Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

AlHilli, Mariam M.; Tran, Christine; Langstraat, Carrie; Martin, John R.; Weaver, Amy L.; McGree, Michaela E.; Mariani, Andrea; Cliby, William A.; Bakkum-Gamez, Jamie N.

doi:10.1016/j.jamcollsurg.2013.04.036

Cited by 29 publications

(15 citation statements)

References 42 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advanced age, worse ECOG performance status, greater ASA score and higher CA-125 were all identified as risk factors for non-home discharges. 10 Advanced age, poor functional status, and inpatient complications were also strongly associated with non-home discharges among patients undergoing colectomy, pancreatectomy, and open abdominal aortic aneurysm repair. 19 …”

Section: Discussionmentioning

confidence: 98%

“…3,9,10 The effect of frailty on patients undergoing urologic surgery, however, has been limited to 30-day morbidity and mortality, 11–14 and its effects on discharge destination have not been well characterized. This is problematic because discharge to a facility can be life changing for an individual, is strongly associated with increased 12 month mortality, 3 and is very costly to society.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preoperative Frailty Is Associated With Discharge to Skilled or Assisted Living Facilities After Urologic Procedures of Varying Complexity

Suskind

Jin

Cooperberg

et al. 2016

Urology

View full text Add to dashboard Cite

Purpose To evaluate the association between frailty and post-operative discharge destination after different types of commonly performed urologic procedures in older patients. Materials and Methods Using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) from 2011–2013, we identified commonly performed inpatient urologic procedures among patients age 65 and older. We then assessed the effect of frailty, measured by the NSQIP Frailty Index (NSQIP-FI), on discharge to a skilled or assisted living facility using logistic regression and assessed the heterogeneity of this effect across procedures using two-level random effects modeling. Results Overall, 1,144 out of 20,794 (5.5%) urologic cases, representing 19 different procedures, resulted in discharge to a skilled or assisted living facility. Cystectomy and large TURBT had the highest percentage at 16.3%. 25% of patients undergoing urology procedures were frail (NSQIP-frailty index [FI] 0.18+), including 9.8% of patients discharged to a facility. Even after adjustment for year, age, race, type of anesthesia, smoking status, recent weight loss, and whether or not the procedure was elective, frailty was strongly associated with discharge to a facility [adjusted OR 3.1 (96% CI 2.5, 3.8) for NSQIP-FI 0.18+ compared to NSQIP FI 0]. This finding was consistent across most procedures of varying complexity with an overall effect of OR 1.6 (95% CI 1.5, 2.0). Conclusions Increasing frailty is associated with discharge to a skilled or assisted living facility across most inpatient urologic procedures evaluated, regardless of complexity. This information is important for preoperative counseling with patients undergoing urologic surgery.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Preoperative Frailty Is Associated With Discharge to Skilled or Assisted Living Facilities After Urologic Procedures of Varying Complexity

Suskind

Jin

Cooperberg

et al. 2016

Urology

View full text Add to dashboard Cite

show abstract

“…Efficiently coordinating transitions of care to postacute providers is also important in terms of cost reduction, especially as some payers move toward reimbursement strategies that bundle acute and postacute care into single episodes of care. 1 Despite these considerations, existing models to predict disposition are rare in all surgical fields [2][3][4][5] and nonexistent in neurosurgical literature. In this article, we use a novel machine learning (ML) technique to predict disposition following meningioma resection from preoperative patient and tumor characteristics.…”

Section: Introductionmentioning

confidence: 99%

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

et al. 2017

View full text Add to dashboard Cite

Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

show abstract

“…12 Predictive models for NHD have been developed in different specialties including orthopedics, gynecology, cardiac and vascular surgery. [13][14][15][16] The use of these models has been associated with decreased postoperative length of stay, cost savings, and improved patient satisfaction. 12,17,18 There are some models to predict patients at risk for NHD following gastric surgery but none for pancreatic surgery.…”

Section: Introductionmentioning

confidence: 99%

Nomogram to predict non-home discharge following pancreaticoduodenectomy in a national cohort of patients

Nassour

Wang

Christie

et al. 2017

HPB

View full text Add to dashboard Cite

show abstract

Risk-Scoring Model for Prediction of Non-Home Discharge in Epithelial Ovarian Cancer Patients

Abstract: Background-Identification of preoperative factors predictive of non-home discharge after surgery for epithelial ovarian cancer (EOC) may aid counseling and optimize discharge planning. We aimed to determine the association between preoperative risk factors and non-home discharge.

Cited by 29 publications

References 42 publications

Preoperative Frailty Is Associated With Discharge to Skilled or Assisted Living Facilities After Urologic Procedures of Varying Complexity

Preoperative Frailty Is Associated With Discharge to Skilled or Assisted Living Facilities After Urologic Procedures of Varying Complexity

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

Nomogram to predict non-home discharge following pancreaticoduodenectomy in a national cohort of patients

Contact Info

Product

Resources

About