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.

Questions/purposes: 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.

Clinical relevance: 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.

Fig. 1A–D

Two implant options for patients with impending pathologic proximal femur fractures are shown. Relatively sick patients with high likelihood of perioperative complications and survival estimates less than 6 months may benefit from intramedullary stabilization. (A) Preoperative and (B) postoperative radiographs are shown. Healthier patients who have a lower likelihood of perioperative complications and more favorable survival estimates of greater than 6 months may require more durable implants, such as the prosthesis shown here. (C) Preoperative and (D) postoperative radiographs are shown. Patients with very short life expectancies of less than 1 month may be candidates for palliative radiotherapy, without surgery.

Fig. 2A–B

The Bayesian Belief Network structure of the (A) 1-month and (B) 6-month models are shown. There are two first-degree associates of 1-month survival. These are the features that are most highly related to the outcome of interest and include the senior surgeon’s estimate of survival and the presence of a completed (as opposed to an impending) pathologic fracture. In comparison, there are six first-degree associates of 6-month survival, including preoperative hemoglobin, absolute lymphocyte count, oncologic diagnosis, presence of a completed pathologic fracture, the number of bone metastases, and the senior surgeon’s estimate of survival.

Fig. 3A–B

The calibration curves show the agreement between observed outcomes and those predicted by the (A) 1-month and (B) 6-month PATHFx models. The shaded region depicts the 95% CI of the predictions. Perfect calibration to the training data should overlie the 45° dotted line. Both models are reasonably well calibrated to the MSKCC training data.

Fig. 4A–B

The decision curves depict the net benefit of the (A) 1-month and (B) 6-month models when applied to the Scandinavian external validation set. Net benefit is defined as a single patient who duly receives the correct treatment based on the model output. Each of the models could be used rather than assume all or none of the patients will survive greater than 1 or 6 months, respectively. However, surgeons requiring a high degree of probability of 6-month survival (B, arrow) before offering endoprostheses should base treatment decisions on the assumption that the patient will not survive greater than 6 months rather than use the PATHFx. This situation can be encountered with very sick patients for whom the risks of arthroplasty outweigh the benefits. In this case, surgeons may choose a less-invasive approach to stabilization, or palliative treatment, depending on the patient’s 1- and 6-month survival estimates.

Fig. 5A–B

This figure from www.pathfx.com shows two typical patient scenarios encountered by surgeons who treat metastatic bone disease. The patient characteristics and PATHFx inputs are shown on the right, and the individualized estimates of survival are displayed as horizontal bar graphs on the left. (A) The first graph shows a very poor survival profile that may help provide surgeons and families with objective information to choose palliative therapy, or in some cases, less-invasive means of surgical stabilization. (B) However, more favorable survival estimates can be used to justify the use of more durable, complicated, and expensive orthopaedic implants, such as conventional, or “tumor” prostheses. ECOG = Eastern Cooperative Oncology Group.