Perioperative outcomes in patients with myeloproliferative neoplasms: a multicentric analysis of 354 surgical procedures
Bibliographic record
Abstract
Polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) are chronic myeloproliferative neoplasms (MPN) whose clinical course is punctuated by risks of both thrombotic and bleeding complications.Although surgery is an established situational risk factor for thrombosis in the general population, 1 this risk is further increased in patients with MPN. 2 In the perioperative setting, the effects of JAK2V617F mutations, elevated hematocrit (Hct), hyperviscosity, and stasis may be amplified, contributing to higher rates of cardiovascular events.3 In addition, concurrent acquired von Willebrand syndrome and inherent or therapeutic platelet (PLT) dysfunction may increase the risk of hemorrhage.One of the most comprehensive studies on this subject to date corroborated high rates of both perioperative thrombosis (7%) and hemorrhage (10.5%) in patients with PV/ET, despite most (74%) having optimally controlled blood counts.2 Importantly, there is currently no consensus or guidelines for perioperative management of MPN, and expert recommendations are based on limited data.4,5 Correspondingly, practices are heterogeneous and potentially inappropriate, as demonstrated by a recent pan-Canadian study.6 Moreover, there are particularly scarce data on MF cohorts and the impact of JAK (Janus kinase) inhibitors, now widespread in this population.This study sought to comparatively assess 90-day perioperative complication rates, risk variables impacting outcomes, and management strategies and their ramifications in a large MPN population, with the goal of informing clinical practice and improving patient outcomes.This study was approved by institutional review boards and written informed patient consent was obtained.Patients diagnosed with PV, ET, and MF according to World Health Organization criteria 7 between August 1981 and October 2021, and enrolled in the Quebec MPN Research Group registry (6 academic and community centers) were included.Consecutive cases where the patient had undergone at least 1 surgical procedure since diagnosis, with available pre-and postoperative data, were analyzed.Data were abstracted on demographics, cardiovascular risk factors, thrombosis/ hemorrhage history, laboratory values at diagnosis and pre-and postsurgery, type of surgery, therapy, and perioperative modifications.End points included surgical (per procedure) and 90-day postsurgery hemorrhage, arterial and venous thrombosis, and mortality.Major thrombotic and hemorrhagic events were defined per convention.8,9 Standard surgical definitions were used to categorize interventions as major (general, orthopedic, cardiovascular, and neurosurgery) 10,11 ; all others were defined as minor.Conventional statistical methods were used (JMP Pro 14.1.0software; SAS Institute, Cary, NC), with P <.05 considered significant.A total of 354 procedures were performed in 184 patients: PV, n = 87 (47%); ET, n = 66 (36%); and MF, n = 31 (17%).Demographic and clinical variables at diagnosis are presented in Table 1.The median age at diagnosis was 64 years (range, 19-89 years); 48% male; 82% JAK2V617F mutated
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".