Prediction of survival after surgical management of femoral metastatic bone disease - A comparison of prognostic models

Prognostic factors and real-life applicability of prognostic models for patients with bone metastases of carcinoma

OBJECTIVE

This study aimed to investigate the factors affecting the survival of patients with bone carcinoma metastases and assess the clinical applicability of existing prognostic models.

METHODS

We retrospectively evaluated 247 patients who presented to our hospital between 2011 and 2021 diagnosed with bone carcinoma metastasis. Demographic data, general health status, primary diagnoses, laboratory and radiological findings, pathological fracture status, treatment methods, and survival times of the patients were recorded, and the effects of these variables on survival time were evaluated. Previously developed Katagiri, Janssen, 2013-Spring, PathFX, and SORG prognostic models were applied, and the predictive performances of these models were evaluated by comparing the predicted survival time with the actual survival time of our patients.

RESULTS

After the multivariate analysis, the following factors were shown to be significantly associated with the survival time of patients: blood hemoglobin and leukocyte levels, lactate dehydrogenase concentration, prognostic nutritional index, body mass index, performance status, medium and fast-growing groups of primary tumors, presence of extraspinal and visceral or brain metastases, and pathological fractures. According to receiver operating characteristics and Brier scores, SORG had the overall highest performance scores, while the Janssen nomogram had the lowest.

CONCLUSION

Our report showed that all prognostic models were clinically applicable, but their performances varied. Among them, the SORG predictive model had the best performance scores overall and is the model the authors suggested for survival prediction among patients with carcinoma bone metastases.

LEVEL OF EVIDENCE

Level IV, Prognostic Study.

Percutaneous Ablation, Osteoplasty, Reinforcement, and Internal Fixation for Pain and Ambulatory Function in Periacetabular Osteolytic Malignancies

Background Osteolytic neoplasms to periacetabular bone frequently cause pain and fractures. Immediate recovery is integral to lifesaving ambulatory oncologic care and maintaining quality of life. Yet, open acetabular reconstructive surgeries are associated with numerous complications that delay cancer treatments. Purpose To determine the effectiveness for short- and long-term pain and ambulatory function following percutaneous ablation, osteoplasty, reinforcement, and internal fixation (AORIF) for periacetabular osteolytic neoplasm. Materials and Methods This retrospective observational study evaluated clinical data from 50 patients (mean age, 65 years ± 14 [SD]; 25 men, 25 women) with osteolytic periacetabular metastases or myeloma. The primary outcome of combined pain and ambulatory function index score (range, 1 [bedbound] through 10 [normal ambulation]) was assessed before and after AORIF at 2 weeks and then every 3 months up to 40 months (overall median follow-up, 11 months [IQR, 4-14 months]). Secondary outcomes included Eastern Cooperative Oncology Group (ECOG) score, infection, transfusion, 30-day readmission, mortality, and conversion hip arthroplasty. Serial radiographs and CT images were obtained to assess the hip joint integrity. The paired t test or Wilcoxon signed-rank test and Kaplan-Meier analysis were used to analyze data. Results Mean combined pain and ambulatory function index scores improved from 4.5 ± 2.4 to 7.8 ± 2.1 (P < .001) and median ECOG scores from 3 (IQR, 2-4) to 1 (IQR, 1-2) (P < .001) at the first 2 weeks after AORIF. Of 22 nonambulatory patients, 19 became ambulatory on their first post-AORIF visit. Pain and functional improvement were retained beyond 1 year, up to 40 months after AORIF in surviving patients. No hardware failures, surgical site infections, readmissions, or delays in care were identified following AORIF. Of 12 patients with protrusio acetabuli, one patient required a conversion hemiarthroplasty at 24 months. Conclusion The ablation, osteoplasty, reinforcement, and internal fixation, or AORIF, technique was effective for short- and long-term improvement of pain and ambulatory function in patients with periacetabular osteolytic neoplasm. © RSNA, 2023.

