A case-control study of readmission to the intensive care unit after cardiac surgery

Med Sci Monit. 2013 Feb 28:19:148-52. doi: 10.12659/MSM.883814.

Abstract

Background: The aim of this study was to identify predictors of repeated admission to the intensive care unit (ICU) of patients who underwent cardiac surgery procedures.

Material/methods: This retrospective study analyzed 169 patients who underwent isolated coronary artery bypass grafting (CABG) between January 2009 and December 2010. The case group contained 54 patients who were readmitted to the ICU during the same hospitalization and the control group comprised 115 randomly selected patients.

Results: Logistic regression analysis revealed that independent predictors for readmission to the ICU after CABG were: older age of patients (odds ratio [OR] 1.04; CI 1.004-1.08); body mass index (BMI)>30 kg/m2 (OR 2.55; CI 1.31-4.97); EuroSCORE II>3.9% (OR 3.56; CI 1.59-7.98); non-elective surgery (OR 2.85; CI 1.37-5.95); duration of operation>4 h (OR 3.44; CI 1.54-7.69); bypass time>103 min (OR 2.5; CI 1.37-4.57); mechanical ventilation>530 min (OR 3.98; CI 1.82-8.7); and postoperative central nervous system (CNS) disorders (OR 3.95; CI 1.44-10.85). The hospital mortality of patients who were readmitted to the ICU was significantly higher compared to the patients who did not require readmission (17% vs. 3.8%, p=0.025).

Conclusions: Identification of patients at risk of ICU readmission should focus on older patients, those who have higher BMI, who underwent non-elective surgery, whose operation time was more than 4 hours, and who have postoperative CNS disorders. Careful optimization of these high-risk patients and caution before discharging them from the ICU may help reduce the rate of ICU readmission, mortality, length of stay, and cost.

MeSH terms

  • Adult
  • Aged
  • Cardiac Surgical Procedures / statistics & numerical data*
  • Case-Control Studies
  • Coronary Artery Bypass
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Logistic Models
  • Multivariate Analysis
  • Patient Readmission / statistics & numerical data*
  • Risk Factors
  • Time Factors