2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a Boosting Algorithm Improve Survival Estimates?

Clinical, oncological, and prognostic differences of patients with subsequent skeletal-related events in bone metastases

Aims

Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments.

Methods

This retrospective study included 3,814 adult patients who received local treatment - surgery and/or radiotherapy - for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher's exact test, and Kaplan-Meier curve.

Results

Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians' experiences when treating the initial SRE are not similar when treating a subsequent SRE.

Conclusion

This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs.

Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review

BACKGROUND

Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases.

METHODS

We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies.

RESULTS

We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney.

CONCLUSIONS

Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.

Machine Learning-Assisted Decision Making in Orthopaedic Oncology

» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long-bone metastases

BACKGROUND

Predictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG-NG, and SORG-MLA) improves their performance.

METHODS

One hundred eighty-five patients surgically treated for long-bone metastases between 2014 and 2019 were divided into three PMA tertiles (small, medium, and large) based on their psoas size on CT. Kaplan-Meier, multivariable regression, and Cox proportional hazards analyses were employed to compare survival between tertiles and examine factors associated with mortality. Logistic regression analysis was used to assess whether incorporating adjusted PMA values enhanced the three SPAs' discriminatory abilities. The clinical utility of incorporating PMA into these SPAs was evaluated by decision curve analysis (DCA).

RESULTS

Patients with small PMA had worse 90-day and 1-year survival after surgery (log-rank test p < 0.001). Patients in the large PMA group had a higher chance of surviving 90 days (odds ratio, OR, 3.72, p = 0.02) and 1 year than those in the small PMA group (OR 3.28, p = 0.004). All three SPAs had increased AUC after incorporation of adjusted PMA. DCA indicated increased net benefits at threshold probabilities >0.5 after the addition of adjusted PMA to these SPAs.

CONCLUSIONS

Decreased PMA on CT is associated with worse survival in surgically treated patients with extremity metastases, even after controlling for three contemporary SPAs. Physicians should consider the additional prognostic value of PMA on survival in patients undergoing consideration for operative management due to extremity metastases.

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.

Does the SORG Machine-learning Algorithm for Extremity Metastases Generalize to a Contemporary Cohort of Patients? Temporal Validation From 2016 to 2020

BACKGROUND

The ability to predict survival accurately in patients with osseous metastatic disease of the extremities is vital for patient counseling and guiding surgical intervention. We, the Skeletal Oncology Research Group (SORG), previously developed a machine-learning algorithm (MLA) based on data from 1999 to 2016 to predict 90-day and 1-year survival of surgically treated patients with extremity bone metastasis. As treatment regimens for oncology patients continue to evolve, this SORG MLA-driven probability calculator requires temporal reassessment of its accuracy.

QUESTION/PURPOSE

Does the SORG-MLA accurately predict 90-day and 1-year survival in patients who receive surgical treatment for a metastatic long-bone lesion in a more recent cohort of patients treated between 2016 and 2020?

METHODS

Between 2017 and 2021, we identified 674 patients 18 years and older through the ICD codes for secondary malignant neoplasm of bone and bone marrow and CPT codes for completed pathologic fractures or prophylactic treatment of an impending fracture. We excluded 40% (268 of 674) of patients, including 18% (118) who did not receive surgery; 11% (72) who had metastases in places other than the long bones of the extremities; 3% (23) who received treatment other than intramedullary nailing, endoprosthetic reconstruction, or dynamic hip screw; 3% (23) who underwent revision surgery, 3% (17) in whom there was no tumor, and 2% (15) who were lost to follow-up within 1 year. Temporal validation was performed using data on 406 patients treated surgically for bony metastatic disease of the extremities from 2016 to 2020 at the same two institutions where the MLA was developed. Variables used to predict survival in the SORG algorithm included perioperative laboratory values, tumor characteristics, and general demographics. To assess the models' discrimination, we computed the c-statistic, commonly referred to as the area under the receiver operating characteristic (AUC) curve for binary classification. This value ranged from 0.5 (representing chance-level performance) to 1.0 (indicating excellent discrimination) Generally, an AUC of 0.75 is considered high enough for use in clinical practice. To evaluate the agreement between predicted and observed outcomes, a calibration plot was used, and the calibration slope and intercept were calculated. Perfect calibration would result in a slope of 1 and intercept of 0. For overall performance, the Brier score and null-model Brier score were determined. The Brier score can range from 0 (representing perfect prediction) to 1 (indicating the poorest prediction). Proper interpretation of the Brier score necessitates a comparison with the null-model Brier score, which represents the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for each patient. Finally, a decision curve analysis was conducted to compare the potential net benefit of the algorithm with other decision-support methods, such as treating all or none of the patients. Overall, 90-day and 1-year mortality were lower in the temporal validation cohort than in the development cohort (90 day: 23% versus 28%; p < 0.001, and 1 year: 51% versus 59%; p<0.001).

RESULTS

Overall survival of the patients in the validation cohort improved from 28% mortality at the 90-day timepoint in the cohort on which the model was trained to 23%, and 59% mortality at the 1-year timepoint to 51%. The AUC was 0.78 (95% CI 0.72 to 0.82) for 90-day survival and 0.75 (95% CI 0.70 to 0.79) for 1-year survival, indicating the model could distinguish the two outcomes reasonably. For the 90-day model, the calibration slope was 0.71 (95% CI 0.53 to 0.89), and the intercept was -0.66 (95% CI -0.94 to -0.39), suggesting the predicted risks were overly extreme, and that in general, the risk of the observed outcome was overestimated. For the 1-year model, the calibration slope was 0.73 (95% CI 0.56 to 0.91) and the intercept was -0.67 (95% CI -0.90 to -0.43). With respect to overall performance, the model's Brier scores for the 90-day and 1-year models were 0.16 and 0.22. These scores were higher than the Brier scores of internal validation of the development study (0.13 and 0.14) models, indicating the models' performance has declined over time.

CONCLUSION

The SORG MLA to predict survival after surgical treatment of extremity metastatic disease showed decreased performance on temporal validation. Moreover, in patients undergoing innovative immunotherapy, the possibility of mortality risk was overestimated in varying severity. Clinicians should be aware of this overestimation and discount the prediction of the SORG MLA according to their own experience with this patient population. Generally, these results show that temporal reassessment of these MLA-driven probability calculators is of paramount importance because the predictive performance may decline over time as treatment regimens evolve. The SORG-MLA is available as a freely accessible internet application at https://sorg-apps.shinyapps.io/extremitymetssurvival/ .Level of Evidence Level III, prognostic study.

Safety and Efficacy of a Single-Stage versus Two-Stage Intramedullary Nailing for Synchronous Impending or Pathologic Fractures of Bilateral Femur for Oncologic Indications: A Systematic Review

Although intramedullary nail (IMN) fixation is the standard of care for most impending and/or complete pathologic fractures of the femur, the optimal timing/sequence of the IMN in cases of synchronous bilateral femoral disease in advanced cancer is not well established. Thus, we compared the outcomes of single-stage (SS) vs. two-stage (TS) IMN of the bilateral femur with a systematic review of the literature on this topic. Bilateral SS and TS IMN cases were identified from 14 studies extracted from four databases according to PRISMA guidelines. Safety (complications, reoperations, mortality, survival, blood loss, and transfusion) and efficacy (length of stay [LOS], time to start rehabilitation and adjuvant therapy, functional scores, and cost) were compared between the groups. A total of 156 IMNs in 78 patients (36 SS and 42 TS) were analyzed. There were one surgical (infection in TS requiring reoperation; p = 0.860) and fifteen medical complications (five in SS, ten in TS; p = 0.045), with SS being associated with lower rates of total and medical complications. Survival, intraoperative mortality, and postoperative same-admission mortality were similar. No cases of implant failure were reported. Data on LOS, rehabilitation, and adjuvant therapy were scarcely reported, although one study favored SS over TS. No study compared cost or functional scores. Our study is the largest and most comprehensive of its kind in supporting the safety and efficacy of a SS bilateral femur IMN approach in these select patients. Further investigations with higher levels of evidence are warranted to optimize treatment protocols for this clinical scenario.

Single-Stage versus Multi-Stage Intramedullary Nailing for Multiple Synchronous Long Bone Impending and Pathologic Fractures in Metastatic Bone Disease and Multiple Myeloma

PURPOSE

Determine whether perioperative outcomes differ between patients who have undergone single or multi-stage IMN procedures for impending or completed pathologic fractures.

