Literature DB >> 10424633

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

Y Zitser-Gurevich1, E Simchen, N Galai, D Braun.   

Abstract

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.

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Year:  1999        PMID: 10424633     DOI: 10.1097/00005650-199907000-00002

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  5 in total

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2.  Predicting readmission risk following coronary artery bypass surgery at the time of admission.

Authors:  Zaher Fanari; Daniel Elliott; Carla A Russo; Paul Kolm; William S Weintraub
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3.  Do hospital length of stay and staffing ratio affect elderly patients' risk of readmission? A nation-wide study of Norwegian hospitals.

Authors:  Torhild Heggestad
Journal:  Health Serv Res       Date:  2002-06       Impact factor: 3.402

4.  Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

Authors:  Milo Engoren; Robert H Habib; John J Dooner; Thomas A Schwann
Journal:  J Clin Monit Comput       Date:  2013-03-16       Impact factor: 2.502

5.  Early Rehospitalization After Prolonged Intensive Care Unit Stay Post Cardiac Surgery: Outcomes and Modifiable Risk Factors.

Authors:  Rizwan A Manji; Rakesh C Arora; Rohit K Singal; Brett M Hiebert; Alan H Menkis
Journal:  J Am Heart Assoc       Date:  2017-02-07       Impact factor: 5.501

  5 in total

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