A compartment modeling approach to reconstruct and analyze gender and age-grouped CoViD-19 Italian data for decision-making strategies.

BACKGROUND: Available national public data are often too incomplete and noisy to be used directly to interpret the evolution of epidemics over time, which is essential for making timely and appropriate decisions. The use of compartment models can be a worthwhile and attractive approach to address this problem. The present study proposes a model compartmentalized by sex and age groups that allows for more complete information on the evolution of the CoViD-19 pandemic in Italy. MATERIAL AND METHODS: Italian public data on CoViD-19 were pre-treated with a 7-day moving average filter to reduce noise. A time-varying susceptible-infected-recovered-deceased (SIRD) model distributed by age and sex groups was then proposed. Recovered and infected individuals distributed by groups were reconstructed through the SIRD model, which was also used to simulate and identify optimal scenarios of pandemic containment by vaccination. The simulation started from realistic initial conditions based on the SIRD model parameters, estimated from filtered and reconstructed Italian data, at different pandemic times and phases. The following three objective functions, accounting for total infections, total deaths, and total quality-adjusted life years (QALYs) lost, were minimized by optimizing the percentages of vaccinated individuals in five different age groups. RESULTS: The developed SIRD model clearly highlighted those pandemic phases in which younger people, who had more contacts and lower mortality, infected older people, characterized by a significantly higher mortality, especially in males. Optimizing vaccination strategies yielded different results depending on the cost function used. As expected, to reduce total deaths, the suggested strategy was to vaccinate the older age groups, whatever the baseline scenario. In contrast, for QALYs lost and total infections, the optimal vaccine solutions strongly depended on the initial pandemic conditions: during phases of high virus diffusion, the model suggested to vaccinate mainly younger groups with a higher contact rate. CONCLUSION: Because of the poor quality and insufficient availability of stratified public pandemic data, ad hoc information filtering and reconstruction procedures proved essential. The time-varying SIRD model, stratified by age and sex groups, provided insights and additional information on the dynamics of CoViD-19 infection in Italy, also supporting decision making for containment strategies such as vaccination.

DNA-recognition process described by MD simulations of the lactose repressor protein on a specific and a non-specific DNA sequence.

The lactose repressor protein may bind DNA in two possible configurations: a specific one, if the DNA sequence corresponds to a binding site, and a non-specific one otherwise. To find its target sequences, the lactose repressor first binds non-specifically to DNA, and subsequently, it rapidly searches for a binding site. Atomic structures of non-specific and specific complexes are available from crystallographic and nuclear magnetic resonance experiments. However, what remains unknown is a detailed description of the steps that transform the non-specific complex into the specific one. Here, how the protein first recognizes its binding site has been studied using molecular dynamics simulations. The picture that emerges is that of a protein that is as mobile when interacting with non-specific DNA sequences as when free in solution. This high degree of mobility allows the protein to rapidly sample different DNA sequences. In contrast, when the protein encounters a binding site, the configuration ensemble collapses, and the protein sliding movements along the DNA sequence become scarce. The binding energies in the specific and non-specific complexes were analysed using the Molecular Mechanics Poisson Boltzmann Surface Area approach. These results represent a first step towards a throughout characterization of the DNA-recognition process.

Effects of the protonation state of the EEEE motif of a bacterial Na(+)-channel on conduction and pore structure.

A distinctive feature of prokaryotic Na(+)-channels is the presence of four glutamate residues in their selectivity filter. In this study, how the structure of the selectivity filter, and the free-energy profile of permeating Na(+) ions are altered by the protonation state of Glu177 are analyzed. It was found that protonation of a single glutamate residue was enough to modify the conformation of the selectivity filter and its conduction properties. Molecular dynamics simulations revealed that Glu177 residues may adopt two conformations, with the side chain directed toward the extracellular entrance of the channel or the intracellular cavity. The likelihood of the inwardly directed arrangement increases when Glu177 residues are protonated. The presence of one glutamate residue with its chain directed toward the intracellular cavity increases the energy barrier for translocation of Na(+) ions. These higher-energy barriers preclude Na(+) ions to permeate the selectivity filter of prokaryotic Na(+)-channels when one or more Glu177 residues are protonated.

A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients.

BACKGROUND: Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes. METHODS: A naïve Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation. RESULTS: After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors. CONCLUSIONS: The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice.

Plethysmographic assessment of tidal expiratory flow limitation.

