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Search Results: 1 - 10 of 597 matches for " Sabino Scolletta "
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Year in review 2010: Critical Care - cardiology
Daniel De Backer, Sabino Scolletta
Critical Care , 2011, DOI: 10.1186/cc10542
Abstract: Vasopressor agents are the cornerstone of therapy for patients with septic shock. However, the time at which vasopressor agents should be introduced is controversial. Hamzaoui and colleagues evaluated the hemodynamic effects of early administration of norepinephrine in 105 patients with septic shock with life-threatening hypotension [1]. These patients had already received a limited amount of fluids (1,000 ml saline). Norepinephrine significantly increased the mean arterial pressure. The cardiac index increased from 3.2 ± 1.0 to 3.6 ± 1.1 l/minute/m2, which can be related to the beta (increase in heart rate or in contractility) or alpha (increase in preload by venous constriction) adrenergic effects of norepinephrine. The heart rate remained unchanged but the stroke volume increased, as a result of an increase in preload (global end-diastolic volume (GEDV) index increased from 694 ± 148 to 742 ± 168 ml/m2) and in contractility (cardiac function index increased from 4.7 ± 1.5 to 5.0 ± 1.6/minute). This effect was also observed in patients with poor cardiac contractility.Another important issue covered is whether beta-blockers agents have some protective effects during critical diseases [2]. In this post hoc analysis of a large trial in 314 critically ill patients with acute respiratory failure, oral beta-blockers at admission were associated with a lower risk of in-hospital (hazard ratio 0.33 (0.14 to 0.74), P = 0.007) and 1-year mortality (hazard ratio 0.29 (0.16 to 0.51), P = 0.0003). The beneficial effect of oral beta-blockers at admission holds true in patients with acute renal failure related to cardiac or noncardiac causes. In addition, administration of oral beta-blockers before hospital discharge had additional beneficial effects on 1-year mortality. These results should be cautiously scrutinized. Information was only available on use of beta-blockers prior to admission and at hospital discharge, but not during the acute stage of the disease. The statement 'c
Why do pulse pressure variations fail to predict the response to fluids in acute respiratory distress syndrome patients ventilated with low tidal volume?
Daniel De Backer, Sabino Scolletta
Critical Care , 2011, DOI: 10.1186/cc10111
Abstract: Fluid management in patients with acute respiratory distress syndrome (ARDS) is particularly difficult [1]. In hemodynamically stable patients fluid restriction is warranted as it decreases the length of need for ventilatory support [2]. However, at the initial phases, patients with ARDS also often present hemodynamic instability and are at risk of tissue hypoperfusion and even tissue hypoxia, which may further contribute to exacerbation of ARDS by boosting activation of inflammation and coagulation [3,4]. Guidance of fluid administration is often complicated by the high pleural pressures, associated with high positive end-expiratory pressure (PEEP) levels, that affect measurements of intravascular pressures. Multiple studies have shown that static indices of preload, being pressures or volumes, often fail to predict the response to fluids. On the contrary, dynamic indices based on heart-lung interactions, such as pulse pressure variations (ΔPP), have repeatedly been found to reliably predict the response to fluids in mechanically ventilated patients.In patients with ARDS, ventilation with low tidal volume is recommended [5]. In patients ventilated with low tidal volume, pulse pressure variations do not predict adequately the response to fluids [6-8]. In this issue of Critical Care, Lakhal and colleagues [1] confirm these findings. In 65 patients with ARDS, Lakhal and colleagues [1] observed that pulse pressure variations moderately predicted the response to fluids and that the predictive value was equivalent to that of pulmonary artery pressure.What does the study by Lakhal and colleagues [1] add to the current literature? First, this trial confirms that pulse pressure variations fail to predict fluid responsiveness in a large series of patients with ARDS ventilated according to current guidelines. Second, this trial tried to evaluate several of the potential mechanisms implicated.In particular, Lakhal and colleagues [1] evaluated the impact of driving pressure. In
