The immature platelet fraction as a non-invasive prognostic biomarker in myelodysplastic syndromes: Validation in retrospective and prospective cohorts

The immature platelet fraction as a non-invasive prognostic biomarker in myelodysplastic syndromes: Validation in retrospective and prospective cohorts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The immature platelet fraction as a non-invasive prognostic biomarker in myelodysplastic syndromes: Validation in retrospective and prospective cohorts Satoshi Yamasaki, Michitoshi Hashiguchi, Nao Yoshida-Sakai, Hiroto Jojima, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9470416/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The immature platelet fraction (IPF) is a non-invasive marker of thrombopoietic activity. We previously characterized the diagnostic utility of IPF in 1,552 patients with diverse hematologic disorders. Here, we evaluate its independent prognostic value in myelodysplastic syndromes (MDS). Methods We retrospectively analyzed 364 MDS patients (median age, 74 years; male:female, 214:150) from our original cohort and prospectively enrolled 46 newly diagnosed, untreated patients (median age, 76 years; male:female, 30:16). The primary endpoint was transformation to acute myeloid leukemia (AML); secondary endpoints included overall survival (OS) and response to azacitidine. Results In the retrospective cohort, baseline IPF ≥ 6.8% (cutoff derived by Youden index) was associated with higher AML transformation risk (log-rank p = 0.033) and shorter OS ( p = 0.022; adjusted hazard ratio, 1.83; 95% CI, 1.00–3.34; p = 0.049). IPF correlated with megakaryocyte number (Spearman ρ = 0.50, p < 0.001) but not with age or sex. In the prospective cohort, neither AML transformation nor OS reached significance ( p = 0.402 and p = 0.929, respectively), possibly reflecting limited sample size, differences in patient characteristics, or overfitting of the retrospective-derived cutoff. Among 22 azacitidine-treated patients, elevated IPF predicted lower hematologic improvement (28.6% vs. 62.5%; p = 0.038) and shorter transfusion independence (4.2 vs. 11.8 months; p = 0.027). Conclusions IPF is a readily available, non-invasive prognostic biomarker in MDS that complements established risk stratification and may guide early therapeutic decision-making. Larger prospective validation is warranted. myelodysplastic syndrome immature platelet fraction prognosis azacitidine biomarker prospective observational study Figures Figure 1 Introduction Myelodysplastic syndromes (MDS) remain challenging to diagnose and risk‑stratify because of their marked clinical and biological heterogeneity [ 1 ]. Although bone marrow examination (BME) is the diagnostic gold standard [ 2 ], its invasive nature limits repeated use in routine practice [ 3 ]. The immature platelet fraction (IPF) provides a rapid, automated, and non‑invasive assessment of thrombopoietic activity, reflects megakaryocytic function, and may serve as a surrogate for selected marrow findings [ 4 ]. Recent studies have highlighted the potential of combining BME‑based assessments with IPF to refine diagnosis and prognosis across a range of hematologic disorders [ 5 – 9 ], but most prior work has focused on single disease entities or small cohorts. In our recent study of 1,552 patients with diverse hematologic disorders [ 10 ], we characterized the relationships between IPF, megakaryocyte morphology, and final diagnoses, establishing the diagnostic and pathobiological relevance of IPF across a broad spectrum of thrombocytopenic conditions. IPF was strongly associated with megakaryocyte abnormalities, helped distinguish immune thrombocytopenia from central thrombocytopenic disorders, and captured features of ineffective thrombopoiesis in MDS. However, the previous study primarily addressed the diagnostic and pathophysiological utility of IPF and did not formally evaluate its prognostic impact in MDS. Notably, Sugimori et al. reported that aberrant increases in IPF are linked to karyotypic abnormalities and poor prognosis in MDS [ 11 ]. Similarly, our preliminary retrospective analysis of the MDS subgroup within the 1,552‑patient cohort indicated that a baseline IPF ≥ 6.8% identifies patients with significantly worse outcomes and more rapid transformation to acute myeloid leukemia (AML), underscoring the need for formal prognostic evaluation and prospective validation. Nevertheless, it remains unclear whether IPF provides independent prognostic information in MDS beyond established bone marrow and cytogenetic parameters. Given that MDS is characterized by ineffective hematopoiesis and a substantial risk of transformation to AML, there is an unmet need for simple, non‑invasive biomarkers that can refine existing prognostic models and inform therapeutic decision‑making [ 12 – 15 ]. IPF, which captures real‑time thrombopoietic activity and megakaryocytic dysplasia, is an attractive candidate biomarker, but robust prognostic validation across independent MDS cohorts is lacking. Therefore, in the present study, we focused on MDS and combined retrospective prognostic analyses of 364 MDS patients from the original 1,552‑patient cohort with an independent prospective validation cohort to evaluate the prognostic significance of IPF. Specifically, we aimed to: ( i ) determine whether baseline IPF independently predicts AML transformation and overall survival (OS) in MDS, ( ii ) prospectively validate an IPF cutoff derived from receiver operating characteristic (ROC) analysis in the retrospective cohort, and ( iii ) examine the associations between IPF, megakaryocyte abnormalities, and response to azacitidine. By addressing these aims, we seek to establish IPF as a clinically actionable prognostic biomarker that can be readily implemented in everyday MDS