Trajectory of PaO2/FiO2 Ratio in Shock After Angiotensin II.

INTRODUCTION: High-dose catecholamines can impair hypoxic pulmonary vasoconstriction and increase shunt fraction. We aimed to determine if Angiotensin II (Ang-2) is associated with improved PaO2/FiO2 and SpO2/FiO2 in patients in shock. METHODS: Adult patients at four tertiary care centers and one community hospital in the United States who received Ang-2 from July 2018-September 2020 were included in this retrospective, observational cohort study. PaO2, SpO2, and FiO2 were measured at 13 timepoints during the 48-h before and after Ang-2 initiation. Piecewise linear mixed models of PaO2/FiO2 and SpO2/FiO2 were created to evaluate hourly changes in oxygenation after Ang-2 initiation. The difference in the proportion of patients with PaO2/FiO2 ≤ 300 mm Hg at the time of Ang-2 initiation and 48 h after was also examined. RESULTS: The study included 254 patients. In the 48 h prior to Ang-2 initiation, oxygenation was significantly declining (hourly PaO2/FiO2 change -4.7 mm Hg/hr, 95% CI - 6.0 to -3.5, p < .001; hourly SpO2/FiO2 change -3.1/hr, 95% CI-3.7 to -2.4, p < .001). Ang-2 treatment was associated with significant improvements in PaO2/FiO2 and SpO2/FiO2 in the 48-h after initiation (hourly PaO2/FiO2 change +1.5 mm Hg/hr, 95% CI 0.5-2.5, p = .003; hourly SpO2/FiO2 change +0.9/hr, 95% CI 0.5-1.2, p < .001). The difference in the hourly change in oxygenation before and after Ang-2 initiation was also significant (pinteraction < 0.001 for both PaO2/FiO2 and SpO2/FiO2). This improvement was associated with significantly fewer patients having a PaO2/FiO2 ≤ 300 mm Hg at 48 h compared to baseline (mean difference -14.9%, 95% CI -25.3% to -4.6%, p = .011). Subgroup analysis found that patients with either a baseline PaO2/FiO2 ≤ 300 mm Hg or a norepinephrine-equivalent dose requirement >0.2 µg/kg/min had the greatest associations with oxygenation improvement. CONCLUSIONS: Ang-2 is associated with improved PaO2/FiO2 and SpO2/FiO2. The mechanisms for this improvement are not entirely clear but may be due to catecholamine-sparing effect or may also be related to improved ventilation-perfusion matching, intrapulmonary shunt, or oxygen delivery.

A Multicenter Observational Cohort Study of Angiotensin II in Shock.

INTRODUCTION: Angiotensin II (Ang-2) is a non-catecholamine vasopressor that targets the renin-angiotensin-aldosterone system by agonism of the angiotensin type 1 receptor. Its utility as a vasopressor and a catecholamine-sparing agent was demonstrated in the pivotal ATHOS-3 trial, and numerous post-hoc analyses have shown reduced mortality in certain subsets of the population. METHODS: Consecutive adult patients at 5 centers who received Ang-2 from 2017-2020 were included in this multicenter, retrospective observational cohort study. Patient demographics, hemodynamics, and adverse events were collected. The primary outcomes of the study were the mean difference in MAP and norepinephrine (NEpi)-equivalent dose at hours 0 and 3 following initiation of Ang-2 therapy. RESULTS: One hundred and sixty-two patients were included in this study. The primary outcomes of an increase in MAP (mean difference 9.3 mmHg, 95% CI 6.4-12.1, p < 0.001) and a reduction in NEpi equivalent dose (mean difference 0.16 µg/kg/min, 95% CI 0.10-0.22, p < 0.001) between hours 0 and 3 were statistically significant. The median time to reach a MAP ≥65 was 16 minutes (IQR 5-60 min). After stratifying patients by the NED dose and number of vasopressors administered prior to the initiation of Ang-2, those with a NED dose < 0.2 µg/kg/min, NED dose < 0.3 µg/kg/min, or those on ≤ 3 vasopressors had a significantly greater reduction in NED by hour 3 than those patients above these thresholds. CONCLUSION: Ang-2 is an effective vasopressor and reduces catecholamine dose significantly. Its effect is rapid, with target MAP obtained within 30 minutes in most patients. Given the critical importance of adequate blood pressure to organ perfusion, Ang-2 should be considered when target MAP cannot be achieved with conventional vasopressors. Ang-2 should be utilized early in the course of shock, before the NED dose exceeds 0.2-0.3 µg/kg/min and before the initiation of the fourth-line vasopressor.

