Pierpaolo Giomarelli

Myocardial and lung injury after cardiopulmonary bypass: role of interleukin (IL)-10.

BACKGROUND Proinflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha), interleukin (IL)-6, and IL-8 play a key role in the inflammatory cascade after cardiopulmonary bypass (CPB) and may induce cardiac and lung dysfunction. Antiinflammatory cytokines such as IL-10 may also significantly limit these complications. Corticosteroid administration before CPB increases blood IL-10 levels and prevents proinflammatory cytokine release. This study examined the association of increased release of IL-10, stimulated by steroid pretreatment, with reduced myocardial and lung injury after CPB. METHODS Twenty patients undergoing coronary artery bypass grafting (CABG) received either preoperative steroid (n = 10, protocol group) or no steroid (n = 10, control group). Perioperative care was standardized, and all caregivers were blinded to treatment group. Seven intervals of blood samples were obtained and assayed for TNF-alpha, IL-6, IL-8, and IL-10. Various hemodynamic and pulmonary measurements were obtained perioperatively. Levels of MB isoenzyme creatine kinase (CK-MB) were also measured. RESULTS In the protocol group, proinflammatory cytokines were significantly reduced while IL-10 levels were much higher after CPB. The protocol group had a lower alveolar-arterial oxygen gradient and higher ratio of arterial oxygen pressure to fraction of inspired oxygen after CPB. Creatine kinase (CK) and CK-MB were reduced in the patients treated with steroid. Correlations were found between plasma cytokines levels and cardiac index, and CK-MB. CONCLUSIONS This study confirms that corticosteroids abolish proinflammatory cytokines release and increase blood IL-10 levels after CPB. Our findings demonstrate a greater release of IL-10 induced by steroid pretreatment, and better heart and lung protection after CPB.

Heparin-coated circuits for high-risk patients: a multicenter, prospective, randomized trial

BACKGROUND Heparin-coated circuits (HCCs) in low-risk cardiac patients who have coronary revascularization have a limited impact on postoperative outcome. In this prospective, randomized investigation, we studied high-risk patients who had cardiac operations with or without HCCs. METHODS A total of 886 patients who had cardiac operations with cardiopulmonary bypass and at least one patient-related or procedure-related risk factor were enrolled in a multicenter study. They were randomly allocated to have cardiopulmonary bypass with Duraflo II HCCs (HCC group, n = 442) or conventional circuits (control group, n = 444). Postoperative outcome was investigated with respect to the occurrence of organ dysfunction. RESULTS HCCs are associated with a shorter intensive care unit and postoperative hospital stay and with a lower rate of patients having a severely impaired clinical outcome (stay in intensive care unit for more than 5 days or death) (relative risk 0.66, p = 0.045). Lung dysfunction rate was significantly lower for the patients in HCC group affected by chronic obstructive pulmonary disease or who had mitral procedure (relative risk, respectively, 0.31, p = 0.018 and 0.05, p = 0.02). Renal dysfunction rate was significantly (p = 0.05) lower for diabetics in the HCC group (relative risk 0.28). CONCLUSIONS When HCCs were used postoperative times decreased and they had a protective effect on lung and kidney function in high-risk patients.

Video-assisted minimally invasive coronary operations without cardiopulmonary bypass: A multicenter study

OBJECTIVE The need to avoid the risks associated with cardiopulmonary bypass has led to the interest in coronary operations without cardiopulmonary bypass. PATIENTS AND METHODS From April 1994 to September 1995, 44 patients (mean age 63.3 +/- 10.0 years, range 43 to 83 years) were selected for video-assisted coronary artery bypass grafting without cardiopulmonary bypass through a small anterior thoracotomy. Mean preoperative ejection fraction was 50.7% +/- 13.4% (range 20% to 65%). Four patients had left ventricular dysfunction (ejection fraction below 35%). Thirty patients had stable angina (26 with class 3 angina) and 14 had unstable angina. One had recurrent angina (redo). In all cases a small (3.5 to 11 cm) anterior thoracotomy (43 left and one right) was performed and the harvesting of the left internal thoracic artery was video-assisted by thoracoscopy. RESULTS The left internal thoracic artery was used in 43 cases to graft the left anterior descending coronary artery; the right thoracic mammary was used in one case to graft the right coronary artery; the radial artery was used in one case to perform a T-graft to the first diagonal and first marginal branches. We recorded one death (2.3%) and one case of postoperative low cardiac output syndrome (2.3%). Perioperative myocardial infarction occurred in two cases (4.5%). We did not record noncardiac complications (cerebrovascular complications, kidney failure, prolonged ventilatory support, or wound complications). Supraventricular and ventricular arrhythmias were never detected. CONCLUSION According to our experience, video-assisted coronary bypass through a small anterior thoracotomy is a new promising technique that can be considered an alternative in most cases to angioplasty and complementary to conventional coronary operations.

Cerebral autoregulation after hypothermic circulatory arrest in operations on the aortic arch.