METHODS

Patients were classified into single-stage single-bone (SSSB), single-stage multiple-bone (SSMB), and multi-stage multiple-bone (MSMB) based on procedure timing and number of bones involved. Outcome variables compared included length of stay (LOS), in-hospital mortality and survival, initiation of rehabilitation and adjuvant therapy, and perioperative complications.

RESULTS

There were 272 IMNs placed in 181 patients (100 males, 81 females, 55.2% and 44.8%, respectively) with a mean age of 66.3 ± 12.1 years. MSMB had significantly longer LOS (24.3 ± 14.2 days) and rehabilitation initiation (3.4 ± 2.5 days) compared to SSSB (8.5 ± 7.7 and 1.8 ± 1.6 days) and SSMB (11.5 ± 7.6 and 2.0 ± 1.6 days) subjects, respectively (both; p < 0.01). Although total perioperative complication rates in SSMB and MSMB were comparable (33.3% vs. 36.0%), they were significantly higher than SSSB (18%) (p = 0.038). MSMB had significantly more (20%) cardiopulmonary complications than SSMB (11.1%) and SSSB (4.5%) (p = 0.027). All groups exhibited comparative survivorship (8.1 ± 8.6, 7.1 ± 7.2, and 11.4 ± 11.8 months) and in-hospital mortality (4.5%, 8.9%, and 4.0%) (all; p > 0.05).

CONCLUSION

In comparison to MSMB, SSMB intramedullary nailing did not result in higher perioperative complication or in-hospital mortality rates in select patients with synchronous long-bone metastases but led to earlier postoperative discharge and initiation of rehabilitation.

Development of a prognostic model in patients with metastatic bone lesions to choose surgical treatment: retrospective study

Choosing surgical management for patients with metastatic bone lesions is one of the essential problems of modern oncology. Surgical interventions are aimed at palliative treatment in most patients with metastatic skeletal lesions. However, curative resections with reconstruction and plasty steps may be considered in selected cases of a solitary metastatic lesion. The life expectancy prognosis based on the histological structure of the tumor is a significant and decisive factor in choosing the appropriate surgery. Aim. To develop a prognostic model for choosing surgical treatment for metastatic bone lesions. Materials and methods. Treatment analysis of 715 patients with a history of surgery for metastatic bone lesions of various localizations is presented. A total of 780 surgical interventions were performed. Surgeries for the complications of bone metastases were mainly performed on the spine (48.5% of all surgeries), followed by long bones with 247 (35%) surgeries, pelvic bones with 81 (11%) interventions, and the chest wall with 40 (5.5%) surgeries. Results. The most unfavorable prognostic factors in patients with metastatic bone lesions are the histological type of the primary tumor of the rapid growth group (risk ratio [RR]=5.11), visceral metastases (RR=3.1), Charlson Comorbidity Index over 10 (RR=3.07) and presence of critical laboratory abnormalities (RR=2.91), as they have the highest rates of impact on survival (over 2.9). Conclusion. The developed 14-point mathematical score of life expectancy prognosis, which includes five oncological and four clinical factors, defines with an accuracy of 91% the risk groups of good (estimated life expectancy over one year), moderate (6 to 12 months), and poor (less than six months) prognosis in patients with metastatic bone lesions.

Evaluation of patients with pathological fractures treated by standard trauma principles but neglecting the underlying malign bone disease

INTRODUCTION

There are several studies in the literature about pathological fractures but almost no information about patients whose pathological fracture caused by a malignant lesion misdiagnosed and treated as a simple fracture. The aim of this study was to investigate patient and fracture characteristics, and outcomes in cases where fractures occurred in the presence of a malign pathology but were treated as simple fractures.

PATIENTS AND METHODS

Cases of malign bone lesions between 2000 and 2020 were retrospectively reviewed. Patients with a final diagnosis of malign bone lesion but whose pathological fractures were treated ignoring the underlying malign bone disease were included. Demographic, clinical and outcome data were collected from patient's medical records and analyzed.

RESULTS

Six patients met the inclusion criteria. Three of the patients were female and the cohort mean age was 56.8 ± 21.8 years at the time of admission. Patient diagnoses were: renal cell carcinoma metastasis (n = 1); colon cancer metastasis (n = 1); chondrosarcoma (n = 2); osteosarcoma (n = 1); and undifferentiated pleomorphic sarcoma of bone (n = 1). In all cases surgical management differed from those that should have been applied if the pathological fracture had been identified. Furthermore, surgical management after definitive histological diagnosis were more aggressive compared to if the malignancy had been identified at first admission. All patients died after a mean follow-up of 16.67 ± 11.7 months and the complication rate was 100%.

CONCLUSION

When a pathological fracture is misdiagnosed and managed as a simple bone fracture, outcomes are extremely poor. In these situations, remedial surgery is more extensive, with increased complication rates and there is poor life expectancy.

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.

Prophylactic surgical treatment using CT-based rigidity analysis vs. after the fact fracture treatment of pathologic femoral lesions

Background

Accurate comparison of prophylactic surgical treatment (PST) to after fracture treatment (AF) of patients with femoral metastatic disease requires more accurately identifying patients for impending fracture, such as with CT-based structural rigidity analysis (CTRA). This study compares a more accurately defined PST group (of impending fractures defined by CTRA) to AF for metastatic femoral disease.

Methods

PST patients were enrolled and treated by the PI in a longitudinal multicenter study of impending pathologic fractures evaluated for accuracy by CTRA. The AF patients were also treated by the senior author and were identified by retrospective chart review. Fifty-five patients were treated surgically for metastatic femoral lesions and were divided into three groups for the purpose of this study: Group I (AF), Group II (PST-high), and Group III (PST-low). Demographic information, comorbidities, and clinical variables of interest were collected by retrospective chart review; cost data was collected by collaboration with our hospital financial personnel (office of the Chief Financial Officer).

Results

Survival showed statistically significant differences favoring Group II. Transfusions in Group I were nearly twice those of Groups II and III, but there was no statistically significant (NS) difference between groups. Estimated blood loss (EBL) was generally with NS difference. Similarly, there were NS differences in LOS between groups. Discharge disposition showed statistically significant differences between groups (P=0.012, global). Discharge to home was highest in Group II (76%) and lowest in Group I (27%). Discharge to rehab was lowest in Group II (24%) and highest in Group I (47%). There were no discharges to hospice or morgue in Group II, while both occurred in Group I. Mean direct and total costs were highest in Group I ($18,837 and $31,997, respectively) and lowest in Group II ($16,094 and $27,357) but the differences were NS.

Conclusions

This study shows benefits of PST over AF in a group of PST patients more accurately defined to have impending pathologic fractures by CTRA definition.

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

Body Composition Predictors of Adverse Postoperative Events in Patients Undergoing Surgery for Long Bone Metastases

Body composition assessed using opportunistic CT has been recently identified as a predictor of outcome in patients with cancer. The purpose of this study was to determine whether the cross-sectional area (CSA) and the attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), and paraspinous and abdominal muscles are the predictors of length of hospital stay, 30-day postoperative complications, and revision surgery in patients treated for long bone metastases.

Clinical Outcome Differences in the Treatment of Impending Versus Completed Pathological Long-Bone Fractures

BACKGROUND

The outcome differences following surgery for an impending versus a completed pathological fracture have not been clearly defined. The purpose of the present study was to assess differences in outcomes following the surgical treatment of impending versus completed pathological fractures in patients with long-bone metastases in terms of (1) 90-day and 1-year survival and (2) intraoperative blood loss, perioperative blood transfusion, anesthesia time, duration of hospitalization, 30-day postoperative systemic complications, and reoperations.

METHODS

We retrospectively performed a matched cohort study utilizing a database of 1,064 patients who had undergone operative treatment for 462 impending and 602 completed metastatic long-bone fractures. After matching on 22 variables, including primary tumor, visceral metastases, and surgical treatment, 270 impending pathological fractures were matched to 270 completed pathological fractures. The primary outcome was assessed with the Cox proportional hazard model. The secondary outcomes were assessed with the McNemar test and the Wilcoxon signed-rank test.