Aspirin Use Is Associated With Decreased Mechanical Ventilation, Intensive Care Unit Admission, and In-Hospital Mortality in Hospitalized Patients With Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease-2019 (COVID-19) is associated with hypercoagulability and increased thrombotic risk in critically ill patients. To our knowledge, no studies have evaluated whether aspirin use is associated with reduced risk of mechanical ventilation, intensive care unit (ICU) admission, and in-hospital mortality. METHODS: A retrospective, observational cohort study of adult patients admitted with COVID-19 to multiple hospitals in the United States between March 2020 and July 2020 was performed. The primary outcome was the need for mechanical ventilation. Secondary outcomes were ICU admission and in-hospital mortality. Adjusted hazard ratios (HRs) for study outcomes were calculated using Cox-proportional hazards models after adjustment for the effects of demographics and comorbid conditions. RESULTS: Four hundred twelve patients were included in the study. Three hundred fourteen patients (76.3%) did not receive aspirin, while 98 patients (23.7%) received aspirin within 24 hours of admission or 7 days before admission. Aspirin use had a crude association with less mechanical ventilation (35.7% aspirin versus 48.4% nonaspirin, P = .03) and ICU admission (38.8% aspirin versus 51.0% nonaspirin, P = .04), but no crude association with in-hospital mortality (26.5% aspirin versus 23.2% nonaspirin, P = .51). After adjusting for 8 confounding variables, aspirin use was independently associated with decreased risk of mechanical ventilation (adjusted HR, 0.56, 95% confidence interval [CI], 0.37-0.85, P = .007), ICU admission (adjusted HR, 0.57, 95% CI, 0.38-0.85, P = .005), and in-hospital mortality (adjusted HR, 0.53, 95% CI, 0.31-0.90, P = .02). There were no differences in major bleeding (P = .69) or overt thrombosis (P = .82) between aspirin users and nonaspirin users. CONCLUSIONS: Aspirin use may be associated with improved outcomes in hospitalized COVID-19 patients. However, a sufficiently powered randomized controlled trial is needed to assess whether a causal relationship exists between aspirin use and reduced lung injury and mortality in COVID-19 patients.

Culture-Negative Septic Shock Compared With Culture-Positive Septic Shock: A Retrospective Cohort Study.

OBJECTIVES: To determine the clinical characteristics and outcomes of culture-negative septic shock in comparison with culture-positive septic shock. DESIGN: Retrospective nested cohort study. SETTING: ICUs of 28 academic and community hospitals in three countries between 1997 and 2010. SUBJECTS: Patients with culture-negative septic shock and culture-positive septic shock derived from a trinational (n = 8,670) database of patients with septic shock. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients with culture-negative septic shock (n = 2,651; 30.6%) and culture-positive septic shock (n = 6,019; 69.4%) were identified. Culture-negative septic shock compared with culture-positive septic shock patients experienced similar ICU survival (58.3% vs 59.5%; p = 0.276) and overall hospital survival (47.3% vs 47.1%; p = 0.976). Severity of illness was similar between culture-negative septic shock and culture-positive septic shock groups ([mean and SD Acute Physiology and Chronic Health Evaluation II, 25.7 ± 8.3 vs 25.7 ± 8.1]; p = 0.723) as were serum lactate levels (3.0 [interquartile range, 1.7-6.1] vs 3.2 mmol/L [interquartile range, 1.8-5.9 mmol/L]; p = 0.366). As delays in the administration of appropriate antimicrobial therapy after the onset of hypotension increased, patients in both groups experienced congruent increases in overall hospital mortality: culture-negative septic shock (odds ratio, 1.56; 95% CI [1.47-1.66]; p < 0.0001) and culture-positive septic shock (odds ratio, 1.65; 95% CI [1.59-1.71]; p < 0.0001). CONCLUSIONS: Patients with culture-negative septic shock behave similarly to those with culture-positive septic shock in nearly all respects; early appropriate antimicrobial therapy appears to improve mortality. Early recognition and eradication of infection is the most obvious effective strategy to improve hospital survival.

Case Volume-Outcomes Associations Among Patients With Severe Sepsis Who Underwent Interhospital Transfer.

