| Title |
A predictive tool particularly designed for elderly myeloma patients presenting with spinal cord compression
|
|---|---|
| Published in |
BMC Cancer, April 2016
|
| DOI | 10.1186/s12885-016-2325-y |
| Pubmed ID | |
| Authors |
Dirk Rades, Antonio Jose Conde-Moreno, Jon Cacicedo, Theo Veninga, Niklas Gebauer, Tobias Bartscht, Steven E. Schild |
| Abstract |
This study was performed to design a predictive tool that allows the estimation of overall survival (OS) of elderly myeloma patients (aged ≥65 years) presenting with myeloma-induced spinal cord compression (SCC). One-hundred-and-sixteen patients irradiated for motor deficits of the legs due to myeloma-induced spinal cord compression were retrospectively evaluated. Ten characteristics were analyzed for OS including age, interval between myeloma diagnosis and radiotherapy, other osseous myeloma lesions, myeloma type, gender, time developing motor deficits, number of affected vertebrae, ECOG-PS, pre-radiotherapy ambulatory status, and fractionation regimen. Characteristics that achieved significance on multivariate analysis were included in the predictive tool. The score for each characteristic was obtained from the 1-year OS rate divided by 10. The sum of these scores represented the prognostic score for each patient. On multivariate analysis, myeloma type (hazard ratio 3.31; 95 %-confidence interval 1.75-6.49; p < 0.001), ECOG-PS (HR 5.33; 95 %-CI 2.67-11.11; p < 0.001), ambulatory status (HR 2.71; 95 % CI 1.65-4.57; p < 0.001), and age (HR 1.95; 95 % CI 1.03-3.78; p = 0.040) were significantly associated with survival. Sum scores ranged from 18 to 32 points. Based on the sum scores, three prognostic groups were designed: 18-19, 21-28 and 29-32 points. The corresponding 1-year survival rates were 0, 43 and 96 %, respectively (p < 0.001). This new predictive tool has been specifically designed for elderly myeloma patients with SCC. It allows estimating the survival prognosis of this patient group and supports the treating physicians when looking for the optimal treatment approach for an individual patient. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 3 | 15% |
| Student > Doctoral Student | 3 | 15% |
| Student > Ph. D. Student | 2 | 10% |
| Lecturer | 2 | 10% |
| Researcher | 2 | 10% |
| Other | 3 | 15% |
| Unknown | 5 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 40% |
| Nursing and Health Professions | 2 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Psychology | 1 | 5% |
| Computer Science | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 5 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2016.
All research outputs
#19,054,237
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Outputs from BMC Cancer
#5,567
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Outputs of similar age
#220,544
of 299,961 outputs
Outputs of similar age from BMC Cancer
#76
of 121 outputs
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