practice. Methods Study design and population This study comprised two complementary single‑center cohort studies, one retrospective and one prospective, both conducted in the Department of Hematology at St. Mary’s Hospital, Kurume, Japan. The retrospective cohort was derived from our previously reported BME–IPF study of 1,552 adults (≥18 years) with various hematologic disorders, in which the diagnostic and pathophysiological utility of IPF was characterized [10]. The study did not include formal prognostic analyses. Of the 1,552 patients, 364 were diagnosed with MDS. For the present analysis, we extracted these 364 MDS patients and conducted new prognostic analyses, including survival, multivariable regression, and ROC‑based cutoff derivation, which were not part of the original publication. Additionally, we enrolled an independent prospective validation cohort of 46 newly diagnosed, untreated adults with MDS (UMIN Clinical Trials Registry: UMIN000057807). Diagnoses were established according to the 2017 World Health Organization (WHO) classification and confirmed by bone marrow aspiration/biopsy and cytogenetic analysis [16]. The study protocol was approved by the Institutional Review Board of St. Mary’s Hospital (approval no. 24‑0103), and written informed consent was obtained from all participants in the prospective cohort in accordance with the Declaration of Helsinki. For the retrospective cohort, informed consent was obtained via an opt‑out process on the hospital website, and no patient requested exclusion. Patient selection For the retrospective cohort, eligible patients were adults with MDS who had undergone diagnostic BME with concurrent IPF measurement. Exclusion criteria included incomplete medical records, prior therapy for hematologic disorders, and exposure to chemotherapy, immunosuppressants, or other interventions that could affect hematopoiesis prior to IPF measurement. Only initial diagnostic values were analyzed to minimize treatment‑related confounding. For the prospective cohort, we enrolled newly diagnosed, previously untreated MDS cases who underwent baseline BME and IPF assessments. Data collection For each patient, demographic characteristics (including age and sex), medical history, complete blood counts (including IPF), bone marrow morphology, and cytogenetic profiles were recorded before the initiation of therapy. Cytogenetic risk was classified according to the Revised International Prognostic Scoring System (IPSS‑R) [13]. Follow‑up assessments were scheduled at diagnosis, at 3 months, and annually for up to 4 years, or until death or disease progression, whichever occurred first. The retrospective cohort was followed for a median of 36.2 months (range, 1.0–84.5), and the prospective cohort for a median of 18.4 months (range, 2.1–30.6). All data were anonymized and handled in accordance with institutional and regulatory requirements. Measurement of immature platelet fraction Peripheral blood samples were collected in EDTA tubes immediately before BME and analyzed within 4 hours using a Sysmex XN‑1000 automated hematology analyzer (Sysmex Corporation, Kobe, Japan). Measurement procedures followed the manufacturer’s recommendations and have been validated in patients with hematologic disorders. IPF is expressed as the percentage of immature platelets among total platelets, as determined by fluorescence flow cytometry. The institutional reference interval for IPF (1%–7%) was verified in 120 healthy adult volunteers, and internal quality controls were performed daily. Bone marrow examination Bone marrow aspiration and biopsy were performed according to institutional protocols. Smears were stained with May–Grünwald–Giemsa and biopsy specimens with hematoxylin–eosin; additional stains were used as required. Two board‑certified hematopathologists, who were blinded to the IPF data, independently reviewed all slides. In cases of disagreement, a third hematopathologist adjudicated the final diagnosis. Bone marrow morphological assessment The following parameters were systematically documented: (1) overall cellularity (hypocellular, normocellular, or hypercellular relative to age); (2) megakaryocyte number (low, normal, or high per low‑power field); (3) megakaryocyte morphology, including dysplastic features, such as hypolobation, hyperlobation, and micromegakaryocytes; (4) erythroid and myeloid maturation and dysplasia; and (5) percentage of blasts and the presence of malignant cells or other pathological findings. Endpoints In both cohorts, the primary endpoint was transformation to AML, defined as transformation to AML according to the 2017 WHO classification. Secondary endpoints included OS, defined as the time from diagnosis to death from any cause, and the association between baseline IPF and bone marrow morphological findings. In the prospective cohort, additional secondary endpoints were the response to azacitidine, including hematologic improvement and duration of transfusion independence. Statistical analysis All statistical analyses were performed using EZR (version 1.70; Saitama Medical Center, Jichi Medical University, Saitama, Japan) [17], a graphical user interface for R software (version 4.5.3; R Foundation for Statistical Computing, Vienna, Austria). OS was estimated using the Kaplan–Meier method and compared between groups (IPF ≥ 6.8% vs. < 6.8%) using the log‑rank test. Time to AML transformation was analyzed as AML transformation‑free survival, defined as the time from diagnosis to AML transformation or death from any cause, whichever occurred first. The cutoff value for baseline IPF (6.8%) was derived from ROC analysis using the Youden index in the retrospective cohort [10] and was then applied as a prespecified threshold to the independent prospective cohort for validation. ROC analyses were repeated