OBJECTIVES: Case volume-outcome associations bolster arguments to regionalize severe sepsis care, an approach that may necessitate interhospital patient transfers. Although transferred patients may most closely reflect care processes involved with regionalization, associations between sepsis case volume and outcomes among transferred patients are unclear. We investigated case volume-outcome associations among patients with severe sepsis transferred from another hospital. DESIGN: Serial cross-sectional study using the Nationwide Inpatient Sample. SETTING: United States nonfederal hospitals, years 2003-2011. PATIENTS: One hundred forty-one thousand seven hundred seven patients (weighted national estimate of 717,732) with severe sepsis transferred from another acute care hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We examined associations between quintiles of annual hospital severe sepsis case volume for the receiving hospital and in-hospital mortality among transferred patients with severe sepsis. Secondary outcomes included hospital length of stay and total charges. Transferred patients accounted for 13.2% of hospitalized severe sepsis cases. In-hospital mortality was 33.2%, with median length of stay 11 days (interquartile range, 5-22), and median total charge $70,722 (interquartile range, $30,591-$159,013). Patients transferred to highest volume hospitals had higher predicted mortality risk, greater number of acutely dysfunctional organs, and lower adjusted in-hospital mortality when compared with the lowest-volume hospitals (odds ratio, 0.80; 95% CI, 0.67-0.90). In stratified analysis (p < 0.001 for interaction of case volume by organ failure), mortality benefit associated with case volume was limited to patients with single organ dysfunction (n = 48,607, 34.3% of transfers) (odds ratio, 0.66; 95% CI, 0.55-0.80). Treatment at highest volume hospitals was significantly associated with shorter adjusted length of stay (incidence rate ratio, 0.86; 95% CI, 0.75-0.98) but not costs (% charge difference, 95% CI: [-]18.8, [-]37.9 to [+]0.3). CONCLUSIONS: Hospital mortality was lowest among patients with severe sepsis who were transferred to high-volume hospitals; however, case volume benefits for transferred patients may be limited to patients with lower illness severity.

Extracorporeal membrane oxygenation for blastomycosis-related acute respiratory distress syndrome: a case series.

PURPOSE: Blastomyces dermatitidis is a dimorphic fungus endemic to North America capable of causing fatal respiratory failure. Acute respiratory distress syndrome (ARDS) complicates up to 10% of pulmonary blastomycosis in hospitalized patients and carries a mortality of 50-90%. This report describes the clinical course of four consecutive patients with blastomycosis-related ARDS treated with venovenous extracorporeal membrane oxygenation (ECMO) during 2009-2014. CLINICAL FEATURES: Four adults were referred from northwestern Ontario, Canada with progressive respiratory illnesses. All patients developed diffuse bilateral opacities on chest radiography and required mechanical ventilation within 6-72 hr. Patients satisfied Berlin criteria for severe ARDS with trough PaO2/F i O2 ratios of 44-61 on positive end-expiratory pressure of 12-24 cm H2O. Wet mount microscopy from respiratory samples showed broad-based yeast consistent with B.dermatitidis. Despite lung protective ventilation strategies with maximal F i O2 (patients A-D), neuromuscular blockade (patients A-D), inhaled nitric oxide (patients A and D), and prone positioning (patient D), progressive hypoxemia resulted in initiation of venovenous ECMO by hours 24-90 of mechanical ventilation with subsequent de-escalation of ventilatory support. In all four cases, ECMO decannulation was performed (7-23 days), mechanical ventilation was withdrawn (18-52 days), and the patients survived to hospital discharge (31-87 days). CONCLUSION: This report describes the successful application of ECMO as rescue therapy in aid of four patients with refractory blastomycosis-associated ARDS. In addition to early appropriate antimicrobial therapy, transfer to an institution experienced with ECMO should be considered when caring for patients from endemic areas with rapidly progressive respiratory failure.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit.

OBJECTIVE: Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness. DESIGN: We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm. SETTING AND PARTICIPANTS: We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022. RESULTS: 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states. CONCLUSION: We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

BACKGROUND: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. OBJECTIVES: To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. METHODS: We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. RESULTS: Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/µL) (34.8%). CONCLUSIONS: Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.

Emerging concepts in optimizing antimicrobial therapy of septic shock: speed is life but a hammer helps too.

