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Insight into COVID-19 intensive care unit (ICU) patient characteristics, rates and risks of invasive mechanical ventilation (IMV) and associated outcomes as well as any regional discrepancies is critical in this pandemic for individual case management and overall resource planning. Electronic searches were performed for reports through May 1 2020 and reports on COVID-19 ICU admissions and outcomes were included using predefined search terms. Relevant data was subsequently extracted and pooled using fixed or random effects meta-analysis depending on heterogeneity. Study quality was assessed by the NIH tool and heterogeneity was assessed by I2 and Q tests. Baseline patient characteristics, ICU and IMV outcomes were pooled and meta-analyzed. Pooled odds ratios (pOR) were calculated for clinical features against ICU, IMV mortality. Subgroup analysis was carried out based on patient regions. A total of twenty-eight studies comprising 12,437 COVID-19 ICU admissions from seven countries were meta-analyzed. Pooled ICU admission rate was 21% [95% CI 0.12-0.34] and 69% of cases needed IMV [95% CI 0.61-0.75]. ICU and IMV mortality were 28.3% [95% CI 0.25-0.32], 43% [95% CI 0.29-0.58] and ICU, IMV duration was 7.78 [95% CI 6.99-8.63] and 10.12 [95% CI 7.08-13.16] days respectively. Besides confirming the significance of comorbidities and clinical findings of COVID-19 previously reported, we found the major correlates with ICU mortality were IMV [pOR 16.46, 95% CI 4.37-61.96], acute kidney injury (AKI) [pOR 12.47, 95% CI 1.52-102.7], and acute respiratory distress syndrome (ARDS) [pOR 6.52, 95% CI 2.66-16.01]. Subgroup analyses confirm significant regional discrepancies in outcomes. This is a comprehensive systematic review and meta-analysis of COVID-19 ICU and IMV cases and associated outcomes. The significant association of AKI, ARDS and IMV with mortality has implications for ICU resource planning for AKI and ARDS as well as suggesting the need for further research into optimal ventilation strategies for COVID-19 patients in the ICU setting. Regional differences in outcome implies a need to develop region specific protocols for ventilatory support as well as overall treatment.

Delirium is a common complication in intensive care unit (ICU) patients. It can lead to various adverse events. In this study, we investigated the effectiveness of combining the use of the PREdiction of DELIRium (PRE-DELIRIC) model for delirium risk assessment and the use of a multicomponent care bundle for delirium assessment, prevention, and care in terms of reductions in the incidence of delirium among surgical ICU patients. This retrospective study included surgical ICU patients who had received PRE-DELIRIC-guided SMART/SmART care (SMART care: SmART bundle plus multidisciplinary team; SmART care: Sleep/sweet sense of home (creating a comforting and restful environment for patients), Assessment (regular and thorough evaluation of patient needs and conditions), Release (revised endotracheal tube care/removal, restraint device care, and immobility reduction for patient comfort), and Time (reorientation of time to optimize patient care schedules) in our hospital between May 2022 and March 2023 (intervention group) and individuals who had received usual care between January 2021 and April 2022 (historical control group). The SmART intervention involves providing care in the following domains: sleep/sweet sense of home, assessment, release, and time. Patients with a PRE-DELIRIC score of >30% received SMART care, which includes multidisciplinary (physicians, pharmacists, respiratory therapists, and physiotherapists) care in addition to SmART care. For the control group, usual care was provided following the guidelines for the prevention and management of pain, agitation, delirium, immobility, and sleep disruption. The primary outcome was delirium incidence during ICU stay, which was assessed using the Intensive Care Delirium Screening Checklist. The secondary outcomes were the duration of ICU stay, rate of unplanned self-extubation, and status of ICU discharge. The intervention and control groups comprised 184 and 197 patients, respectively; their mean ages were 63.7 ± 18.4 years and 62.4 ± 19.5 years, respectively. The incidence of delirium was significantly lower (p = 0.001) in the intervention group (22.3%) than in the control group (47.7%). Our findings suggest that the PRE-DELIRIC-guided SMART/SmART care intervention is effective in preventing and managing delirium among surgical ICU patients. [Display omitted] •We adopted a novel approach combining the assessment of delirium risk by using the PRE-DELIRIC model and the provision of SMART/SmART care in surgical intensive care units (ICUs).•The combined approach was effective in risk stratification and delirium management in ICU patients.•The incidence of delirium was significantly lower in the intervention group than in the control group.•Enhanced patient comfort, communication, and adjustments for COVID-19 visiting restrictions were key intervention strategies.•Our findings underscore the potential of integrated models and tailored interventions in optimizing delirium management in ICUs.

