were associated with increased mortality, with variable magnitude across subgroups. This might be a modifiable factor to improve mortality.

Association between high-flow nasal cannula use and mortality in patients with sepsis-induced acute lung injury: a retrospective propensity score-matched cohort study.

BACKGROUND: High-flow nasal cannula (HFNC) has emerged as a promising noninvasive method for delivering oxygen to critically ill patients, particularly those with sepsis and acute lung injury. However, uncertainties persist regarding its therapeutic benefits in this specific patient population. METHODS: This retrospective study utilized a propensity score-matched cohort from the Medical Information Mart in Intensive Care-IV (MIMIC-IV) database to explore the correlation between HFNC utilization and mortality in patients with sepsis-induced acute lung injury. The primary outcome was 28-day all-cause mortality. RESULTS: In the propensity score-matched cohort, the 28-day all-cause mortality rate was 18.63% (95 out of 510) in the HFNC use group, compared to 31.18% (159 out of 510) in the non-HFNC group. The use of HFNC was associated with a lower 28-day all-cause mortality rate (hazard ratio [HR] = 0.53; 95% confidence interval [CI] = 0.41-0.69; P < 0.001). HFNC use was also associated with lower ICU mortality (odds ratio [OR] = 0.52; 95% CI = 0.38-0.71; P < 0.001) and lower in-hospital mortality (OR = 0.51; 95% CI = 0.38-0.68; P < 0.001). Additionally, HFNC use was found to be associated with a statistically significant increase in both the ICU and overall hospitalization length. CONCLUSIONS: These findings indicate that HFNC may be beneficial for reducing mortality rates among sepsis-induced acute lung injury patients; however, it is also associated with longer hospital stays.

Long-term survival in stroke patients: insights into triglyceride-glucose body mass index from ICU data.

BACKGROUND: The Triglyceride Glucose-Body Mass Index (TyG-BMI) has been established as a robust indicator of insulin resistance (IR), reflecting metabolic health across various populations. In general, lower TyG-BMI values are often associated with better metabolic health outcomes and a reduced risk of adverse health events in non-critically ill populations. Previous studies have highlighted a significant negative association between TyG-BMI and all-cause mortality (ACM) among critically ill atrial fibrillation patients. Given the high prevalence and severe outcomes associated with stroke, understanding how TyG-BMI at the time of ICU admission correlates with ACM in critically ill stroke patients becomes imperative. This study aims to assess the correlation between TyG-BMI and ACM in this specific patient cohort, exploring how traditional associations between TyG-BMI and metabolic health may differ in the context of acute, life-threatening illness. METHODS: Patient data were retrieved by accessing the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database, categorizing patients into three groups on the basis of TyG-BMI tertiles. The study evaluated both primary and secondary outcomes: the primary outcomes included the 90-day, 180-day, and 1-year ACM, while secondary outcomes encompassed ICU, in-hospital, and 30-day ACM. Our study employed the Kaplan-Meier (K-M) curve method for outcome comparison across the groups while utilizing multivariate Cox proportional-hazards regression models and restricted cubic splines (RCS) to explore TyG-BMI association with these outcomes. Additionally, interaction and subgroup analyses were performed, focusing on different mortality time points. RESULTS: Among a cohort of 1707 individuals diagnosed with stroke, the average age was 68 years (interquartile range [IQR]: 58-78 years), with 946 (55.42%) of the participants being male. The analysis of K-M curves suggested that patients having a lower TyG-BMI level faced a heightened risk of long-term ACM, whereas the short-term ACM exhibited no statistically significant differences across the three TyG-BMI groups. Furthermore, Cox proportional-hazards regression analysis validated a statistically significant increased risk of long-term ACM among patients belonging to the lowest TyG-BMI tertile. Additionally, RCS analysis results demonstrated L-shaped correlations between the TyG-BMI index and both short- and long-term ACM. These findings underscore the TyG-BMI predictive value for long-term mortality in stroke patients, highlighting a nuanced relationship that varies over different time frames. The results revealed no interactions between TyG-BMI and the stratified variables, with the exception of age. CONCLUSION: In our study, lower TyG-BMI levels in critically ill stroke patients are significantly related to a higher risk of long-term ACM within the context of the United States. This finding suggests the potential of TyG-BMI as a marker for stratifying long-term risk in this patient population. However, it's crucial to note that this association was not observed for short-term ACM, indicating that the utility of TyG-BMI may be more pronounced in long-term outcome prediction. Additionally, our conclusion that TyG-BMI could serve as a reliable indicator for managing and stratifying stroke patients over the long term is preliminary. To confirm our findings and assess the universal applicability of TyG-BMI as a prognostic tool, it is crucial to conduct rigorously designed research across various populations.

