Significant delay and decreased chance of treatment for acute ischemic stroke patients on remote outer islets of China compared with the main island: the PUTUO Study.

INTRODUCTION: Data from acute ischemic stroke patients throughout 2021 from one district of an archipelago city of China were collected and analyzed retrospectively to determine the management difference due to time lags from onset of symptoms to the arrival at the stroke center (FMCT) of two regions: main island (MI) and outer islets (OIs). METHODS: All patients information from 1 January to 31 December 2021 was retrieved through the electronic medical records system of the only stroke center in MI. After screening and exclusion, each patient's medical record was reviewed by two neurologists separately. Before OI patients were allocated to a group, their residential addresses at onset of the stroke were confirmed by telephone. Comparisons were analyzed between the two regions for gender, age, pre-stroke risk factors and peri-admission management parameters. RESULTS: A total of 326 patients met the inclusion criteria: 300 from the MI group and 26 for the OI group. Intergroup comparisons for gender, age and most of the risk factors showed no significant difference. FMCT were shown to be significantly distinct (p<0.001). Hospitalization expenses also showed significant difference. The odds ratio of the definite treatment IV thrombolysis was 0.131 (OI group to MI group range: 0.017-0.987, p=0.021). CONCLUSION: The diagnosis and treatment of acute ischemic stroke patients from OIs was significantly postponed compared to those from MI. Therefore, new effective and efficient solutions are urgently needed.

Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury.

BACKGROUND: Acute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized. METHODS: Sepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI. RESULTS: A total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong's test). CONCLUSIONS: Our study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BACKGROUND: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. METHODS: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. RESULTS: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups. CONCLUSIONS: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. TRIAL REGISTRATION: ISRCTN, ISRCTN12233792 . Registered November 20th, 2017.

Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation.

OBJECTIVES: Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis. DESIGN: Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours. SETTING: The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019. PATIENTS: Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours. CONCLUSIONS: This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.

Effectiveness of anisodamine for the treatment of critically ill patients with septic shock: a multicentre randomized controlled trial.

BACKGROUND: Septic shock is characterized by an uncontrolled inflammatory response and microcirculatory dysfunction. There is currently no specific agent for treating septic shock. Anisodamine is an agent extracted from traditional Chinese medicine with potent anti-inflammatory effects. However, its clinical effectiveness remains largely unknown. METHODS: In a multicentre, open-label trial, we randomly assigned adults with septic shock to receive either usual care or anisodamine (0.1-0.5 mg per kilogram of body weight per hour), with the anisodamine doses adjusted by clinicians in accordance with the patients' shock status. The primary end point was death on hospital discharge. The secondary end points were ventilator-free days at 28 days, vasopressor-free days at 28 days, serum lactate and sequential organ failure assessment (SOFA) score from days 0 to 6. The differences in the primary and secondary outcomes were compared between the treatment and usual care groups with the χ2 test, Student's t test or rank-sum test, as appropriate. The false discovery rate was controlled for multiple testing. RESULTS: Of the 469 patients screened, 355 were assigned to receive the trial drug and were included in the analyses-181 patients received anisodamine, and 174 were in the usual care group. We found no difference between the usual care and anisodamine groups in hospital mortality (36% vs. 30%; p = 0.348), or ventilator-free days (median [Q1, Q3], 24.4 [5.9, 28] vs. 26.0 [8.5, 28]; p = 0.411). The serum lactate levels were significantly lower in the treated group than in the usual care group after day 3. Patients in the treated group were less likely to receive vasopressors than those in the usual care group (OR [95% CI] 0.84 [0.50, 0.93] for day 5 and 0.66 [0.37, 0.95] for day 6). CONCLUSIONS: There is no evidence that anisodamine can reduce hospital mortality among critically ill adults with septic shock treated in the intensive care unit. Trial registration ClinicalTrials.gov ( NCT02442440 ; Registered on 13 April 2015).

Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen.

BACKGROUND: Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. METHODS: Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. RESULTS: A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. CONCLUSIONS: Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.

Efficacy and safety of corticosteroids for septic shock in immunocompromised patients: A cohort study from MIMIC.