Chronic obstructive pulmonary disease (COPD) patients often experience tidal expiratory flow-limitation (tEFL), a condition causing respiratory and cardiovascular detrimental effects. As the appearance of tEFL should increase expiratory (Rexp) relative to inspiratory (Rins) resistance, we hypothesized that Rexp/Rins can be used to detect tEFL. Rexp/Rins was measured with a commercial plethysmograph in 109 healthy subjects and, before and after bronchodilation (BD), in 64 COPD patients, 36 with and 28 without tEFL according to the NEP technique. Before BD, the median (interquartile range) of Rexp/Rins was significantly greater (P < 0.001) in COPD patients with tEFL (2.47(3.06;7.07)) than in COPD patients without tEFL (1.63(1.44;1.82)) and in healthy subjects (1.52(1.35;1.62)). In COPD patients Rexp/Rins above 1.98 predicted the presence of tEFL with 96 % specificity and 92 % sensitivity, Rexp2/Rins performing even better. After BD the predictive ability of Rexp/Rins slightly declined, but remained elevated. The non-invasive measurement of Rexp/Rins is an easy, inexpensive, routinely usable method to detect tEFL in spontaneously breathing COPD subjects.

A simple clinical model for planning transfusion quantities in heart surgery.

BACKGROUND: Patients undergoing heart surgery continue to be the largest demand on blood transfusions. The need for transfusion is based on the risk of complications due to poor cell oxygenation, however large transfusions are associated with increased morbidity and risk of mortality in heart surgery patients. The aim of this study was to identify preoperative and intraoperative risk factors for transfusion and create a reliable model for planning transfusion quantities in heart surgery procedures. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Heliox administration in anesthetized rabbits with spontaneous inspiratory flow limitation.

We investigated the effects of heliox administration (80% helium in O2) on tidal inspiratory flow limitation (tIFL) occurring in supine anesthetized spontaneously breathing rabbits, regarded as an animal model of obstructive apnea-hypopnea syndrome. 22 rabbits were instrumented to record oro-nasal mask flow, airway opening, tracheal and esophageal pressures, and diaphragm and genioglossus electromyographic activities while breathing either room air or heliox, and, in 12 rabbits, also during the application of continuous positive airway pressure (CPAP; 6 cmH2O). For the group, heliox increased peak inspiratory flow, ventilation (18 ± 11%), peak inspiratory tracheal and dynamic transpulmonary pressures, but in no animal eliminated tIFL, as instead CPAP did in all. Muscle activities were unaffected by heliox. In the presence of IFL the increase in flow with heliox (ΔV̇ifl) varied markedly among rabbits (2 to 49%), allowing the distinction between responders and non-responders. None of the baseline variables discriminated responders and non-responders. However, fitting the Rohrer equation (R = K1 + K2V̇) to the tracheal pressure-flow relationship over the first 0.1 s of inspiration while breathing air allowed such discrimination on the basis of larger K2 in responders (0.005 ± 0.002 versus 0.002 ± 0.001 cmH2O·s2·ml-2; P < 0.001), suggesting a corresponding difference in the relative contribution of laminar and turbulent flow. The differences in ΔV̇ifl between responders and non-responders were simulated by modeling the collapsible segment of the upper airways as a non-linear resistor and varying its pressure-volume curve, length, and diameter, thus showing the importance of mechanical and geometrical factors in determining the response to heliox in the presence of tIFL.NEW & NOTEWORTHY In an obstructive sleep apnea rabbit model, heliox never abolishes tidal inspiratory flow limitation (IFL), but increases inspiratory flow and tidal volume, substantially in some and nearly nil in other animals. Positive response to heliox cannot be predicted on the basis of breathing pattern characteristics or upper airway resistance that preceded IFL onset, but is related to the mechanical and geometrical features of upper airway collapsible segment, as indicated by model simulation.

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system.

BACKGROUND: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. METHODS: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. RESULTS: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. CONCLUSIONS: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context.

Helium-oxygen ventilation in the presence of expiratory flow-limitation: a model study.