AAOS Clinical Practice Guideline Summary: Treatment of Metastatic Carcinoma and Myeloma of the Femur

The Musculoskeletal Tumor Society, in partnership with American Society of Clinical Oncology and American Society for Radiation Oncology, has developed a clinical practice guideline to assist providers with the care of patients with metastatic carcinoma and myeloma of the femur. The guideline was developed by an Expert Panel consisting of representatives of all three organizations by American Academy of Orthopaedic Surgeons (AAOS) methodologists using the AAOS standardized guideline development process. A systematic review of the available evidence was conducted, and the identified evidence was rated was rated for quality and potential for bias. Recommendations were developed based on this evidence in a standardized fashion. The guideline was approved by the guideline approval bodies of all three organizations. Thirteen recommendations were synthesized covering relevant subtopics such as imaging, use of bone-modifying agents, radiation therapy, and surgical reconstruction. The consensus of the expert panel was that bone-modifying agents may assist in reducing the incidence of femur fracture, regardless of tumor histology. The panel recommended the use of radiation therapy to decrease the rate of femur fractures for patients considered at increased risk. The panel recommended arthroplasty be considered to improve patient function and decrease the need of postoperative radiation therapy in patients with pathologic fractures in the femur.

Predicting survival of patients with spinal metastatic disease using PathFx 3.0 - A validation study of 668 patients in Sweden

Introduction

PathFx is a computer-based prediction model for estimating survival of patients with bone metastasis. The model has been validated in several studies, but this is the first validation using exclusively patients with spinal metastases.

Research question

Is PathFx 3.0 a tool useful for predicting survival for patients with spinal metastatic disease?

Material and methods

668 patients (67% male, median age 67 years) presenting with spinal metastases at two university hospitals in Sweden 1991-2014 were included. Of those, the majority (82%, n ​= ​551) underwent surgery. Data on all patients was analyzed with PathFx version 3.0, generating a probability of survival at 1, 3, 6, 12, 18 and 24 months. The predictions were compared to real survival data and the precision in estimation was evaluated with Receiver-Operating Characteristic curve (ROC) analysis where the Area Under Curve (AUC) was calculated. Brier score and decision curve analyses were also assessed.

Results

The AUC for 1-, 3-, 6- and 12 months survival predictions were 0.64 (95% CI 0.5-0.71), 0.71 (95% CI 0.67-0.75), 0.70 (95% CI 0.66-0.77) and 0.74 (95% CI 0.70-0.78). For 18- and 24 months survival the AUC were 0.74 (95% CI 0.69-0.78) and 0.76 (95% CI 0.72-0.81). The Brier scores were all 0.23 or lower depending on the estimated survival time.

Discussion and conclusion

PathFx 3.0 is a reasonably reliable tool for predicting survival in patients with spinal metastatic disease. As the PathFx computer model can be updated to reflect advancements in oncology, we suggest this type of model, rather than rigid point-based scoring systems, to be used for estimating survival in patients with metastatic spinal disease in the future.

Can a Bayesian belief network for survival prediction in patients with extremity metastases (PATHFx) be externally validated in an Asian cohort of 356 surgically treated patients?

BACKGROUND AND PURPOSE

Predicted survival may influence the treatment decision for patients with skeletal extremity metastasis, and PATHFx was designed to predict the likelihood of a patient dying in the next 24 months. However, the performance of prediction models could have ethnogeographical variations. We asked if PATHFx generalized well to our Taiwanese cohort consisting of 356 surgically treated patients with extremity metastasis.

PATIENTS AND METHODS

We included 356 patients who underwent surgery for skeletal extremity metastasis in a tertiary center in Taiwan between 2014 and 2019 to validate PATHFx's survival predictions at 6 different time points. Model performance was assessed by concordance index (c-index), calibration analysis, decision curve analysis (DCA), Brier score, and model consistency (MC).

RESULTS

The c-indexes for the 1-, 3-, 6-, 12-, 18-, and 24-month survival estimations were 0.71, 0.66, 0.65, 0.69, 0.68, and 0.67, respectively. The calibration analysis demonstrated positive calibration intercepts for survival predictions at all 6 timepoints, indicating PATHFx tended to underestimate the actual survival. The Brier scores for the 6 models were all less than their respective null model's. DCA demonstrated that only the 6-, 12-, 18-, and 24-month predictions appeared useful for clinical decision-making across a wide range of threshold probabilities. The MC was < 0.9 when the 6- and 12-month models were compared with the 12-month and 18-month models, respectively.

INTERPRETATION

In this Asian cohort, PATHFx's performance was not as encouraging as those of prior validation studies. Clinicians should be cognizant of the potential decline in validity of any tools designed using data outside their particular patient population. Developers of survival prediction tools such as PATHFx might refine their algorithms using data from diverse, contemporary patients that is more reflective of the world's population.

