Impact of Kidney Dysfunction on Overall Survival in Myeloproliferative Neoplasms: A Single-Center Retrospective Study
Bibliographic record
Abstract
Introduction: Myeloproliferative neoplasms (MPN) are clonal hematopoietic stem cell disorders that can lead to the deposition of fibrous tissue, and by a propensity toward extramedullary hematopoiesis. The progression of kidney function and frequency of chronic kidney disease (CKD) in patients with MPN is unknown, although CKD is linked to increased mortality. In our study, we aimed to investigate the relationship between kidney function as measured by glomerular filtration rate (eGFR) and MPN disease and risk of thrombosis. Moreover, we analyzing the risk factors for CKD, impact of different treatment modalities and effect of CKD on survival. Patients & Method: We haveretrospectively screened 762 patients between Jan 1989 to May 2019 with MPN at Princess Margaret Cancer Centre. Diagnosis, of Polycythemia (PV), essential thrombocytosis (ET), and myelofibrosis (MF; comprising PMF, post-ET-PMF, and post-PV-PMF) was required as defined by the WHO classification. Patients with other MPNs (CNL, CEL, HES, MDS/MPN, MPN-U, Mastocytosis accelerating phase MPN and blast phase) or missing serial creatinine measurement or their CKD attributed to other causes (diabetic nephropathy, High blood pressure, polycystic kidney disease obstructive uropathy, Glomerulonephritis before MPN diagnosis and other) were excluded, resulting in study sample of 232 patients. The total cohort was subdivided according to the calculated eGFR, (ml/min/1.73m2) into eGFR1 (≥90, n=154), eGFR2 (60-89, n=12), and eGFR3 (<60, n=66). eGFR was collected retrospectively, because of small number of patients, we combined eGFR2 and eGFR3 in one group. Overall Survival (OS) was calculated using the Kaplan-Meier and log-rank test was used to assess impact variables of interest. Cox proportional hazards model was used to assess for prognostic factors of OS as well as to assess the joint effect of potential prognostic factors. Results: Median age for the total cohort was 58 years (range;18-88.3), 54% were male. Median follow-up duration was 86 months (range:45.5-135). Diagnosis of MPN involved ET; n=21(9%)), PV; n=32 (14%) and MF; n=179 (77%). JAK2V617F status was documented in 219 patients; 67% were positive; of 106 patients analyzed for CARL 40.6% were positive. Cardiovascular risk factors were higher in PV and MF than in ET (59.4%, 57% &38%) respectively. A higher uric acid and LDH levels were found in PV and MF. A total of 19% had a history of thrombosis and rate was higher in ET (28.6%) than PV (21.9%) or MF (16.8%). Further patient's characteristics summarized in Table1. Kidney biopsy performed in 8 patients. The most prominent histological finding included focal segmental glomerulosclerosis (n=3), Ig A nephropathy (n=3), mesangial hypercellularity and sclerosis, extramedullary haematopoiesis (n=1) and lupus nephritis (n=1). The risk factors for CKD based on eGFR group are presented in (Table 2). MPN diagnosis is a significant risk factor for kidney function (p=0.0117). In MF a higher rate of eGFR 88.5% compared with PV and ET. JAK2V617 demonstrates a significant impact on abnormal eGFR (p=0.05);. IPSS and DIPSS score are a significant risk factor for kidney dysfunction (p=0.0014, and 0.0074 respectively). High uric acid levels and neutrophil counts were higher in high eGFR group (p=0.0014, and 0.0074 respectively). No association found between thrombosis and high eGFR. Hydroxyurea treatment does not have an impact on CKD, however, more patients received Ruxolitinb in abnormal eGFR (p=0.001). CKD has significant impact on OS. The 5 years OS for eGFR1 was 75% (95% CI 67%-81%), and for abnormal eGFR was 63.9% (95% CI 52%-73%), p=0004 (Fig 1). Conclusion: Higher incidence of kidney dysfunction associated in MF compared to PV and ET, there was no association between thrombosis and high eGFR as we excluded all other cause of CKD. MF with abnormal eGFR associated with high DIPSS score. MPN patients with kidney dysfunction significantly affects OS, which indicate close monitoring and prospective study is required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".