CMV-Related Hemorrhagic Retinal Vasculitis in a Multiple Myeloma Patient on Daratumumab Therapy: A Case Report
Bibliographic record
Abstract
PURPOSE: To report a case of cytomegalovirus (CMV)-related hemorrhagic retinal vasculitis in a patient with multiple myeloma (MM) on daratumumab, a trial cereblon E3 ligase modulatory drug (CELMoD), dexamethasone, and acyclovir, and discuss clinical implications for CMV prophylaxis. METHODS: Case report, narrative review of CMV reactivation risk in MM patients on daratumumab and antiviral agent efficacy for CMV prophylaxis. RESULTS: A 63-year-old female presented with 3 days of progressive unilateral vision loss in the right eye to the level of counting fingers. She had a history of relapsed and refractory MM and autologous stem cell transplant (ASCT). At the time of presentation, she was receiving daratumumab, a trial CELMoD, dexamethasone, and acyclovir. Posterior segment exam demonstrated trace vitreous cells (0.5+ vitritis as per SUN criteria) and scattered hemorrhages with multifocal intraluminal vascular whitening, aligned with infectious posterior uveitis and suggestive of panretinal occlusive vasculitis. Optical coherence tomography showed inner macular edema and epiretinal membrane formation. CMV reactivation was confirmed with PCR of anterior chamber fluid and blood. CONCLUSION: Patients with MM on daratumumab are at increased risk of opportunistic reactivations including CMV, potentially due to daratumumab's immunomodulatory side effects. Our patient developed CMV-related hemorrhagic retinal vasculitis despite low-dose acyclovir, which provides limited protection against CMV reactivation in CMV seropositive individuals. This case report therefore offers casuistic support for ophthalmic screening for CMV reactivation or CMV prophylaxis with letermovir in this patient population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".