Effect of mycophenolate mofetil on the white blood cell count and the frequency of infection in systemic lupus erythematosus.

Leukopenia is a common manifestation of SLE. Addition of immunosuppressive therapy in a SLE patient who is already leukopenic is a clinical concern. It could worsen leukopenia, increase the risk of infection, or both. The aim of this study was to analyze the immediate effect of mycophenolate mofetil on the white blood cell count and the rate of infection in SLE patients. Two hundred and forty-four patients within the Hopkins Lupus Cohort who were newly started on mycophenolate mofetil were included in the study. The white blood cell count and interval infection history on the day mycophenolate mofetil was started were compared with the white blood cell count and interval infection history at the next visit. The study was based on 244 patients who began taking mycophenolate mofetil in the cohort. The study population included 47 % African Americans, 44 % Caucasians, and 9 % other ethnicities. There was a slight but not statistically significant increase in the white blood cell count (6.63 vs. 7.01), after starting mycophenolate mofetil. Patients with a baseline white blood cell count \u3c3000/mm(3) did have a statistically significant increase in the white blood cell count after starting mycophenolate mofetil (2.57 vs. 5.13, P = 0.0047). We also found a statistically significant increase in the risk of bacterial infection (but not viral infection) after starting mycophenolate mofetil (4 vs. 9 %, P = 0.0036). Leukopenia does not worsen with mycophenolate mofetil. However, mycophenolate mofetil appears to slightly increase the rate of bacterial (but not viral) infection

The effect of adding comorbidities to current centers for disease control and prevention central-line–associated bloodstream infection risk-adjustment methodology

BACKGROUNDRisk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.METHODSUsing a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.RESULTSOverall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.CONCLUSIONSOur risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.Infect Control Hosp Epidemiol 2017;38:1019–1024</jats:sec

Quantum Dots with Disorder and Interactions: A Solvable Large-g Limit

We show that problem of interacting electrons in a quantum dot with chaotic boundary conditions is solvable in the large-g limit, where g is the dimensionless conductance of the dot. The critical point of the $g=\infty$ theory (whose location and exponent are known exactly) that separates strong and weak-coupling phases also controls a wider fan-shaped region in the coupling-1/g plane, just as a quantum critical point controls the fan in at T>0. The weak-coupling phase is governed by the Universal Hamiltonian and the strong-coupling phase is a disordered version of the Pomeranchuk transition in a clean Fermi liquid. Predictions are made in the various regimes for the Coulomb Blockade peak spacing distributions and Fock-space delocalization (reflected in the quasiparticle width and ground state wavefunction).Comment: 4 pages, 2 figure

Consensus on circulatory shock and hemodynamic monitoring. Task force of the European Society of Intensive Care Medicine.

OBJECTIVE: Circulatory shock is a life-threatening syndrome resulting in multiorgan failure and a high mortality rate. The aim of this consensus is to provide support to the bedside clinician regarding the diagnosis, management and monitoring of shock. METHODS: The European Society of Intensive Care Medicine invited 12 experts to form a Task Force to update a previous consensus (Antonelli et al.: Intensive Care Med 33:575-590, 2007). The same five questions addressed in the earlier consensus were used as the outline for the literature search and review, with the aim of the Task Force to produce statements based on the available literature and evidence. These questions were: (1) What are the epidemiologic and pathophysiologic features of shock in the intensive care unit ? (2) Should we monitor preload and fluid responsiveness in shock ? (3) How and when should we monitor stroke volume or cardiac output in shock ? (4) What markers of the regional and microcirculation can be monitored, and how can cellular function be assessed in shock ? (5) What is the evidence for using hemodynamic monitoring to direct therapy in shock ? Four types of statements were used: definition, recommendation, best practice and statement of fact. RESULTS: Forty-four statements were made. The main new statements include: (1) statements on individualizing blood pressure targets; (2) statements on the assessment and prediction of fluid responsiveness; (3) statements on the use of echocardiography and hemodynamic monitoring. CONCLUSIONS: This consensus provides 44 statements that can be used at the bedside to diagnose, treat and monitor patients with shock

Dynamic and volumetric variables reliably predict fluid responsiveness in a porcine model with pleural effusion

Background: The ability of stroke volume variation (SVV), pulse pressure variation (PPV) and global end-diastolic volume (GEDV) for prediction of fluid responsiveness in presence of pleural effusion is unknown. The aim of the present study was to challenge the ability of SVV, PPV and GEDV to predict fluid responsiveness in a porcine model with pleural effusions. Methods: Pigs were studied at baseline and after fluid loading with 8 ml kg−1 6% hydroxyethyl starch. After withdrawal of 8 ml kg−1 blood and induction of pleural effusion up to 50 ml kg−1 on either side, measurements at baseline and after fluid loading were repeated. Cardiac output, stroke volume, central venous pressure (CVP) and pulmonary occlusion pressure (PAOP) were obtained by pulmonary thermodilution, whereas GEDV was determined by transpulmonary thermodilution. SVV and PPV were monitored continuously by pulse contour analysis. Results: Pleural effusion was associated with significant changes in lung compliance, peak airway pressure and stroke volume in both responders and non-responders. At baseline, SVV, PPV and GEDV reliably predicted fluid responsiveness (area under the curve 0.85 (p<0.001), 0.88 (p<0.001), 0.77 (p = 0.007). After induction of pleural effusion the ability of SVV, PPV and GEDV to predict fluid responsiveness was well preserved and also PAOP was predictive. Threshold values for SVV and PPV increased in presence of pleural effusion. Conclusions: In this porcine model, bilateral pleural effusion did not affect the ability of SVV, PPV and GEDV to predict fluid responsiveness

Diamagnetic Persistent Currents and Spontaneous Time-Reversal Symmetry Breaking in Mesoscopic Structures

Recently, new strongly interacting phases have been uncovered in mesoscopic systems with chaotic scattering at the boundaries by two of the present authors and R. Shankar. This analysis is reliable when the dimensionless conductance of the system is large, and is nonperturbative in both disorder and interactions. The new phases are the mesoscopic analogue of spontaneous distortions of the Fermi surface induced by interactions in bulk systems and can occur in any Fermi liquid channel with angular momentum $m$. Here we show that the phase with $m$ even has a diamagnetic persistent current (seen experimentally but mysterious theoretically), while that with $m$ odd can be driven through a transition which spontaneously breaks time-reversal symmetry by increasing the coupling to dissipative leads.Comment: 4 pages, three eps figure

Electronically available patient claims data improve models for comparing antibiotic use across hospitals: Results from 576 US facilities

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission\u27s antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals\u27 utilization rankings