BACKGROUND: Corticosteroids have been widely used as adjunct therapy for septic shock for many decades, but both the efficacy and safety remain unclear. The study was designed to investigate overall benefits and potential risks of corticosteroids in immunocompromised patients with septic shock. METHODS: The Medical Information Mart for Intensive Care III (MIMIC-III) database was employed to conduct a cohort study. Immunocompromised patients with septic shock were enrolled and categorized by whether exposure to intravenous corticosteroids. Cox Proportional-Hazards models were used to control for confounders and assess the relationship between corticosteroids use and mortality. RESULTS: A total of 866 patients were enrolled in this study, including 395 in the corticosteroids group and 471 in the non-corticosteroids group. Corticosteroids infusion was not associated with improved 30-day mortality in overall immunocompromised population [34.7% vs 32.1%; adjusted hazard ratio (HR) 1.11, 95% confidence interval (CI) 0.87-1.43, p = 0.37]. The mortality effects were similar in 90-day, 180-day, 1-year and hospital mortality. For the subgroup of patients with metastatic cancer, corticosteroids infusion was associated with a statistically significant increase in the 30-day mortality risk (HR 1.58, 95% CI 1.06-2.37; p = 0.02). Corticosteroids had adverse effects on hemodynamic stability, prolonged ICU and hospital duration, and increased risk of hyperglycemia. CONCLUSIONS: Corticosteroids therapy for the maintenance of blood pressure was not associated with improved mortality or hemodynamic stability in overall immunocompromised population with septic shock. Future randomized clinical trials are required to validate the effects of corticosteroids for septic shock in the special immunocompromised population.

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

Gene correlation network analysis to identify regulatory factors in sepsis.

BACKGROUND AND OBJECTIVES: Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. METHODS: The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. RESULTS: A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). CONCLUSIONS: The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.

BACKGROUND: Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine learning to identify clinical phenotypes for critically ill patients with MV in intensive care units (ICUs). METHODS: A retrospective cohort study was conducted with 5013 patients who had undergone MV and treatment in the Department of Critical Care Medicine, Peking Union Medical College Hospital. Statistical and machine learning methods were used. All the data used in this study, including demographics, vital signs, circulation parameters and mechanical ventilator parameters, etc., were automatically extracted from the electronic health record (EHR) system. An external database, Medical Information Mart for Intensive Care III (MIMIC III), was used for validation. RESULTS: Phenotypes were derived from a total of 4009 patients who underwent MV using a latent profile analysis of 22 variables. The associations between the phenotypes and disease severity and clinical outcomes were assessed. Another 1004 patients in the database were enrolled for validation. Of the five derived phenotypes, phenotype I was the most common subgroup (n = 2174; 54.2%) and was mostly composed of the postoperative population. Phenotype II (n = 480; 12.0%) led to the most severe conditions. Phenotype III (n = 241; 6.01%) was associated with high positive end-expiratory pressure (PEEP) and low mean airway pressure. Phenotype IV (n = 368; 9.18%) was associated with high driving pressure, and younger patients comprised a large proportion of the phenotype V group (n = 746; 18.6%). In addition, we found that the mortality rate of Phenotype IV was significantly higher than that of the other phenotypes. In this subgroup, the number of patients in the sequential organ failure assessment (SOFA) score segment (9,22] was 198, the number of deaths was 88, and the mortality rate was higher than 44%. However, the cumulative 28-day mortality of Phenotypes IV and II, which were 101 of 368 (27.4%) and 87 of 480 (18.1%) unique patients, respectively, was significantly higher than those of the other phenotypes. There were consistent phenotype distributions and differences in biomarker patterns by phenotype in the validation cohort, and external verification with MIMIC III further generated supportive results. CONCLUSIONS: Five clinical phenotypes were correlated with different disease severities and clinical outcomes, which suggested that these phenotypes may help in understanding heterogeneity in MV treatment effects.

Defining persistent critical illness based on growth trajectories in patients with sepsis.

BACKGROUND: Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients. METHODS: This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class. RESULTS: The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15 days after intensive care unit admission in the overall population. Only a minority of the study subjects (n = 643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27 days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8 days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness. CONCLUSIONS: While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

Prevalence and impact of acute renal impairment on COVID-19: a systematic review and meta-analysis.