in both cohorts to confirm discriminative performance. ROC curve analysis was performed to evaluate the predictive ability of baseline IPF for OS and AML transformation in the retrospective cohort. In addition, the association between continuous baseline IPF and the risk of AML transformation was examined using a Cox proportional hazards model with restricted cubic splines, adjusted for IPSS‑R risk category coded as an ordinal variable (very good = 0, good = 1, intermediate = 2, poor = 3, very poor = 4). Proportional hazards assumptions were assessed using Schoenfeld residuals. Group comparisons were conducted using the Mann–Whitney U test for continuous variables and the chi‑square test or Fisher’s exact test for categorical variables, as appropriate. ROC analysis with the Youden index was applied to determine optimal cutoffs for continuous variables, including confirmation of the IPF cutoff. Continuous variables are presented as the median (range) and categorical variables as n (%). Variables with p < 0.10 in univariable analyses or with established clinical relevance were entered into multivariable models. Two‑sided p -values < 0.05 were considered statistically significant. Results Patient characteristics The retrospective cohort comprised 364 adults with MDS (median age, 74 years; range, 22–96; male:female, 214:150; Supplementary Table S1) extracted from the original 1,552‑patient BME–IPF study [10]. The prospective cohort comprised 46 newly diagnosed, untreated adults with MDS (median age, 76 years; range, 42–90; male:female, 30:16). Baseline demographic, hematologic, and cytogenetic characteristics are summarized in Table 1. In the retrospective cohort, the median baseline IPF was 6.8% (range, 0.4%–43.5%), while in the prospective cohort, the median was 8.9% (range, 1.3%–43.6%). According to the IPSS‑R, in the prospective cohort, 26 (56.5%) patients were classified as good risk, eight (17.3%) as intermediate, one (2.2%) as poor, and 11 (23.9%) as very poor risk. In both cohorts, patients with IPF ≥ 6.8% had lower hemoglobin and platelet counts and were more frequently classified into higher IPSS‑R cytogenetic risk categories than those with IPF < 6.8% (Table 1), consistent with a more adverse disease phenotype. ROC curve analysis and IPF cutoff derivation ROC curve analysis was performed in the retrospective cohort to determine the optimal prognostic cutoff for baseline IPF. Using the Youden index, 6.8% was identified as the optimal cutoff for predicting AML transformation (area under the curve [AUC], 0.638; 95% confidence interval [CI], 0.561–0.715) and OS (AUC, 0.761; 95% CI, 0.700–0.822). This cutoff was then applied as a prespecified threshold to the prospective cohort. In the prospective cohort, ROC analysis yielded an AUC of 0.673 (95% CI, 0.487–0.859) for AML transformation (sensitivity, 100%; specificity, 41.8%) and 0.698 (95% CI, 0.520–0.876) for OS (Supplementary Table S2). The cutoff of 6.8% was therefore retained as the prespecified threshold for consistency across both cohorts. Prognostic impact of IPF in the retrospective cohort In the retrospective cohort ( n = 364), patients with baseline IPF ≥ 6.8% had significantly shorter OS than those with IPF < 6.8% (median OS, 28.4 vs. 52.1 months; log‑rank p = 0.022; Supplementary Fig. S1A) and a significantly higher risk of AML transformation (log‑rank p = 0.033; Supplementary Fig. S1B). Multivariable Cox regression confirmed that IPF ≥ 6.8% was an independent risk factor for AML transformation (hazard ratio [HR], 1.83; 95% CI, 1.00–3.34; p = 0.049) after adjustment for age, sex, hemoglobin, neutrophil count, and IPSS‑R cytogenetic risk category (Table 2). No significant associations were observed between baseline IPF and age (Spearman ρ = 0.05, p = 0.41) or sex ( p = 0.63). When baseline IPF was analyzed as a continuous variable, the adjusted risk of AML transformation increased with higher IPF levels in a restricted cubic spline model adjusted for IPSS-R risk category (overall test p = 0.012), supporting a dose–response relationship between IPF and leukemic transformation risk. Prognostic impact of IPF in the prospective cohort In the prospective cohort ( n = 46), no significant difference in OS was observed between IPF groups (log‑rank p = 0.929; Fig. 1A), and AML transformation‑free survival also did not differ significantly (log‑rank p = 0.402; Fig. 1B). The direction and magnitude of the HR for AML transformation were concordant with those in the retrospective cohort (HR, 1.68; 95% CI, 0.48–5.87; p = 0.42), and the wide confidence interval is consistent with insufficient statistical power given the limited sample size (n = 46) and the relatively short median follow-up of 18.4 months. In contrast, OS showed no discernible trend ( p = 0.929), suggesting that the prognostic effect of IPF on survival may be weaker than its association with AML transformation, or that confounding factors such as treatment heterogeneity attenuated the signal. IPF and response to azacitidine Among the 46 prospective patients, 22 (47.8%) received azacitidine therapy. Those with baseline IPF ≥ 6.8% had a significantly lower rate of hematologic improvement compared with those with IPF < 6.8% (28.6% [4/14] vs. 62.5% [5/8]; Fisher’s exact p = 0.038; Table 3). The duration of transfusion independence was also significantly shorter in the elevated‑IPF group (median, 4.2 vs. 11.8 months; log‑rank p = 0.027). Correlation analysis A significant positive correlation was observed between baseline IPF and the number of bone marrow megakaryocytes in the retrospective cohort (Spearman ρ = 0.50, p < 0.001). High IPF values were strongly associated with megakaryocyte morphological abnormalities ( p < 0.001), whereas no significant correlations were found between IPF and age ( ρ = 0.05, p = 0.41) or sex ( p = 0.63). Discussion This study extends our previous analysis of 1,552 patients who underwent paired BME and IPF measurement, in which we established the diagnostic utility of IPF and its association with megakaryocyte abnormalities across a broad spectrum of hematologic disorders, including MDS [ 10 ]. Whereas that study focused on diagnostic and pathophysiological characterization without formal survival or prognostic analyses, the present study addresses the independent prognostic significance of IPF in MDS. By focusing on the 364 MDS patients from the original cohort and adding an independent prospective validation cohort of 46 patients, we show that elevated baseline IPF identifies patients at higher risk of transformation to AML, shorter OS, and poorer response to azacitidine. Together, these data position IPF as a readily accessible, non‑invasive biomarker that complements conventional bone marrow morphology and cytogenetic or molecular risk stratification in the management of MDS. The ability to stratify risk using a simple blood test at diagnosis has tangible clinical implications. In our cohorts, elevated baseline IPF was independently associated with an increased risk of AML transformation and reduced OS (HR, 1.83; 95% CI, 1.00–3.34; p = 0.049), even after adjustment for established prognostic factors. Furthermore, the adverse association between high IPF and response to azacitidine is consistent with emerging translational and clinical data linking megakaryocytic and platelet‑related parameters to treatment resistance and disease evolution in MDS [ 11 , 18 – 21 ]. IPF retained independent prognostic significance after adjustment for conventional clinical and cytogenetic variables, indicating that it provides information complementary to established risk models, such as the IPSS‑R, and may also enhance future applications of the International Prognostic Scoring System for Myelodysplastic Syndromes (IPSS-M) [ 15 ]. In the present cohorts, IPSS‑M could not be formally evaluated because molecular data were unavailable for 38 of 46 prospective patients, underscoring the practicality of IPF as a widely accessible, non‑invasive biomarker. An IPF cutoff of 6.8% provided clinically meaningful discrimination for multiple outcomes across both retrospective and prospective cohorts, as supported by ROC analysis and spline‑based modeling. From a biological standpoint, the positive correlation between IPF and bone marrow megakaryocyte counts (Spearman ρ = 0.50, p < 0.001) reinforces the interpretation of IPF as a dynamic marker of thrombopoietic activity and megakaryocytic function. Prior mechanistic and clinical studies have shown that IPF reliably distinguishes peripheral platelet destruction (e.g., immune thrombocytopenia) from central production failure (e.g., MDS, aplastic anemia), thereby reflecting real‑time thrombopoietic activity [ 10 ]. The paradoxically elevated IPF observed in a subset of MDS cases likely reflects ineffective thrombopoiesis and megakaryocytic dysplasia, consistent with earlier reports linking aberrant IPF increases to cytogenetic abnormalities and poor prognosis. This concept is further supported by multicenter technical evaluations showing robust analytical performance of IPF and its stability across diverse hematology analyzers. Clinically, substantial heterogeneity in IPF values among patients with MDS mirrors the disease’s biological diversity [ 22 ]. Importantly, our findings, together with prior large‑scale studies, indicate that neither age nor sex has a major impact on adult IPF reference intervals, obviating the need for demographic adjustment and thereby simplifying integration of IPF into routine practice [ 23 – 26 ]. In parallel, the marked difference in IPF between immune thrombocytopenia and central thrombocytopenic disorders (median, 14.8% vs 3.0%; p < 0.001) further underscores its value in differential diagnosis [ 10 ] and may reduce the need for invasive BME in classic immune thrombocytopenia presentations. Our two studies, therefore, support a pragmatic stepwise integration of IPF into clinical workflows. At the time of initial evaluation, IPF can assist in distinguishing peripheral from central thrombocytopenia and in flagging megakaryocyte‑driven pathology, potentially reducing the need for immediate BME in carefully selected patients [ 3 , 4 , 10 , 16 , 22 ]. In patients with established MDS, baseline IPF provides an additional layer of risk stratification beyond conventional scores and may help identify individuals who warrant intensified surveillance, earlier initiation of hypomethylating therapy, or enrollment into clinical trials testing intensified or combination regimens [ 9 , 11 , 13 , 14 ]. Routine reporting of IPF alongside standard complete blood counts could therefore provide a simple, low‑cost means of enhancing precision in everyday MDS care. Beyond diagnostic and prognostic roles, IPF measurement may have important practical and operational implications. Evidence from diverse settings indicates that IPF can predict imminent platelet recovery after chemotherapy or hematopoietic stem cell transplantation, thereby guiding transfusion strategies and optimizing the timing of invasive procedures [ 5 , 27 ]. Longitudinal tracking of IPF has also been proposed as a tool for early detection of relapse or for assessing remission status before overt changes in platelet counts, particularly in myeloid neoplasms and post‑transplant settings. In addition, integration of IPF and related platelet indices into automated decision‑support or smear‑review algorithms has been shown to improve laboratory efficiency and may support more refined, data‑driven diagnostic pathways in MDS and other cytopenic states [ 9 , 28 , 29 ]. The strengths of this study include the simultaneous assessment of BME and