Functional and Survival Outcomes of Patients following the Harrington Procedure for Complex Acetabular Metastatic Lesions

Background: The Harrington surgical technique makes it possible to manage complex, extensive bone lesions using pins and cement to consolidate bone for acetabular cup positioning. However, it may be associated with a high reoperation rate, and the functional results of this surgery are not precisely described in the literature. Methods: In a monocentric retrospective study including all patients operated on using the Harrington procedure associated with THA between 2005 and 2020, we aimed to assess preoperative and postoperative function, reoperation-free survival, and overall survival. Results: Functional improvement was significant for Parker scores (preoperative: 3.6 ± 2.0; 6-month follow-up: 6.6 ± 3.2; 12-month follow-up: 7.6 ± 2.1) and Musculoskeletal Tumor Society (MSTS) scores (preoperative: 31.1 ± 16.2%; 6-month follow-up: 67.7 ± 30.6%; 12-month follow-up: 82.4 ± 24.0%). Of the 21 patients included, the reoperation-free survival rate was 76.1% [CI 95%: 58.1–99.7] at six and twelve months, with the main complications being pin migration (50.0%) and infection (25%). The patient overall survival rate was 76.2% [95% CI: 59.9–96.7] at six months and 61.9% [95% CI: 59.9–96.7] at 12 months. Discussion: These results underlined significant functional improvements following a conventional Harrington procedure, with acceptable reoperation rates.

Treatment of Bone Metastasis

The incidence of metastatic bone disease is increasing, as patients with cancer are living longer [...]

The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities—A Comparison of Prognostic Models

Individualized survival prognostic models for symptomatic patients with appendicular metastatic bone disease are key to guiding clinical decision-making for the orthopedic surgeon. Several prognostic models have been developed in recent years; however, most orthopedic surgeons have not incorporated these models into routine practice. This is possibly due to uncertainty concerning their accuracy and the lack of comparison publications and recommendations. Our aim was to conduct a review and quality assessment of these models. A computerized literature search in MEDLINE, EMBASE and PubMed up to February 2022 was done, using keywords: “Bone metastasis”, “survival”, “extremity” and “prognosis”. We evaluated each model’s performance, assessing the estimated discriminative power and calibration accuracy for the analyzed patients. We included 11 studies out of the 1779 citations initially retrieved. The 11 studies included seven different models for estimating survival. Among externally validated survival prediction scores, PATHFx 3.0, 2013-SPRING and potentially Optimodel were found to be the best models in terms of performance. Currently, it is still a challenge to recommend any of the models as the standard for predicting survival for these patients. However, some models show better performance status and other quality characteristics. We recommend future, large, multicenter, prospective studies to compare between PATHfx 3.0, SPRING 2013 and OptiModel using the same external validation dataset.

Machine learning algorithms to estimate 10-Year survival in patients with bone metastases due to prostate cancer: toward a disease-specific survival estimation tool

Background

Prognostic indicators, treatments, and survival estimates vary by cancer type. Therefore, disease-specific models are needed to estimate patient survival. Our primary aim was to develop models to estimate survival duration after treatment for skeletal-related events (SREs) (symptomatic bone metastasis, including impending or actual pathologic fractures) in men with metastatic bone disease due to prostate cancer. Such disease-specific models could be added to the PATHFx clinical-decision support tool, which is available worldwide, free of charge. Our secondary aim was to determine disease-specific factors that should be included in an international cancer registry.

Methods

We analyzed records of 438 men with metastatic prostate cancer who sustained SREs that required treatment with radiotherapy or surgery from 1989–2017. We developed and validated 6 models for 1-, 2-, 3-, 4-, 5-, and 10-year survival after treatment. Model performance was evaluated using calibration analysis, Brier scores, area under the receiver operator characteristic curve (AUC), and decision curve analysis to determine the models’ clinical utility. We characterized the magnitude and direction of model features.

Results

The models exhibited acceptable calibration, accuracy (Brier scores < 0.20), and classification ability (AUCs > 0.73). Decision curve analysis determined that all 6 models were suitable for clinical use. The order of feature importance was distinct for each model. In all models, 3 factors were positively associated with survival duration: younger age at metastasis diagnosis, proximal prostate-specific antigen (PSA) < 10 ng/mL, and slow-rising alkaline phosphatase velocity (APV).

Conclusions

We developed models that estimate survival duration in patients with metastatic bone disease due to prostate cancer. These models require external validation but should meanwhile be included in the PATHFx tool. PSA and APV data should be recorded in an international cancer registry.