BACKGROUND: The aim of this study is to assess the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 patients and to determine the association of acute kidney injury (AKI) with the severity and prognosis of COVID-19 patients. METHODS: The electronic database of Embase and PubMed were searched for relevant studies. A meta-analysis of eligible studies that reported the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 was performed. The incidences of AKI were compared between severe versus non-severe patients and survivors versus non-survivors. RESULTS: A total of 24 studies involving 4963 confirmed COVID-19 patients were included. The proportions of patients with elevation of sCr and BUN levels were 9.6% (95% CI 5.7-13.5%) and 13.7% (95% CI 5.5-21.9%), respectively. Of all patients, 57.2% (95% CI 40.6-73.8%) had proteinuria, 38.8% (95% CI 26.3-51.3%) had proteinuria +, and 10.6% (95% CI 7.9-13.3%) had proteinuria ++ or +++. The overall incidence of AKI in all COVID-19 patients was 4.5% (95% CI 3.0-6.0%), while the incidence of AKI was 1.3% (95% CI 0.2-2.4%), 2.8% (95% CI 1.4-4.2%), and 36.4% (95% CI 14.6-58.3%) in mild or moderate cases, severe cases, and critical cases, respectively. Meanwhile, the incidence of AKI was 52.9%(95% CI 34.5-71.4%), 0.7% (95% CI - 0.3-1.8%) in non-survivors and survivors, respectively. Continuous renal replacement therapy (CRRT) was required in 5.6% (95% CI 2.6-8.6%) severe patients, 0.1% (95% CI - 0.1-0.2%) non-severe patients and 15.6% (95% CI 10.8-20.5%) non-survivors and 0.4% (95% CI - 0.2-1.0%) survivors, respectively. CONCLUSION: The incidence of abnormal urine analysis and kidney dysfunction in COVID-19 was high and AKI is closely associated with the severity and prognosis of COVID-19 patients. Therefore, it is important to increase awareness of kidney dysfunction in COVID-19 patients.

Corticosteroid therapy in critically ill patients with COVID-19: a multicenter, retrospective study.

BACKGROUND: Corticoid therapy has been recommended in the treatment of critically ill patients with COVID-19, yet its efficacy is currently still under evaluation. We investigated the effect of corticosteroid treatment on 90-day mortality and SARS-CoV-2 RNA clearance in severe patients with COVID-19. METHODS: 294 critically ill patients with COVID-19 were recruited between December 30, 2019 and February 19, 2020. Logistic regression, Cox proportional-hazards model and marginal structural modeling (MSM) were applied to evaluate the associations between corticosteroid use and corresponding outcome variables. RESULTS: Out of the 294 critically ill patients affected by COVID-19, 183 (62.2%) received corticosteroids, with methylprednisolone as the most frequently administered corticosteroid (175 accounting for 96%). Of those treated with corticosteroids, 69.4% received corticosteroid prior to ICU admission. When adjustments and subgroup analysis were not performed, no significant associations between corticosteroids use and 90-day mortality or SARS-CoV-2 RNA clearance were found. However, when stratified analysis based on corticosteroid initiation time was performed, there was a significant correlation between corticosteroid use (≤ 3 day after ICU admission) and 90-day mortality (logistic regression adjusted for baseline: OR 4.49, 95% CI 1.17-17.25, p = 0.025; Cox adjusted for baseline and time varying variables: HR 3.89, 95% CI 1.94-7.82, p < 0.001; MSM adjusted for baseline and time-dependent variants: OR 2.32, 95% CI 1.16-4.65, p = 0.017). No association was found between corticosteroid use and SARS-CoV-2 RNA clearance even after stratification by initiation time of corticosteroids and adjustments for confounding factors (corticosteroids use ≤ 3 days initiation vs no corticosteroids use) using MSM were performed. CONCLUSIONS: Early initiation of corticosteroid use (≤ 3 days after ICU admission) was associated with an increased 90-day mortality. Early use of methylprednisolone in the ICU is therefore not recommended in patients with severe COVID-19.

Performance of the MEDS score in predicting mortality among emergency department patients with a suspected infection: a meta-analysis.

OBJECTIVES: To carry out a meta-analysis to examine the prognostic performance of the Mortality in Emergency Department Sepsis (MEDS) score in predicting mortality among emergency department patients with a suspected infection. METHODS: Electronic databases-PubMed, Embase, Scopus, EBSCO and the Cochrane Library-were searched for eligible articles from their respective inception through February 2019. Sensitivity, specificity, likelihood ratios and receiver operator characteristic area under the curve were calculated. Subgroup analyses were performed to explore the prognostic performance of MEDS in selected populations. RESULTS: We identified 24 studies involving 21 246 participants. The pooled sensitivity of MEDS to predict mortality was 79% (95% CI 72% to 84%); specificity was 74% (95% CI 68% to 80%); positive likelihood ratio 3.07 (95% CI 2.47 to 3.82); negative likelihood ratio 0.29 (95% CI 0.22 to 0.37) and area under the curve 0.83 (95% CI 0.80 to 0.86). Significant heterogeneity was seen among included studies. Meta-regression analyses showed that the time at which the MEDS score was measured and the cut-off value used were important sources of heterogeneity. CONCLUSION: The MEDS score has moderate accuracy in predicting mortality among emergency department patients with a suspected infection. A study comparison MEDS and qSOFA in the same population is needed.

