How PATHFx works
PATHFx is capable of generating the likelihood of survival at several time-points post-surgery, or post-radiation treatment.
By providing basic clinical and physiologic variables, clinicians are able derive a personalized, objective, and unbiased “survival trajectory” for each patient.
Shared decision-making. PATHFx v2.0 offers control by limiting the amount of uncertainty. The tool provides objective estimates of survival, while allowing clinicians with expertise to provide a weighted estimate. Clinicians, families and patients may then use this information to determine the best course of action. For instance, those with short life expectancies may benefit most from palliative radiotherapy or less invasive surgery including intramedullary nailing or other fixation techniques[1, 5, 10]. In contrast, patients with longer survival estimates are generally thought to require more durable reconstructive options that can be expected to last many years, but increase both the perioperative risk and the duration of rehabilitation[2, 4, 8, 9, 11].
What’s different in PATHFx v2.0?
Everything. We re-imagined how the PATHFx tool could integrate with providers, electronic health records, registries and industry. Version 2.0 uses open source architecture in an effort to stimulate collaboration, ease compatibility and streamline the development of new models. In doing so, we created PATHFx v2.0, from scratch, using machine-learning code, developed in-house, specifically for this purpose. The new models include nearly two decades of clinical experience gleaned since the development of the original PATHFx models. We updated the machine learning algorithms to keep pace with the ever-evolving industry standard, and fine-tuned the imputation strategies to better account for missing data in this unique patient population.
Staying current is vital, given the variety of new treatments available for patients with advanced malignancy, including checkpoint inhibition and adoptive immunotherapy. In addition, we’ve developed PATHFx v2.0 to be useful in patients who are undergoing palliative radiotherapy for symptomatic lesions, thereby broadening its indications for use.
U/I U/X
Off the bat, you’ll notice an all-new User Interface and User Experience. This includes examples of “how to treat” and also an opportunity to “ask the experts,” posed to our network of international collaborators. The dialogue includes references to industry, necessary offer creative implant solutions for this challenging patient population.
We created each of the all-new features in PATHFx v2.0 with one goal in mind: Empower clinicians to offer the right intervention(s) to the right patient at the right time.
Validation
PATHFx v2.0 is validated using large international datasets which have been the focus of prior external validation studies[3, 6, 7], in addition to patients who have been treated with palliative radiotherapy alone.
1. Alvi HM, Damron TA. Prophylactic stabilization for bone metastases, myeloma, or lymphoma: do we need to protect the entire bone? Clin Orthop Relat Res. 2013;471:706–714.
2. Forsberg JA, Wedin R, Bauer H. Which implant is best after failed treatment for pathologic femur fractures? Clin Orthop Relat Res. 2013;471:735–740.
3. Forsberg JA, Wedin R, Bauer HCF, Bauer HC, Hansen BH, Laitinen M, Trovik CS, Keller JO, Keller J, Boland PJ, Healey JH. External validation of the Bayesian Estimated Tools for Survival (BETS) models in patients with surgically treated skeletal metastases. BMC Cancer. 2012;12:493.
4. Manoso M, Healey JH. Metastatic Cancer To the Bone. In: DeVita VT, Rosenberg S, Hellman S, eds. Cancer. 7 ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008:2368–2371.
5. Mavrogenis AF, Angelini A, Vottis C, Pala E, Calabrò T, Papagelopoulos PJ, Ruggieri P. Modern Palliative Treatments for Metastatic Bone Disease. The Clinical Journal of Pain. 2016;32:337–350.
6. Ogura K, Gokita T, Shinoda Y, Kawano H, Takagi T, Ae K, Kawai A, Wedin R, Forsberg JA. Can A Multivariate Model for Survival Estimation in Skeletal Metastases (PATHFx) Be Externally Validated Using Japanese Patients? Clin Orthop Relat Res. 2017;475:1–8.
7. Piccioli A, Spinelli MS, Forsberg JA, Wedin R, Healey JH, Ippolito V, Daolio PA, Ruggieri P, Maccauro G, Gasbarrini A, Biagini R, Piana R, Fazioli F, Luzzati A, Di Martino A, Nicolosi F, Camnasio F, Rosa MA, Campanacci DA, Denaro V, Capanna R. How do we estimate survival? External validation of a tool for survival estimation in patients with metastatic bone disease-decision analysis and comparison of three international patient populations. BMC Cancer. 2015;15:424.
8. Steensma M, Boland PJ, Morris CD, Athanasian E, Healey JH. Endoprosthetic treatment is more durable for pathologic proximal femur fractures. Clin Orthop Relat Res. 2012;470:920–926.
9. Steensma M, Healey JH. Trends in the surgical treatment of pathologic proximal femur fractures among Musculoskeletal Tumor Society members. Clin Orthop Relat Res. 2013;471:2000–2006.
10. Ward WG, Holsenbeck S, Dorey FJ, Spang J, Howe D. Metastatic disease of the femur: surgical treatment. Clin Orthop Relat Res. 2003:S230–44.
11. Wedin R, Bauer HC, Wersäll P. Failures after operation for skeletal metastatic lesions of long bones. Clin Orthop Relat Res. 1999:128–139.
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