A comparison between air and heliox (80% helium-20% oxygen) ventilation was performed using a mathematical, non-linear dynamic, morphometric model of the respiratory system. Different obstructive conditions, all causing expiratory flow limitation (EFL), were simulated during mechanical ventilation to evaluate and interpret the effects of heliox on tidal EFL and dynamic hyperinflation. Relative to air ventilation, intrinsic positive end-expiratory pressure did not change with heliox if the obstruction was limited to the peripheral airways, i.e. beyond the seventh generation. When central airways were also involved, heliox reduced dynamic hyperinflation (DH) if the flow-limiting segment remained in the fourth to seventh airway generation during the whole expiration, but produced only minor effects if, depending on the contribution of peripheral to total apparent airway resistance, the flow-limiting segment moved eventually to the peripheral airways. In no case did heliox abolish EFL occurring with air ventilation, indicating that any increase in driving pressure would be without effect on DH. Hence, to the extent that chronic obstructive pulmonary disease (COPD) affects primarily the peripheral airways, and causes EFL through the same mechanisms operating in the model, heliox administration should not be expected to appreciably reduce DH in the majority of COPD patients who are flow-limited at rest.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

BACKGROUND: Popular 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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 comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part I: model planning.

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.

A multivariate Bayesian model for assessing morbidity after coronary artery surgery.

INTRODUCTION: 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.

Bias-Exchange Metadynamics Simulations: An Efficient Strategy for the Analysis of Conduction and Selectivity in Ion Channels.

Conduction through ion channels possesses two interesting features: (i) different ionic species are selected with high-selectivity and (ii) ions travel across the channel with rates approaching free-diffusion. Molecular dynamics simulations have the potential to reveal how these processes take place at the atomic level. However, analysis of conduction and selectivity at atomistic detail is still hampered by the short time scales accessible by computer simulations. Several algorithms have been developed to "accelerate" sampling along the slow degrees of freedom of the process under study and thus to probe longer time scales. In these algorithms, the slow degrees of freedom need to be defined in advance, which is a well-known shortcoming. In the particular case of ion conduction, preliminary assumptions about the number and type of ions participating in the permeation process need to be made. In this study, a novel approach for the analysis of conduction and selectivity based on bias-exchange metadynamics simulations was tested. This approach was compared with umbrella sampling simulations, using a model of a Na(+)-selective channel. Analogous conclusions resulted from both techniques, but the computational cost of bias-exchange simulations was lower. In addition, with bias-exchange metadynamics it was possible to calculate free energy profiles in the presence of a variable number and type of permeating ions. This approach might facilitate the definition of the set of collective variables required to analyze conduction and selectivity in ion channels.

Digital dermoscopy analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study.

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

A naïve approach for deriving scoring systems to support clinical decision making.

RATIONALE, AIMS AND OBJECTIVES: Scoring systems are frequently proposed in medicine to summarize a set of qualitative and quantitative items by means of a numeric score. Their design often requires modelling ability and subjective judgments. This can make it difficult to adapt a scoring system to a clinical setting different from that in which the system was developed. The objective of this study was to discuss an approach to derive scoring systems, which can be easily modified and matched to any scenario. METHODS: A naïve Bayes approach was used to develop a scoring system that is completely defined by descriptive tables obtained by frequency counts from the training set. The approach was implemented to build a locally customized scoring system for planning transfusion requirements after cardiac surgery. The performance of this system was evaluated and compared with that of a logistic regression model designed using the same predictors. The working sample was a set of 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. RESULTS: The area under the receiver operating characteristic curve was equal to 0.811 and 0.824 for the scoring system and for the logistic regression model, respectively. This result proves that this global index of discrimination capacity was virtually identical and very good for both models. The values of sensitivity, specificity and overall correct-classification percentage obtained by the leave-one-out method were practically the same for the two models (73.9% versus 75.3%). CONCLUSIONS: An easy-fitting and trustworthy scoring system can be directly developed using a naïve Bayes approach. The simplicity of its design allows the system to be customized to any specific institution and updated regularly. This aspect has important practical implications because it can encourage the use of scoring systems among clinicians, enabling their performance to be properly assessed in a wider clinical context.

A functional mathematical model to simulate the single-breath nitrogen washout.

