Predicting non-home discharge in epithelial ovarian cancer patients: External validation of a predictive model.
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2018-10
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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.
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Connor, Elizabeth V, Erica M Newlin, J Eric Jelovsek and Mariam M AlHilli (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.
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John E Jelovsek
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 clinical prediction models using traditional biostatistics and machine learning approaches. These “individualized” patient-centered prediction tools aim to improve decision-making regarding the prevention of lower urinary tract symptoms (LUTS) and other pelvic floor disorders after childbirth (PMID:29056536), de novo stress urinary incontinence and other patient-perceived outcomes after pelvic organ prolapse surgery, risk of transfusion during gynecologic surgery, and urinary outcomes after mid-urethral sling surgery (PMID: 26942362). He also has significant expertise in leading trans-disciplinary teams through NIH-funded multi-center research networks and international settings. As alternate-PI for the Cleveland Clinic site in the NICHD Pelvic Floor Disorders Network, he was principal investigator on the CAPABLe trial (PMID: 31320277), one of the largest multi-center trials for fecal incontinence studying anal exercises with biofeedback and loperamide for the treatment of fecal incontinence. He was the principal investigator of the E-OPTIMAL study (PMID: 29677302), describing the long-term follow up sacrospinous ligament fixation compared to uterosacral ligament suspension for apical vaginal prolapse. He was also primary author on research establishing the minimum important clinical difference for commonly used measures of fecal incontinence. Currently, he serves as co-PI in the NIDDK Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01DK097780-05) where he has been involved in studies in the development of Symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index-29 (LURN SI-29) and LURN SI-10 questionnaires for men and women with LUTS. He is also the site-PI for the PREMIER trial (1R01HD105892): Patient-Centered Outcomes of Sacrocolpopexy versus Uterosacral Ligament Suspension for the Treatment of Uterovaginal Prolapse.
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