Predicting non-home discharge in epithelial ovarian cancer patients: External validation of a predictive model.
Abstract
OBJECTIVE:To externally validate a model predicting non-home discharge in women undergoing
primary cytoreductive surgery (CRS) for epithelial ovarian cancer (EOC). METHODS:Women
undergoing primary CRS via laparotomy for EOC at three tertiary medical centers in
an academic health system from January 2010 to December 2015 were included. Patients
were excluded if they received neoadjuvant chemotherapy, had a non-epithelial malignancy,
were not undergoing primary cytoreduction, or lacked documented model components.
Non-home discharge included skilled nursing facility, acute rehabilitation facility,
hospice, or inpatient death. The predicted probability of non-home discharge was calculated
using age, pre-operative CA-125, American Society of Anesthesiologists (ASA) score
and Eastern Cooperative Oncology Group (ECOG) performance status as described in the
previously published predictive model. Model discrimination was calculated using a
concordance index and calibration curves were plotted to characterize model performance
across the cohort. RESULTS:A total of 204 admissions met inclusion criteria. The overall
rate of non-home discharge was 12% (95% CI 8-18%). Mean age was 60.8 years (SD 11.0).
Median length of stay (LOS) was significantly longer for patients with non-home discharge
(8 vs. 5 days, P < 0.001). The predictive model had a concordance index of 0.86 (95%
CI 0.76-0.93), which was similar to model performance in the original study (CI 0.88).
The model provided accurate predictions across all probabilities (0 to 100%). CONCLUSIONS:Non-home
discharge can be accurately predicted using preoperative clinical variables. Use of
this validated non-home discharge predictive model may facilitate preoperative patient
counseling, early discharge planning, and potentially decrease cost of care.
Type
Journal articleSubject
HumansNeoplasms, Glandular and Epithelial
Ovarian Neoplasms
Length of Stay
Patient Discharge
Nomograms
Retrospective Studies
Counseling
Aged
Middle Aged
Hospices
Patient Care Planning
Female
Preoperative Period
Tertiary Care Centers
Cytoreduction Surgical Procedures
Carcinoma, Ovarian Epithelial
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https://hdl.handle.net/10161/19759Published Version (Please cite this version)
10.1016/j.ygyno.2018.08.011Publication Info
Connor, Elizabeth V; Newlin, Erica M; Jelovsek, J Eric; & AlHilli, Mariam M (2018). Predicting non-home discharge in epithelial ovarian cancer patients: External validation
of a predictive model. Gynecologic oncology, 151(1). pp. 129-133. 10.1016/j.ygyno.2018.08.011. Retrieved from https://hdl.handle.net/10161/19759.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
John E Jelovsek
F. Bayard Carter Distinguished Professor of Obstetrics and Gynecology
Dr. Jelovsek is the F. Bayard Carter Distinguished Professor of OBGYN at Duke University
and serves as Director of Data Science for Women’s Health. He is Board Certified in
OBGYN by the American Board of OBGYN and in Female Pelvic Medicine & Reconstructive
Surgery by the American Board of OBGYN and American Board of Urology. He has an active
surgical practice in urogynecology based out of Duke Raleigh. He has expertise as
a clinician-scientist in developing and evaluating clini

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