RESULTS

The 90-day survival rate did not differ between the groups (HR, 1.13 [95% CI, 0.81 to 1.56]; p = 0.48), but the 1-year survival rate was worse for completed pathological fractures (46% versus 38%) (HR, 1.28 [95% CI, 1.02 to 1.61]; p = 0.03). With regard to secondary outcomes, completed pathological fractures were associated with higher intraoperative estimated blood loss (p = 0.03), a higher rate of perioperative blood transfusions (p = 0.01), longer anesthesia time (p = 0.04), and more reoperations (OR, 2.50 [95% CI, 1.92 to 7.86]; p = 0.03); no differences were found in terms of the rate of 30-day postoperative complications or the duration of hospitalization.

CONCLUSIONS

Patients undergoing surgery for impending pathological fractures had lower 1-year mortality rates and better secondary outcomes as compared with patients undergoing surgery for completed pathological fractures when accounting for 22 covariates through propensity matching. Patients with an impending pathological fracture appear to benefit from prophylactic stabilization as stabilizing a completed pathological fracture seems to be associated with increased mortality, blood loss, rate of blood transfusions, duration of surgery, and reoperation risk.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

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.

Body composition predictors of mortality in patients undergoing surgery for long bone metastases

Background and Objectives

Body composition measurements using computed tomography (CT) may serve as imaging biomarkers of survival in patients with and without cancer. This study assesses whether body composition measurements obtained on abdominal CTs are independently associated with 90‐day and 1‐year mortality in patients with long‐bone metastases undergoing surgery.

Methods

This single institutional retrospective study included 212 patients who had undergone surgery for long‐bone metastases and had a CT of the abdomen within 90 days before surgery. Quantification of cross‐sectional areas (CSA) and CT attenuation of abdominal subcutaneous adipose tissue, visceral adipose tissue, and paraspinous and abdominal muscles were performed at L4. Multivariate Cox proportional‐hazards analyses were performed.

Results

Sarcopenia was independently associated with 90‐day mortality (hazard ratio [HR] = 1.87; 95% confidence interval [CI] = 1.11–3.16; p = 0.019) and 1‐year mortality (HR = 1.50; 95% CI = 1.02–2.19; p = 0.038) in multivariate analysis while controlling for clinical variables such as primary tumors, comorbidities, and chemotherapy. Abdominal fat CSAs and muscle attenuation were not associated with mortality.

Conclusions

The presence of sarcopenia assessed by CT is predictive of 90‐day and 1‐year mortality in patients undergoing surgery for long‐bone metastases. This body composition measurement can be used as novel imaging biomarker supplementing existing prognostic tools to optimize patient selection for surgery and improve shared decision making.

The preoperative machine learning algorithm for extremity metastatic disease can predict 90-day and 1-year survival: An external validation study

BACKGROUND

The prediction of survival is valuable to optimize treatment of metastatic long-bone disease. The Skeletal Oncology Research Group (SORG) machine-learning (ML) algorithm has been previously developed and internally validated. The purpose of this study was to determine if the SORG ML algorithm accurately predicts 90-day and 1-year survival in an external metastatic long-bone disease patient cohort.

METHODS

A retrospective review of 264 patients who underwent surgery for long-bone metastases between 2003 and 2019 was performed. Variables used in the stochastic gradient boosting SORG algorithm were age, sex, primary tumor type, visceral/brain metastases, systemic therapy, and 10 preoperative laboratory values. Model performance was calculated by discrimination, calibration, and overall performance.

RESULTS

The SORG ML algorithms retained good discriminative ability (area under the cure [AUC]: 0.83; 95% confidence interval [CI]: 0.76-0.88 for 90-day mortality and AUC: 0.84; 95% CI: 0.79-0.88 for 1-year mortality), calibration, overall performance, and decision curve analysis.

CONCLUSION

The previously developed ML algorithms demonstrated good performance in the current study, thereby providing external validation. The models were incorporated into an accessible application (https://sorg-apps.shinyapps.io/extremitymetssurvival/) that may be freely utilized by clinicians in helping predict survival for individual patients and assist in informative decision-making discussion before operative management of long bone metastatic lesions.

Clinical Features and Serological Markers Risk Model Predicts Overall Survival in Patients Undergoing Breast Cancer and Bone Metastasis Surgeries

Background

Surgical therapy of breast cancer and bone metastasis can effectively improve the prognosis of breast cancer. However, after the first operation, the relationship between preoperative indicators and outcomes in patients who underwent metastatic bone surgery remained to be studied. Purpose 1. Recognize clinical and laboratory prognosis factors available to clinical doctors before the operation for bone metastatic breast cancer patients. 2. Develop a risk prediction model for 3-year postoperative survival in patients with breast cancer bone metastasis.

Methods

From 2014 to 2020, patients who suffered from breast cancer bone metastasis and received therapeutic procedures in our institution were included for analyses (n=145). For patients who underwent both breast cancer radical surgery and bone metastasis surgery, comprehensive datasets of the parameters of interest (clinical features, laboratory factors, and patient prognoses) were collected (n=69). We performed Multivariate Cox regression to identify factors that were associated with postoperative outcome. 3-year survival prediction model and nomograms were established by 100 bootstrapping. Its benefit was evaluated by calibration plot, C-index, and decision curve analysis. The Surveillance, Epidemiology, and End Results database was also used for external validation.

Results

Radiotherapy for primary cancer, pathological type of metastatic breast cancer, lymph node metastasis, elevated serum alkaline phosphatase, lactate dehydrogenase were associated with postoperative prognosis. Pathological types of metastatic breast cancer, multiple bone metastasis, organ metastases, and elevated serum lactate dehydrogenase were associated with 3-year survival. Then those significant variables and serum alkaline phosphatase counts were integrated to construct nomograms for 3-year survival. The C-statistic of the established predictive model was 0.83. The calibration plot presents a graphical representation of calibration. In the decision curve analysis, the benefits are higher than those of the extreme curve. The receiver operating characteristic of the external validation of the model was 0.82, indicating a favored fitting degree of the two models.

Conclusion

Our study suggests that several clinical features and serological markers can predict the overall survival among the patients who are about to receive bone metastasis surgery after breast cancer surgery. The model can guide the preoperative evaluation and clinical decision-making for patients. Level of evidence Level III, prognostic study.

Prognostic Factors Influencing Survival and a Treatment Pattern Analysis of Conventional Palliative Radiotherapy for Patients with Bone Metastases

BACKGROUND

Treatment indication for bone metastases is influenced by patient prognosis. Single-fraction radiotherapy (SFRT) was proven equally effective as multiple fractionation regimens (MFRT) but continues to be underused.

OBJECTIVE

Primary objectives: (a) to identify prognostic factors for overall survival and (b) to analyze treatment patterns of palliative radiotherapy (proportion of SFRT indication and predictive factors of radiotherapy regimen) for bone metastases.

METHODS

582 patients with bone metastases who underwent conventional radiotherapy between January 1st 2014-31 December 2017 were analyzed. The Cox proportional hazard model was used to identify predictors of overall survival. For the treatment pattern analysis, 677 radiotherapy courses were evaluated. The logistic regression model was used to identify potential predictors of radiotherapy regimen.

RESULTS

The 3-year overall survival was 15%. Prognostic factors associated with poor overall survival were multiple bone metastases [hazard ratio (HR = 5.4)], poor performance status (HR = 1.5) and brain metastases (HR = 1.37). SFRT prescription increased from 41% in 2017 to 51% in 2017. Predictors of SFRT prescription were a poor performance status [odds ratio (OR = 0.55)], lung (OR = 0.49) and urologic primaries (OR = 0.33) and the half-body lower site of irradiation (OR = 0.59). Spinal metastases were more likely to receive MFRT (OR = 2.09).

CONCLUSIONS

Based on the prognostic factors we identified, a selection protocol for patients candidates for palliative radiotherapy to bone metastases could be established, in order to further increase SFRT prescription in our institution.

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review

Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed.Material and methods - We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics.Results - Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635-26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73-0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis.Interpretation - ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported.

Albumin and Survival in Extremity Metastatic Bone Disease: An Analysis of Two Independent Datasets

INTRODUCTION

Numerous prognostication models have been developed to estimate survival in patients with extremity metastatic bone disease, but few include albumin despite albumin's role in malnutrition and inflammation. The purpose of this study was to examine two independent datasets to determine the value for albumin in prognosticating survival in this population.