Association Between the Use of Sodium Bicarbonate and Mortality in Acute Kidney Injury Using Marginal Structural Cox Model.

OBJECTIVE: Acute kidney injury with metabolic acidosis is common in critically ill patients. This study assessed the associations between the use of IV sodium bicarbonate and mortality of patients with acute kidney injury and acidosis. DESIGN: The study was conducted by using data from Beth Israel Deaconess Medical Center, which included several ICUs such as coronary care unit, cardiac surgery recovery unit, medical ICU, surgical ICU, and trauma-neuro ICU. Marginal structural Cox model was used to assess the relationship between receipt of sodium bicarbonate and hospital mortality, allowing pH, PaCO2, creatinine, and bicarbonate concentration as time-varying predictors of sodium bicarbonate exposure while adjusting for baseline characteristics of age, gender, Sequential Organ Failure Assessment score, acute kidney injury stage, Elixhauser score, quick Sequential Organ Failure Assessment, and Simplified Acute Physiology Score II. SETTING: A large U.S.-based critical care database named Medical Information Mart for Intensive Care. PATIENTS: Patients with Kidney Disease: Improving Global Outcomes acute kidney injury stage greater than or equal to 1 (> 1.5 (Equation is included in full-text article.)baseline creatinine) and one measurement of acidosis (pH ≤ 7.2). Baseline creatinine was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 3,406 eligible patients, 836 (24.5%) had received sodium bicarbonate treatment. Patients who received sodium bicarbonate treatment had a higher Sequential Organ Failure Assessment (9 vs 7; p < 0.001), lower pH (7.16 vs 7.18; p < 0.001), and bicarbonate concentration (16.51 ± 7.04 vs 20.57 ± 6.29 mmol/L; p < 0.001) compared with those who did not receive sodium bicarbonate. In the marginal structural Cox model by weighing observations with inverse probability of receiving sodium bicarbonate, sodium bicarbonate treatment was not associated with mortality in the overall population (hazard ratio, 1.16; 95% CI, 0.98-1.42; p = 0.132), but it appeared to be beneficial in subgroups of pancreatitis (hazard ratio, 0.53; 95% CI, 0.28-0.98; p = 0.044) and severe acidosis (pH < 7.15; hazard ratio, 0.75; 95% CI, 0.58-0.96; p = 0.024). Furthermore, sodium bicarbonate appeared to be beneficial in patients with severe bicarbonate deficit (< -50 kg·mmol/L). CONCLUSIONS: In the analysis by adjusting for potential confounders, there is no evidence that IV sodium bicarbonate is beneficial for patients with acute kidney injury and acidosis. Although the study suggested potential beneficial effects in some highly selected subgroups, the results need to be validated in experimental trials.

Declining Mortality in Patients With Acute Respiratory Distress Syndrome: An Analysis of the Acute Respiratory Distress Syndrome Network Trials.

