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Record W2971830193 · doi:10.1182/blood-2018-99-117930

Monoclonal Gammopathy of Undetermined Significance - Patient Characteristics and Referral Patterns

2018· article· en· W2971830193 on OpenAlexaffabout
Holly Lee, Lesley Street, Jason Tay, Jennifer Grossman, John F. Thaell, Dawn Goodyear, Sylvia McCulloch, Peter Duggan, Paola Neri, Víctor H. Jiménez‐Zepeda

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsMonoclonal gammopathy of undetermined significanceMedicineMultiple myelomaPopulationInternal medicineParaproteinemiaHematologyReferralIncidence (geometry)CohortPediatricsMonoclonalImmunologyFamily medicineMonoclonal antibody

Abstract

fetched live from OpenAlex

Abstract Introduction Monoclonal gammopathy of undetermined significance (MGUS) is a prevalent hematological condition among elderly population with incidence rates of 3% and 5% over the age of 50 and 70 years, respectively (Kyle et al., 2018). It is considered a premalignant state of multiple myeloma with a risk of progression of 1% per year. It has also been shown that MGUS patients have a shorter survival compared to the age- and sex-matched cohort (Kyle et al., 2018). Recent reports have highlighted the clinical significance of monoclonal gammopathy and the organ injury that may result from the effects of paraproteinemia (Fermand et al., 2018). With new insights into the significance of monoclonal gammopathy, the purpose of our study was to characterize the MGUS patient population referred at our hematology clinics by observing the reason for monoclonal protein testing and assessing patient comorbidities at the time of MGUS diagnosis. Methods We collaborated with three different clinicians who see the majority of MGUS patients at the University of Calgary Medical Group (UCMG) clinics. Patients who were referred and diagnosed with MGUS at the hematology clinic at UCMG since 2014 were assessed. Retrospective chart reviews were performed and reasons for monoclonal testing were recorded as indicated in referral request notes or initial consult notes. Data on patient comorbidities was collected as indicated in the initial consult notes. MGUS risk stratification was calculated per previous reports (Katzmann et al., 2013; Kyle et al., 2018) Results A total of 606 MGUS patients were seen at our clinic from February 2014 to June 2018. There were 565 patient charts available for complete review. 56% of the patients were male. Median age was 72 and median follow up was 2 years. MGUS risk stratification showed that 33.2% had low, 47.9% intermediate-low, 17.8% intermediate-high, and 1.2% high-risk MGUS. There were 55.5% IgG-MGUS, 20.5% IgM-MGUS, 12.3% IgA-MGUS, and 8.4% light chain-MGUS patients. 3% had biclonal gammopathy. Patient comorbidities at time of diagnosis are reported in table 1. The most common conditions were hypertension (50.4%), dyslipidemia (33.1%), chronic kidney disease (22.4%), diabetes (21.6%), coronary artery disease (18.6%), and solid tumors (14.6%). The most common solid tumors were prostate cancer (22/ 83, 25%), colon cancer (12/83, 14.4%), and breast cancer (10/83, 12.0%). Of the 565 patient charts, 140 had either missing referral sheets or had no record of the reasons for paraproteinemia investigations in the notes. In the rest of the 425 patients, the most common reason for monoclonal protein testing by referring physician was for renal dysfunction, which included work up for acute kidney injury, chronic kidney injury, proteinuria and hematuria. The next most common reason was work up of neuropathy, followed by anemia, and constitutional symptoms (figure 1). The patients in the 'Others' group had various reasons for testing including work up for seizure, forgetfulness, stroke, headache, chronic pancreatitis, multiple sclerosis, myasthenia gravis, family history of myeloma, history of venous thromboembolism, splenomegaly, bronchiectasis, chest pain, and as part of routine physical and blood donor testing. Discussion and Conclusion MGUS is often incidentally detected as part of a work up for other medical conditions, and our results reveal that there is a variety of reasons for which monoclonal testing is performed. With recent developments in our understanding of the significance of monoclonal gammopathy and its association with certain renal and organ damage (Fermand et al., 2018; Leung et al., 2012), there may be a change in how the paraproteinemia investigations are utilized by clinicians in different disciplines. It will be important to recognize and establish appropriate indications for testing. Furthermore, MGUS patients present with a wide range of comorbidities at the time of diagnosis. Interdisciplinary care will play a key role in discerning how much of the organ dysfunction and patients' symptoms are secondary to their underlying medical conditions versus the effect of monoclonal gammopathy. Disclosures McCulloch: Celgene: Honoraria; Takeda: Other: Travel expenses. Neri:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.032
GPT teacher head0.292
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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