Background Microcirculatory dysfunction develops in both septic and cardiogenic shock patients, and it is associated with poor prognosis in patients with septic shock. Information on the association between microcirculatory dysfunction and prognosis in cardiogenic shock patients with venoarterial extracorporeal membrane oxygenation (VA-ECMO) support is limited. Methods Sublingual microcirculation images were recorded using an incident dark-field video microscope at the following time points: within 12 h (T1), 24 h (T2), 48 h (T3), 72 h (T4), and 96 h (T5) after VA-ECMO placement. If a patient could be weaned off VA-ECMO, sublingual microcirculation images were recorded before and after VA-ECMO removal. Microcirculatory parameters were compared between 28-day nonsurvivors and survivors with VA-ECMO support. In addition, the microcirculation and clinical parameters were assessed as prognostic tests of 28-day mortality, and patients were divided into three subgroups according to microcirculation parameters for survival analysis. Results Forty-eight patients were enrolled in this study. At T1, the observed heart rate, mean arterial pressure, inotropic score and lactate level of 28-day nonsurvivors and survivors did not differ significantly, but the perfused small vessel density (PSVD) and proportion of perfused vessels (PPV) were lower in the 28-day nonsurvivors than in the survivors. The PSVD and PPV were slightly superior to lactate levels in predicting 28-day mortality (area under curve of 0.68, 0.70, and 0.62, respectively). The subgroup with the lowest PSVD (< 15 mm/mm 2 ) and PPV (< 64%) values exhibited less favorable survival compared with the other two subgroups. Conclusions Early microcirculatory parameters could be used to predict the survival of cardiogenic shock patients with VA-ECMO support. Trial registration ClinicalTrials.gov, NCT02393274 . Registered on 19 March 2015.

Background We aimed to develop and validate models for predicting intensive care unit (ICU) mortality of critically ill adult patients as early as upon ICU admission. Methods Combined data of 79,657 admissions from two teaching hospitals’ ICU databases were used to train and validate the machine learning models to predict ICU mortality upon ICU admission and at 24 h after ICU admission by using logistic regression, gradient boosted trees (GBT), and deep learning algorithms. Results In the testing dataset for the admission models, the ICU mortality rate was 7%, and 38.4% of patients were discharged alive or dead within 1 day of ICU admission. The area under the receiver operating characteristic curve (0.856, 95% CI 0.845–0.867) and area under the precision-recall curve (0.331, 95% CI 0.323–0.339) were the highest for the admission GBT model. The ICU mortality rate was 17.4% in the 24-hour testing dataset, and the performance was the highest for the 24-hour GBT model. Conclusion The ADM models can provide crucial information on ICU mortality as early as upon ICU admission. 24 H models can be used to improve the prediction of ICU mortality for patients discharged more than 1 day after ICU admission.

[...]the volume or type of fluid supplemented changed over different time periods [2]. [...]the indication, initiation timing, and facilities of renal replacement therapy might change over time. Furosemide may be considered after restoration of adequate blood pressure and tissue perfusion. [...]a maximum dose of 0.04 units/min of vasopressin was used in this study, and the SSC guidelines recommend a lower fixed dose of 0.03 units/min vasopressin [5].

To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission. Retrospective cohort study. Single university teaching hospital in Taipei, Taiwan. 6238 critically ill patients from August 2020 to August 2021. Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance. Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835–0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780–0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911–0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792–0.886) was the highest for the 24H LR model. Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission. •A certain proportion of critically ill patients develop delirium within the first 24h after ICU admission.•Traditional delirium prediction models make a prediction time of 24h after ICU admission cannot predict early delirium.•Developing machine learning model to predict delirium upon ICU admission is crucial for early intervention.•Make a second prediction at 24h after ICU admission can improve the prediction of delirium occurred after 24h.

Microcirculatory dysfunction plays a crucial role in renal ischemia/reperfusion (IR)-induced injury. Dexmedetomidine was reported to ameliorate IR-induced acute kidney injury. This study investigated the effects of dexmedetomidine on renal microcirculation after IR-induced acute kidney injury in rats. In total, 50 rats were randomly allocated to the following five groups (10 in each group): Sham, Control‒IR, Dex (dexmedetomidine) ‒Sham, Dex‒IR, and IR‒Dex group. The microcirculation parameters included total small vessel density, perfused small vessel density (PSVD), proportion of perfused small vessels, microvascular flow index, and tissue oxygen saturation (StO 2 ) were recorded. The repeated measures analysis showed that PSVD on renal surface was higher in the Dex‒IR group than in the Control‒IR group (3.5 mm/mm 2 , 95% confidence interval [CI] 0.6 to 6.4 mm/mm 2 , P = 0.01). At 240 min, StO 2 on renal surface was lower in the Control‒IR group than in the Sham group (– 7%, 95% CI − 13 to − 1%, P = 0.021), but StO 2 did not differ significantly among the Sham, Dex‒IR, and IR‒Dex groups. Our results showed that pretreatment with dexmedetomidine improved renal microcirculation in rats with IR-induced acute kidney injury. However, the adverse effects of low mean arterial pressure and heart rate might offset the protective effect of dexmedetomidine on organ injury.

