Development and comparison of 1-year survival models in patients with primary bone sarcomas:External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model

BACKGROUND: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.’s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort. MATERIAL AND METHODS: The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000–June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models. RESULTS: External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077–0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12–0.16). CONCLUSION: External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort

The impact of comorbidity on mortality in Danish sarcoma patients from 2000-2013: A nationwide population-based multicentre study

<div><p>Introduction</p><p>Sarcoma is a rare type of cancer. The incidence increases with age and elderly patients may have comorbidity that affects the prognosis. The aim of this study was to describe the type and prevalence of comorbidity in a nationwide population-based study in Denmark from 2000–2013 and to analyse the impact of the different comorbidities on mortality.</p><p>Material and methods</p><p>The Danish Sarcoma Registry is a national clinical database containing all patients with sarcoma in the extremities or trunk wall from 2000 and onwards. By linking data to other registries, we were able to get patient information on an individual level including date and cause of death as well as the comorbidity type up to 10 years prior to the sarcoma diagnosis. Based on diseases in the Charlson Comorbidity Index, we pooled the patients into six categories: no comorbidity, cardiopulmonary disease, gastrointestinal disease, neurovascular disease, malignant neoplasms, and miscellaneous (diabetes, renal and connective tissue diseases). 2167 patients were included.</p><p>Results</p><p>The prevalence of comorbidity was 20%. For patients with localized disease, comorbidity increased the disease-specific mortality significantly (HR 1.70 (95% CI 1.36–2.13)). For patients with metastatic disease at the time of diagnosis, comorbidity did not affect the disease-specific mortality (HR 1.05 (95% CI 0.78–1.42)). The presence of another cancer diagnosis within 10 years prior to the sarcoma diagnosis was the only significant independent prognostic factor of disease-specific mortality with an increase of 66% in mortality rate compared to patients with no comorbidity (HR 1,66 (95% CI 1.22–2.25)).</p><p>Conclusion</p><p>Comorbidity is a strong independent prognostic factor of mortality in patients with localized disease. This study emphasizes the need for optimizing the general health of comorbid patients in order to achieve a survival benefit from treatment of patients with localized disease, as this is potentially modifiable.</p></div

Patient characteristics (N: 2167) by Charlson comorbidity index score and by comorbidity categories.

<p>Patient characteristics (N: 2167) by Charlson comorbidity index score and by comorbidity categories.</p

Crude and adjusted analyses for the association between type of comorbidity, and the overall and disease-specific mortality in patients with localized and metastatic disease respectively (n = 2167).

<p>Crude and adjusted analyses for the association between type of comorbidity, and the overall and disease-specific mortality in patients with localized and metastatic disease respectively (n = 2167).</p

Estimates of impact of comorbidity on overall mortality and disease-specific mortality.

<p>Kaplan-Meier estimates of the impact of comorbidity on overall mortality for patients with (A) localized disease and (B) metastatic disease. Cumulative incidence curves of the impact of comorbidity in disease-specific mortality in (C) localized disease and (D) metastatic disease.</p

Charlson comorbidity index, number of patients in each comorbidity category and matching scores for the 19 medical conditions.

<p>Charlson comorbidity index, number of patients in each comorbidity category and matching scores for the 19 medical conditions.</p