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke.

BACKGROUND/AIM: We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations. MATERIALS AND METHODS: We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions. RESULTS: A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit. CONCLUSIONS: Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Optimizing patient outcomes: a comprehensive evaluation of protocolized sedation in intensive care settings: a systematic review and meta-analysis.

INTRODUCTION: Amidst the routine utilization of protocolized sedation in ventilated ICU patients, existing management guidelines exhibit a lack of unanimous recommendations for its widespread adoption. This study endeavors to comprehensively assess the effectiveness and safety of protocolized sedation in critically ill ventilated patients. OBJECTIVE: The primary objective of this study was to systematically review and conduct a meta-analysis of clinical trials comparing protocolized sedation with standard management in critically ill ventilated patients. Key outcomes under scrutiny include ICU and hospital mortality, ventilation days, duration of ICU stay, and incidents of self-extubation. The evaluation incorporates the Risk of Bias 2 (RoB2) tool to assess the quality of included studies. Data analysis utilizes a random-effects model for relative risk (RR) and mean differences. Subgroup analysis categorizes sedation protocols into algorithmic or daily interruption, addressing potential heterogeneity. Additionally, a GRADE evaluation is performed to ascertain the overall certainty of the evidence. RESULTS: From an initial pool of 1504 records, 10 studies met the inclusion criteria. Protocolized sedation demonstrated a reduced RR for mortality (RR: 0.80, 95% CI 0.68-0.93, p < 0.01, I2 = 0%) and a decrease in ventilation days (mean difference: - 1.12, 95% CI - 2.11 to - 0.14, p = 0.03, I2 = 84%). Furthermore, there was a notable reduction in ICU stay (mean difference: - 2.24, 95% CI - 3.59 to - 0.89, p < 0.01, I2 = 81%). However, incidents of self-extubation did not exhibit a significant difference (RR: 1.20, 95% CI 0.49-2.94, p = 0.69, I2 = 35%). Subgroup analyses effectively eliminated heterogeneity (I2 = 0%), and the GRADE evaluation yielded moderate results for mortality, ventilation days, and ICU duration. CONCLUSION: Protocolized sedation, whether implemented algorithmically or through daily interruption, emerges as a safe and effective approach when compared to standard management in ventilated ICU patients. The findings from this study contribute valuable insights to inform evidence-based practices in sedation management for this critical patient population.

Lactate-to-albumin ratio: A promising predictor of 28-day all-cause mortality in critically Ill patients with acute ischemic stroke.

INTRODUCTION: Numerous diseases have been found to be associated with the lactate-to-albumin ratio (LAR), as confirmed by existing research. This study aims to investigate the relationship between LAR within 24 hours of admission and a 28-day mortality rate in patients manifesting ischemic stroke. METHODS: This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.1) database. We included adult patients with acute ischemic stroke (AIS) who were admitted to the intensive care unit. The primary outcome entailed evaluating the ability of LAR to predict death at 28-day of hospital admission in patients with AIS. RESULTS: A total of 502 patients with ischemic stroke were enrolled in the study, of which 185 (36.9 %) died within 28 days after hospital admission. We identified a linear association between LAR and mortality risk. Compared with the reference group (first LAR tertile), the 28-day mortality was increased in the highest tertile; the fully adjusted HR value was 1.21 (1.08 to 1.40). the Area Under the Curve (AUC) value for LAR was 58.26 % (95 % CI: 53.05 % - 63.46 %), which was higher than that for arterial blood lactate (AUC = 56.88 %) and serum albumin (AUC = 55.29 %) alone. It was not inferior even when compared to SOFA (AUC = 56.28 %). The final subgroup analysis exhibited no significant interaction of LAR with each subgroup (P for interaction: 0.079 - 0.848). CONCLUSION: In our study, LAR emerged as a promising predictor of all-cause mortality in acute ischemic stroke patients within 28 days of admission.

A randomized clinical trial to evaluate the effect of post-intensive care multidisciplinary consultations on mortality and the quality of life at 1 year.