Background Severe vitamin D deficiency (SVDD) dramatically increases the risks of mortality, infections, and many other diseases. Studies have reported higher prevalence of vitamin D deficiency in patients with critical illness than general population. This multicenter retrospective cohort study develops and validates a score-based model for predicting SVDD in patients with critical illness. Methods A total of 662 patients with critical illness were enrolled between October 2017 and July 2020. SVDD was defined as a serum 25(OH)D level of < 12 ng/mL (or 30 nmol/L). The data were divided into a derivation cohort and a validation cohort on the basis of date of enrollment. Multivariable logistic regression (MLR) was performed on the derivation cohort to generate a predictive model for SVDD. Additionally, a score-based calculator (the SVDD score) was designed on the basis of the MLR model. The model’s performance and calibration were tested using the validation cohort. Results The prevalence of SVDD was 16.3% and 21.7% in the derivation and validation cohorts, respectively. The MLR model consisted of eight predictors that were then included in the SVDD score. The SVDD score had an area under the receiver operating characteristic curve of 0.848 [95% confidence interval (CI) 0.781–0.914] and an area under the precision recall curve of 0.619 (95% CI 0.577–0.669) in the validation cohort. Conclusions This study developed a simple score-based model for predicting SVDD in patients with critical illness. Trial registration : ClinicalTrials.gov protocol registration ID: NCT03639584. Date of registration: May 12, 2022. Graphical Abstract

Introduction Fluid resuscitation is an indispensable procedure in the acute management of hemorrhagic shock for restoring tissue perfusion, particularly microcirculation in splanchnic organs. Resuscitation fluids include crystalloids, hypertonic saline (HTS), and synthetic colloids, and their selection affects the recovery of microcirculatory blood flow and reactive oxygen species (ROS) formation, which is often evident in the kidney, following reperfusion. In this study, the effects of acute resuscitation with 0.9 % saline (NS), 3 % HTS, 4 % succinylated gelatin (GEL), and 6 % hydroxyethyl starch (HES) 130/0.4 were compared in a hemorrhagic shock rat model to analyze restoration of microcirculation among various splanchnic organs and the gracilis muscle and reperfusion-induced renal ROS formation. Methods A total of 96 male Wistar rats were subjected to sham operation (sham group), hemorrhagic shock (control group), and resuscitation with NS, HTS, GEL and HES. Two hours after resuscitation, changes in the mean arterial pressure (MAP), serum lactate level and the microcirculatory blood flow among various splanchnic organs, namely the liver, kidney, and intestine (mucosa, serosal muscular layer, and Peyer’s patch), and the gracilis muscle, were compared using laser speckle contrast imaging. Renal ROS formation after reperfusion was investigated using an enhanced in vivo chemiluminescence (CL) method. Results Microcirculatory blood flow was less severely affected by hemorrhaging in the liver and gracilis muscle. Impairment of microcirculation in the kidney was restored in all resuscitation groups. Resuscitation in the NS group failed to restore intestinal microcirculation. Resuscitation in the HTS, GEL, and HES groups restored intestinal microcirculatory blood flow. By comparison, fluid resuscitation restored hemorrhagic shock-induced hypotension and decreased lactatemia in all resuscitation groups. Reperfusion-induced in vivo renal ROS formation was significantly higher in the GEL and HES groups than in the other groups. Conclusion Although fluid resuscitation with NS restored the MAP and decreased lactatemia following hemorrhagic shock, intestinal microcirculation was restored only by other volume expanders, namely 3 % HTS, GEL, and HES. However, reperfusion-induced renal ROS formation was significantly higher when synthetic colloids were used.

Immunosuppression increases the risk of nosocomial infection in patients with chronic critical illness. This exploratory study aimed to determine the immunometabolic signature associated with nosocomial infection during chronic critical illness. We prospectively recruited patients who were admitted to the respiratory care center and who had received mechanical ventilator support for more than 10 days in the intensive care unit. The study subjects were followed for the occurrence of nosocomial infection until 6 weeks after admission, hospital discharge, or death. The cytokine levels in the plasma samples were measured. Single-cell immunometabolic regulome profiling by mass cytometry, which analyzed 16 metabolic regulators in 21 immune subsets, was performed to identify immunometabolic features associated with the risk of nosocomial infection. During the study period, 37 patients were enrolled, and 16 patients (43.2%) developed nosocomial infection. Unsupervised immunologic clustering using multidimensional scaling and logistic regression analyses revealed that expression of nuclear respiratory factor 1 (NRF1) and carnitine palmitoyltransferase 1a (CPT1a), key regulators of mitochondrial biogenesis and fatty acid transport, respectively, in natural killer (NK) cells was significantly associated with nosocomial infection. Downregulated NRF1 and upregulated CPT1a were found in all subsets of NK cells from patients who developed a nosocomial infection. The risk of nosocomial infection is significantly correlated with the predictive score developed by selecting NK cell-specific features using an elastic net algorithm. Findings were further examined in an independent cohort of COVID-19-infected patients, and the results confirm that COVID-19-related mortality is significantly associated with mitochondria biogenesis and fatty acid oxidation pathways in NK cells. In conclusion, this study uncovers that NK cell-specific immunometabolic features are significantly associated with the occurrence and fatal outcomes of infection in critically ill population, and provides mechanistic insights into NK cell-specific immunity against microbial invasion in critical illness.