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• W3036147535 abstract "Where Are We Now? Annually, as many as 600,000 patients in the United States may have metastatic bone disease [2]. Estimates suggest metastatic bone disease accounts for 17% of annual medical costs nationally [18]. Although myeloma ranks fourth in the prevalence of metastatic bone disease in the United States, among tumors, it is the most likely to be accompanied by metastases (which occur in nearly 30% of patients with this diagnosis) and metastatic bone disease has the highest mean cost per patient among all cancers [17], as discussed by Toci et al. [18] in this issue of Clinical Orthopaedics and Related Research®. It is therefore important to consider patients with metastatic bone disease and those with myeloma independently. As orthopaedic surgeons, we contribute to the cost of metastatic bone disease in part by our prophylactic treatment of impending fractures and fixation of pathologic fractures. Because the cost of care is low for patients who undergo prophylactic fixation, determining which lesions are at risk for fracture is important [5]. However, cost is only one aspect that should be considered in making a clinical decision regarding prophylactic fixation. Two studies have shown longer mean survival and reduced immediate postoperative death with fixation than with treatment after a fracture [4, 22]. Other potential clinical benefits include improved pain control, improved ambulatory capability, lower blood loss, and less transfusion [4, 22]. Despite the importance of identifying an impending pathologic fracture, predicting the fracture risk remains imperfect. Universally available tools such as the Mirels scoring system, Harrington’s criteria, Carnesale’s “conventional definition,” and L-cort are based on radiographic (plain film or CT) and limited clinical data, such as the lesion’s location and the presence or absence of pain [6]. Although the sensitivity of identifying lesions at risk is generally high, specificity is generally low with these techniques [14, 20]. Other CT-based tools (such as a structural rigidity analysis, finite-element modeling, positron emission tomography-CT, parametric response modeling, and machine learning) have been shown to be more accurate, but they are not widely available [7, 10, 14, 19, 21]. Clinical and CT-based tools do not account for differences in underlying cancers. The expected survival duration of patients with metastatic bone disease differs widely and is the longest with myeloma, breast, and prostate cancer and the shortest with lung cancer. Effects on bone integrity, responsiveness to radiotherapy, and available adjunctive treatments also vary considerably among various cancer types, and these differences affect the risk of pathologic fracture. In this issue, Toci et al. [18] take a first and exciting step in adding a disease-specific perspective to predicting impending fractures. Their findings suggest that, compared with the Mirels score, their novel scoring system using five variables that were selected based on a stepwise regression analysis and receiver operating characteristic curves improves sensitivity, specificity, and positive and negative predictive values for identifying impending pathologic fractures in patients with myeloma. This system uses information readily available to the clinician without a sophisticated analysis and therefore represents a welcome advance. Where Do We Need to Go? Until more sophisticated and accurate CT and/or positron emission tomography-CT-based fracture prediction tools become widely available, physicians who treat metastatic bone disease need criteria that are valid and easy to apply. Given the deficiencies in available clinical prediction tools (including the Mirels, Harrington, Carnesale, and L-cort systems), new criteria are needed. These tools should be based not only on factors derived directly by the physician from radiographs and/or CT scans, but also on factors specific to the patient, availability and efficacy of adjunctive therapies, and other variables affecting expected patient survival. Treatment and survival factors are disease-specific. We need to identify which combination of these specific factors is the most important in determining the risk of fracture and indications for prophylactic treatment, not only overall but for also patients with specific underlying cancers. Toci et al. [18] have provided such a tool for multiple myeloma, but the development of similar tools for other specific diseases would be equally beneficial. The importance of identifying these factors and defining better criteria for diagnosing and treating impending pathologic fractures is crucial from two perspectives. First and most importantly, prophylactic stabilization benefits the patient for the reasons stated previously, so we need to know how best to prevent fracture and its adverse outcomes. Second, we need to avoid overtreatment. Patients who undergo prophylactic surgery when nonoperative treatment is appropriate are unnecessarily subjected to the risks of surgery. Moreover, with increased attention to healthcare costs, overtreatment generates an unnecessary economic burden. How Do We Get There? One way to identify important factors for diagnosing an impending pathologic fracture is through disease-specific studies such as the current one by Toci et al. [18]. Similar studies for patients with breast, lung, and primary renal carcinomas in particular would be welcome additions. Beyond the initial test cohorts, however, validation cohorts will establish efficacy for independent groups of patients in a broader array of clinical situations. Beyond retrospective, disease-specific studies, incorporation of survival data into a prediction of the risk of fractures is essential. Numerous survival prediction tools have been reported for appendicular and spinal bone lesions [1, 6, 12, 16]. Among survival tools for patients with appendicular metastatic bone disease and myeloma, PATHFx (www.PathFx.org) [3, 9, 15] is the most predictive of 3-month and 6-month survival, important timepoints for surgeons to consider when making surgical treatment decisions [13]. Among similar tools for spinal lesions, the Skeletal Oncology Research Group nomogram is the most predictive of 30-day and 90-day survival [1]. These tools have been validated in validation cohorts. PATHFx is the only survival tool available online [8]. Integration of patient survival data with either clinical or purely radiologic impending fracture prediction tools would account for variables not currently considered and would be a potentially important advance in providing guidance for treating these lesions. There is a precedent for this sort of integration. In the prediction of osteoporotic fractures, the Fracture Risk Assessment Tool integrates clinical data with radiologic data (femoral-neck bone mineral density) [11]. Developing a similar tool for patients with metastatic disease and myeloma should be an important and realistic goal in orthopaedics. Ideally, such a tool would incorporate a CT rigidity analysis, finite-element modeling, or positron emission tomography-CT for a biomechanical assessment and PATHFx for survival prediction." @default.
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• W3036147535 date "2020-06-16" @default.
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• W3036147535 title "CORR Insights®: Can a Novel Scoring System Improve on the Mirels Score in Predicting the Fracture Risk in Patients with Multiple Myeloma?" @default.
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