PURPOSE: Critical illness is associated with long-term increased mortality and impaired quality of life (QoL). We assessed whether multidisciplinary consultations would improve outcome at 12 months (M12) after intensive care unit (ICU) discharge. METHODS: We performed an open, multicenter, parallel-group, randomized clinical trial. Eligible are patients discharged alive from ICU in 11 French hospitals between 2012 and 2018. The intervention group had a multidisciplinary face-to-face consultation involving an intensivist, a psychologist, and a social worker at ICU discharge and then at M3 and M6 (optional). The control group had standard post-ICU follow-up. A consultation was scheduled at M12 for all patients. The QoL was assessed using the EuroQol-5 Dimensions-5 Level (Euro-QoL-5D-5L) which includes five dimensions (mobility, self-care, usual activities, pain, and anxiety/depression), each ranging from 1 to 5 (1: no, 2: slight, 3: moderate, 4: severe, and 5: extreme problems). The primary endpoint was poor clinical outcome defined as death or severe-to-extreme impairment of at least one EuroQoL-5D-5L dimension at M12. The information was collected by a blinded investigator by phone. Secondary outcomes were functional, psychological, and cognitive status at M12 consultation. RESULTS: 540 patients were included (standard, n = 272; multidisciplinary, n = 268). The risk for a poor outcome was significantly greater in the multidisciplinary group than in the standard group [adjusted odds ratio 1.49 (95% confidence interval, (1.04-2.13)]. Seventy-two (13.3%) patients died at M12 (standard, n = 32; multidisciplinary, n = 40). The functional, psychological, and cognitive scores at M12 did not statistically differ between groups. CONCLUSIONS: A hospital-based, face-to-face, intensivist-led multidisciplinary consultation at ICU discharge then at 3 and 6 months was associated with poor outcome 1 year after ICU.

Complementarity of nutrition screening with Global Leadership Initiative on Malnutrition criteria for diagnosing malnutrition in critically ill patients: A comparison study of Nutritional Risk Screening 2002 and modified Nutrition Risk in the Critically Ill Score.

BACKGROUND AND AIM: Critical illness induces hypermetabolism and hypercatabolism, increasing nutrition risk (NR). Early NR identification is crucial for improving outcomes. We assessed four nutrition screening tools (NSTs) complementarity with the Global Leadership Initiative on Malnutrition (GLIM) criteria in critically ill patients. METHODS: We conducted a comparative study using data from a cohort involving five intensive care units (ICUs), screening patients for NR using NRS-2002 and modified-NUTRIC tools, with three cutoffs (≥3, ≥4, ≥5), and malnutrition diagnosed by GLIM criteria. Our outcomes of interest included ICU and in-hospital mortality, ICU and hospital length of stay (LOS), and ICU readmission. We examined accuracy metrics and complementarity between NSTs and GLIM criteria about clinical outcomes through logistic regression and Cox regression. We established a four-category independent variable: NR(-)/GLIM(-) as the reference, NR(-)/GLIM(+), NR(+)/GLIM(-), and NR(+)/GLIM(+). RESULTS: Of the 377 patients analyzed (median age 64 years [interquartile range: 54-71] and 53.8% male), NR prevalence varied from 87% to 40.6%, whereas 64% presented malnutrition (GLIM criteria). NRS-2002 (score ≥4) showed superior accuracy for GLIM-based malnutrition. Multivariate analysis revealed mNUTRIC(+)/GLIM(+) increased >2 times in the likelihood of ICU and in-hospital mortality, ICU and hospital LOS, and ICU readmission compared with the reference group. CONCLUSION: No NST exhibited satisfactory complementarity to the GLIM criteria in our study, emphasizing the necessity for comprehensive nutrition assessment for all patients, irrespective of NR status. We recommend using mNUTRIC if the ICU team opts for nutrition screening, as it demonstrated superior prognostic value compared with NRS-2002, and applying GLIM criteria in all patients.

Intensive Care Unit Mortality Trends during the First Two Years of the COVID-19 Pandemic in Greece: A Multi-Center Retrospective Study.

Data on COVID-19 mortality among patients in intensive care units (ICUs) from Eastern and/or Southern European countries, including Greece, are limited. The purpose of this study was to evaluate the ICU mortality trends among critically ill COVID-19 patients during the first two years of the pandemic in Greece and to further investigate if certain patients' clinical characteristics contributed to this outcome. We conducted a multi-center retrospective observational study among five large university hospitals in Greece, between February 2020 and January 2022. All adult critically ill patients with confirmed COVID-19 disease who required ICU admission for at least 24 h were eligible. In total, 1462 patients (66.35% males) were included in this study. The mean age of this cohort was 64.9 (±13.27) years old. The 28-day mortality rate was 35.99% (n = 528), while the overall in-hospital mortality was 50.96% (n = 745). Cox regression analysis demonstrated that older age (≥65 years old), a body mass index within the normal range, and a delay in ICU admission from symptom onset, as well as worse baseline clinical severity scores upon ICU admission, were associated with a greater risk of death. Mortality of critically ill COVID-19 patients was high during the first two years of the pandemic in Greece but comparable to other countries. Risk factors for death presented in this study are not different from those that have already been described for COVID-19 in other studies.

