title: Can We Estimate Short- and Intermediate-term Survival in Patients Undergoing Surgery for Metastatic Bone Disease?
abstract: BACKGROUND
  Objective means of estimating survival can be used to guide surgical decision-making and to risk-stratify patients for clinical trials. Although a free, online tool ( www.pathfx.org ) can estimate 3- and 12-month survival, recent work, including a survey of the Musculoskeletal Tumor Society, indicated that estimates at 1 and 6 months after surgery also would be helpful. Longer estimates help justify the need for more durable and expensive reconstructive options, and very short estimates could help identify those who will not survive 1 month and should not undergo surgery. Thereby, an important use of this tool would be to help avoid unsuccessful and expensive surgery during the last month of life.
  
  
  OBJECTIVE
  We seek to provide a reliable, objective means of estimating survival in patients with metastatic bone disease. After generating models to derive 1- and 6-month survival estimates, we determined suitability for clinical use by applying receiver operator characteristic (ROC) (area under the curve [AUC] > 0.7) and decision curve analysis (DCA), which determines whether using PATHFx can improve outcomes, but also discerns in which kinds of patients PATHFx should not be used.
  
  
  METHODS
  We used two, existing, skeletal metastasis registries chosen for their quality and availability. Data from Memorial Sloan-Kettering Cancer Center (training set, n = 189) was used to develop two Bayesian Belief Networks trained to estimate the likelihood of survival at 1 and 6 months after surgery. Next, data from eight major referral centers across Scandinavia (n = 815) served as the external validation set-that is, as a means to test model performance in a different patient population. The diversity of the data between the training set from Memorial Sloan-Kettering Cancer Center and the Scandinavian external validation set is important to help ensure the models are applicable to patients in various settings with differing demographics and treatment philosophies. We considered disease-specific, laboratory, and demographic information, and the surgeon's estimate of survival. For each model, we calculated the area under the ROC curve (AUC) as a metric of discriminatory ability and the Net Benefit using DCA to determine whether the models were suitable for clinical use.
  
  
  RESULTS
  On external validation, the AUC for the 1- and 6-month models were 0.76 (95% CI, 0.72-0.80) and 0.76 (95% CI, 0.73-0.79), respectively. The models conferred a positive net benefit on DCA, indicating each could be used rather than assume all patients or no patients would survive greater than 1 or 6 months, respectively.
  
  
  CONCLUSIONS
  Decision analysis confirms that the 1- and 6-month Bayesian models are suitable for clinical use.
  
  
  CONCLUSIONS
  These data support upgrading www.pathfx.org with the algorithms described above, which is designed to guide surgical decision-making, and function as a risk stratification method in support of clinical trials. This updating has been done, so now surgeons may use any web browser to generate survival estimates at 1, 3, 6, and 12 months after surgery, at no cost. Just as short estimates of survival help justify palliative therapy or less-invasive approaches to stabilization, more favorable survival estimates at 6 or 12 months are used to justify more durable, complicated, and expensive reconstructive options.
authors.affiliation: Regenerative Medicine, Naval Medical Research Center, 503 Robert Grant Avenue, Silver Spring, MD, 20910, USA. jaforsberg@me.com.|Uniformed Services University-Walter Reed Department of Surgery, Bethesda, MD, USA. jaforsberg@me.com.|Section of Orthopaedics and Sports Medicine, Department of Molecular Medicine and Surgery, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden. jaforsberg@me.com.
authors.forename: Jonathan A
authors.initials: JA
authors.lastname: Forsberg
authors.affiliation: Section of Orthopaedics and Sports Medicine, Department of Molecular Medicine and Surgery, Karolinska Institute, Karolinska University Hospital, Stockholm, Sweden.
authors.forename: Rikard
authors.initials: R
authors.lastname: Wedin
authors.affiliation: Orthopaedic Surgery Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center and Weill College of Medicine, Cornell University, New York, NY, USA.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Boland
authors.affiliation: Orthopaedic Surgery Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center and Weill College of Medicine, Cornell University, New York, NY, USA.
authors.forename: John H
authors.initials: JH
authors.lastname: Healey
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D001859:Bone Neoplasms / Q000401:mortality / Q000556:secondary* / Q000601:surgery*; D003661:Decision Support Techniques; D006801:Humans; D009519:New York City; D010027:Osteotomy / Q000009:adverse effects / Q000401:mortality; D011237:Predictive Value of Tests; D012372:ROC Curve; D012042:Registries; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012537:Scandinavian and Nordic Countries; D013997:Time Factors; D016896:Treatment Outcome