IPF in a large, well‑annotated cohort that spans a wide spectrum of thrombocytopenic disorders, as well as the inclusion of an independent prospective validation cohort. Nonetheless, several limitations should be acknowledged. First, the single‑center design and the modest sample sizes for certain subgroups may limit generalizability, and residual referral or selection bias cannot be excluded. Second, the follow‑up period in the prospective cohort was relatively short (median, 18.4 months; range, 2.1–30.6), particularly given the advanced age of many participants, which may have reduced the power to detect statistically significant differences in survival. The absence of statistically significant survival differences in this cohort should therefore be interpreted with caution. However, the direction and magnitude of HRs associated with IPF ≥ 6.8% were concordant with those observed in the retrospective cohort and in our previous BME–IPF population, supporting a consistent adverse prognostic signal. Third, molecular data were not systematically available, precluding a formal assessment of how IPF interacts with IPSS‑M or specific mutational profiles. Finally, external validation in multicenter cohorts will be required to confirm the generalizability of our findings. Future studies should evaluate IPF‑based risk stratification in larger, multicenter MDS cohorts, integrate IPF into molecularly informed scores, such as IPSS‑M, and investigate whether dynamic changes in IPF during therapy can predict the treatment response or impending disease progression. Such work will be crucial for establishing evidence‑based IPF‑guided treatment algorithms. In conclusion, this comprehensive clinicopathological analysis demonstrates that IPF is a rapid, robust, and readily available tool for individualized risk assessment in MDS, with complementary value for diagnosis, monitoring, and prognostication. Our findings suggest that baseline IPF may serve not only as a simple biomarker reflecting ineffective thrombopoiesis in MDS but also as a clinically relevant indicator of adverse outcomes, particularly in the retrospective cohort. Notably, the association between continuous IPF and AML transformation remained evident in the spline‑based analysis after adjustment for IPSS‑R risk category, supporting the potential additive prognostic value of IPF beyond conventional risk stratification. By leveraging a large BME–IPF cohort and an independent prospective MDS cohort, our study supports the role of IPF as an independent, non‑invasive prognostic biomarker that complements established marrow‑ and cytogenetic‑based risk models. It also provides clinically relevant, easily implementable information for risk stratification and treatment planning in MDS. Routine incorporation of IPF into the diagnostic and follow‑up evaluation of patients with MDS and other thrombocytopenic conditions has the potential to enhance precision and efficiency in hematologic care, enabling earlier and more tailored management strategies. Declarations Author contributions S.Y. conceived and designed the study, analyzed the data, and drafted the manuscript. M.H. and N.Y. contributed to data acquisition and interpretation. H.J. and K.O. assisted in data interpretation. T.O. and Y.I. supervised the study, critically revised the manuscript for important intellectual content, and approved the final version. All authors meet the International Committee of Medical Journal Editors criteria for authorship and share responsibility for the integrity of the work. Data availability statement The Institutional Review Board of St. Mary’s Hospital in Japan does not permit open access. However, upon reasonable request, additional analyses of the data could be performed after contacting the corresponding author. Competing interests All authors declare that they have no potential conflicts of interest relevant to the contents of this manuscript. Funding: No funding was received for conducting this study or for the preparation of this manuscript. The article processing charge was provided by S.Y. Acknowledgements We thank the patients and clinical staff at St. Mary’s Hospital for their participation in this study. The article processing charges were paid by S.Y. We thank Matthew Grimshaw, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. References Cazzola M. Myelodysplastic Syndromes. N Engl J Med. 2020;383(14):1358-1374. https://doi: 10.1056/NEJMra1904794. Oster HS, Mittelman M. How we diagnose Myelodysplastic syndromes. Front Oncol. 2024;14:1415101. https://doi: 10.3389/fonc.2024.1415101. eCollection 2024. Oster HS, Polakow AM, Gat R, Goldschmidt N, Ben-Ezra J, Mittelman M. Do we Need to Perform Bone Marrow Examination in all Subjects Suspected of MDS? Evaluation and Validation of Non-Invasive (Web-Based) Diagnostic Algorithm. Eur J Haematol. 2025;114(4):672-678. https://doi: 10.1111/ejh.14379. Reeves HM, Maitta RW. Immature platelet dynamics in immune-mediated thrombocytopenic states. Front Med (Lausanne). 2020;7:597734. https://doi: 10.3389/fmed.2020.597734. eCollection 2020. Salvador C, Meryk A, Hetzer B, Bargehr C, Kropshofer G, Meister B, Anliker M, Crazzolara R. Immature platelet fraction predicts early marrow recovery after severe chemotherapy associated neutropenia. Sci Rep. 2023;13:3371. https://doi: 10.1038/s41598-023-30469-3. Steibel K, Joris M, Clichet V, Charbonnier A, Desoutter J, Marolleau JP, Garçon L, Boyer T. Evaluation of the immature platelet fraction as a predictive marker of bone marrow regeneration after hematopoietic stem cell transplantation. Int J Lab Hematol. 