MATERIALS AND METHODS

Extremity metastatic bone disease patients undergoing surgical management were identified from two independent populations. Population 1: Retrospective chart review at two tertiary care centers. Population 2: A large, national, North American multicenter surgical registry with 30-day follow-up. Bivariate and multivariate analyses were used to examine albumin's value for prognostication at 1-, 3-, and 12-month after surgery.

RESULTS

In Population 1, 1,090 patients were identified with 1-, 3-, and 12-month mortality rates of 95 (8.8%), 305 (28.9%), and 639 (62.0%), respectively. In Population 2, 1,675 patients were identified with one-month postoperative mortality rates of 148 (8.8%). In both populations, hypoalbuminemia was an independent prognostic factor for mortality at 30 days. In the institutional set, hypoalbuminemia was additionally associated with 3- and 12-month mortality.

CONCLUSIONS

Hypoalbuminemia is a marker for mortality in extremity metastatic bone disease. Further consideration of this marker could improve existing prognostication models in this population.

LEVEL OF EVIDENCE

III.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

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.

Association of Obesity With Survival Outcomes in Patients With Cancer: A Systematic Review and Meta-analysis

Importance

Obesity, defined as a body mass index (BMI) greater than 30, is associated with a significant increase in the risk of many cancers and in overall mortality. However, various studies have suggested that patients with cancer and no obesity (ie, BMI 20-25) have worse outcomes than patients with obesity.

Objective

To assess the association between obesity and outcomes after a diagnosis of cancer.

Data Sources

PubMed, the Cochrane Library, and EMBASE were searched from inception to January 2020.

Study Selection

Studies reporting prognosis of patients with obesity using standard BMI categories and cancer were included. Studies that used nonstandard BMI categories, that were limited to children, or that were limited to patients with hematological malignant neoplasms were excluded. Screening was performed independently by multiple reviewers. Among 1892 retrieved studies, 203 (17%) met inclusion criteria for initial evaluation.

Data Extraction and Synthesis

The Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines were reporting guideline was followed. Data were extracted by multiple independent reviewers. Risk of death, cancer-specific mortality, and recurrence were pooled to provide an adjusted hazard ratio (HR) with a 95% CI . A random-effects model was used for the retrospective nature of studies.

Main Outcomes and Measures

The primary outcome of the study was overall survival (OS) in patients with cancer, with and without obesity. Secondary end points were cancer-specific survival (CSS) and progression-free survival (PFS) or disease-free survival (DFS). The risk of events was reported as HRs with 95% CIs, with an HR greater than 1 associated with a worse outcome among patients with obesity vs those without.

Results

A total of 203 studies with 6 320 365 participants evaluated the association of OS, CSS, and/or PFS or DFS with obesity in patients with cancer. Overall, obesity was associated with a reduced OS (HR, 1.14; 95% CI, 1.09-1.19; P < .001) and CSS (HR, 1.17; 95% CI, 1.12-1.23; P < .001). Patients were also at increased risk of recurrence (HR, 1.13; 95% CI, 1.07-1.19; P < .001). Conversely, patients with obesity and lung cancer, renal cell carcinoma, or melanoma had better survival outcomes compared with patients without obesity and the same cancer (lung: HR, 0.86; 95% CI, 0.76-0.98; P = .02; renal cell: HR, 0.74; 95% CI, 0.53-0.89; P = .02; melanoma: HR, 0.74; 95% CI, 0.57-0.96; P < .001).

Conclusions and Relevance

In this study, obesity was associated with greater mortality overall in patients with cancer. However, patients with obesity and lung cancer, renal cell carcinoma, and melanoma had a lower risk of death than patients with the same cancers without obesity. Weight-reducing strategies may represent effective measures for reducing mortality in these patients.

Serum alkaline phosphatase is a prognostic marker in bone metastatic disease of the extremity

Purpose

The purpose of this study was to determine the prognostic value of serum alkaline phosphatase for treatment decision making in metastatic bone disease.

Methods

1090 patients who underwent surgery for extremity metastatic disease were retrospectively identified at two tertiary care centers. The association between alkaline phosphatase and mortality was assessed by bivariate and multivariate analyses.

Results

Three-month and one-year mortality rates were 305 (29%) and 639 (62%), respectively. Alkaline phosphatase was associated with mortality at both three months and one year.

Conclusion

Serum alkaline phosphatase may be a useful marker in prognostic algorithms for patients with extremity metastatic disease.

Thirty-day Postoperative Complications After Surgery For Metastatic Long Bone Disease Are Associated With Higher Mortality at 1 Year

BACKGROUND

The benefits of surgical treatment of a metastasis of the extremities may be offset by drawbacks such as potential postoperative complications. For this group of patients, the primary goal of surgery is to improve quality of life in a palliative setting. A better comprehension of factors associated with complications and the impact of postoperative complications on mortality may prevent negative outcomes and help surgeons in surgical decision-making.

QUESTIONS/PURPOSES

(1) What is the risk of 30-day postoperative complications after surgical treatment of osseous metastatic disease of the extremities? (2) What predisposing factors are associated with a higher risk of 30-day complications? (3) Are minor and major 30-day complications associated with higher mortality at 1 year?

METHODS

Between 1999 and 2016, 1090 patients with osseous metastatic disease of the long bones treated surgically at our institution were retrospectively included in the study. Surgery included intramedullary nailing (58%), endoprosthetic reconstruction (22%), plate-screw fixation (14%), dynamic hip screw fixation (2%), and combined approaches (4%). Surgery was performed if patients were deemed healthy enough to proceed to surgery and wished to undergo surgery. All data were retrieved by manually reviewing patients' records. The overall frequency of complications, which were defined using the Clavien-Dindo classification system, was calculated. We did not include Grade I complications as postoperative complications and complications were divided into minor (Grade II) and major (Grades III-V) complications. A multivariate logistic regression analysis was used to identify factors associated with 30-day postoperative complications. A Cox regression analysis was used to assess the association between postoperative complications and overall survival.

RESULTS

Overall, 31% of the patients (333 of 1090) had a postoperative complication within 30 days. The following factors were independently associated with 30-day postoperative complications: rapidly growing primary tumors classified according to the modified Katagiri classification (odds ratio 1.6; 95% confidence interval, 1.1-2.2; p = 0.011), multiple bone metastases (OR 1.6; 95% CI, 1.1-2.3; p = 0.008), pathologic fracture (OR 1.5; 95% CI, 1.1-2.0; p = 0.010), lower-extremity location (OR 2.2; 95% CI, 1.6-3.2; p < 0.001), hypoalbuminemia (OR 1.7; 95% CI, 1.2-2.4; p = 0.002), hyponatremia (OR 1.5; 95% CI, 1.0-2.2; p = 0.044), and elevated white blood cell count (OR 1.6; 95% CI, 1.1-2.4; p = 0.007). Minor and major postoperative complications within 30 days after surgery were both associated with greater 1-year mortality (hazard ratio 1.6; 95% CI, 1.3-1.8; p < 0.001 and HR 3.4; 95% CI, 2.8-4.2, respectively; p < 0.001).

CONCLUSION

Patients with metastatic disease in the long bones are vulnerable to postoperative adverse events. When selecting patients for surgery, surgeons should carefully assess a patient's cancer status, and several preoperative laboratory values should be part of the standard work-up before surgery. Furthermore, 30-day postoperative complications decrease survival within 1 year after surgery. Therefore, patients at a high risk of having postoperative complications are less likely to profit from surgery and should be considered for nonoperative treatment or be monitored closely after surgery.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Development and Internal Validation of Machine Learning Algorithms for Preoperative Survival Prediction of Extremity Metastatic Disease

BACKGROUND

A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models.

QUESTIONS/PURPOSES

The purposes of this study were (1) to develop machine learning algorithms for 90-day and 1-year survival in patients who received surgical treatment for a bone metastasis of the extremity, and (2) to use these algorithms to identify those clinical factors (demographic, treatment related, or surgical) that are most closely associated with survival after surgery in these patients.

