The Concept of Risk Assessment and Being Unfit for Surgery.

The concept of risk assessment and the identification of surgical unfitness for vascular intervention is a particularly controversial issue today as the minimally invasive surgical population has increased not only in volume but also in complexity (comorbidity profile) and age, requiring an improved pre-operative selection and definition of high risk. A practical step by step (three steps, two points for each) approach for surgical risk assessment is suggested in this review. As a general rule, the identification of a "high risk" patient for vascular surgery follows a step by step process where the risk is clearly defined, quantified (when too "high"?), and thereby stratified based on the procedure, the patient, and the hospital, with the aid of predictive risk scores. However, there is no standardized, updated, and objective definition for surgical unfitness today. The major gap in the current literature on the definition of high risk in vascular patients explains the lack of sound validated predictive systems and limited generalizability of risk scores in vascular surgery. In addition, the concept of fitness is an evolving tool and many traditional high risk criteria and definitions are no longer valid. Given the preventive purpose of most vascular procedures performed in elderly asymptomatic patients, the decision to pursue or withhold surgery requires realistic estimates not only regarding individual peri-operative mortality, but also life expectancy, healthcare priorities, and the patient's primary goals, such as prolongation of life versus maintenance of independence or symptom relief. The overall "frailty" and geriatric risk burden, such as cognitive, functional, social, and nutritional status, are variables that should be also included in the analyses for stratification of surgical risk in elderly vascular patients.

A simplified model for mitral valve dynamics.

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics are little known. A simple non-linear rotational spring model is developed and implemented to capture the dynamics of the mitral valve. A measured pressure difference curve was used as the input into the model, which represents an applied torque to the anatomical valve chords. A range of mechanical model hysteresis states were investigated to find a model that best matches reported animal data of chord movement during a heartbeat. The study is limited by the use of one dataset found in the literature due to the highly invasive nature of getting this data. However, results clearly highlight fundamental physiological issues, such as the damping and chord stiffness changing within one cardiac cycle, that would be directly represented in any mitral valve model and affect behaviour in dysfunction. Very good correlation was achieved between modeled and experimental valve angle with 1-10% absolute error in the best case, indicating good promise for future simulation of cardiac valvular dysfunction, such as mitral regurgitation or stenosis. In particular, the model provides a pathway to capturing these dysfunctions in terms of modeled stiffness or elastance that can be directly related to anatomical, structural defects and dysfunction.

Patient specific identification of the cardiac driver function in a cardiovascular system model.

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically.

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets.

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care.