Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients?

Ogura, Koichi; Gokita, Tabu; Shinoda, Yusuke; Kawano, Hirotaka; Takagi, Tatsuya; Ae, Keisuke; Kawai, Akira; Wedin, Rikard; Forsberg, Jonathan A.

doi:10.1007/s11999-017-5389-3

Cited by 29 publications

(23 citation statements)

References 13 publications

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“…Each has demonstrable prognostic value in estimating survival in patients with advanced cancer [12, 2, 23, 22, 24, 17 -21, 41, 15, 11, 7]. In doing so, we produced models with high discriminatory capacities in an independent cohort, similar to the internal validation statistics reported by Janssen et al [8] as well as the external validation of PATHFX by Forsberg et al [18], Piciolli et al [13] and Ogura et al [37]. This is an important observation, since models exhibit a decrease in discriminative ability when presented with unknown records during external validation.…”

Section: Limitationssupporting

confidence: 54%

“…Piccioli et al [13] observed similar findings in DCA analysis of the PATHFX 3-month model although they found a threshold at 0.90, which is increased to our findings (0.52). Ogura et al [37] observed something similar for their 1 month (0.80) and 3month prediction model (0.60). This large difference of net benefit across threshold probabilities can be explained by our two cohorts being very homogeneous (most variables have the same distribution in the training and the validation cohorts and no significant difference in survival pattern was observed) in contrast to the training and various validation cohorts of the PATHFx.…”

Section: Limitationsmentioning

confidence: 54%

“…This may result in overfitting of the analysis in a BBN and result in overly optimistic results in DCA. As illustrated by the validation of PATHFx [37,13,14], it is a problem to find a cohort of patients having surgical treatment for MBDex with limited missing data. As such, our priority was to use a validation cohort with a minimum of missing data instead of an independent cohort with large amounts of missing data.…”

Section: Limitationsmentioning

confidence: 99%

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Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study

Sørensen¹,

Silvius²,

Khullar³

et al. 2018

TOORTHJ

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Background: Predicting survival for patients with metastatic bone disease in the extremities (MBDex) is important for ensuring the implant will outlive the patient. Hitherto, prediction models for these patients have been constructed using subjective assessments, mostly lacking biochemical variables. Objectives: To develop a prediction model for survival after surgery due to MBDex using biochemical variables and externally validate the model. Methods: We created Bayesian Belief Network models to estimate likelihood of survival 1, 3, 6, and 12 months after surgery using 140 patients. We validated the models using the data of 130 other patients and calculated the area under the Receiver Operator Characteristic curve (ROC). Variables included: hemoglobin, neutrophil-count, C-reactive protein, alkaline phosphatase, primary cancer, Karnofsky-score, ASA-score, visceral metastases, bone metastases, days from diagnose of primary cancer to index surgery for MBDex, ischemic heart disease, diabetes, fracture/impending-fracture and age. Results: Survival probabilities were influenced by all biochemical variables. Validation showed ROC for the 1, 3, 6, and 12-months model: 68% (C.I.: 55%-80%), 69% (C.I.: 60%-78%), 81% (C.I.: 74%-87%) and 84% (C.I.: 77%-90%). Conclusion: Biochemical markers can be incorporated into a prediction model for survival in patients having surgery for MBDex allowing surgeons to offer more objective and individualized treatment options.

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Section: Limitationssupporting

confidence: 54%

Section: Limitationsmentioning

confidence: 54%

Section: Limitationsmentioning

confidence: 99%

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Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study

Sørensen¹,

Silvius²,

Khullar³

et al. 2018

TOORTHJ

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“…No new patient data was developed or accessed. To externally validate the results, one Western data set including 815 patients (Scandinavia) 5 and one Eastern (Japanese) data set with 261 patients were used 8 . Both data sets were used to validate the first‐generation PathFx models and have been described in detail (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…The tool was then externally validated in Scandinavian 5 and Italian 6 patients. The models were then expanded to predict 1‐ and 6‐month survival, 7 which were, in turn, externally validated in Japanese patients 8 . Finally, 18‐ and 24‐month models were added to provide a comprehensive survival profile.…”

Section: Introductionmentioning

confidence: 99%

Validation of PATHFx 2.0: An open‐source tool for estimating survival in patients undergoing pathologic fracture fixation

Overmann¹,

Clark²,

Tsagozis

et al. 2020

Journal Orthopaedic Research

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Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost‐effective solution. Using open‐source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open‐source methods and externally validated in two unique patient populations. The training set of a well‐characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1‐month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open‐source software and externally validated it in two unique patient populations, which can be used as a cost‐effective option to guide surgical decisions in patients with metastatic bone disease.

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Prophylactic Procedures for Orthopedic Pathologies

Akpınar

Özkan

Reddy

et al. 2021

Prophylactic Surgery

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Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients?

Abstract: Level II, prognostic study.

Cited by 29 publications

References 13 publications

Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study

Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study

Validation of PATHFx 2.0: An open‐source tool for estimating survival in patients undergoing pathologic fracture fixation

Prophylactic Procedures for Orthopedic Pathologies

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