Prediction of hospital mortality among critically ill patients in a single centre in Asia: comparison of artificial neural networks and logistic regression-based model.

INTRODUCTION: This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. METHODS: This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. RESULTS: In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. CONCLUSION: Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

Value of serum iron and urine neutrophil gelatinase-associated lipocalin in predicting the mortality of critically ill patients with sepsis.

INTRODUCTION: We aimed to investigate the association of iron metabolism-related parameters with 60-day mortality in critically ill patients with sepsis. METHODS: Serum or urine concentrations of iron metabolism-related parameters on intensive care unit admission were measured in a prospective cohort of 133 eligible patients with sepsis according to the Sepsis-3 criteria, and these values were compared between survivors and nonsurvivors, categorized according to their 60-day survival status. Cox regression analyses were performed to examine the association between iron parameters and 60-day mortality. Kaplan-Meier methods were used to illustrate the differences in survival between different iron parameters. RESULTS: Of the 133 patients included in the study, 61 (45.8%) had died by day 60. After adjusting for confounding variables, higher concentrations of serum iron (cut-off 9.5 µmol/mL) and higher concentrations of urine neutrophil gelatinase-associated lipocalin (uNGAL; cut-off 169.3 ng/mL) were associated with a significantly greater risk of death in the Cox regression analysis. These two biomarkers combined with Sequential Organ Failure Assessment (SOFA) scores increased the area under the receiver operating characteristic (AUROC) curve to 0.85. DISCUSSION: These findings suggest that higher concentrations of serum iron and uNGAL are each associated with higher 60-day mortality, and they add significant accuracy to this prediction in combination with SOFA. Abbreviations: uNGAL: urine neutrophil gelatinase-associated lipocalin; ICU: intensive care unit; SOFA: Sequential Organ Failure Assessment; APACHE II: the Acute Physiology and Chronic Health Evaluation II; ELISA: enzyme-linked immunosorbent assay; HR: hazard ratio; CIs: confidence intervals; WBC: white blood cell; TBIL: total bilirubin.

Association of active mobilisation variables with adverse events and mortality in patients requiring mechanical ventilation in the intensive care unit: a systematic review and meta-analysis.

BACKGROUND: Mobilisation during critical illness is now included in multiple clinical practice guidelines. However, a large, randomised trial and systematic review have recently identified an increased probability of adverse events and mortality in patients who received early active mobilisation in the intensive care unit (ICU). We aimed to determine the effects of mobilisation compared with usual care on adverse events and mortality in an acute ICU setting. In subgroup analyses, we specifically aimed to investigate possible sources of harm, including the timing and duration of mobilisation achieved, ventilation status, and admission diagnosis. METHODS: In this systematic review with frequentist and Bayesian analyses, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, SPORTDiscus, SCOPUS, Web of Science, and PEDro electronic databases, as well as clinical trial registries (ICTRP and ClinicalTrials.gov), from inception to March 16, 2023, without language restrictions. Eligible studies were randomised controlled trials that examined active mobilisation compared with either no mobilisation or mobilisation commencing later, or at a lower frequency or intensity, in adults who were critically ill during or after a period of mechanical ventilation in an acute ICU setting. Two authors independently screened reports, extracted data, and assessed the risk of bias using the Cochrane risk-of-bias tool (version 1). The primary outcome was the number of adverse events that occurred during the implementation of mobilisation, with the effect of mobilisation on mortality being the secondary outcome. Risk ratios (RRs) with 95% CIs were calculated in R (version 4.0.3) using random-effects modelling, with Bayesian analysis completed to calculate the probability of treatment harm (ie, RR >1). Subgroup analyses were completed to investigate the association of various factors of mobilisation on adverse events and mortality: duration of mobilisation (longer [≥20 min per day] vs shorter [<20 min per day]), timing of commencement (early [≤72 h from ICU admission] vs late [>72 h from ICU admission]), ventilation status at commencement (all patients mechanically ventilated vs all patients extubated), and ICU admission diagnosis (surgical vs medical). This study was registered with PROSPERO, CRD42022369272. FINDINGS: After title and abstract screening of 14 440 studies and review of 466 full texts, 67 trials with 7004 participants met inclusion criteria, with 59 trials contributing to the meta-analysis. Of the 67 included studies, 15 (22%) did not mention adverse events and 13 (19%) reported no adverse events occurring across the trial period. Overall, we found no effect of mobilisation compared with usual care on the occurrence of adverse events (RR 1·09 [95% CI 0·69-1·74], p=0·71; I2 91%; 32 731 events, 20 studies; very low certainty), with a 2·96% occurrence rate (693 events in 23 395 intervention sessions; 25 studies). Mobilisation did not have any effect on mortality (RR 0·98 [95% CI 0·87-1·12], p=0·81; I2 0%; n=6218, 58 studies; moderate certainty). Subgroup analysis was hindered by the large amount of data that could not be allocated and analysed, making the results hypothesis generating only. INTERPRETATION: Implementation of mobilisation in the ICU was associated with a less than 3% chance of an adverse event occurring and was not found to increase adverse events or mortality overall, providing reassurance for clinicians about the safety of performing this intervention. Subgroup analyses did not clearly identify any specific variable of mobilisation implementation that increased harm. FUNDING: None.

Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer the causality. METHODS: A total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care-IV and eICU-Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival-associated prognostic factors. RESULTS: The optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in-hospital survival, .81 for 30-day survival in internal testing and .87 for in-hospital survival in external testing. Important factors for in-hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30-day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone. CONCLUSIONS: Our interpretable machine learning model for predicting in-hospital and 30-day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability. This model can assist clinicians in managing these patients to improve the discrimination of high-risk patients.

A longitudinal study to determine if all critically ill patients should be considered at nutrition risk or is there a highly accurate screening tool to be adopted?

BACKGROUND: Nutrition risk is prevalent in intensive care unit (ICU) settings and related to poor prognoses. We aimed to evaluate the concurrent and predictive validity of different nutrition risk screening tools in the ICU. METHODS: Data were collected between 2019 and 2022 in six ICUs (n = 450). Nutrition risk was evaluated by modified Nutrition Risk in Critically ill (mNUTRIC), Nutritional Risk Screening (NRS-2002), Malnutrition Screening Tool (MST), Malnutrition Universal Screening Tool (MUST), and Nutritional Risk in Emergency (NRE-2017). Accuracy and agreement of the tools were assessed; logistic regression was used to verify the association between nutrition risk and prolonged ICU stay; Cox regression was used for mortality in the ICU, both with adjustment for confounders. RESULTS: NRS-2002 ≥5 showed the best accuracy (0.63 [95% CI, 0.58-0.69]) with mNUTRIC, and MST with NRS-2002 ≥5 (0.76 [95% CI, 0.71-0.80]). All tools had a poor/fair agreement with mNUTRIC (k = 0.019-0.268) and moderate agreement with NRS-2002 ≥5 (k = 0.474-0.503). MUST (2.26 [95% CI 1.40-3.63]) and MST (1.69 [95% CI, 1.09-2.60]) predicted death in the ICU, and the NRS-2002 ≥5 (1.56 [95% CI 1.02-2.40]) and mNUTRIC (1.86 [95% CI, 1.26-2.76]) predicted prolonged ICU stay. CONCLUSION: No nutrition risk screening tool demonstrated a satisfactory concurrent validity; only the MUST and MST predicted ICU mortality and the NRS-2002 ≥5 and mNUTRIC predicted prolonged ICU stay, suggesting that it could be appropriate to adopt the ESPEN recommendation to assess nutrition status in patients with ≥48 h in the ICU.

Decreased percentages of plasmacytoid dendritic cells predict survival in critically ill patients.

Critically ill patients admitted to intensive care units (ICUs) experience a broad variety of life-threatening conditions. Irrespective of the initial cause of hospitalization, many experience systemic immune dysregulation. Dendritic cells (DCs) are the most potent antigen-presenting cells and play a pivotal role in regulating the immune response by linking the innate to the adaptive immune system. The aim of this study was to analyze whether DCs or their respective subsets are associated with 30-d mortality in an unselected patient cohort admitted to a medical ICU with a cardiovascular focus. A total of 231 patients were included in this single-center prospective observational study. Blood was drawn at admission and after 72 h. Subsequently, flow cytometry was utilized for the analysis of DCs and their respective subsets. In the total cohort, low percentages of DCs were significantly associated with sepsis, respiratory failure, and septic shock. In particular, a significantly lower percentage of circulating plasmacytoid DCs (pDCs) was found to be a strong and independent predictor of 30-d mortality after adjustment for demographic and clinical variables with an hazard ratio of 4.2 (95% confidence interval: 1.3-13.3, P = 0.015). Additionally, low percentages of pDCs were correlated with additional markers of inflammation and organ dysfunction. In conclusion, we observed low percentages of DCs in patients admitted to an ICU experiencing sepsis, respiratory failure, and cardiogenic shock, suggesting their depletion as a contributing mechanism for the development of immune paralysis. In our cohort, pDCs were identified as the most robust subset to predict 30-d mortality.