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Development and prospective validation of a model estimating risk of readmission in cancer patients
Author(s) -
Schmidt Carl R.,
Hefner Jennifer,
McAlearney Ann S.,
Graham Lisa,
Johnson Kristen,
MoffattBruce Susan,
Huerta Timothy,
Pawlik Timothy M.,
White Susan
Publication year - 2018
Publication title -
journal of surgical oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.201
H-Index - 111
eISSN - 1096-9098
pISSN - 0022-4790
DOI - 10.1002/jso.24968
Subject(s) - medicine , prospective cohort study , emergency medicine , incidence (geometry) , logistic regression , emergency department , cancer , physics , psychiatry , optics
Hospital readmissions among cancer patients are common. While several models estimating readmission risk exist, models specific for cancer patients are lacking. Methods A logistic regression model estimating risk of unplanned 30‐day readmission was developed using inpatient admission data from a 2‐year period ( n = 18 782) at a tertiary cancer hospital. Readmission risk estimates derived from the model were then calculated prospectively over a 10‐month period ( n = 8616 admissions) and compared with actual incidence of readmission. Results There were 2478 (13.2%) unplanned readmissions. Model factors associated with readmission included: emergency department visit within 30 days, >1 admission within 60 days, non‐surgical admission, solid malignancy, gastrointestinal cancer, emergency admission, length of stay >5 days, abnormal sodium, hemoglobin, or white blood cell count. The c‐statistic for the model was 0.70. During the 10‐month prospective evaluation, estimates of readmission from the model were associated with higher actual readmission incidence from 20.7% for the highest risk category to 9.6% for the lowest. Conclusions An unplanned readmission risk model developed specifically for cancer patients performs well when validated prospectively. The specificity of the model for cancer patients, EMR incorporation, and prospective validation justify use of the model in future studies designed to reduce and prevent readmissions.