Outcomes and prognostic factors after surgery for bone metastases in the extremities and pelvis: A retrospective analysis of 140 patients

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Pathological fracture, visceral metastasis and lung cancer were negative prognostic factors for patients with bone metastases in the extremities and pelvis.

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Complications occurred in every fourth patient within the first 30 postoperative days.

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No significant differences in short- and long-term outcomes were observed between endoprosthetic replacement and internal fixation.

Background

Methods

Results

Conclusions

International Validation of the SORG Machine-learning Algorithm for Predicting the Survival of Patients with Extremity Metastases Undergoing Surgical Treatment

BACKGROUND

The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.

QUESTIONS/PURPOSES

(1) Is the SORG-MLA generalizable to Taiwanese patients for predicting 90-day and 1-year mortality? (2) Is the ECOG score an independent factor associated with 90-day and 1-year mortality while controlling for SORG-MLA predictions?

METHODS

All 356 patients who underwent surgery for long-bone metastases between 2014 and 2019 at one tertiary care center in Taiwan were included. Ninety-eight percent (349 of 356) of patients were of Han Chinese descent. The median (range) patient age was 61 years (25 to 95), 52% (184 of 356) were women, and the median BMI was 23 kg/m2 (13 to 39 kg/m2). The most common primary tumors were lung cancer (33% [116 of 356]) and breast cancer (16% [58 of 356]). Fifty-five percent (195 of 356) of patients presented with a complete pathologic fracture. Intramedullary nailing was the most commonly performed type of surgery (59% [210 of 356]), followed by plate screw fixation (23% [81 of 356]) and endoprosthetic reconstruction (18% [65 of 356]). Six patients were lost to follow-up within 90 days; 30 were lost to follow-up within 1 year. Eighty-five percent (301 of 356) of patients were followed until death or for at least 2 years. Survival was 82% (287 of 350) at 90 days and 49% (159 of 326) at 1 year. The model's performance metrics included discrimination (concordance index [c-index]), calibration (intercept and slope), and Brier score. In general, a c-index of 0.5 indicates random guess and a c-index of 0.8 denotes excellent discrimination. Calibration refers to the agreement between the predicted outcomes and the actual outcomes, with a perfect calibration having an intercept of 0 and a slope of 1. The Brier score of a prediction model must be compared with and ideally should be smaller than the score of the null model. A decision curve analysis was then performed for the 90-day and 1-year prediction models to evaluate their net benefit across a range of different threshold probabilities. A multivariate logistic regression analysis was used to evaluate whether the ECOG score was an independent prognosticator while controlling for the SORG-MLA's predictions. We did not perform retraining/recalibration because we were not trying to update the SORG-MLA algorithm in this study.

RESULTS

The SORG-MLA had good discriminatory ability at both timepoints, with a c-index of 0.80 (95% confidence interval 0.74 to 0.86) for 90-day survival prediction and a c-index of 0.84 (95% CI 0.80 to 0.89) for 1-year survival prediction. However, the calibration analysis showed that the SORG-MLAs tended to underestimate Taiwanese patients' survival (90-day survival prediction: calibration intercept 0.78 [95% CI 0.46 to 1.10], calibration slope 0.74 [95% CI 0.53 to 0.96]; 1-year survival prediction: calibration intercept 0.75 [95% CI 0.49 to 1.00], calibration slope 1.22 [95% CI 0.95 to 1.49]). The Brier score of the 90-day and 1-year SORG-MLA prediction models was lower than their respective null model (0.12 versus 0.16 for 90-day prediction; 0.16 versus 0.25 for 1-year prediction), indicating good overall performance of SORG-MLAs at these two timepoints. Decision curve analysis showed SORG-MLAs provided net benefits when threshold probabilities ranged from 0.40 to 0.95 for 90-day survival prediction and from 0.15 to 1.0 for 1-year prediction. The ECOG score was an independent factor associated with 90-day mortality (odds ratio 1.94 [95% CI 1.01 to 3.73]) but not 1-year mortality (OR 1.07 [95% CI 0.53 to 2.17]) after controlling for SORG-MLA predictions for 90-day and 1-year survival, respectively.

