Predictors for Skeletal-Related Events in Patients with Advanced Multiple Myeloma.
Bibliographic record
Abstract
Abstract Most patients with advanced multiple myeloma (MM) develop bone lesions during their disease course. Myeloma bone disease can result in potentially debilitating and life threatening skeletal-related events (SREs) such as pathologic fracture, spinal cord compression, the need for palliative radiotherapy (RT) or surgery to bone, and hypercalcemia of malignancy. Bone-targeted therapies that prevent or delay SRE onset may maintain quality of life (QOL) and functional independence in patients with advanced MM. Yet, the risk factors for SREs in this patient population are not fully understood. Exploratory analyses were conducted to identify potential SRE risk factors in patients with bone lesions from MM who received either zoledronic acid or pamidronate every 3 to 4 weeks for up to 24 months in a large, randomized trial. Patients with complete baseline demographics, disease characteristics, and markers of bone metabolism information available were included (n=282). Dichotomous variables included sex, race (white/other), narcotic analgesics (yes/no), Eastern Cooperative Oncology Group performance status (active/impaired), prior SRE (yes/no), and values with a defined upper limit of normal (creatinine, lymphocyte %, hemoglobin, serum glutamic oxaloacetic transaminase, albumin, lactate dehydrogenase [LDH], and calcium). Continuous variables included age, weight, cancer duration, Functional Assessment of Cancer Therapy-General score, Brief Pain Inventory (BPI) score, and bone markers (eg, urinary N-telopeptide of type I collagen [NTX], deoxypyridinoline [DPD]). Paraprotein type was also included. Univariate and multivariate analyses to determine relative risks (RR) for reduced time to first SRE associated with baseline variables using Cox regression models were developed, and those that were not significant at the 5% level were removed by backward elimination to generate a reduced model. In the reduced multivariate model, lower weight (RR=0.94 per 5-kg increase; P=.021), higher BPI scores (RR=1.16 per 1-unit increase; P < .001), race other than white (RR=0.60; P=.028), need for narcotic analgesics (RR=1.61; P=.017), and high levels of NTX (RR=1.68 per 100-nmol/mmol creatinine increase; P=.005) significantly correlated with reduced time to first SRE. Pathologic fracture and RT to bone were the most common SREs; in multivariate models, lower weight and higher BPI scores were associated with increased RRs of both fractures and RT to bone. Race and DPD levels were also significant covariates for fractures, whereas high levels of LDH correlated significantly with need for RT. Because bone resorption marker levels were significant covariates, the correlation between baseline NTX and time to first SRE was assessed. High baseline NTX (≥ 50 nmol/mmol creatinine) was associated with increased risk of shorter time to first SRE: by a significant 67% in the zoledronic acid group (n=210; P=.015) and by a 57% trend in the pamidronate group (n=108; P=.114). Taken together, lower weight and pain parameters (eg, BPI or narcotic analgesics) correlated consistently with skeletal morbidity risks in patients with advanced MM. Treatments that facilitate the restoration of bone homeostasis, as evidenced by bone marker normalization, may reduce the risk of SREs, thus maintaining QOL in this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".