METHODS

All 1090 patients who underwent surgical treatment for a long-bone metastasis at two institutions between 1999 and 2017 were included in this retrospective study. The median age of the patients in the cohort was 63 years (interquartile range [IQR] 54 to 72 years), 56% of patients (610 of 1090) were female, and the median BMI was 27 kg/m (IQR 23 to 30 kg/m). The most affected location was the femur (70%), followed by the humerus (22%). The most common primary tumors were breast (24%) and lung (23%). Intramedullary nailing was the most commonly performed type of surgery (58%), followed by endoprosthetic reconstruction (22%), and plate screw fixation (14%). Missing data were imputed using the missForest methods. Features were selected by random forest algorithms, and five different models were developed on the training set (80% of the data): stochastic gradient boosting, random forest, support vector machine, neural network, and penalized logistic regression. These models were chosen as a result of their classification capability in binary datasets. Model performance was assessed on both the training set and the validation set (20% of the data) by discrimination, calibration, and overall performance.

RESULTS

We found no differences among the five models for discrimination, with an area under the curve ranging from 0.86 to 0.87. All models were well calibrated, with intercepts ranging from -0.03 to 0.08 and slopes ranging from 1.03 to 1.12. Brier scores ranged from 0.13 to 0.14. The stochastic gradient boosting model was chosen to be deployed as freely available web-based application and explanations on both a global and an individual level were provided. For 90-day survival, the three most important factors associated with poorer survivorship were lower albumin level, higher neutrophil-to-lymphocyte ratio, and rapid growth primary tumor. For 1-year survival, the three most important factors associated with poorer survivorship were lower albumin level, rapid growth primary tumor, and lower hemoglobin level.

CONCLUSIONS

Although the final models must be externally validated, the algorithms showed good performance on internal validation. The final models have been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/extremitymetssurvival/. Pending external validation, clinicians may use this tool to predict survival for their individual patients to help in shared treatment decision making.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

Predicting survival in older patients treated for cervical spine fractures: development of a clinical survival score

BACKGROUND CONTEXT

Emerging literature has identified the importance of pretreatment health and functional status as influential in the prognostication of survival. A comprehensive, accessible, predictive model for survival following cervical spine fracture has yet to be developed.

PURPOSE

To develop an accessible and intuitive predictive model for survival in individuals aged 50 and older treated for cervical spine fractures.

STUDY DESIGN

Retrospective review of records from two tertiary care centers (2009-2016).

PATIENT SAMPLE

Patients age 50 and older who received operative or nonoperative management for cervical fractures.

OUTCOME MEASURES

One-year mortality was the primary outcome with 3-month and 2-year mortality considered secondarily.

METHODS

Multivariable logistic regression was used to identify factors independently associated with mortality. The magnitude and precision of the relationship with 1-year mortality for statistically significant variables determined weighting in the scoring system subsequently developed. Score performance was tested through multivariable regression and bootstrap simulation. In a sensitivity test, the performance of the score developed for 1-year mortality was assessed using figures for the 3-month and 2-year time-points.

RESULTS

We included 1,758 patients. Mortality rates were 12% at 3 months, 17% at 1 year, and 21% at 2 years. Following multivariable testing age, injury severity score and Glasgow coma scale demonstrated the strongest predictive values for a base score, followed by serum albumin and ambulatory status. The resultant composite score ranged from 0 (base score≤4, albumin≤3.5 g/dL, and dependent/nonambulator at presentation) to a maximum of 4 (base score≥5, albumin>3.5 g/dL, and independent ambulator at presentation). Following multivariable analysis, when compared to patients with a score of 4, significantly increased odds of 1-year mortality were appreciated for those with scores of 3 (odds ratio [OR] 7.35; 95% confidence interval [CI] 3.77, 14.32), 2 (OR 8.43; 95% CI 4.66, 15.25), 1 (OR 17.47; 95% CI 9.81, 31.11), and 0 (OR 26.58; 95% CI 13.87, 50.92). Score performance was unchanged in bootstrap testing and sensitivity analyses.

CONCLUSIONS

We have developed a useful prognostic utility capable of informing survival in individuals age 50 and older, following cervical spine fractures. The score can be applied to adjust patient expectations, anticipate outcomes, and as an adjunct to decision-making in the postinjury period.

Incidence of surgical interventions for metastatic bone disease in the extremities: a population-based cohort study

BACKGROUND

The incidence of surgery due to metastatic bone disease in the extremities (MBDex) and postoperative survival remain uninvestigated in the population. The aim of the current study was: to identify (1) incidence, demographics and survival of a population-based cohort of patients having surgery for MBDex (2) rate of referrals and referral pattern to a musculoskeletal tumour centre (MTC).

MATERIAL AND METHOD

A prospective study of a consecutive population-based cohort of patients having surgery for MBDex from 2014 to 2016. Patient demographics, indication for surgery, oncological status, and postoperative survival was obtained from patient interviews, surveillance scans and patient records.

RESULTS

We identified 164 patients treated for 175 bone lesions resulting in an incidence of MBDex surgery of 48.6 lesions/million inhabitants/year and a 10% risk of undergoing surgery for MBDex for every year liven with metastatic bone disease. The most common primary cancers were breast, lung, renal, prostate and myeloma. Twenty-nine lesions represented debut of cancer and 22 lesions debut of relapse of a previous cancer. Overall one-year survival was 41% (95% C.I.: 33%-48%). Fifty-nine percent of patients were referred for treatment at MTC. Patients referred had better prognostic baseline characteristic than patients treated at secondary surgical centres (SSC) (lower ASA score (p < .001), no visceral metastasis (p < .001), lower age (p < .001) and less aggressive primary cancer (p < .001)). The one-year probability of overall survival was higher for MTC patients compared to SSC patients (p < .001).

CONCLUSIONS

Present study describes a prospective population-based cohort of patients having surgery for MBDex identifying incidence and postoperative survival. Referral of patient is biased by selection where 'long-term survivors' are referred for treatment at MTC. We can, however, not exclude that treatment centre influences chance of survival after surgery for MBDex although our study was not designed to identify any potential influence.

The Impact of Vancomycin and Cefazolin as Standard Preoperative Antibiotic Prophylaxis on Surgical Site Infections Following Instrumented Spinal Fusion

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

To assess whether administration of prophylactic vancomycin, in addition to cefazolin decreased revision surgeries for postoperative infection (SSI) as well as the need for revisions overall.

SUMMARY OF BACKGROUND DATA

In 2010 our institution implemented an antibiotic prophylaxis regimen consisting of intravenous vancomycin and cefazolin that applied to all patients receiving surgical implants. The impact of this change in prophylactic antibiotic regimen on SSIs following instrumented spinal fusions remains unknown.

METHODS

We conducted a prepost analysis evaluating the effect of the change in antibiotic prophylaxis on SSIs following instrumented spinal fusions. We collected data on all eligible patients over the course of 2005 to 2009 and 2011 to 2015. We used logistic regression techniques to evaluate unadjusted results for the prophylactic antibiotic protocol on all revision surgeries, as well as those for SSI, followed by sequential adjustments for sociodemographic factors and surgical characteristics.

RESULTS

Revision surgeries performed for a diagnosis of infection were reduced from a rate of 4% (n = 57) in the period 2005 to 2009 to 2% (n = 44) over 2011 to 2015 (P < 0.001). At the same time, the incidence of revision surgeries for any cause was also reduced (14% in 2005-2009 vs. 9% in 2011-2015; P < 0.001). In adjusted analysis, the odds of a revision procedure for SSI were reduced by 50% following introduction of the protocol (OR 0.50; 95% CI 0.33, 0.76). No significant difference in the organisms responsible for SSI was identified between 2005 and 2009 and 2011 and 2015 (P = 0.22).

CONCLUSION

This natural experiment has shown some utility for a preoperative prophylactic antibiotic regimen of vancomycin and cefazolin, including meaningful reductions in revision procedures performed for SSI. This is the first effort we are aware of to consider a uniform institutional protocol that employs the use of intravenous vancomycin and cefazolin as prophylactic agents.

LEVEL OF EVIDENCE

2.

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

Background

Operative fixation for femoral metastatic bone disease is based on the principles of reducing pain and restoring function. Recent literature has proposed a number of prognostic models for appendicular metastatic bone disease. The aim of this study was to compare the accuracy of proposed soring systems in the setting of femoral metastatic bone disease in order to provide surgeons with information to determine the most appropriate scoring system in this setting.