OBJECTIVES: There has been multiple advances in the management of acute respiratory distress syndrome, but the temporal trends in acute respiratory distress syndrome-related mortality are not well known. This study aimed to investigate the trends in mortality in acute respiratory distress syndrome patients over time and to explore the roles of daily fluid balance and ventilation variables in those patients. DESIGN: Secondary analysis of randomized controlled trials conducted by the Acute Respiratory Distress Syndrome Network from 1996 to 2013. SETTING: Multicenter study involving Acute Respiratory Distress Syndrome Network trials. PATIENTS: Patients with acute respiratory distress syndrome. INTERVENTIONS: None. MEASURES AND MAIN RESULTS: Individual patient data from 5,159 acute respiratory distress syndrome patients (excluding the Late Steroid Rescue Study trial) were enrolled in this study. The crude mortality rate decreased from 35.4% (95% CI, 29.9-40.8%) in 1996 to 28.3% (95% CI, 22.0-34.7%) in 2013. By adjusting for the baseline Acute Physiology and Chronic Health Evaluation III, age, ICU type, and admission resource, patients enrolled from 2005 to 2010 (odds ratio, 0.61; 95% CI, 0.50-0.74) and those enrolled after 2010 (odds ratio, 0.73; 95% CI, 0.58-0.92) were associated with lower risk of death as compared to those enrolled before 2000. The effect of year on mortality decline disappeared after adjustment for daily fluid balance, positive end-expiratory pressure, tidal volume, and plateau pressure. There were significant trends of declines in daily fluid balance, tidal volume, and plateau pressure and an increase in positive end-expiratory pressure over the 17 years. CONCLUSIONS: Our study shows an improvement in the acute respiratory distress syndrome-related mortality rate in the critically ill patients enrolled in the Acute Respiratory Distress Syndrome Network trials. The effect was probably mediated via decreased tidal volume, plateau pressure, and daily fluid balance and increased positive end-expiratory pressure.

Perceived differences between intensivists and infectious diseases consultants facing antimicrobial resistance: a global cross-sectional survey.

To identify differences in perception on multi-drug-resistant (MDR) organisms and their management at intensive care units (ICU). A cross-sectional survey was conducted. A proposal addressing a pathogen priority list (PPL) for ICU, arising from the TOTEM study, was compared with a sample of global experts in infections in critically ill patients. The survey was responded by 129 experts. Globally, ESBL Enterobacteriaceae, followed by carbapenem-resistant Acinetobacter baumannii and carbapenem-resistant Klebsiella pneumoniae, were the main concerns. Some differences in opinion were identified between 63 (49%) ICU physicians (ICU/anesthesiology) and 43 (33%) infectious disease consultants (ID physicians/microbiologists). The pathogens most concerning in the ICU for intensivists were ESBL Enterobacteriaceae (38%) versus carbapenem-resistant A. baumannii (48.3%) for ID consultants, (p < 0.05). Increasing number of ID consultants over intensivists (26% vs 14%) reported difficulty in choosing initial therapy for carbapenem-resistant A. baumannii. For intensivists, the urgent measures to limit development of antibiotic resistance were headed by cohort measures (26.3%) versus increasing nurse/patient ratio (32.5%) for ID consultants, (p < 0.05). Regarding effectiveness to prevent MDR development and spread, education programs (42.4%) were the priority for intensivists versus external consultation (35.7%) for ID consultants. Finally, both groups agreed that carbapenem resistance was the most pressing concern (> 70%) regarding emerging resistance. Differences in priorities regarding organisms, infection control practices, and educational priorities were visualized between ID/clinical microbiologists and ICU/anesthesiologists. Multi-disciplinary collaboration is required to achieve best care for ICU patients with severe infections.

A global priority list of the TOp TEn resistant Microorganisms (TOTEM) study at intensive care: a prioritization exercise based on multi-criteria decision analysis.

The World Health Organization (WHO) proposed a global priority pathogen list (PPL) of multidrug-resistant (MDR) bacteria. Our current objective was to provide global expert ranking of the most serious MDR bacteria present at intensive care units (ICU) that have become a threat in clinical practice. A proposal addressing a PPL for ICU, arising from the WHO Global PPL, was developed. Based on the supporting data, the pathogens were grouped in three priority tiers: critical, high, and medium. A multi-criteria decision analysis (MCDA) was used to identify the priority tiers. After MCDA, mortality, treatability, and cost of therapy were of highest concern (scores of 19/20, 19/20, and 15/20, respectively) while dealing with PPL, followed by healthcare burden and resistance prevalence. Carbapenem-resistant (CR) Acinetobacter baumannii, carbapenemase-expressing Klebsiella pneumoniae (KPC), and MDR Pseudomonas aeruginosa were identified as critical organisms. High-risk organisms were represented by CR Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and extended-spectrum beta-lactamase (ESBL) Enterobacteriaceae. Finally, ESBL Serratia marcescens, vancomycin-resistant Enterococci, and TMP-SMX-resistant Stenotrophomonas maltophilia were identified as medium priority. We conclude that education, investigation, funding, and development of new antimicrobials for ICU organisms should focus on carbapenem-resistant Gram-negative organisms.

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care.

BACKGROUND AND OBJECTIVES: Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI. METHODS: AKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1. MAIN RESULTS: Of the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively). CONCLUSIONS: The XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.