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.

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.

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.

Importance of sociodemographic and morbidity aspects in measuring health-related quality of life: performances of three tools: comparison of three questionnaire scores.

Background: Since health-related quality of life (HRQL) measures are numerous, comparisons have been suggested.

Aim: To compare three HRQL measures: SF6D, HUI3 and EQ5D.

Methods: Three questionnaires (SF36, HUI3, EQ5D) were administered to 1,011 patients attending 16 general practices in two Italian cities.

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.

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.

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.