Methods

A retrospective cohort analysis of patients who underwent surgical management of femoral metastatic bone disease at a single institution were included. A pre-operative predicted survival for all 114 patients was retrospectively calculated utilising the revised Katagiri model, PathFx model, SSG score, Janssen nomogram, OPTModel and SPRING 13 nomogram. Univariate and multivariate Cox regression proportional hazard models were constructed to assess the role of prognostic variables in the patient group. Area under the receiver characteristics and Brier scores were calculated for each prognostic model from comparison of predicted survival and actual survival of patients to quantify the accuracy of each model.

Results

For the femoral metastatic bone disease patients treated with surgical fixation, multivariate analysis demonstrated a number of pre-operative factors associated with survival in femoral metastatic bone disease, consistent with established literature. The OPTIModel demonstrated the highest accuracy at predicting 12-month (Area Under the Curve [AUC] = 0.79) and 24-month (AUC = 0.77) survival after surgical management. PathFx model was the most accurate at predicting 3-month survival (AUC = 0.70) and 6-month (AUC = 0.70) survival. The PathFx model was successfully externally validated in the femoral patient dataset for all time periods.

Conclusions

Among six prognostic models assessed in the setting of femoral metastatic bone disease, the present study observed the most accurate model for 3-month, 6-month, 12-month and 24-month survival. The results of this study may be utilised by the treating surgical team to determine the most accurate model for the required time period and therefore improve decision-making in the care of patients with femoral metastatic bone disease.

Pathologic fractures of the distal femur: Current concepts and treatment options

Pathologic fractures of the distal femur caused by bone metastases are not as common as those in the proximal femur but provide great difficulty to adequately treat. This systematic review shows that insufficient literature exists to draw clinically relevant conclusions for essential questions, such as "what factors indicate an endoprosthetic reconstruction for distal femur pathologic fractures?" Due to paucity of literature in the systematic review, a current concepts review (including treatment flowchart), based on instructional reviews and experience, was also performed.

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

BACKGROUND

Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time.

QUESTIONS/PURPOSES

The purposes of this study were (1) to develop machine-learning algorithms for the prediction of 5-year survival in patients with chondrosarcoma; and (2) to deploy the best algorithm as an accessible web-based app for clinical use.

METHODS

All patients with a microscopically confirmed diagnosis of conventional or dedifferentiated chondrosarcoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2010. SEER covers approximately 30% of the US population and consists of demographic, tumor characteristic, treatment, and outcome data. In total, 1554 patients met the inclusion criteria. Mean age at diagnosis was 52 years (SD 17), ranging from 7 to 102 years; 813 of the 1554 patients were men (55%); and mean tumor size was 8 cm (SD 6), ranging from 0.1 cm to 50 cm. Exact size was missing in 340 of 1544 patients (22%), grade in 88 of 1544 (6%), tumor extension in 41 of 1544 (3%), and race in 16 of 1544 (1%). Data for 1-, 3-, 5-, and 10-year overall survival were available for 1533 (99%), 1512 (98%), 1487 (96%), and 977 (63%) patients, respectively. One-year survival was 92%, 3-year survival was 82%, 5-year survival was 76%, and 10-year survival was 54%. Missing data were imputed using the nonparametric missForest method. Boosted decision tree, support vector machine, Bayes point machine, and neural network models were developed for 5-year survival. These models were chosen as a result of their capability of predicting two outcomes based on prior work on machine-learning models for binary classification. The models were assessed by discrimination, calibration, and overall performance. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0 with 1.0 being perfect discrimination and 0.5 that the model is no better than chance at making a prediction. The Brier score measures the squared difference between the predicted probability and the actual outcome. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. The Brier scores of the models are compared with the null model, which is calculated by assigning each patient a probability equal to the prevalence of the outcome.

RESULTS

Four models for 5-year survival were developed with c-statistics ranging from 0.846 to 0.868 and Brier scores ranging from 0.117 to 0.135 with a null model Brier score of 0.182. The Bayes point machine was incorporated into a freely available web-based application. This application can be accessed through https://sorg-apps.shinyapps.io/chondrosarcoma/.

CONCLUSIONS

Although caution is warranted, because the prediction model has not been validated yet, healthcare providers could use the online prediction tool in daily practice when survival prediction of patients with chondrosarcoma is desired. Future studies should seek to validate the developed prediction model.

LEVEL OF EVIDENCE

Level III, prognostic study.

What Factors Are Associated With Implant Breakage and Revision After Intramedullary Nailing for Femoral Metastases?

BACKGROUND

Actual and impending pathologic fractures of the femur are commonly treated with intramedullary nails because they provide immediate stabilization with a minimally invasive procedure and enable direct weightbearing. However, complications and revision surgery are prevalent, and despite common use, there is limited evidence identifying those factors that are associated with complications.

QUESTIONS/PURPOSES

Among patients treated with intramedullary nailing for femoral metastases, we asked the following questions: (1) What is the cumulative incidence of local complications? (2) What is the cumulative incidence of implant breakage and what factors are associated with implant breakage? (3) What is the cumulative incidence of revision surgery and what factors are associated with revision surgery?

METHODS

Between January 2000 and December 2015, 245 patients in five centers were treated with intramedullary nails for actual and impending pathologic fractures of the femur caused by bone metastases. During that period, the general indications for intramedullary nailing of femoral metastases were impending fractures of the trochanter region and shaft and actual fractures of the trochanter region if sufficient bone stock remained; nails were used for lesions of the femoral shaft if they were large or if multiple lesions were present. Of those treated with intramedullary nails, 51% (117) were actual fractures and 49% (111) were impending fractures. A total of 60% (128) of this group were women; the mean age was 65 years (range, 29-93 years). After radiologic followup (at 4-8 weeks) with the orthopaedic surgeon, because of the palliative nature of these treatments, subsequent in-person followup was performed by the primary care provider on an as-needed basis (that is, as desired by the patient, without any scheduled visits with the orthopaedic surgeon) throughout each patient's remaining lifetime. However, there was close collaboration between the primary care providers and the orthopaedic team such that orthopaedic complications would be reported. A total of 67% (142 of 212) of the patients died before 1 year, and followup ranged from 0.1 to 175 months (mean, 14.4 months). Competing risk models were used to estimate the cumulative incidence of local complications (including persisting pain, tumor progression, and implant breakage), implant breakage separately, and revision surgery (defined as any reoperation involving the implant other than débridement with implant retention for infection). A cause-specific multivariate Cox regression model was used to estimate the association of factors (fracture type/preoperative radiotherapy and fracture type/use of cement) with implant breakage and revision, respectively.

RESULTS

Local complications occurred in 12% (28 of 228) of the patients and 6-month cumulative incidence was 8% (95% confidence interval [CI], 4.7-11.9). Implant breakage occurred in 8% (18 of 228) of the patients and 6-month cumulative incidence was 4% (95% CI, 1.4-6.5). Independent factors associated with increased risk of implant breakage were an actual (as opposed to impending) fracture (cause-specific hazard ratio [HR_cs], 3.61; 95% CI, 1.23-10.53, p = 0.019) and previous radiotherapy (HR_cs, 2.97; 95% CI, 1.13-7.82, p = 0.027). Revisions occurred in 5% (12 of 228) of the patients and 6-month cumulative incidence was 2.2% (95% CI, 0.3-4.1). The presence of an actual fracture was independently associated with a higher risk of revision (HR_cs, 4.17; 95% CI, 0.08-0.82, p = 0.022), and use of cement was independently associated with a lower risk of revision (HR_cs, 0.25; 95% CI, 1.20-14.53, p = 0.025).

CONCLUSIONS

The cumulative incidence of local complications, implant breakage, and revisions is low, mostly as a result of the short survival of patients. Based on these results, surgeons should consider use of cement in patients with intramedullary nails with actual fractures and closer followup of patients after actual fractures and preoperative radiotherapy. Future, prospective studies should further analyze the effects of adjuvant therapies and surgery-related factors on the risk of implant breakage and revisions.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Prognostic role of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with bone metastases

Background

Skeletal metastases are a common problem in patients with cancer, and surgical decision making depends on multiple factors including life expectancy. Identification of new prognostic factors can improve survival estimation and guide healthcare providers in surgical decision making. In this study, we aim to determine the prognostic value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in patients with bone metastasis.

