Addressing Unmet Needs in the Management of Drug-Induced Secondary Erythrocytosis: Results from an International Physician Survey
Bibliographic record
Abstract
Erythrocytosis is an increasingly common reason for hematology referral. JAK2-unmutated secondary erythrocytosis (SE) is more prevalent than polycythemia vera (PV), yet less extensively characterized. One of the fastest-rising causes of SE is medication use (25% of cases), frequently associated with testosterone therapy (TT), and recently described, sodium-glucose cotransporter 2 inhibitors (SGLT2i) (AJH, 2023). Data on risk factors for SE and thrombosis risk imputable to these drugs are scarce. Further, no data-driven therapeutic guidelines exist, making it challenging to manage these cases. The aim of this study was to assess prevalence of drug-induced SE in current practice, clinical perceptions and concerns regarding these entities, and management patterns. An online survey consisting of 8 questions was distributed via email to community and academic clinical hematologists. Between July 6-28, 2024, a total of 58 physicians completed the survey, including 49 (84%) from Canada, 8 (14%) from the United States, and 1 (2%) from Italy. The median number of SE cases respondents had evaluated over the past year was 10 (range 2-200), 30% of which (range 0-100%) were reportedly drug-induced. When queried about management of SGLT2i-mediated SE, the majority of physicians (39%) disclosed they continued therapy regardless of hemoglobin (Hb)/hematocrit (Hct) levels; 20.5% each responded they decreased SGLT2i dose or stopped therapy; 13% and 7% respectively replied they had not encountered this scenario, or didn't know how to manage it. When asked about specific Hb/Hct triggers that concerned them, the majority responded they either didn't have a trigger or used an individualized approach (30%); 28% and 24% replied Hct above 50-52% and 54-55% respectively. Additional responses included Hct 45%, 60%, and Hb 180-200 g/L (2-6% each). Factors that informed practice included: differential triggers for men/women, consideration of patient co-morbidities and risk/benefit ratio of pursuing SGLT2i, and presence of associated symptoms. Some physicians referenced guidelines from which they extrapolated to direct their approach: BJHaem, 2018 (Hct<55% for idiopathic erythrocytosis); endocrinology-based JCEM, 2018 (Hct<54%); others appropriated data from seminal studies in PV. When questioned about management of TT-induced erythrocytosis, 54% reported reducing TT dose, while 20% stopped therapy; 11% responded a combination thereof; 7.5% each either continued therapy or individualized their approach. The most common concerning Hb/Hct trigger while on TT therapy was 54-55%, followed by 50-52%, versus no trigger/individualized approach per 33%, 31% and 20% of respondents respectively. Other triggers included Hct >45%, 60%, or Hb from 180-200 (2-7% each). When asked if concerned about thrombosis in the setting of drug-induced SE, the vast majority replied “yes” (69%). Others either reported no concern (17%) or being equivocal/undecided (14%). Some nuanced their responses, specifying they were concerned in the setting of TT but not SGLT2i therapy. Others expressed concern, but specified this was less so than in cases of PV. A portion of respondents noted cardiovascular co-morbidities and degree of Hct/Hb elevation as being factors that influenced their level of apprehension. Finally, when asked if clinical practice guidelines for management of drug-induced SE would be helpful, 100% of respondents answered “yes.” Additional comments underscored the marked heterogeneity in clinical practice and “lack of clear consensus on management.” Others conveyed the importance of such guidelines being evidence-based, reiterating how “robust data on thrombosis risk in this setting are currently lacking.” A respondent relayed the value specifically, of “tools for shared decision-making concerning risks of Hct elevation vs benefit of continuation (of) both drug classes.” Categorically, respondents indicated management of drug-induced SE was a clear unmet need. Results from this broad-based survey with international contributions patently highlight the prevalence of drug-induced SE in practice (~30% of SE), the degree of heterogeneity in management, appreciable concern for thrombosis - especially when SE is TT-driven, and a critical unmet need for evidence-based management guidelines. Additional studies on mechanisms of drug-induced SE and associated thrombotic risk are effectively warranted.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".