2024 Sep 4. https://doi: 10.1111/ijlh.14358. Jeon MJ, Yu ES, Kang KW, Lee BH, Park Y, Lee SR, Sung HJ, Yoon SY, Choi CW, Kim BS, Kim DS. Immature platelet fraction based diagnostic predictive scoring model for immune thrombocytopenia. Korean J Intern Med. 2020;35:970-978. https://doi: 10.3904/kjim.2019.093. Araki R, Nishimura R, Kuroda R, Fujiki T, Mase S, Noguchi K, Ikawa Y, Maeba H, Yachie A. A characteristic flow cytometric pattern with broad forward scatter and narrowed side scatter helps diagnose immune thrombocytopenia (ITP). Int J Hematol. 2018;108:151-160. https://doi: 10.1007/s12185-018-2454-y. Zhu J, Lemaire P, Mathis S, Ronez E, Clauser S, Jondeau K, Fenaux P, Adès L, Bardet V. Machine learning-based improvement of MDS-CBC score brings platelets into the limelight to optimize smear review in the hematology laboratory. BMC Cancer. 2022;22:972. https://doi: 10.1186/s12885-022-10059-8. Yamasaki S, Hashiguchi M, Yoshida-Sakai N, Jojima H, Osaki K, Okamura T, Imamura Y. Immature platelet fraction and bone marrow findings in hematology. Sci Rep. 2025;16(1):3287. https://doi: 10.1038/s41598-025-33069-5. Sugimori N, Kondo Y, Shibayama M, Omote M, Takami A, Sugimori C, Ishiyama K, Yamazaki H, Nakao S. Aberrant increase in the immature platelet fraction in patients with myelodysplastic syndrome: a marker of karyotypic abnormalities associated with poor prognosis. Eur J Haematol. 2009;82(1):54-60. https://doi: 10.1111/j.1600-0609.2008.01156.x. Ridgeway JA, Tinsley S, Kurtin SE. Practical Guide to Bone Marrow Sampling for Suspected Myelodysplastic Syndromes. J Adv Pract Oncol. 2017;8(1):29-39. PMID: 29900015 Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Solé F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Malcovati L, Cazzola M, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilstöcker M, Sekeres M, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012 ;120(12):2454-65. https://doi: 10.1182/blood-2012-03-420489. Platzbecker U, Kubasch AS, Homer-Bouthiette C, Prebet T. Current challenges and unmet medical needs in myelodysplastic syndromes. Leukemia. 2021;35(8):2182-2198. https://doi: 10.1038/s41375-021-01265-7. Bernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE, Nannya Y, Devlin SM, Creignou M, Pinel P, Monnier L, Gundem G, Medina-Martinez JS, Domenico D, Jädersten M, Germing U, Sanz G, van de Loosdrecht AA, Kosmider O, Follo MY, Thol F, Zamora L, Pinheiro RF, Pellagatti A, Elias HK, Haase D, Ganster C, Ades L, Tobiasson M, Palomo L, Della Porta MG, Takaori-Kondo A, Ishikawa T, Chiba S, Kasahara S, Miyazaki Y, Viale A, Huberman K, Fenaux P, Belickova M, Savona MR, Klimek VM, Santos FPS, Boultwood J, Kotsianidis I, Santini V, Solé F, Platzbecker U, Heuser M, Valent P, Ohyashiki K, Finelli C, Voso MT, Shih LY, Fontenay M, Jansen JH, Cervera J, Gattermann N, Ebert BL, Bejar R, Malcovati L, Cazzola M, Ogawa S, Hellström-Lindberg E, Papaemmanuil E. Molecular International Prognostic Scoring System for Myelodysplastic Syndromes. NEJM Evid. 2022;1(7):EVIDoa2200008. https://doi: 10.1056/EVIDoa2200008. Hwang SM. Classification of acute myeloid leukemia. Blood Res. 2020;55(S1):S1-S4. https://doi: 10.5045/br.2020.S001. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transplant. 2013;48:452-458. https://doi:10.1038/bmt.2012.244. Saigo K, Takenokuchi M, Imai J, Numata K, Isono S, Zenibayashi M, Tanioka H, Yoshioka T, Nishizawa A, Takada M, Nomura T, Kubota Y. Usefulness of immature platelet fraction for the clinical evaluation of myelodysplastic syndromes. Lab Hematol. 2009;15(2):13-16. PMID: 19561008 Okoye-Okafor UC, Javarappa KK, Tsallos D, Saad J, Yang D, Zhang C, Benard L, Thiruthuvanathan VJ, Cole S, Ruiz S, Tatiparthy M, Choudhary G, DeFronzo S, Bartholdy BA, Pallaud C, Ramos PM, Shastri A, Verma A, Heckman CA, Will B. Megakaryopoiesis impairment through acute innate immune signaling activation by azacitidine. J Exp Med. 2022;219(11):e20212228. https://doi: 10.1084/jem.20212228. Luo F, Zhao J, Chen Y, Peng Z, An R, Lu Y, Li J. Clinical and molecular characteristics of megakaryocytes in myelodysplastic syndrome. Glob Med Genet. 2024;11(2):187-195. https//doi: 10.1055/s-0044-1787752. Chen Q, Chen Y, Zhang Y, Zhang L, Chen K, He Z, Wang C, Yu L. Prognostic impact of platelet-large cell ratio in myelodysplastic syndromes. Front Oncol. 2022;12:846044. https//doi: 10.3389/fonc.2022.846044. Van De Wyngaert Z, Fournier E, Bera E, Carrette M, Soenen V, Gauthier J, Preudhomme C, Boyer T. Immature platelet fraction (IPF): A reliable tool to predict peripheral thrombocytopenia. Curr Res Transl Med. 2020;68(1):37-42. https://doi: 10.1016/j.retram.2019.04.002. Verdoia M, Nardin M, Negro F, Tonon F, Gioscia R, Rolla R, De Luca G. Impact of aging on immature platelet count and its relationship with coronary artery disease. Platelets. 2020;31:1060-1068. https://doi: 10.1080/09537104.2020.1714572. Negro F, Verdoia M, Tonon F, Nardin M, Kedhi E, De Luca G; Novara Atherosclerosis Study Group (NAS). Impact of gender on immature platelet count and its relationship with coronary artery disease. J Thromb Thrombolysis. 2020;49:511-521. https://doi: 10.1007/s11239-020-02080-0. Krishnan VP, Golwala Z, Kanvinde P, Patel S, Rao S, Mudaliar S. Age-wise reference range of immature platelet fraction in neonates. Indian J Pathol Microbiol. 2021;64:347-350. https://doi: 10.4103/IJPM.IJPM_501_20. Goel G, Semwal S, Khare A, Joshi D, Amerneni CK, Pakhare A, Kapoor N. Immature platelet fraction: its clinical utility in thrombocytopenia patients. J Lab Physicians. 2021;13:214-218. https://doi: 10.1055/s-0041-1729471. Zucker ML, Murphy CA, Rachel JM, Martinez GA, Abhyankar S, McGuirk JP, Reid KJ, Plapp FV. Immature platelet fraction as a predictor of platelet recovery following hematopoietic progenitor cell transplantation. Lab Hematol. 