Predictors for hospital readmission after cardiac surgery.

BACKGROUND AND AIM OF THE STUDY: Unplanned hospital readmissions are responsible for increased health care costs and have direct influence on patient quality of life. The aim of the study was to determine the predictors for hospital readmission after open-heart surgery. METHODS: Prospective study analyzed all patients who underwent cardiac surgery in the year 2012. Follow-up period was one year from the date of operation. Patients were divided in two groups based on their readmission status. RESULTS: In the study period of one year, 1268 patients who underwent cardiac surgery were included. A total of 121 patients (9.54%) were readmitted within one year after the operation. The main reasons for readmission were congestive heart failure (17.3%), sternal dehiscence (14.9%), rhythm and conduction disturbances (14.9%), wound infection (11.6%), recurrent angina pectoris (11.6%), and pericardial effusion (10.7%). Independent predictors for hospital readmission were previous stroke (p = 0.002), chronic heart failure (p < 0.0005), and postoperative pericardial effusion (p = 0.006). CONCLUSIONS: Our study determined risk factors and predictors for hospital readmission after cardiac surgery. This may help to reduce readmission rates.

Locally derived system for cardiac operative risk evaluation.

INTRODUCTION: During the last two decades, many authors have found that European Systems for Cardiac Operative Risk Evaluation (additive and logistic models) overestimate the risk in cardiac surgery. The new European model has recently been introduced as an update to previous versions. The aim of the study was to investigate the significance of locally derived system for cardiac operative risk evaluation and to compare its predictive power with the existing European systems. MATERIAL AND METHODS: For developing a local risk prediction model, data from 2681 patients submitted to cardiac surgery at the Institute of Cardiovascular Diseases Vojvodina have thoroughly been collected. Logistic regression analysis was used to construct a local model for prediction of outcome. The evaluation of the local model and three European systems was performed by comparing the observed and expected hospital mortality. RESULTS: The difference between the predicted and observed mortality regardless of the type of surgery was statistically insignificant for the additive European system (p=0.073) and the local model (p=0.134). The logistic European system overestimated the operative risk, while the new European model underestimated mortality. In coronary surgery, all models, except the logistic European system, performed well. In valvular surgery, the new European model and the local model underestimated mortality significantly, while the additive and logistic European models performed well. In combined surgery, the new European system significantly underestimated mortality (p=0.029), while the local model performed well (p=0.252). CONCLUSION: The locally derived model shows satisfactory results, with good calibration and discriminative power. The local model specifically outperforms all other European systems in terms of discriminatory power in combined surgery subset.