BACKGROUND The purpose of this study was to determine whether patients who undergo thoracic aorta repairs with the aid of hypothermic circulatory arrest experience impairments in cerebral autoregulation, and to ascertain the influence of three different techniques of cerebral protection on autoregulatory function. METHODS Sixty-seven patients undergoing elective aortic arch procedures with hypothermic circulatory arrest were tested for cerebral dynamic autoregulation using continuous transcranial Doppler velocity and blood pressure recordings. Twenty-three patients were treated using hypothermic circulatory arrest without adjuncts (group 1), 25 using antegrade cerebral perfusion (group 2), and 19 using retrograde cerebral perfusion (group 3). RESULTS There were no hospital deaths. Two major strokes occurred in this series; 9 patients experienced temporary neurologic dysfunction: in all these patients severe impairment of cerebral autoregulation was observed. Cerebral autoregulation in the immediate postoperative period was preserved only in patients treated with antegrade cerebral perfusion. Severe impairments were observed in the other two groups in which the degree of autoregulatory response was inversely correlated to the duration of the cerebral protection time during hypothermic circulatory arrest. Postoperative improvement of autoregulatory function was observed in the majority of patients. Our data suggest the exposure to brain damage in the presence of autoregulation impairment, thus indicating that postoperative hypotensive phases may further contribute to neurologic impairment. CONCLUSIONS The status of cerebral autoregulation in the postoperative period after hypothermic circulatory arrest procedures is profoundly altered. The degree of impairment is influenced by the cerebral protection technique. This study indicates the beneficial role of antegrade perfusion during hypothermic circulatory arrest for the preservation of this function and suggests that postoperative cerebral autoregulation impairment can be regarded as an expression of central nervous system injury.

Predicting transfusions in cardiac surgery: the easier, the better: the Transfusion Risk and Clinical Knowledge score.

Background and Objectives  Allogeneic blood products transfusions are associated with an increased morbidity and mortality risk in cardiac surgery. At present, a few transfusion risk scores have been proposed for cardiac surgery patients. The present study is aimed to develop and validate a risk score based on adequate statistical analyses joint with a clinical selection of a limited (five) number of preoperative predictors.

Predictive role of pretransplant serum CXCL10 for cardiac acute rejection.

Background. The detection of acute rejection in heart transplantation remains an important feature of transplant management, especially in the early phase. Frequent surveillance with endomyocardial biopsy is necessary, even though it is an invasive procedure and carries a certain risk. Hence, noninvasive biomarkers able to predict acute rejection could be a further helpful tool in patient management. The interferon-&ggr;-inducible chemokine CXCL10 is required for initiation and development of graft failure caused by acute or chronic rejection. It has been reported that CXCL10 serum level is predictive of graft loss in kidney graft recipients. In the present study, we investigated whether pretransplant CXCL10 serum level may be a predictive noninvasive biomarker in heart transplant (HTx) recipients, as well. Methods. Sera from 143 patients undergoing orthotopic heart transplantation were collected before surgery and tested for CXCL10 and CCL22 and compared with serum samples from healthy subjects. Results. We found that basal CXCL10 serum levels in HTx recipients were significantly higher than in healthy subjects, whereas no difference was seen in CCL22 levels. Among HTx recipients, CXCL10 serum levels of rejectors were significantly higher than in nonrejectors. Our results showed that CXCL10 was a significant independent risk factor of several variables and had the highest predictive value for early acute heart rejection, with 160 pg/mL cutoff value. Conclusions. In HTx recipients, measurement of pretransplant CXCL10 serum levels could be a clinically useful tool for predicting cardiac acute rejection, especially in the early posttransplant period.

An adjusted EuroSCORE model for high-risk cardiac patients

OBJECTIVE To verify the accuracy and precision of the logistic European system for cardiac operative risk evaluation (EuroSCORE) in high-risk cardiac surgery patients and to develop and externally validate a new system of recalibration. METHODS The development series included 4279 high-risk patients who had undergone cardiac operations at the IRCCS Policlinico S. Donato. Performance, accuracy, and precision of the logistic EuroSCORE were assessed in this series, using a deciles-based comparison between expected and observed mortality rates, a receiver operating characteristic analysis, and a Hosmer-Lemeshow test for calibration. Differences between predicted and observed mortality rates were mathematically evaluated to develop an adjusted logistic EuroSCORE. This adjusted risk score was subsequently validated with the same approach on an external series of 1459 high-risk patients who had undergone cardiac operations at the Siena hospital. RESULTS The adjusted logistic EuroSCORE was based on five different correction factors applied to the crude logistic EuroSCORE depending on its value. At the external validation, this model provided a good performance, with observed mortality rates not significantly different from the expected in 8 out of 10 deciles of risk distribution. The adjusted EuroSCORE had the same moderate balanced accuracy of the crude logistic EuroSCORE (area under the curve: 0.695), with a better precision (Hosmer-Lemeshow calibration test: chi(2): 3.6, p=0.891). CONCLUSIONS Recalibration of the logistic EuroSCORE in high-risk patients is needed due to its tendency to overestimate the mortality risk. The application of a variable correction factor results in a better performance, increased precision, with unaltered balanced accuracy.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

BackgroundDifferent 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.MethodsModels 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.ResultsScoring 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.ConclusionKnowledge 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.

Systemic arterial waveform analysis and assessment of blood flow during extracorporeal circulation

Background: 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.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

BackgroundPopular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.MethodsEight 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.ResultsScoring 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.ConclusionAlthough 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.