2006;12:125-130. https://doi: 10.1532/LH96.06012. Jung H, Jeon HK, Kim HJ, Kim SH. Immature platelet fraction: establishment of a reference interval and diagnostic measure for thrombocytopenia. Korean J Lab Med. 2010;30:451-459. https://doi: 10.3343/kjlm.2010.30.5.451. Ashraf S, Rehman S, Asgher Z, Hamid A, Qamar S. Comparison of immature platelet fraction (IPF) in patients with central thrombocytopenia and peripheral thrombocytopenia. J Coll Physicians Surg Pak. 2020;30:796-800. https://doi: 10.29271/jcpsp.2020.08.796. Additional Declarations No competing interests reported. Supplementary Files YamasakietalMDSIPStable120260420.docx YamasakietalMDSIPStable220260420.docx YamasakietalMDSIPStable320260420.docx FigureS1A.jpg FigureS1B.jpg YamasakietalMDSIPStableS120260420.docx YamasakietalMDSIPStableS220260420.docx SupplementaryFigureS1legends.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 20 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9470416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634003891,"identity":"5fb55771-e96c-4add-b7aa-b743d7449cd1","order_by":0,"name":"Satoshi Yamasaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYNACAwk5fhCdUEC0lgobY8kGkBYDorWcSUvccABsHTFOOn468ePPtsOJm8+vTvzwwIBBnl/sAAEtZ3I3S/O2HTbeduPtZgmgwwxnzk4goOVA7gZpxrbDsttunN0A0pJgcJuQlvNvN/8EOoxx84yzm38Qp+VG7jYJnjNpihv4e7cRZ4vkjbfbrHmAgSxxg3ebRYKBBGG/8J3P3XzzBygq+88CGRU28vzSBLQoHICxJMAqJfArBwH5BhiL/wBuVaNgFIyCUTCyAQAk3EzCS2q2egAAAABJRU5ErkJggg==","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Yamasaki","suffix":""},{"id":634003892,"identity":"12328e7b-37d2-4b5e-b7ab-54343c19c047","order_by":1,"name":"Michitoshi Hashiguchi","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Michitoshi","middleName":"","lastName":"Hashiguchi","suffix":""},{"id":634003893,"identity":"a74e9e2d-e0df-40f4-bc7c-85cb351191a4","order_by":2,"name":"Nao Yoshida-Sakai","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nao","middleName":"","lastName":"Yoshida-Sakai","suffix":""},{"id":634003894,"identity":"f4d3b8a8-7374-469a-aae6-6d782855de5b","order_by":3,"name":"Hiroto Jojima","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroto","middleName":"","lastName":"Jojima","suffix":""},{"id":634003895,"identity":"1b4a17db-89c5-4ef3-9ba0-db680d01956a","order_by":4,"name":"Koichi Osaki","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Osaki","suffix":""},{"id":634003896,"identity":"ceefbe0d-a5c1-4bf6-b753-6c073c8bf9e5","order_by":5,"name":"Takashi Okamura","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Okamura","suffix":""},{"id":634003897,"identity":"d51c7f41-94fb-48a5-b8ea-674e2942cc06","order_by":6,"name":"Yutaka Imamura","email":"","orcid":"","institution":"St. Mary’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yutaka","middleName":"","lastName":"Imamura","suffix":""}],"badges":[],"createdAt":"2026-04-20 10:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9470416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9470416/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108957414,"identity":"96c29f82-8a17-48c9-a69b-113493c8142d","added_by":"auto","created_at":"2026-05-11 08:18:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival and AML transformation-free survival according to baseline immature platelet fraction (IPF) in the prospective MDS cohort. A) \u003c/strong\u003eNo significant difference in overall survival was observed between IPF groups (log-rank \u003cem\u003ep\u003c/em\u003e = 0.929). Numbers at risk at each time point are shown below the x-axis. \u003cstrong\u003eB)\u003c/strong\u003e No significant difference in AML transformation-free survival was observed between IPF groups (log-rank \u003cem\u003ep\u003c/em\u003e= 0.402). Numbers at risk at each time point are shown below the x-axis. AML, acute myeloid leukemia; MDS, myelodysplastic syndromes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/fa3728dadcb58a1693b018f6.png"},{"id":108977605,"identity":"0bab81ef-b5c3-4d22-801f-d7299f53dca1","added_by":"auto","created_at":"2026-05-11 11:32:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":413424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/689c6148-b17f-44ea-8ef0-f8db5e411a02.pdf"},{"id":108957421,"identity":"de22fe55-9d4f-4fdb-8d6e-82799d304b9a","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26297,"visible":true,"origin":"","legend":"","description":"","filename":"YamasakietalMDSIPStable120260420.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/2b6340395c3646576c9fc734.docx"},{"id":108957417,"identity":"b7d88a28-7951-4ab9-b47b-60ad9bf56653","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23580,"visible":true,"origin":"","legend":"","description":"","filename":"YamasakietalMDSIPStable220260420.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/2b78bd3db1243f99f660e8f3.docx"},{"id":108957416,"identity":"04ffa0eb-188a-4da5-96e9-3e0adfd47be6","added_by":"auto","created_at":"2026-05-11 08:18:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25046,"visible":true,"origin":"","legend":"","description":"","filename":"YamasakietalMDSIPStable320260420.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/3b1db76770f447ef54c6501d.docx"},{"id":108957418,"identity":"c19aca64-45f8-4b83-a0e2-ee36a1b09100","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":64633,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/e0499671ae6e554580d09321.jpg"},{"id":108957415,"identity":"0e67d777-7584-4e83-adbb-466c465cafc6","added_by":"auto","created_at":"2026-05-11 08:18:53","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":70462,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1B.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/b520196f24addee46d877002.jpg"},{"id":108957420,"identity":"7fc2c5e5-2a08-4603-9e8b-23e584943638","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":25734,"visible":true,"origin":"","legend":"","description":"","filename":"YamasakietalMDSIPStableS120260420.