TY - JOUR
T1 - Prediction of readmissions after CABG using detailed follow-up data
T2 - The Israeli CABG study (ISCAB)
AU - Zitser-Gurevich, Yana
AU - Simchen, Elisheva
AU - Galai, Noya
AU - Braun, Dalit
PY - 1999/7
Y1 - 1999/7
N2 - OBJECTIVE. To use detailed pre-discharge follow-up data to predict readmissions within 3 months after Coronary Artery Bypass Grafting (CABG). SETTINGS AND DESIGN. 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. METHODS. 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. RESULTS. 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). CONCLUSIONS. 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.
AB - OBJECTIVE. To use detailed pre-discharge follow-up data to predict readmissions within 3 months after Coronary Artery Bypass Grafting (CABG). SETTINGS AND DESIGN. 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. METHODS. 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. RESULTS. 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). CONCLUSIONS. 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.
KW - Hospital readmission
KW - Prediction
KW - Quality of care
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U2 - 10.1097/00005650-199907000-00002
DO - 10.1097/00005650-199907000-00002
M3 - Article
C2 - 10424633
AN - SCOPUS:0033159748
SN - 0025-7079
VL - 37
SP - 625
EP - 636
JO - Medical care
JF - Medical care
IS - 7
ER -