Methods

One thousand and twelve patients from two tertiary referral centers between 2002 and 2014 met the inclusion criteria. Bivariate and multivariate Cox regression analyses were performed to determine the association of NLR and PLR with survival.

Results

At 3 months, 84.0% of the patients with low NLR were alive versus 61.3% of the patients with a high NLR (p < 0.001), and 75.8% of the patients with a low PLR were alive versus 55.6% of the patients with a high PLR (p < 0.001). Both elevated NLR and elevated PLR were independently associated with worse survival (hazard ratio (HR): 1.311; 95% confidence interval (CI): 1.117–1.538; p = 0.001) and (HR: 1.358; 95% CI: 1.152–1.601; p < 0.001), respectively.

Conclusion

This study showed both NLR and PLR to be independently associated with survival in patients who were treated for skeletal metastasis.

External Validation and Optimization of the SPRING Model for Prediction of Survival After Surgical Treatment of Bone Metastases of the Extremities

BACKGROUND

Survival predictions before surgery for metastatic bone disease in the extremities (based on statistical models and data of previous patients) are important for choosing an implant that will function for the remainder of the patient's life. The 2008-SPRING model, presented in 2016, enables the clinician to predict expected survival before surgery for metastatic bone disease in the extremities. However, to maximize the model's accuracy, it is necessary to maintain and update the patient database to refit the prediction models achieving more accurate calibration.

QUESTIONS/PURPOSES

The purposes of this study were (1) to refit the 2008-SPRING model for prediction of survival before surgery for metastatic bone disease in the extremities with a more modern cohort; and (2) to evaluate the performance of the refitted SPRING model in a population-based cohort of patients having surgery for metastatic bone disease in the extremities.

METHODS

We produced the 2013-SPRING model by adding to the 2008-SPRING model (n = 130) a cohort of patients from a consecutive institutional database of patients who underwent surgery for bone metastases in the extremities with bone resection and reconstruction between 2009 and 2013 at a highly specialized surgical center in Denmark (n = 140). Currently the model is only available as the nomogram fully available in the current article, which is sufficient to use in daily clinical work, but we are working on making the tool available online. As such, the 2013-SPRING model was produced using a consecutive cohort of patients (n = 270) treated during an 11-year period (2003-2013) called the training cohort, all treated with bone resection and reconstruction. We externally validated the 2008-SPRING and the 2013-SPRING models in a prospective cohort (n = 164) of patients who underwent surgery for metastatic bone disease in the extremities from May 2014 to May 2016, called the validation cohort. The validation cohort was identified from a cross-section of the Danish population who were treated for metastatic lesions (using endoprostheses and internal fixation) in the extremities at five secondary surgical centers and one highly specialized surgical center. This cross-section is representative of the Danish population and no patients were treated outside the included centers as a result of public healthcare settings. The indications for surgery for training and the validation cohort were pathologic fracture, impending fracture, or intractable pain despite radiation. Exact date of death was known for all patients as a result of the Danish Civil Registration System and no loss to followup existed. In the training cohort, 150 patients (out of 270 [56%]) and in the validation cohort 97 patients (out of 164 [59%]) died of disease within 1 year postoperatively. The 2013 model did not differ from the 2008 model and included hemoglobin, complete fracture/impending fracture, visceral and multiple bone metastases, Karnofsky Performance Status, and the American Society of Anesthesiologists score and primary cancer. The models were evaluated by area under the receiver operating characteristic curve (AUC ROC) and Brier score (the lower the better).

RESULTS

The 2013-SPRING model was successfully refitted with a cohort using more patients than the 2008-SPRING model. Comparison of performance in external validation between the 2008 and 2013-SPRING models showed the AUC ROC was increased by 3% (95% confidence interval [CI], 0%-5%; p = 0.027) and 2% (95% CI, 0%-4%; p = 0.013) at 3-month and 6-month survival predictions, respectively, but not at 12 months at 1% (95% CI, 0%-3%; p = 0.112). Brier score was improved by -0.018 (95% CI, -0.032 to -0.004; p = 0.011) for 3-month, -0.028 (95% CI, -0.043 to -0.0123; p < 0.001) for 6-month, and -0.014 (95% CI, -0.025 to -0.002; p = 0.017) for 12-month survival prediction.

CONCLUSIONS

We improved the SPRING model's ability to predict survival after surgery for metastatic bone disease in the extremities. As such, the refitted 2013-SPRING model gives the surgeon a tool to assist in the decision-making of a surgical implant that will serve the patient for the remainder of their life. The 2013-SPRING model may provide increased quality of life for patients with bone metastasis because potential implant failures can be minimized by precise survival prediction preoperatively and the model is freely available and ready to use from the current article.

LEVEL OF EVIDENCE

Level I, diagnostic study.

Patient survival following joint replacement due to metastatic bone disease - comparison of overall patient and prostheses survival between cohorts treated in two different time-periods

BACKGROUND

Improvements in medical treatment for cancer have increased survival of cancer patients. We hypothesize that improvement in cancer treatment is reflected in increased survival after surgical intervention for metastatic bone disease (MBD) and that subsequent revision surgery does not pose a risk for survival.

METHODS

We identified a retrospective consecutive cohort who received bone resection and reconstruction (BRR) with implants (including total joint replacements (with or without wide resection) or bone reconstruction with an intercalary spacer or revisions procedures for failed implants with BBR technique) due to MBD from 2003 to 2008 (early cohort) and 2009 to 2013 (late cohort) at a tertiary referral center. We registered epidemiological data, type of implant (primary or a revision implant), patient survival (Kaplan-Meier), implant survival (competing risk analysis) and complications to surgery.

RESULTS

Three hundred and eleven procedures were performed in 291 patients (289 primary BRR (270 patients, early cohort n = 130 late cohort n = 140) and 22 revision BRR (21 patients)). Overall survival was 44% (95% confidence intervals (95% CI): 39-50) and 32% (95% CI: 27-38) after 1 and 2 years. No difference in survival between the early and late cohort was found (p = .458), or between primary and revision BRR (p = .465). Time from diagnosis of cancer to surgery was shorter in the early cohort (p < .001). The cumulative incidence of failure of implant was 2% (95% CI: 0-3%) at 1 year and 3% (95% CI: 1-6%) at 2 years. One year cumulative implant failure for revision implants was 5% (95% CI: 0-13%) at 1 and 2 years. The risk of failure was not statistical significant between primary and revision implant (p = .293) in competing risk analysis.

DISCUSSION

We were not able to identify an increased survival after surgery for MBD over time, however, we found an increased interval from diagnosis to surgery for MBD. This study suggests that revision surgery for MBD does not pose a risk for survival.

An Easy-to-Use Prognostic Model for Survival Estimation for Patients with Symptomatic Long Bone Metastases

BACKGROUND

A survival estimation for patients with symptomatic long bone metastases (LBM) is crucial to prevent overtreatment and undertreatment. This study analyzed prognostic factors for overall survival and developed a simple, easy-to-use prognostic model.

METHODS

A multicenter retrospective study of 1,520 patients treated for symptomatic LBM between 2000 and 2013 at the radiation therapy and/or orthopaedic departments was performed. Primary tumors were categorized into 3 clinical profiles (favorable, moderate, or unfavorable) according to an existing classification system. Associations between prognostic variables and overall survival were investigated using the Kaplan-Meier method and multivariate Cox regression models. The discriminatory ability of the developed model was assessed with the Harrell C-statistic. The observed and expected survival for each survival category were compared on the basis of an external cohort.

RESULTS

Median overall survival was 7.4 months (95% confidence interval [CI], 6.7 to 8.1 months). On the basis of the independent prognostic factors, namely the clinical profile, Karnofsky Performance Score, and presence of visceral and/or brain metastases, 12 prognostic categories were created. The Harrell C-statistic was 0.70. A flowchart was developed to easily stratify patients. Using cutoff points for clinical decision-making, the 12 categories were narrowed down to 4 categories with clinical consequences. Median survival was 21.9 months (95% CI, 18.7 to 25.1 months), 10.5 months (95% CI, 7.9 to 13.1 months), 4.6 months (95% CI, 3.9 to 5.3 months), and 2.2 months (95% CI, 1.8 to 2.6 months) for the 4 categories.

CONCLUSIONS

This study presents a model to easily stratify patients with symptomatic LBM according to their expected survival. The simplicity and clarity of the model facilitate and encourage its use in the routine care of patients with LBM, to provide the most appropriate treatment for each individual patient.

