Monoclonal Immunoglobulin Deposition Disease - a Review of Current and Emerging Data
Bibliographic record
Abstract
Introduction Monoclonal immunoglobulin deposition disease (MIDD) is a rare complication of abnormal B and plasma cells, characterized by non-fibrillar, Congo-red negative deposits of monoclonal immunoglobulin molecules. These include light chain (LCDD), heavy chain (HCDD), or combined (LHCDD), and can involve several organs, most notably the kidney. Clone directed treatments for MIDD, including chemotherapy and autologous stem cell transplantation (ASCT), are used to reduce abnormal immunoglobulin deposition and restore organ function. Organ transplantation is considered in select cases. However, data is limited in terms of prognosis, association of MIDD with other hematological disorders, and outcomes in the evolving treatment landscape. Objectives The primary aim of this scoping review is to identify and collate existing data on clinical manifestations, disease associations, and treatment options in MIDD. This will help provide evidence-based recommendations and inform future research. Methods We conducted a literature search through EMBASE, MEDLINE, and CINAHL. Identified studies were imported onto Covidence and screened by two independent reviewers. Primary studies of patients with MIDD were included. Conference abstracts and dissertations were included if they synthesized data on MIDD. Case reports, reviews, and guideline reports were excluded. Outcomes of interest include disease type, risk factors, systemic manifestations, natural history, and prognosis. Data was also collected on disease association, intervention (observation, immunosuppression, chemotherapy, ASCT, organ transplantation, novel agents), and prognostic variables. Standardized scoping review methodology and PRISMA guidelines were used for protocol development. Results A total of 16,673 records were identified through literature search, of which 125 were used. Most studies were case series, with larger retrospective cohort studies providing data on treatment options and transplantation in MIDD. Grey literature search was also performed and yielded no additional results. LCDD was identified as the most common MIDD subtype. Organ involvement was most commonly renal, followed by hepatic and cardiac. Study endpoints included overall survival, renal survival (i.e., time to initiation of renal replacement therapy), hematologic response, and organ response. Biochemical parameters such as serum creatinine and proteinuria were measured. Single organ involvement was more common than multi-organ, and disease response was primarily assessed using laboratory parameters. There was substantial heterogeneity in treatment. These included corticosteroids, plasma exchange, and chemotherapy - typically bortezomib-containing regimens - followed by ASCT in eligible patients. Renal response rates of up to 79% (n=28) were noted in a larger patient cohort. Patients who demonstrated renal response fared better than non-renal responders did (overall survival of 87% vs 60% at 27.3 months, n=255). In patients who underwent ASCT (either as consolidation or in the case of relapse/progression), most patients achieved hematologic CR (71%, n=14). ASCT was relatively well-tolerated, with early transplant-related mortality limited to one patient across 9 studies with data on ASCT (n=69). Patients appeared to have more favorable outcomes with MIDD/MGRS compared to MIDD in the setting of myeloma (VGPR/CR 58.9% vs 47.9%, n=255). Renal response appeared to correlate with deeper hematologic responses. Post renal transplant allograft loss has been noted upon MIDD relapse. Discussion Limited data exists on MIDD and its subtypes. Potential reasons include newer classification schema, low incidence, disease heterogeneity, need for histopathologic diagnosis, and frequently asymptomatic initial course. This scoping review outlines patient characteristics, disease subtypes, and outcomes with treatment options including ASCT and organ transplantation. MIDD appears to respond to chemotherapy and ASCT; however, responses depend on the subtype, associated disease process, and degree of organ involvement. Renal response may have significant prognostic implications. Limitations of this review include a paucity of prospective data, lack of studies using novel plasma cell-directed therapies, and small sample sizes. Further data analysis is ongoing, and emerging prospective data is of interest.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".