CONCLUSION

SORG-MLAs retained good discriminatory ability in Taiwanese patients with long-bone metastases, although their actual survival time was slightly underestimated. More international validation and incremental value studies that address factors such as the ECOG score are warranted to refine the algorithms, which can be freely accessed online at https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Comparison between different prognostic models to be used for metastatic bone disease on appendicular skeleton in a Chilean population

PURPOSE

Several preoperation prognosis models used on the treatment of metastatic bone disease on appendicular skeleton have been devised. The purpose of this study was to compare the performance of different survival prognostic models on patients with metastatic bone disease in long bones in a Chilean population.

METHODS

This is a multicentric retrospective study. We retrospectively reviewed the medical records of 136 patients who were confirmed with metastatic bone disease of the appendicular skeleton and who were treated surgically from 2016 to 2019. The minimum follow-up time was 12 months. All patients were assessed using four appendicular metastatic bone disease scoring systems. A preoperative predicted survival time for all 136 patients was retrospectively calculated making use of the revised Katagiri, PathFx, Optimodel and IOR score model.

RESULTS

The PathFx model demonstrated an accuracy at predicting 3 (area under the curve [AUC] = 0.61) and 6-month (AUC = 0.65) survival time after surgical management. IOR score model demonstrated an accuracy at predicting 12-month survival time (AUC = 0.64). The survival rate reached the 44% in a year. The median survival time to death or last follow-up time was 14.9 months (SD ± 15).

CONCLUSION

PathFx score model demonstrated the highest accuracy at predicting a survival time of 3 and 6 months. IOR score model was the most accurate measure at predicting a survival time of 12-months. To our knowledge, this is the first study reporting a comparative analysis of metastatic bone disease with predicting models in a country located in Latin America. PathFx's and IOR score models are the ones to be used in the Chilean population as the predictive models in metastatic bone disease of the appendicular skeleton.

Current Overview of Treatment for Metastatic Bone Disease

The number of patients with bone metastasis increases as medical management and surgery improve the overall survival of patients with cancer. Bone metastasis can cause skeletal complications, including bone pain, pathological fractures, spinal cord or nerve root compression, and hypercalcemia. Before initiation of treatment for bone metastasis, it is important to exclude primary bone malignancy, which would require a completely different therapeutic approach. It is essential to select surgical methods considering the patient’s prognosis, quality of life, postoperative function, and risk of postoperative complications. Therefore, bone metastasis treatment requires a multidisciplinary team approach, including radiologists, oncologists, and orthopedic surgeons. Recently, many novel palliative treatment options have emerged for bone metastases, such as stereotactic body radiation therapy, radiopharmaceuticals, vertebroplasty, minimally invasive spine stabilization with percutaneous pedicle screws, acetabuloplasty, embolization, thermal ablation techniques, electrochemotherapy, and high-intensity focused ultrasound. These techniques are beneficial for patients who may not benefit from surgery or radiotherapy.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review

Background and purpose - External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines.Material and methods - We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting.Results - We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43-89), with 6 items being reported in less than 4/18 of the studies.Interpretation - Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Fracture Risk of Long Bone Metastases: A Review of Current and New Decision-Making Tools for Prophylactic Surgery

Simple Summary

Long bone metastases are frequently a pivotal point in the oncological history of patients. Weakening of the bone results in pathologic fractures that not only compromise patient function but also their survival. Therefore, the main issue for tumor boards remains timely assessment of the risk of fracture, as this is a key consideration in providing preventive surgery while also avoiding overtreatment. As the Mirels scoring system takes into account both the radiological and the clinical criteria, it has been used worldwide since the 1990s. However, due to increasing concern regarding the lack of accuracy, new thresholds have been defined for the identification of impending fractures that require prophylactic surgery, on the basis of axial cortical involvement and biomechanical models involving quantitative computed tomography. The aim of this review is to establish a state-of-the-art of the risk assessment of long bone metastases fractures, from simple radiologic scores to more complex multidimensional bone models, in order to define new decision-making tools.

Abstract

Long bone pathological fractures very much reflect bone metastases morbidity in many types of cancer. Bearing in mind that they not only compromise patient function but also survival, identifying impending fractures before the actual event is one of the main concerns for tumor boards. Indeed, timely prophylactic surgery has been demonstrated to increase patient quality of life as well as survival. However, early surgery for long bone metastases remains controversial as the current fracture risk assessment tools lack accuracy. This review first focuses on the gold standard Mirels rating system. It then explores other unique imaging thresholds such as axial or circumferential cortical involvement and the merits of nuclear imaging tools. To overcome the lack of specificity, other fracture prediction strategies have focused on biomechanical models based on quantitative computed tomography (CT): computed tomography rigidity analysis (CT-RA) and finite element analysis (CT-FEA). Despite their higher specificities in impending fracture assessment, their limited availability, along with a need for standardization, have limited their use in everyday practice. Currently, the prediction of long bone pathologic fractures is a multifactorial process. In this regard, machine learning could potentially be of value by taking into account clinical survival prediction as well as clinical and improved CT-RA/FEA data.