Current research suggests that combination antimicrobial therapy where a given pathogen is covered by two or more antibiotics with differing antimicrobial mechanisms may be useful in a subset of critically ill patients, particularly those with septic shock. This paper elucidates the circumstances under which combination therapy may be useful by reinforcing the observation that beneficial effects are noted in critically ill patients with septic shock and, possibly, severe sepsis. Clinicians may wish to consider this approach with such patients.

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU.

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.

A boosting inspired personalized threshold method for sepsis screening.

Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.

Early Detection of Septic Shock Onset Using Interpretable Machine Learners.

BACKGROUND: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient's progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. METHOD: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. RESULTS: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. CONCLUSION: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

Predictors and outcomes of healthcare-associated infections in COVID-19 patients.

INTRODUCTION: Healthcare-associated infections (HAI) after viral illnesses are important sources of morbidity and mortality. This has not been extensively studied in hospitalized COVID-19 patients. METHODS: This study included all COVID-19-positive adult patients (≥18 years) hospitalized between 01 March and 05 August 2020 at the current institution. The Centers for Disease Control and Prevention definition of HAI in the acute care setting was used. The outcomes that were studied were rates and types of infections and in-hospital mortality. Several multivariable logistic regression models were constructed to examine characteristics associated with development of HAI. RESULTS: Fifty-nine (3.7%) of 1565 patients developed 140 separate HAIs from 73 different organisms: 23 were Gram-positive, 39 were Gram-negative and 11 were fungal. Patients who developed HAI did not have higher odds of death (OR 0.85, 95% CI 0.40-1.81, p = 0.69). HAIs were associated with the use of tocilizumab (OR 5.04, 95% CI 2.4-10.6, p < 0.001), steroids (OR 3.8, 95% CI 1.4-10, p = 0.007), hydroxychloroquine (OR 3.0, 95% CI 1.0-8.8, p = 0.05), and acute kidney injury requiring hemodialysis (OR 3.7, 95% CI 1.1-12.8, p = 0.04). CONCLUSIONS: HAI were common in hospitalized Covid-19 patients. Tocilizumab and steroids were associated with increased risk of HAIs.

Integrated Digital Health System Tools to Support Decision Making and Treatment Preparation in CKD: The PREPARE NOW Study.

RATIONALE & OBJECTIVE: Digital health system tools to support shared decision making and preparation for kidney replacement treatments for patients with chronic kidney disease (CKD) are needed. STUDY DESIGN: Descriptive study of the implementation of digital infrastructure to support a patient-centered health system intervention. SETTING & PARTICIPANTS: 4 CKD clinics within a large integrated health system. EXPOSURE: We developed an integrated suite of digital engagement tools to support patients' shared decision making and preparation for kidney failure treatments. Tools included an automated CKD patient registry and risk prediction algorithm within the electronic health record (EHR) to identify and prioritize patients in need of nurse case management to facilitate shared decision making and preparation for kidney replacement treatments, an electronic patient-facing values clarification tool, a tracking application to document patients' preparation for treatments, and an EHR work flow to broadcast patients' treatment preferences to all health care providers. OUTCOMES: Uptake and acceptability. ANALYTIC APPROACH: Mixed methods. RESULTS: From July 1, 2017, through June 30, 2018, the CKD registry identified 1,032 patients in 4 nephrology clinics, of whom 243 (24%) were identified as high risk for progressing to kidney failure within 2 years. Kidney Transitions Specialists enrolled 117 (48%) high-risk patients by the end of year 1. The values tool was completed by 30/33 (91%) patients who attended kidney modality education. Nurse case managers used the tracking application for 100% of patients to document 287 planning steps for kidney replacement therapy. Most (87%) high-risk patients had their preferred kidney replacement modality documented and displayed in the EHR. Nurse case managers reported that the tools facilitated their identification of patients needing support and their navigation activities. LIMITATIONS: Single institution, short duration. CONCLUSIONS: Digital health system tools facilitated rapid identification of patients needing shared and informed decision making and their preparation for kidney replacement treatments. FUNDING: This work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Project Program Award (IHS-1409-20967). TRIAL REGISTRATION: ClinicalTrials.gov NCT02722382.

A survival benefit of combination antibiotic therapy for serious infections associated with sepsis and septic shock is contingent only on the risk of death: a meta-analytic/meta-regression study.

