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.


CONCLUSIONS
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.
Barbini
Emanuela
E
Department of Surgery and Bioengineering, University of Siena, Siena, Italy. barbini4@unisi.it
Cevenini
Gabriele
G
Scolletta
Sabino
S
Biagioli
Bonizella
B
Giomarelli
Pierpaolo
P
Barbini
Paolo
P
D000465:Algorithms; D001499:Bayes Theorem; D006348:Cardiac Surgical Procedures / Q000009:adverse effects*; D005260:Female; D006801:Humans; D007362:Intensive Care Units / Q000592:standards / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D009017:Morbidity; D016571:Neural Networks, Computer; D010359:Patient Readmission / Q000706:statistics & numerical data; D011183:Postoperative Complications / Q000453:epidemiology*; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000706:statistics & numerical data