Estimating Survival in Patients with Operable Skeletal Metastases: An Application of a Bayesian Belief Network

BACKGROUND: Accurate estimations of life expectancy are important in the management of patients with metastatic cancer affecting the extremities, and help set patient, family, and physician expectations. Clinically, the decision whether to operate on patients with skeletal metastases, as well as the choice of surgical procedure, are predicated on an individual patient's estimated survival. Currently, there are no reliable methods for estimating survival in this patient population. Bayesian classification, which includes bayesian belief network (BBN) modeling, is a statistical method that explores conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. Thus, BBN models are being used with increasing frequency in a variety of diagnoses to codify complex clinical data into prognostic models. The purpose of this study was to determine the feasibility of developing bayesian classifiers to estimate survival in patients undergoing surgery for metastases of the axial and appendicular skeleton. METHODS: We searched an institution-owned patient management database for all patients who underwent surgery for skeletal metastases between 1999 and 2003. We then developed and trained a machine-learned BBN model to estimate survival in months using candidate features based on historical data. Ten-fold cross-validation and receiver operating characteristic (ROC) curve analysis were performed to evaluate the BNN model's accuracy and robustness. RESULTS: A total of 189 consecutive patients were included. First-degree predictors of survival differed between the 3-month and 12-month models. Following cross validation, the area under the ROC curve was 0.85 (95% CI: 0.80-0.93) for 3-month probability of survival and 0.83 (95% CI: 0.77-0.90) for 12-month probability of survival. CONCLUSIONS: A robust, accurate, probabilistic naïve BBN model was successfully developed using observed clinical data to estimate individualized survival in patients with operable skeletal metastases. This method warrants further development and must be externally validated in other patient populations

Posterior estimates of survival at 12 months (10 most frequent cases).

<p>The 12-month posterior estimates of survival characterizing the data set by most- to least-frequent cases. The ten most likely cases were selected from 128 possible permutations.</p

Kaplan-Meier curves showing overall survival for patients by diagnosis group.

<p>The overall survival of patients in Group 1 was significantly lower than that of patients in Groups 2 and 3 at the 3-month time point^∞ (<i>p</i><0.0001, log-rank test). Overall survival was significantly different between all groups at the 12-month time point* (<i>p</i><0.0001, log-rank test).</p

Three-month BBN model with posterior distributions depicted as proportions (%) of the training population.

<p>As shown, there are five first-degree predictors of 3-month survival: the surgeon's estimate of survival (“surgeon_estimate_of_survival”), preoperative hemoglobin concentration (“hemoglobin”), preoperative absolute lymphocyte count (“absolute_lymphocyte_count”), ECOG performance status (“ECOG”), and the presence of a completed pathologic fracture (“completed_path_fx”). The network structure indicates that the primary oncologic diagnosis (“dx_grouping”) and the presence of visceral metastases (“visceral_mets”) are both first-degree associates of the surgeon's estimate node.</p

Network features used in the final BBN models.

<p>Features included in the final BBN models. Each feature, its label, description, and possible node states are shown. Continuous variables are represented as categorical variables.</p

Posterior estimates of survival at 3 months (10 most frequent cases).

<p>The 3-month posterior estimates of survival characterizing the data set by most- to least-frequent cases. The ten most likely cases were selected from 256 possible permutations.</p

Correlation of procalcitonin and cytokine expression with dehiscence of wartime extremity wounds

Background: Despite technological advances in the treatment of severe extremity trauma, the timing of wound closure remains the subjective clinical decision of the treating surgeon. Traditional serum markers are poor predictors of wound-healing. The objective of this study was to evaluate the cytokine and chemokine profiles of severe extremity wounds prior to closure to determine if wound effluent markers can be used to predict healing. Methods: Serum and effluent (exudate) samples were collected prospectively from adult volunteers with multiple high-energy penetrating extremity wounds sustained during military combat. Samples were collected prior to definitive wound closure or flap coverage. Wounds were followed clinically for six weeks. The primary clinical outcome measures were wound-healing and dehiscence. Control serum samples were collected from normal age and sex-matched adult volunteers. All samples were analyzed for the following cytokines and chemokines: procalcitonin; eotaxin; granulocyte macrophage colony stimulating factor; interferon (IFN)-γ; interleukin (IL)-1 through 8, KD, 12, 13, and 15; IFN-γ inducible protein-10; monocyte chemotactic protein-1; macrophage inflammatory protein-1α the protein regulated on activation, normal T expressed and secreted (RANTES); and tumor necrosis factor (TNF)-α. Results: Fifty wounds were analyzed in twenty patients. Four of the fifty wounds dehisced. An increased rate of wound dehiscence was observed in patients with a concomitant closed head injury as well as in those with an associated arterial injury of the affected limb (p \u3c 0.05). Among the serum chemokines and cytokines, only serum procalcitonin levels correlated with wound dehiscence (p \u3c 0.05). Effluent analysis showed that, compared with wounds that healed, wounds that dehisced were associated with elevated procalcitonin, decreased RANTES protein, and decreased IL-13 concentrations (p \u3c 0.05). No wound with an effluent procalcitonin concentration of \u3c220 pg/mL, an IL-13 concentration of \u3e12 pg/mL, or a RANTES protein concentration of \u3e1000 pg/mL failed to heal. Conclusions: Effluent procalcitonin, IL-13, and RANTES protein levels as well as serum procalcitonin levels correlate with wound dehiscence following closure of severe open extremity wounds. These preliminary results indicate that effluent biomarker analysis may be an objective means of determining the timing of traumatic wound closure. Level of Evidence: Prognostic Level I. See Instructions to Authors for a complete description of levels of evidence. Copyright © 2008 by The Journal of Bone and Joint Surgery, Incorporated