1,720,982 research outputs found
Problematiche anestesiologiche.
No full textil paziente anziano con frattura di femore costituisce per l’anestesista una occasione importante per mettere a punto molte tematiche di competenza , dalla valutazione del rischio e delle coomorbilità preoperatorie all’impiego di tecniche di anestesia , di monitoraggio e di trattamento intensivo evolute e successivamente di valutazione di qualità della cura che rispettino le regole fondamentali della medicina dell’evidenza
A multivariate Bayesian model for assessing morbidity after coronary artery surgery.
No full textIntroduction: Although most risk-stratification scores are derived from preoperative patient variables, there are several intraoperative and postoperative variables that can influence prognosis. Higgins and colleagues previously evaluated the contribution of preoperative, intraoperative and postoperative predictors to the outcome. We developed a Bayes linear model to discriminate morbidity risk after coronary artery bypass grafting and compared it with three different score models: the Higgins' original scoring system, derived from the patient's status on admission to the intensive care unit (ICU), and two models designed and customized to our patient population. Methods: 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. Results: 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. Conclusion: 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
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning
Full text linkAbstract Background Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications. Methods 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. Results 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. Conclusion 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.</p
Systemic arterial waveform analysis and assessment of blood flow during extracorporeal circulation
No full textBackground: The pressure recording analytical method (PRAM) is a method for real-time beat-to-beat quantification of peripheral blood flow based on the analysis of arterial waveform morphology. Since PRAM can be implemented in any conditions of flow, whether physiological or artificial, we assessed its accuracy in patients undergoing cardiac surgery during extracorporeal circulation (ECC), using the roller-pump device as the reference gold standard. Methods: We prospectively studied 32 patients undergoing elective coronary surgery. Flow values obtained by PRAM from the radial artery were compared with simultaneous values by thermodilution in physiological conditions of flow and with the roller-pump device readings during ECC. Results: Before and after ECC, the overall estimates of flow measured by PRAM closely agreed with thermodilution (mean difference 0.07±0.40 L/min). During ECC, PRAM estimates of flow also closely correlated with simultaneous pump readings (mean difference 0.11±0.33 L/min). At time of weaning from ECC, two patterns of hemodynamic adaptation were documented by PRAM following resumption of cardiac contraction: in most patients (n =26; 80%), cardiac output (CO) was stable (reduction ≤ 10% compared to the steady ECC phase); six patients (20%) showed a fall in CO exceeding 10% and up to 38%. Conclusions: PRAM provided accurate, continuous quantification of peripheral blood flow during each phase of cardiac surgery, including ECC, and allowed early recognition of patients with low CO during weaning from the pump. © 2006 Edward Arnold (Publishers) Ltd
Therapeutic hypothermia after cardiac arrest: results and prehospital protocol implementation.
No full textErrors in the arterial blood pressure measurement.
No full textntroduction The artefacts affecting arterial wave morphology may compromise recorded values of arterial blood pressure (ABP) and can lead to therapeutic errors. The aim of this study is to evaluate the errors between invasive and noninvasive arterial pressure values, the incidence of artefacts due to an inadequate dynamic response of the transducer-tubing system, and their detection by the ICU staff. Methods Seventy-five consecutive patients (50 male, mean age 55 ± 18) admitted to the ICU for heterogeneous pathologies were enrolled. Inclusion criteria were: the presence of an intra-arterial catheter (IAC) for invasive blood pressure monitoring, and age >18 years. Pregnancy was excluded. At admission and every time the IAC was replaced we acquired invasive systolic, diastolic, and medium arterial pressure values (I-SP, I-DP, I-MP) during hemodynamic stability (variations of mean arterial pressure <10%); at the same time, noninvasive systolic and diastolic arterial pressure values (Ni-SP, Ni-DP) were measured with a sphygmomanometer at the same arm of the IAC. Noninvasive medium arterial pressure (Ni-MP) was calculated as follows: (SP + 2DP) / 3. At every time of the study, before ABP value acquisition, medical and nursing staff answered a questionnaire on the reliability of the arterial waveform. The staff could perform the fast flush test if considered appropriate. However, the fast flush test was executed by the main investigator at the end of questionnaire in all patients. Bland–Altman analysis was performed.
Results We compared 130 pairs of Ni-SP, Ni-DP and Ni-MP and I-SP, I-DP and I-MP. The mean bias between Ni-SP and I-SP was –11 mmHg (limit of agreement (LoA) –43.6 to 21.4 mmHg). The mean bias between Ni-DP and I-DP and between Ni-MP and I-MP was 6.1 mmHg (LoA –15.5 to 27.7 mmHg) and 0.37 mmHg (LoA –21.0 to 21.7 mmHg), respectively. We performed the fast flush test 130 times; an inadequate dynamic response of the transducer-tubing system was observed 55 times: in 45 cases the arterial signal was underdumped and in 10 cases was overdumped. The arterial dumping was correctly detected by the medical staff in 95% of cases, by nursing staff and postgraduates in 35% of cases.
Conclusion The bias between invasive and noninvasive ABP measure can be relevant and mislead in the therapeutic management. These errors can be avoided by identifying the artefacts that affect arterial signal and so the ICU staff must pay attention to the recognition of arterial dumping in critically ill patients
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