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Med Care. 1999 Jul;37(7):625-36.

Prediction of readmissions after CABG using detailed follow-up data: the Israeli CABG Study (ISCAB)

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  • 1Department of Health Services Research, Ministry of Health, Israel.



To use detailed pre-discharge follow-up data to predict readmissions within 3 months after Coronary Artery Bypass Grafting (CABG).


A prospective nationwide study (ISCAB) of 4,835 patients undergoing isolated CABG in Israel in 1994. Survivors of the initial hospitalization were candidates for the readmission study.


Patient information was prospectively collected from preoperative interviews and hospital follow-up. Readmissions' data were obtained from the National Hospital Admission Registry. Logistic and multinomial models were constructed for total and cause-specific readmissions, respectively.


Of CABG survivors, 1,094 (24.1%) were rehospitalized within 3 months of the original surgery. Significant multivariate predictors of total readmissions included the following: preoperative co-morbidities; operative factors; immediate post-operative complications and socio-demographic characteristics as well as provider characteristics. However, the logistic model had low predictive power (c-statistic = 0.65). The heterogeneous reasons for readmissions were classified into specific serious cardiac diagnoses (19.0%), other cardiac reasons (35.4%), specific infections at the site of the operation (10.2%), other infections (7.3%), and various other reasons (23.0%). The multinomial model for cause-specific readmissions caused by either serious cardiac reasons or wound infection had a higher predictive value (c-statistics of 0.75, 0.72, respectively).


Total readmissions after CABG in Israel were difficult to predict, even with an extensive pre-discharge follow-up data. We propose that reasons for readmission vary from true emergencies to nonspecific causes, with the latter related to a lack of support services in the community. We suggest that cause-specific rehospitalizations could be a better outcome for evaluating quality of care.

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