A four-parameter linear model for analysing cardiorespiratory data in post-operative cardiac patients.
This paper investigates the possibility of characterizing the differences between normal- and high-risk postoperative cardiac patients on the basis of four parameters related to a simple linear model of cardiorespiratory performances. The model comprises three subsystems representing cardiac, vascular and respiratory functions, respectively. These parameters, determined from physiological variables measured in the Intensive Care Unit, seem useful for clinical evaluation of patient status. In fact, their values quantify the improved cardiovascular and respiratory response that normal-risk patients exhibit to increasing metabolic needs after hypothermic treatment, with less utilization of blood oxygen reserve. In addition, a set of three parameters derived from the proposed four allows a prediction of patient class membership with an error lower than 7% when used with a Bayes quadratic classifier.
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Dipartimento di Elettronica Informatica e Sistemistica, Università di Bologna, Italy.
Avanzolini
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Barbini
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Gnudi
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D006348:Cardiac Surgical Procedures / Q000009:adverse effects; D002319:Cardiovascular System / Q000503:physiopathology*; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D008991:Monitoring, Physiologic; D011182:Postoperative Care / Q000379:methods; D012137:Respiratory System / Q000503:physiopathology*; D018570:Risk Assessment; D012720:Severity of Illness Index