51 research outputs found

    The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

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    Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

    Les tests au synacthène® en réanimation (comparaison du test à 1 [mu]g versus 250 [mu]g)

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    LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

    Use of venous-to-arterial carbon dioxide tension difference to guide resuscitation therapy in septic shock

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    International audienceThe mixed venous-to-arterial carbon dioxide (CO2) tension difference [P (v-a) CO2] is the difference between carbon dioxide tension (PCO2) in mixed venous blood (sampled from a pulmonary artery catheter) and the PCO2 in arterial blood. P (v-a) CO2 depends on the cardiac output and the global CO2 production, and on the complex relationship between PCO2 and CO2 content. Experimental and clinical studies support the evidence that P (v-a) CO2 cannot serve as an indicator of tissue hypoxia, and should be regarded as an indicator of the adequacy of venous blood to wash out the total CO2 generated by the peripheral tissues. P (v-a) CO2 can be replaced by the central venous-to-arterial CO2 difference (ΔPCO2), which is calculated from simultaneous sampling of central venous blood from a central vein catheter and arterial blood and, therefore, more easy to obtain at the bedside. Determining the ΔPCO2 during the resuscitation of septic shock patients might be useful when deciding when to continue resuscitation despite a central venous oxygen saturation (ScvO2) > 70% associated with elevated blood lactate levels. Because high blood lactate levels is not a discriminatory factor in determining the source of that stress, an increased ΔPCO2 (> 6 mmHg) could be used to identify patients who still remain inadequately resuscitated. Monitoring the ΔPCO2 from the beginning of the reanimation of septic shock patients might be a valuable means to evaluate the adequacy of cardiac output in tissue perfusion and, thus, guiding the therapy. In this respect, it can aid to titrate inotropes to adjust oxygen delivery to CO2 production, or to choose between hemoglobin correction or fluid/inotrope infusion in patients with a too low ScvO2 related to metabolic demand. The combination of P (v-a) CO2 or ΔPCO2 with oxygen-derived parameters through the calculation of the P (v-a) CO2 or ΔPCO2/arteriovenous oxygen content difference ratio can detect the presence of global anaerobic metabolism

    A comparison between measured and calculated central venous oxygen saturation in critically ill patients.

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    BACKGROUND:Central venous oxygen saturation (ScvO2) is often used to help to guide resuscitation of critically ill patients. The standard gold technique for ScvO2 measurement is the co-oximetry (Co-oximetry_ScvO2), which is usually incorporated in most recent blood gas analyzers. However, in some hospitals, those machines are not available and only calculated ScvO2 (Calc_ScvO2) is provided. Therefore, we aimed to investigate the agreement between Co-oximetry_ScvO2 and Calc_ScvO2 in a general population of critically ill patients and septic shock patients. METHODS:A total of 100 patients with a central venous catheter were included in the study. One hundred central venous blood samples were collected and analyzed using the same point-of-care blood gas analyzer, which provides both the calculated and measured ScvO2 values. Bland and Altman plot, intra-class correlation coefficient (ICC), and Cohen's Kappa coefficient were used to assess the agreement between Co-oximetry_ScvO2 and Calc_ScvO2. Multiple linear regression analysis was performed to investigate the independent explanatory variables of the difference between Co-oximetry_ScvO2 and Calc_ScvO2. RESULTS:In all population, Bland and Altman's analysis showed poor agreement (+4.5 [-7.1, +16.1]%) between the two techniques. The ICC was 0.754 [(95% CI: 0.393-0.880), P< 0.001], and the Cohen's Kappa coefficient, after categorizing the two variables into two groups using a cutoff value of 70%, was 0.470 (P <0.001). In septic shock patients (49%), Bland and Altman's analysis also showed poor agreement (+5.6 [-6.7 to 17.8]%). The ICC was 0.720 [95% CI: 0.222-0.881], and the Cohen's Kappa coefficient was 0.501 (P <0.001). Four independent variables (PcvO2, Co-oximetry_ScvO2, venous pH, and Hb) were found to be associated with the difference between the measured and calculated ScvO2 (adjusted R2 = 0.8, P<0.001), with PcvO2 being the main independent explanatory variable because of its highest absolute standardized coefficient. The area under the receiver operator characteristic curves (AUC) of PcvO2 to predict Co-oximetry_ScvO2 ≥ 70% was 0.911 [95% CI: 0.837-0.959], in all patients, and 0.903 [95% CI: 0.784-0.969], in septic shock patients. The best cutoff value was ≥ 36 mmHg (sensitivity, 88%; specificity, 83%), in all patients, and ≥ 35 mmHg (sensitivity, 94%; specificity, 71%) in septic shock patients. CONCLUSIONS:The discrepancy between the measured and calculated ScvO2 is clinically not acceptable. We do not recommend the use of calculated ScvO2 to guide resuscitation in critically ill patients. In situations where the Co-oximetry technique is not available, relying on PcvO2 to predict the measured ScvO2 value above or below 70% could be an option

    Use of sodium-chloride difference and corrected anion gap as surrogates of Stewart variables in critically ill patients.