C-reactive protein and tumour diagnosis predict survival in patients treated surgically for long bone metastases

PURPOSE

Surgical options for long bone metastases include intramedullary nail fixation or prosthetic reconstruction. Patients with a short life expectancy may benefit from less invasive surgery such as intramedullary nail fixation, while patients with a long life expectancy could be treated with more invasive surgery such as prosthetic reconstruction. The purpose of our study was to analyze the survival of patients treated surgically for long bone metastases, determining the prognostic factors affecting survival and analyzing the surgical complications and reoperation rates. Based on our results, we developed a prognostic score that helps to choose the best treatment for these patients. In addition, we compared the performance of our prognostic score with other previous prognostic models.

METHOD

We investigated prospectively potential clinical and laboratory prognostic factors in 159 patients with metastatic bone disease who underwent surgery with intramedullary nail fixation or prosthetic reconstruction. Clinical data were collected, recording the following data: age and sex of patients, primary tumour and time of diagnosis, number (single or multiple) and presentation (synchronous or metachronous) of bone metastases, presence of visceral metastases. The following laboratory data were analyzed: hemoglobin, leukocyte counts, lymphocyte counts, platelets count, alkaline phosphatase, and C-reactive protein.

RESULTS

Our study showed that pathological C-reactive protein and primary tumour diagnosis were significant negative independent prognostic factors at 12-month survival. Based on our results, we created a score using C-reactive protein and primary tumour diagnosis, creating three different prognostic groups: (A) good prognosis primary tumour and physiological CRP with probability of survival at 12 months of 88.9 [80.1-98.5]; (B) bad prognosis primary tumour and physiological CRP or good prognosis primary tumour and pathological CRP with a probability of survival at 12 months of 56.7 [45.4-70.7]; (C) bad prognosis primary tumour and pathological CRP with a probability of survival at 12 months of 12.5 [5.0-28.3]. Using ROC multiple analysis, our score (AUC = 0.816) was the most accurate in predicting a 12-month survival compared to previous prognostic models.

DISCUSSION

Patients treated surgically for long bone metastases with a life expectancy over 12 months should be treated with more durable reconstruction, while patients with a life expectancy less than 12 months should be treated with less invasive surgery. The diagnosis of primary cancer and C-reactive protein are two very simple data which every orthopaedic surgeon in any community hospital can easily rely on for any decision-making in the surgical treatment of a complex patient as with a patient with skeletal metastases.

CONCLUSION

Our prognostic score based on only two simple variables (C-reactive protein and primary tumour diagnosis) was able to predict the 12-month survival of patients treated surgically for long bone metastases and could be helpful in choosing the best treatment for these patients.

Utility of the Current Procedural Terminology Codes for Prophylactic Stabilization for Defining Metastatic Femur Disease

Introduction:

Cohorts from the electronic health record are often defined by the Current Procedural Terminology (CPT) codes. The error prevalence of CPT codes for patients receiving surgical treatment of metastatic disease of the femur has not been investigated, and the predictive value of coding ontologies to identify patients with metastatic disease of the femur has not been adequately discussed.

Methods:

All surgical cases at a single academic tertiary institution from 2010 through 2015 involving prophylactic stabilization of the femur or fixation of a pathologic fracture of the femur were identified using the CPT and International Classification of Disease (ICD) codes. A detailed chart review was conducted to determine the procedure performed as documented in the surgical note and the patient diagnosis as documented in the pathology report, surgical note, and/or office visit notes.

Results:

We identified 7 CPT code errors of 171 prophylactic operations (4.1%) and one error of 71 pathologic fracture fixation s(1.4%). Of the 164 prophylactic operations that were coded correctly, 87 (53.0%) had metastatic disease. Of the 70 pathologic operations that were coded correctly, 41 (58%) had metastatic disease.