suPAR as a prognostic biomarker in sepsis
Katia Donadello, Sabino Scolletta, Cecilia Covajes, Jean-Louis Vincent
BMC Medicine , 2012, DOI: 10.1186/1741-7015-10-2
Abstract: Sepsis is defined as the clinical syndrome resulting from the presence of both infection and a systemic inflammatory response [1]. Sepsis involves the activation of inflammatory and anti-inflammatory mediators, cellular and humoral reactions, and micro- and macro-circulatory alterations. Despite improvements in the management of critically ill patients with serious infections, sepsis is still the leading cause of death in critically ill patients [2]. Early diagnosis of sepsis is vital because rapid, appropriate therapy is associated with improved outcomes [3]. There is, therefore, a need for better techniques to facilitate the diagnosis of sepsis and to monitor its course. Various biomarkers, biological molecules that are characteristic of normal or pathogenic processes and can be easily and objectively measured, have been proposed as being of potential use for sepsis diagnosis, therapeutic guidance, and/or prognostication [4,5], although their exact role remains undefined [3]. The two biomarkers that have been most widely studied and used in patients with sepsis are C-reactive protein (CRP) and procalcitonin (PCT). Levels of both these biomarkers have been demonstrated to be raised in patients with sepsis making them useful diagnostic indicators [6,7]. Importantly, because they lack specificity for sepsis and levels may be raised in other inflammatory diseases, these biomarkers are more useful for ruling out sepsis than for ruling it in, that is, a completely normal value makes a diagnosis of sepsis very unlikely. PCT, in particular, has also been used for therapeutic guidance in patients with various types of infection [7].Recently, the soluble form of the urokinase-type plasminogen activator receptor (suPAR) has attracted scientific interest because it seems to discriminate better than some other biomarkers among patients with different severities of illness [8]. In this narrative review, we discuss the available literature on suPAR in sepsis and provide a descri
Remifentanil in critically ill cardiac patients
Ruggeri Laura,Landoni Giovanni,Guarracino Fabio,Scolletta Sabino
Annals of Cardiac Anaesthesia , 2011,
Abstract: Remifentanil has a unique pharmacokinetic profile, with a rapid onset and offset of action and a plasmatic metabolism. Its use can be recommended even in patients with renal impairment, hepatic dysfunction or poor cardiovascular function. A potential protective cardiac preconditioning effect has been suggested. Drug-related adverse effects seem to be comparable with other opioids. In cardiac surgery, many randomized controlled trials demonstrated that the potential benefits of the use of remifentanil not only include a profound protection against intraoperative stressful stimuli, but also rapid postoperative recovery, early weaning from mechanical ventilation, and extubation. Remifentanil shows ideal properties of sedative agents being often employed for minimally invasive cardiologic techniques, such as transcatheter aortic valve implantation and radio frequency treatment of atrial flutter, or diagnostic procedures such as transesophageal echocardiography. In intensive care units remifentanil is associated with a reduction in the time to tracheal extubation after cessation of the continuous infusion; other advantages could be more evident in patients with organ dysfunction. Effective and safe analgesia can be provided in case of short and painful procedures (i.e. chest drain removal). In conclusion, thanks to its peculiar properties, remifentanil will probably play a major role in critically ill cardiac patients.