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    INTRODUCTION: To investigate whether the difference between sodium and chloride ([Na(+)] - [Cl(-)]) and anion gap corrected for albumin and lactate (AG(corr)) could be used as apparent strong ion difference (SID(app)) and strong ion gap (SIG) surrogates (respectively) in critically ill patients. METHODS: A total of 341 patients were prospectively observed; 161 were allocated to the modeling group, and 180 to the validation group. Simple regression analysis was used to construct a mathematical model between SID(app) and [Na(+)] - [Cl(-)] and between SIG and AG(corr) in the modeling group. Area under the receiver operating characteristic (ROC) curve was also measured. The mathematical models were tested in the validation group. RESULTS: in the modeling group, SID(app) and SIG were well predicted by [Na(+)] - [Cl(-)] and AG(corr) (R(2) = 0.973 and 0.96, respectively). Accuracy values of [Na(+)] - [Cl(-)] for the identification of SID(app) acidosis (47.5 mEq/L) were 0.992 (95% confidence interval [CI], 0.963-1) and 0.998 (95%CI, 0.972-1), respectively. The accuracy of AG(corr) in revealing SIG acidosis (>8 mEq/L) was 0.974 (95%CI: 0.936-0.993). These results were validated by showing excellent correlations and good agreements between predicted and measured SID(app) and between predicted and measured SIG in the validation group (R(2) = 0.977; bias = 0±1.5 mEq/L and R(2) = 0.96; bias = -0.2±1.8 mEq/L, respectively). CONCLUSIONS: SID(app) and SIG can be substituted by [Na(+)] - [Cl(-)] and by AG(corr) respectively in the diagnosis and management of acid-base disorders in critically ill patients

    Determinants of noninvasive ventilation success or failure in morbidly obese patients in acute respiratory failure.

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    PurposeAcute respiratory failure (ARF) is a common life-threatening complication in morbidly obese patients with obesity hypoventilation syndrome (OHS). We aimed to identify the determinants of noninvasive ventilation (NIV) success or failure for this indication.MethodsWe prospectively included 76 consecutive patients with BMI>40 kg/m2 diagnosed with OHS and treated by NIV for ARF in a 15-bed ICU of a tertiary hospital.ResultsNIV failed to reverse ARF in only 13 patients. Factors associated with NIV failure included pneumonia (n = 12/13, 92% vs n = 9/63, 14%; pConclusionsMultiple organ failure and pneumonia were the main factors associated with NIV failure and death in morbidly obese patients in hypoxemic ARF. On the opposite, NIV was constantly successful and could be safely pushed further in case of severe hypercapnic acute respiratory decompensation of OHS

    Correlation and agreement between observed and predicted strong ion gap (SIG) in the cross-validation group.

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    <p>Panel A shows the agreement between observed and predicted SIG (bias = −0.2, limits of agreement 95% = −2.1 to 1.6 mEq/L). Panel B shows the correlation between observed and predicted SIG (R2 = 0.96, P<0.0001).</p

    Subgroups analysis in the septic shock patients of the cross-validation group according to the presence of acute kidney injury and of acute respiratory failure.

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    <p>AKI, acute kidney injury; ARF, acute respiratory failure, SID<sub>app</sub>, apparent strong ion difference; SIG, strong ion gap; AG<sub>corr</sub>, anion gap corrected for albumin and lactate; ICC, intraclass correlation coefficient; CI, confidence interval. Agreement is expressed as bias, (95% limits of agreement).</p

    Subgroups analysis of acid-base variables, agreements and intraclass correlation coefficients between observed and predicted values of SID<sub>app</sub> and of SIG, and kappa coefficients between SID<sub>app</sub> and its surrogate and between SIG and its surrogate in the cross-validation group.

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    <p>SID<sub>app</sub>, apparent strong ion difference; SIG, strong ion gap; AG<sub>corr</sub>, anion gap corrected for albumin and lactate; ICC, intraclass correlation coefficient; CI, confidence interval. Metabolic acidosis = SBE<−2 mEq/L, reference range = −2 mEq/L≤SBE≤+2 mEq/L, and metabolic alkalosis = SBE>+2 mEq/L. Agreement is expressed as bias, (95% limits of agreement). All others data are expressed as median with range (minimum, maximum).</p
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