Determinants of duration of ICU stay after coronary artery bypass graft surgery

Abstract

Prediction of duration of a patient's stay in the ICU after cardiac surgery is difficult. In 652 consecutive adult patients undergoing elective coronary artery bypass graft (CABG) surgery, we analysed prospectively preoperative and immediate postoperative variables thought to influence duration of stay in the ICU. With univariate analysis, we found that age, preoperative left ventricular ejection fraction, bypass time, aortic cross-clamp time, blood transfusions and the number of inotropic agents administered in the immediate postoperative period (for at least 6 h) were significant correlates of duration of stay in the ICU. However, logistic regression analysis showed that the number of inotropes was the most important determinant of stay in the ICU, with an overall prediction accuracy of 94.8%. The main cause of prolonged stay in the ICU (more than 2 days) was low cardiac output syndrome. We conclude that analysis of perioperative variables enhanced our ability to accurately predict duration of stay in the ICU in cardiac surgery patients. The number of inotropic agents administered during the first 6 h after operation was the most important determinant of duration of stay in the ICU.

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery

“…When too many concessions had to be made before the model could be applied to our data, we excluded the prediction model from this validation study. 8,10,12,13,15,25 Therefore, we do not think that the use of registry data has significantly influenced our conclusions. On the contrary, by using registry data we validated the performance of the models in daily clinical practice, which was specifically the aim of our study.…”

“…In February 2008, the MEDLINE and PreMEDLINE databases were searched for studies on prediction models for PICULOS after cardiac 2,11,13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28] we further restricted our analysis to prediction models that used a threshold for PICULOS within the bounds of 24 to 72 hours.…”

“…[5][6][7][8][9][10][11][12][13] Various prediction models have been developed to preoperatively identify patients with an increased risk for postoperative complications and prolonged ICU stay. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Interestingly, all of these prediction models were derived from samples including different patients, as reflected by the different distributions of patient and outcome characteristics. Hence, which model should be preferred in which situation is still unclear.…”

“…As a consequence, we only used this method for the predictors "family history" in the Parsonnet model 35,36 and "preoperative hemoglobin level" in the model of Huijskes et al 17,37 If neither of these methods could be applied, the model was excluded from the analysis. 13,25 …”

“…To allow for a fair comparison of the models, a threshold for observed PICULOS had to be chosen. On the basis of the literature 15,17,21,22,25,26,28 and current clinical practice, we defined observed PICULOS as an ICU length of stay of Ͼ48 hours.In comparing the performance of the models, we focused on discrimination and calibration. The discrimination performance of a model indicates the extent to which the model distinguishes between patients with and without prolonged ICU stay.…”