High mixed venous oxygen saturation levels do not exclude fluid responsiveness in critically ill septic patients
Dimitrios Velissaris, Charalampos Pierrakos, Sabino Scolletta, Daniel De Backer, Jean Vincent
Critical Care , 2011, DOI: 10.1186/10326
Abstract: This observational study was conducted in a 32-bed university hospital medicosurgical ICU. The hemodynamic response to a fluid challenge was evaluated in 65 critically ill patients with severe sepsis. Patients were divided into two groups (responders and nonresponders) according to their cardiac index (CI) response to the challenge (>10% or <10%).Of the 65 patients, 34 (52%) were fluid responders. Baseline SvO2, CI, heart rate (HR) and mean arterial pressure (MAP) were not statistically different between groups. The responders had lower pulmonary artery occlusion pressure (PAOP) and central venous pressure (CVP) at baseline than the nonresponders. After the fluid challenge, there were no differences between the two groups in MAP, CVP, PAOP or HR. There was no correlation between changes in CI or stroke volume index and baseline SvO2. Receiver operating characteristic analysis showed that SvO2 was not a predictor of fluid responsiveness.The response of septic patients to a fluid challenge is independent of baseline SvO2. The presence of a high SvO2 does not necessarily exclude the need for further fluid administration.Patients with severe sepsis and septic shock typically have decreased vascular tone, with a high cardiac index (CI), low systemic vascular resistance and elevated mixed venous oxygen saturation (SvO2). Fluid resuscitation is essential for the restoration and maintenance of adequate intravascular volume to improve and maintain organ perfusion [1-4]. Natural or artificial colloids or crystalloids may be used for this purpose, as no differences in outcome have been reported related to the type of fluid [5]. As fluid requirements are not easily determined, a fluid challenge technique should be used on a repeated basis according to the patient's response (for example, an increase in blood pressure) and tolerance (for example, excessive increase in cardiac filling pressure) [6-8].By rearranging the Fick equation, SvO2 can be defined as the balance between fou
Intra-arrest hypothermia during cardiac arrest: a systematic review
Sabino Scolletta, Fabio Taccone, Per Nordberg, Katia Donadello, Jean-Louis Vincent, Maaret Castren
Critical Care , 2012, DOI: 10.1186/cc11235
Abstract: We performed a systematic search of PubMed, EMBASE, CINAHL, the Cochrane Library and Ovid/Medline databases using "arrest" OR "cardiac arrest" OR "heart arrest" AND "hypothermia" OR "therapeutic hypothermia" OR "cooling" as keywords. Only studies using intra-arrest therapeutic hypothermia (IATH) were selected for this review. Three authors independently assessed the validity of included studies and extracted data regarding characteristics of the studied cohort (animal or human) and the main outcomes related to the use of IATH: Mortality, neurological status and cardiac function (particularly, rate of ROSC).A total of 23 animal studies (level of evidence (LOE) 5) and five human studies, including one randomized controlled trial (LOE 1), one retrospective and one prospective controlled study (LOE 3), and two prospective studies without a control group (LOE 4), were identified. IATH improved survival and neurological outcomes when compared to normothermia and/or hypothermia after ROSC. IATH was also associated with improved ROSC rates and with improved cardiac function, including better left ventricular function, and reduced myocardial infarct size, when compared to normothermia.IATH improves survival and neurological outcome when compared to normothermia and/or conventional hypothermia in experimental models of CA. Clinical data on the efficacy of IATH remain limited.Use of mild therapeutic hypothermia, or "targeted temperature management" as recently suggested [1], has been recommended in cardiac arrest (CA) patients since the publication of two randomized clinical trials in 2002, the results of which demonstrated a significant improvement in neurologically intact survival for comatose CA patients presenting with ventricular fibrillation (VF) or ventricular tachycardia (VT) [2,3]. Current guidelines suggest that mild therapeutic hypothermia should also be considered in patients presenting with other rhythms although this has been less well studied [4].Although therap
A multivariate Bayesian model for assessing morbidity after coronary artery surgery
Bonizella Biagioli, Sabino Scolletta, Gabriele Cevenini, Emanuela Barbini, Pierpaolo Giomarelli, Paolo Barbini
Critical Care , 2006, DOI: 10.1186/cc4951