A nonlinear dynamic model is proposed to reproduce and interpret the influence of pulmonary inhomogeneities on the single-breath nitrogen washout (SBNW) curve. The model is characterized by two parallel zones. In each zone, the upper airways are described by a Rohrer resistor. Intermediate airways are represented as a collapsible segment, the volume of which depends on transmural pressure. Smaller airways are described by a resistance which increases when transpulmonary pressure decreases. The respiratory region is modeled as a Voigt element. Three different conditions were simulated: a reference case, characterized by airway-parameter values for normal conditions, and two pathological states corresponding to different levels of disease. In the reference case, a straight line was a good approximation of SBNW phase III and the last point of departure of the nitrogen trace from this line unambiguously identified the onset of phase IV. The slope of phase III rose with disease severity (from a 1.1% increase in nitrogen concentration per 1000 ml of expired volume in the reference case to 3.6% and 7.7% in the pathological cases) and the distinction between phases III and IV became less evident. The results obtained indicate that the slope of phase III depends primarily on nitrogen-concentration differences between lung zones, as determined by different mechanical properties of the respiratory airways. In spite of the simplified representation of the lungs, the similarity of the simulation results to actual data suggests that the proposed model describes important physiological mechanisms underlying changes observed during SBNW in normal and pathological patients.

A naïve Bayes classifier for planning transfusion requirements in heart surgery.

RATIONALE, AIMS AND OBJECTIVES: Transfusion of allogeneic blood products is a key issue in cardiac surgery. Although blood conservation and standard transfusion guidelines have been published by different medical groups, actual transfusion practices after cardiac surgery vary widely among institutions. Models can be a useful support for decision making and may reduce the total cost of care. The objective of this study was to propose and evaluate a procedure to develop a simple locally customized decision-support system. METHODS: We analysed 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. Univariate statistical tests were performed to identify a set of preoperative and intraoperative variables as likely independent features for planning transfusion quantities. These features were utilized to design a naïve Bayes classifier. Model performance was evaluated using the leave-one-out cross-validation approach. All computations were done using spss and matlab code. RESULTS: The overall correct classification percentage was not particularly high if several classes of patients were to be identified. Model performance improved appreciably when the patient sample was divided into two classes (transfused and non-transfused patients). In this case the naïve Bayes model correctly classified about three quarters of patients with 71.2% sensitivity and 78.4% specificity, thus providing useful information for recognizing patients with transfusion requirements in the specific scenario considered. CONCLUSIONS: Although the classifier is customized to a particular setting and cannot be generalized to other scenarios, the simplicity of its development and the results obtained make it a promising approach for designing a simple model for different heart surgery centres needing a customized decision-support system for planning transfusion requirements in intensive care unit.

Estimation of expiratory resistance: a simulation study.

It is of particular importance to detect and quantify obstructive pathological conditions in mechanically ventilated patients, especially in the presence of expiratory flow limitation (EFL), in order to help the clinicians in the choice of the most appropriate ventilation and pharmacological strategies. Aim of this work is to test by simulation a non invasive procedure for estimating the total apparent expiratory resistance of the respiratory system (Rtae). The proposed procedure is based on a time-varying two-element viscoelastic model characterized by the variable resistance Rtae and by a constant compliance estimated by the end-inspiratory occlusion technique. A non linear, dynamic, morphometric model of respiratory mechanics, based on Weibel's representation of the tracheobronchial tree, was used to simulate normal and obstructive respiratory conditions, leading to EFL, during artificial ventilation. The proposed resistance was computed in all simulated cases when the 50% and the 75% of tidal volume has been exhaled during a normal expiration. Rtae appeared to be dependent on the degree of airway obstruction and could provide useful information on how the airway compression varies during expiration.

A simulation study of expiratory flow limitation in obstructive patients during mechanical ventilation.

Although normal lungs may be represented satisfactorily by symmetrical architecture, pathological conditions generally require accounting for asymmetrical branching of the bronchial tree, since lung heterogeneity may be significant in respiratory diseases. In the present study, a recently proposed symmetrical dynamic morphometric model of the human lung, based on Weibel's regular dichotomy, was adapted to simulate different physiopathological scenarios of lung heterogeneity. The asymmetrical architecture was mimicked by modeling different conductive airway compartments below the main bronchi, each compartment being characterized by regular branching. The respiratory zone and chest wall were described by a Voigt body and a constant elastance, respectively. Simulation results allowed us to investigate the influence of the main mechanisms involved in expiratory flow limitation and dynamic hyperinflation in mechanically ventilated COPD patients. In brief, they showed that convective gas acceleration plays a key role in reproducing a negative relationship between driving pressure and expiratory flow. Moreover, reduced lung elastance due to emphysema resulted in a remarkable increase in dynamic hyperinflation, although it did not significantly modify expiratory flow limitation. Finally, the presence of a normal lung compartment masked pathological behaviors, preventing standard techniques from revealing expiratory flow limitation in affected compartments.