LEVEL OF EVIDENCE

Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Utility of Adding Magnetic Resonance Imaging to Computed Tomography Alone in the Evaluation of Cervical Spine Injury: A Propensity-Matched Analysis

STUDY DESIGN

Adult patients who received computed tomography (CT) alone or CT-magnetic resonance imaging (MRI) for the evaluation of cervical spine injury.

OBJECTIVE

To evaluate the utility of CT-MRI in the diagnosis of cervical spine injury using propensity-matched techniques.

SUMMARY OF BACKGROUND DATA

The optimal evaluation (CT alone vs. CT and MRI) for patients with suspected cervical spine injury in the setting of blunt trauma remains controversial.

METHODS

The primary outcome was the identification of a cervical spine injury, with decision for surgery and change in management considered secondarily. A propensity score was developed based on the likelihood of receiving evaluation with CT-MRI, and this score was used to balance the cohorts and develop two groups of patients around whom there was a degree of clinical equipoise in terms of the imaging protocol. Logistic regression was used to evaluate for significant differences in injury detection in patients evaluated with CT alone as compared to those receiving CT-MRI.

RESULTS

Between 2007 and 2014, 8060 patients were evaluated using CT and 693 with CT-MRI. Following propensity-score matching, each cohort contained 668 patients. There were no significant differences between the two groups in baseline characteristics. The odds of identifying a cervical spine injury were significantly higher in the CT-MRI group, even after adjusting for prior injury recognition on CT (odds ratios 2.6; 95% confidence interval 1.7-4.0; P < 0.001). However, only 53/668 patients (8%) in the CT-MRI group had injuries identified on MRI not previously recognized by CT. Only a minority of these patients (n = 5/668, 1%) necessitated surgical intervention.

CONCLUSION

In this propensity-matched cohort, the addition of MRI to CT alone identified missed injuries at a rate of 8%. Only a minority of these were serious enough to warrant surgery. This speaks against the standard addition of MRI to CT-alone protocols in cervical spine evaluation after trauma.

LEVEL OF EVIDENCE

3.

Decision support systems for incurable non-small cell lung cancer: a systematic review

BACKGROUND

Individually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients.

METHODS

A systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness.

RESULTS

The systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor.

CONCLUSIONS

Many DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness.

Prognostic Factors for Failure of Antibiotic Treatment in Patients With Osteomyelitis of the Spine

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

The aim of this study was to identify factors independently associated with antibiotic treatment failure in patients with spinal osteomyelitis.

SUMMARY OF BACKGROUND DATA

There are few studies that have identified risk factors for antibiotic treatment failure in medically managed spinal osteomyelitis. Identifying such factors could help to identify patients who can be treated solely with antibiotics.

METHODS

All patients who underwent antibiotic treatment for spinal osteomyelitis in one of our institutions between January 1, 2001 and January 1, 2015 were identified. Patients who underwent surgery before the start of the antibiotic treatment were excluded.

RESULTS

We included 215 patients with a mean age of 58 years; 63 (29%) patients had failure of antibiotic treatment. Diabetes (hazard ratio [HR] 1.69, 95% confidence interval [CI] 1.03-2.79, P = 0.037), fever (HR 1.61, 95% CI 0.93-2.79, P = 0.088), osteomyelitis at an additional site (HR 5.17, 95% CI 2.63-27.9, P = 0.001), and the presence of an epidural abscess (HR 1.91, 95% CI 1.05-3.45, P = 0.033) were associated with failure of antibiotic treatment. In the multivariate Cox regression analysis, diabetes (HR 1.69, 95% CI 1.03-2.79, P = 0.019), osteomyelitis at an additional site (HR 8.26, 95% CI 2.51-27.2, P = 0.001), fever (HR 1.77, 95% CI 1.00-3.12, P = 0.050), and the presence of an epidural abscess (HR 1.82, 95% CI 1.06-3.13, P = 0.030) were independently associated with failure of antibiotic treatment.

CONCLUSION

Antibiotic treatment failed in 29% of patients; diabetes, current other osteomyelitis, and having an epidural abscess were independently associated with failure of antibiotic treatment.

LEVEL OF EVIDENCE

3.

Diagnostic algorithm, prognostic factors and surgical treatment of metastatic cancer diseases of the long bones and spine

Oncological management of skeletal metastases has changed dramatically in the last few decades. A significant number of patients survive for many years with their metastases.

Surgeons are more active and the technical repertoire is broader, from plates to intramedullary devices to (tumour) endoprostheses.

The philosophy of treatment should be different in the case of a trauma-related fracture and a pathological fracture. A proper algorithm for establishing a diagnosis and evaluation of prognostic factors helps in planning the surgical intervention.

The aim of palliative surgery is usually to eliminate pain and to allow the patient to regain his/her mobility as well as to improve the quality of life through minimally invasive techniques using life-long durable devices.

In a selected group of patients with an oncologically controlled primary tumour site and a solitary bone metastasis with positive prognostic factors, which meet the criteria for radical excision (approximately 10% to 15% of the cases), a promising three to five years of survival may be achieved, especially in cases of metastases from breast and kidney cancer.

Spinal metastases require meticulous evaluation because decisions on treatment mostly depend on the tumour type, segmental stability, the patient’s symptoms and general state of health.

Advanced radiotherapy combined with minimally invasive surgical techniques (minimally invasive stabilisation and separation surgery) provides durable local control with a low complication rate in a number of patients.

Cite this article: EFORT Open Rev 2017;2:372-381.

Treatment of pathological fractures of the long bones

Bone metastases of the long bones often lead to pain and pathological fractures. Local treatment consists of radiotherapy or surgery. Treatment strategies are strongly based on the risk of the fracture and expected survival.

Diagnostic work-up consists of CT and biopsy for diagnosis of the primary tumour, bone scan or PET-CT for dissemination status, patient history and blood test for evaluation of general health, and biplanar radiograph or CT for evaluation of the involved bone.

A bone lesion with an axial cortical involvement of >30 mm has a high risk of fracturing and should be stabilised surgically.

Expected survival should be based on primary tumour type, performance score, and presence of visceral and cerebral metastases.

Radiotherapy is the primary treatment for symptomatic lesions without risk of fracturing. The role of post-operative radiotherapy remains unclear.

Main surgical treatment options consist of plate fixation, intramedullary nails and (endo) prosthesis. The choice of modality depends on the localisation, extent of involved bone, and expected survival. Adjuvant cement should be considered in large lesions for better stabilisation.

Cite this article: Willeumier JJ, van der Linden YM, van de Sande MAJ, Dijkstra PDS. Treatment of pathological fractures of the long bones. EFORT Open Rev 2016;1:136–145. DOI: 10.1302/2058-5241.1.000008.

Metastatic fractures of long limb bones

The diagnosis of pathological fracture should be considered routinely in patients with long limb-bone fractures. Investigations must be performed to establish the diagnosis of pathological fracture then to determine that the bone lesion is a metastasis. In over 85% of cases, the clinical evaluation combined with a detailed analysis of the radiographs is sufficient to determine that the fracture occurred at a tumour site. Aetiological investigations establish that the tumour is a metastasis. In some patients, the diagnosis of metastatic cancer antedates the fracture. When this is not the case, a diagnostic strategy should be devised, with first- to third-line investigations. When these fail to provide the definitive diagnosis, a surgical biopsy should be performed. The primaries most often responsible for metastatic bone disease are those of the breast, lung, kidney, prostate, and thyroid gland. However, the survival gains provided by newly introduced treatments translate into an increased frequency of bone metastases from other cancers. The optimal treatment of a pathological fracture is preventive. The Mirels score is helpful for determining whether preventive measures are indicated. When selecting a treatment for a pathological fracture, important considerations are the type of tumour, availability of effective adjuvant treatments, and general health of the patient. Metastatic fractures are best managed by a multidisciplinary team. The emergent treatment should start with optimisation of the patient's general condition, in particular by identifying and treating metabolic disorders (e.g., hypercalcaemia) and haematological disorders. Treatment decisions also depend on the above-listed general factors, location of the tumour, and size of the bony defect. Prosthetic reconstruction is preferred for epiphyseal fractures and internal fixation for diaphyseal fractures.