Abstract: We analyzed 88 operative risk factors; 1,090 consecutive adult patients who underwent coronary artery bypass grafting were studied. Training and testing data sets of 740 patients and 350 patients, respectively, were used. A stepwise approach enabled selection of an optimal subset of predictor variables. Model discrimination was assessed by receiver operating characteristic (ROC) curves, whereas calibration was measured using the Hosmer-Lemeshow goodness-of-fit test.A set of 12 preoperative, intraoperative and postoperative predictor variables was identified for the Bayes linear model. Bayes and locally customized score models fitted according to the Hosmer-Lemeshow test. However, the comparison between the areas under the ROC curve proved that the Bayes linear classifier had a significantly higher discrimination capacity than the score models. Calibration and discrimination were both much worse with Higgins' original scoring system.Most prediction rules use sequential numerical risk scoring to quantify prognosis and are an advanced form of audit. Score models are very attractive tools because their application in routine clinical practice is simple. If locally customized, they also predict patient morbidity in an acceptable manner. The Bayesian model seems to be a feasible alternative. It has better discrimination and can be tailored more easily to individual institutions.Since the mid-1980s, many predictive models for the assessment of cardiac postoperative mortality have gained popularity in the medical community [1]. Because much has happened in the field of cardiac surgery in recent years, mortality is now low and morbidity has been suggested as both a valid end point and a more attractive target for developing operative risk models [2]. General severity-of-illness models can be inaccurate when applied to specific groups of patients, even if they are valid for comparing outcomes in large numbers of patients [3], and the inaccuracy of these models makes them inap
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning
Emanuela Barbini, Gabriele Cevenini, Sabino Scolletta, Bonizella Biagioli, Pierpaolo Giomarelli, Paolo Barbini
BMC Medical Informatics and Decision Making , 2007, DOI: 10.1186/1472-6947-7-35
Abstract: Models based on Bayes rule, k-nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. k-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.Different quantitative approaches are frequently used in medical practice to estimate the risk of mortality and morbidity (severity-of-illness) of critical patients [1-4]. Particular attention has been paid to
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example
Gabriele Cevenini, Emanuela Barbini, Sabino Scolletta, Bonizella Biagioli, Pierpaolo Giomarelli, Paolo Barbini
BMC Medical Informatics and Decision Making , 2007, DOI: 10.1186/1472-6947-7-36
Abstract: Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery
A simple clinical model for planning transfusion quantities in heart surgery
Felicetta Simeone, Federico Franchi, Gabriele Cevenini, Antonino Marullo, Vittorio Fossombroni, Sabino Scolletta, Bonizella Biagioli, Pierpaolo Giomarelli, Paolo Barbini
BMC Medical Informatics and Decision Making , 2011, DOI: 10.1186/1472-6947-11-44
Abstract: We performed an observational study on 3315 consecutive patients who underwent cardiac surgery between January 2000 and December 2007. To estimate the number of packs of red blood cells (PRBC) transfused during heart surgery, we developed a multivariate regression model with discrete coefficients by selecting dummy variables as regressors in a stepwise manner. Model performance was assessed statistically by splitting cases into training and testing sets of the same size, and clinically by investigating the clinical course details of about one quarter of the patients in whom the difference between model estimates and actual number of PRBC transfused was higher than the root mean squared error.Ten preoperative and intraoperative dichotomous variables were entered in the model. Approximating the regression coefficients to the nearest half unit, each dummy regressor equal to one gave a number of half PRBC. The model assigned 4 units for kidney failure requiring preoperative dialysis, 2.5 units for cardiogenic shock, 2 units for minimum hematocrit at cardiopulmonary bypass less than or equal to 20%, 1.5 units for emergency operation, 1 unit for preoperative hematocrit less than or equal to 40%, cardiopulmonary bypass time greater than 130 minutes and type of surgery different from isolated artery bypass grafting, and 0.5 units for urgent operation, age over 70 years and systemic arterial hypertension.The regression model proved reliable for quantitative planning of number of PRBC in patients undergoing heart surgery. Besides enabling more rational resource allocation of costly blood-conservation strategies and blood bank resources, the results indicated a strong association between some essential postoperative variables and differences between the model estimate and the actual number of packs transfused.Despite published blood conservation and transfusion guidelines, transfusion practices in heart-surgery patients differ widely between physicians and institutions. For ex
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