Infectious Risk in Multiple Myeloma Patients Undergoing Treatment with Teclistamab
Bibliographic record
Abstract
Introduction Bispecific antibodies (BsAbs) have become a key component of treatment in relapsed/refractory multiple myeloma (MM). However, despite promising response rates demonstrated in clinical trials, BsAbs have been shown to have a distinct adverse event profile including increased risk of infections as compared with conventional treatment regimens. The aim of this study was to explore the infectious risk of MM patients on active treatment with Teclistamab. Methods This was a retrospective study of MM patients treated with Teclistamab at Taussig Cancer Center from December 16th 2022 to March 31st 2024. Patients who completed step up dosing and received at least one dose of Teclistamab at maintenance dose were included in the study. The study period spanned from the date of the first dose of Teclistamab up to 6 months after the last dose of Teclistamab, change in therapy or death. Infections which required inpatient hospitalization occurring during the study period were included in the study. Neutropenia was defined as absolute neutropenia count <1500 cells/uL. Hypogammaglobulinemia was defined as serum IgG levels below 700 mg/dL. Data were collected on baseline patient characteristics, MM disease characteristics, and infection characteristics. The primary endpoint was rate of infections per patient-year. Results A total of 58 MM patients who were treated with Teclistamab at Taussig Cancer Center were screened for the study. 46/58 (79.3%) of those patients met inclusion criteria and were included in the study. Among the 46 patients included in the study, there were 20 infectious episodes that required inpatient hospitalization, occurring among 18 patients. The median age of the 46 patients was 66 with an interquartile range (IQR) of 62-74. Patients had broad exposure to novel agents including proteasome inhibitors (46, 100.0%), immunomodulatory drugs (44, 95.7%) and monoclonal antibodies (44, 95.7%). Other notable treatment exposures included CAR T-cell therapy (15, 32.6%), and autologous stem cell transplant (3, 6.5%). 6/9 (66.7%) of patients who underwent previous CAR T-cell therapy and 2/3 (66.7%) of patients who underwent previous autologous stem cell transplant developed an infection requiring hospitalization. No statistically significant difference was found in patients with infection and prior CAR T-cell therapy or autologous hematopoietic stem cell transplant. The median number of lines of therapy from MM diagnosis to start of Teclistamab therapy was 7 (IQR: 5.5-8). The median time from start of Teclistamab therapy till first infectious episode was 84.5 days (IQR: 27-177). Bacterial infections were most common (12/20, 60%), with the most common organism being Streptococcus spp. (2/12, 17%). The most common source of infection was respiratory (10/20, 50%). Neutropenia was present at diagnosis for 5/20 (25%) infectious episodes with 4/5 (80%) of these episodes having Grade 4 neutropenia. Hypogammaglobulinemia occurred during the study period in 43/46 (93.5%) patients with a median IgG nadir of 168 (IQR: 118-320). 22/43 (51.2%) patients received intravenous immunoglobulin (IVIG) during the study period, with 3/22 (13.6%) of these patients going on to develop an infection that required inpatient hospitalization. Of the 21 patients not administered IVIG, 13/21 (61.9%) went on to develop an infection that required inpatient hospitalization (p-value .0016). The median length of hospital stay for all infectious episodes was 7.5 days (IQR: 4-12.5). 6/20 (30%) infectious episodes required ICU stay with a median length of stay of 4.5 days (IQR: 2-18). The overall rate of infections per patient-year was 0.82. The rate of infections per patient-year for the 22 patients with hypogammaglobulinemia who did receive IVIG was .28. The rate of infections per patient-year for the 21 patients with hypogammaglobulinemia who did not receive IVIG was 1.5 (p-value = .06). The overall infection-related mortality rate was 3/20 (15%). Conclusion Multiple myeloma patients treated with Teclistamab experienced a high rate of infections requiring hospitalization and ICU care while on active treatment regardless of prior CAR T-cell and autologous hemopoietic stem cell transplant. Preventive measures, such as administration of prophylactic IVIG to patients with hypogammaglobulinemia, improved outcomes and should be taken to reduce infectious risk in this susceptible 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".