OBJECTIVE: To assess whether a potential benefit with combination antibiotic therapy is restricted to the most critically ill subset of patients, particularly those with septic shock. DATA SOURCES: OVID MEDLINE (1950-October 2009), EMBASE (1980-October 2009), the Cochrane Central Register of Controlled Trials (to third quarter 2009), the ClinicalTrial.gov database, and the SCOPUS database. STUDY SELECTION: Randomized or observational studies of antimicrobial therapy of serious bacterial infections potentially associated with sepsis or septic shock. Fifty studies met entry criteria. DATA EXTRACTION: Study design, mortality/clinical response, and other variables were extracted independently by two reviewers. When possible, study datasets were split into mutually exclusive groups with and without shock or critical illness. DATA SYNTHESIS: Although a pooled odds ratio indicated no overall mortality/clinical response benefit with combination therapy (odds ratio, 0.856; 95% confidence interval, 0.71-1.03; p = .0943; I = 45.1%), stratification of datasets by monotherapy mortality risk demonstrated substantial benefit in the most severely ill subset (monotherapy risk of death >25%; odds ratio of death, 0.51; 95% confidence interval, 0.41-0.64; I = 8.6%). Of those datasets that could be stratified by the presence of shock/critical illness, the more severely ill group consistently demonstrated increased efficacy of a combination therapy strategy (odds ratio, 0.49; 95% confidence interval, 0.35-0.70; p < .0001; I = 0%). An increased risk of death was found in low-risk patients (risk of death

Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality.

PURPOSE: Hospital occupancy (HospOcc) pressures often lead to longer intensive care unit (ICU) stay after physician recognition of discharge readiness. We evaluated the relationships between HospOcc, extended ICU stay, and patient outcomes. MATERIALS AND METHODS: 7-year retrospective cohort study of 8500 alive discharge encounters from 4 adult ICUs of a tertiary hospital. We estimated associations between i) HospOcc and ICU transfer delay; and ii) ICU transfer delay and hospital mortality. RESULTS: Median (IQR) ICU transfer delay was 4.8 h (1.6-11.7), 1.4% (119) suffered in-hospital death, and 4% (341) were readmitted. HospOcc was non-linearly related with ICU transfer delay, with a spline knot at 80% (mean transfer delay 8.8 h [95% CI: 8.24, 9.38]). Higher HospOcc level above 80% was associated with longer transfer delays, (mean increase 5.4% per % HospOcc increase; 95% CI, 4.7 to 6.1; P < .001). Longer ICU transfer delay was associated with increasing odds of in-hospital death or ICU readmission (odds ratio 1.01 per hour; 95% CI 1.00 to 1.01; P = .04) but not with ICU readmission alone (OR 1.01 per hour; 95% CI 1.00 to 1.01, P = .14). CONCLUSIONS: ICU transfer delay exponentially increased above a threshold hospital occupancy and may be associated with increased hospital mortality.

Fungicidal versus fungistatic therapy of invasive Candida infection in non-neutropenic adults: a meta-analysis.

The purpose of this study was to determine whether fungicidal versus fungistatic pharmacotherapy of invasive candidiasis/candidemia yields superior outcomes. Data sources included MEDLINE (1966-June 2017), EMBASE (1980-June 2017), PubMed (1966-June 2017), Global Health-Ovid (inception to June 2017), LILACS Virtual Health Library (inception to June 2017) and the Cochrane Central Register of Controlled Trials (to 2nd quarter 2017). The ClinicalTrial.gov database, the SCOPUS database, SIGLE (System for Information on Grey Literature) and Google Scholar were also utilised to search for relevant studies. Randomised studies of any pharmacotherapy of invasive candidiasis including candidemia using a fungicidal (amphotericin B or echinocandin compound) versus a fungistatic (triazole) compound in adolescent or adult non-neutropenic patients. Eight studies met the inclusion criteria. Pooled odds ratios demonstrated an advantage of fungicidal therapy with respect to early therapeutic success (OR 1.61, 95% CI 1.27-2.03, p < 0.0001, I2 = 0%) and persistence or recurrence of infection (OR 0.51, 95% CI 0.35-0.74, p = 0.0005, I2 = 0%) but no advantage for late survival (OR 0.97, 95% CI 0.77-1.21, p = 0.77, I2 = 0%). Fungicidal therapy of invasive candidiasis and candidemia is associated with a higher probability of early therapeutic success and decreased probability of persistent or recurrent infection. However, there is no improvement in survival.