Discussion:

The error prevalence was low in both prophylactic stabilization and pathologic fixation groups (4.1% and 1%, respectively). The structured data (CPT and ICD-9 codes) had a positive predictive value for patients having metastatic disease of 53% for patients in the prophylactic stabilization group and 58% for patients in the pathologic fixation group. The CPT codes and ICD codes assessed in this analysis do provide a useful tool for defining a population in which a moderate proportion of individuals have metastatic disease in the femur at an academic medical center. However, verification is necessary.

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

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.

Clinical and functional comparison of dynamic hip screws and intramedullary nails for treating proximal femur metastases in older individuals

Objective

To compare the outcomes of dynamic hip screws (DHS) and intramedullary nailing (IMN) in the treatment of extra-capsular metastatic carcinoma of the proximal femur.

Methods

A retrospective case analysis method was used to examine data of patients with proximal metastatic cancer of the femur who were treated with internal fixation in Department of Orthopaedics, Beijing Friendship Hospital, from January 2007 to December 2018. Blood loss, postoperative pain, functional score, length of stay, and survival rates were compared, and postoperative complications were assessed.

Results

Complete follow-up data were available for 33 patients. The mean follow-up period was 12.2±3.6 (range: 9−32) months and the average age was 72.3±4.7 (range: 59−83) years old. There were 20 females and 13 males. Twenty-three patients had undergone IMN and 10 DHS, according to bone defects and the patient’s overall condition. The median survival time was 10 months in the IMN group and 11 months in the DHS group. Duration of surgery (t=−7.366, P<0.001) and length of hospital stay (t=−3.509, P<0.001) differed significantly between the two groups. There was one case of breakage of internal fixation in the IMN group.

Conclusions

There was no significant difference between DHS and IMN in terms of surgical efficacy. IMN and DHS were different in terms of surgical time and hospital stay. However, due to the limited number of cases in this study, multi-factor analysis has not been performed and needs to be further verified in future analysis. When developing a surgical plan, it is recommended to consider the patient’s condition and the surgeon’s experience.

Current concepts in the surgical treatment of skeletal metastases

Symptomatic metastatic bone disease affects a large proportion of patients with malignant tumours and significantly impairs patients’ quality of life. There are still controversies regarding both surgical indications and methods, mainly because of the relatively few high-quality studies in this field. Generally, prosthetic reconstruction has been shown to result in fewer implant failures and should be preferred in patients with a good prognosis. Survival estimation tools should be used as part of preoperative planning. Adjuvant treatment, which relies on radiotherapy and inhibition of osteoclast function may also offer symptomatic relief and prevent implant failure. In this review we discuss the epidemiology, indications for surgery, preoperative planning, surgical techniques and adjuvant treatment of metastatic bone disease.

Fracture risk assessment and clinical decision making for patients with metastatic bone disease

Metastatic breast, prostate, lung, and other cancers often affect bone, causing pain, increasing fracture risk, and decreasing function. Management of metastatic bone disease (MBD) is clinically challenging when there is potential but uncertain risk of pathological fracture. Management of MBD has become a major focus within orthopedic oncology with respect to fracture and impending fracture care. If impending skeletal-related events (SREs), particularly pathologic fracture, could be predicted, increasing evidence suggests that prophylactic surgical treatment improves patient outcomes. However, current fracture risk assessment and radiographic metrics do not have high accuracy and have not been combined with relevant patient survival tools. This review first explores the prevalence, incidence, and morbidity of MBD and associated SREs for different cancer types. Strengths and limitations of current fracture risk scoring systems for spinal stability and long bone fracture are highlighted. More recent computed tomography (CT)-based structural rigidity analysis (CTRA) and finite element (FE) analysis methods offer advantages of increased specificity (true negative rate), but are limited in availability. Other fracture prediction approaches including parametric response mapping and positron emission tomography/computed tomography measures show early promise. Substantial new information to inform clinical decision-making includes measures of survival, clinical benefits, and economic analysis of prophylactic treatment compared to after-fracture stabilization. Areas of future research include use of big data and machine learning to predict SREs, greater access and refinement of CTRA/FE approaches, combination of clinical survival prediction tools with radiographically based fracture risk assessment, and net benefit analysis for fracture risk assessment and prophylactic treatment.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases

BACKGROUND

PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate.

QUESTION/PURPOSES

Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions.

METHODS

After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]).

RESULTS

The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use.

CONCLUSIONS

We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools.

LEVEL OF EVIDENCE

Level III, therapeutic study.