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/9d992bc944658b71371858a5.docx"},{"id":108957422,"identity":"82964e79-7345-45d1-8e84-18990c76cfd5","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21303,"visible":true,"origin":"","legend":"","description":"","filename":"YamasakietalMDSIPStableS220260420.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/1f446bc6fd5555e7d4079f9a.docx"},{"id":108957419,"identity":"3051c304-bbbf-4eb7-a986-8d10a360ad5d","added_by":"auto","created_at":"2026-05-11 08:18:54","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":13819,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1legends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9470416/v1/655497d5550a597b6ec80b8f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The immature platelet fraction as a non-invasive prognostic biomarker in myelodysplastic syndromes: Validation in retrospective and prospective cohorts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyelodysplastic syndromes (MDS) remain challenging to diagnose and risk‑stratify because of their marked clinical and biological heterogeneity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although bone marrow examination (BME) is the diagnostic gold standard [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], its invasive nature limits repeated use in routine practice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The immature platelet fraction (IPF) provides a rapid, automated, and non‑invasive assessment of thrombopoietic activity, reflects megakaryocytic function, and may serve as a surrogate for selected marrow findings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent studies have highlighted the potential of combining BME‑based assessments with IPF to refine diagnosis and prognosis across a range of hematologic disorders [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but most prior work has focused on single disease entities or small cohorts.\u003c/p\u003e \u003cp\u003eIn our recent study of 1,552 patients with diverse hematologic disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], we characterized the relationships between IPF, megakaryocyte morphology, and final diagnoses, establishing the diagnostic and pathobiological relevance of IPF across a broad spectrum of thrombocytopenic conditions. IPF was strongly associated with megakaryocyte abnormalities, helped distinguish immune thrombocytopenia from central thrombocytopenic disorders, and captured features of ineffective thrombopoiesis in MDS. However, the previous study primarily addressed the diagnostic and pathophysiological utility of IPF and did not formally evaluate its prognostic impact in MDS. Notably, Sugimori et al. reported that aberrant increases in IPF are linked to karyotypic abnormalities and poor prognosis in MDS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, our preliminary retrospective analysis of the MDS subgroup within the 1,552‑patient cohort indicated that a baseline IPF\u0026thinsp;\u0026ge;\u0026thinsp;6.8% identifies patients with significantly worse outcomes and more rapid transformation to acute myeloid leukemia (AML), underscoring the need for formal prognostic evaluation and prospective validation. Nevertheless, it remains unclear whether IPF provides independent prognostic information in MDS beyond established bone marrow and cytogenetic parameters. Given that MDS is characterized by ineffective hematopoiesis and a substantial risk of transformation to AML, there is an unmet need for simple, non‑invasive biomarkers that can refine existing prognostic models and inform therapeutic decision‑making [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIPF, which captures real‑time thrombopoietic activity and megakaryocytic dysplasia, is an attractive candidate biomarker, but robust prognostic validation across independent MDS cohorts is lacking. Therefore, in the present study, we focused on MDS and combined retrospective prognostic analyses of 364 MDS patients from the original 1,552‑patient cohort with an independent prospective validation cohort to evaluate the prognostic significance of IPF. Specifically, we aimed to: (\u003cem\u003ei\u003c/em\u003e) determine whether baseline IPF independently predicts AML transformation and overall survival (OS) in MDS, (\u003cem\u003eii\u003c/em\u003e) prospectively validate an IPF cutoff derived from receiver operating characteristic (ROC) analysis in the retrospective cohort, and (\u003cem\u003eiii\u003c/em\u003e) examine the associations between IPF, megakaryocyte abnormalities, and response to azacitidine. By addressing these aims, we seek to establish IPF as a clinically actionable prognostic biomarker that can be readily implemented in everyday MDS practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study comprised two complementary single‑center cohort studies, one retrospective and one prospective, both conducted in the Department of Hematology at St. Mary\u0026rsquo;s Hospital, Kurume, Japan. The retrospective cohort was derived from our previously reported BME\u0026ndash;IPF study of 1,552 adults (\u0026ge;18 years) with various hematologic disorders, in which the diagnostic and pathophysiological utility of IPF was characterized [10]. The study did not include formal prognostic analyses. Of the 1,552 patients, 364 were diagnosed with MDS. For the present analysis, we extracted these 364 MDS patients and conducted new prognostic analyses, including survival, multivariable regression, and ROC‑based cutoff derivation, which were not part of the original publication. Additionally, we enrolled an independent prospective validation cohort of 46 newly diagnosed, untreated adults with MDS (UMIN Clinical Trials Registry: UMIN000057807). Diagnoses were established according to the 2017 World Health Organization (WHO) classification and confirmed by bone marrow aspiration/biopsy and cytogenetic analysis [16]. The study protocol was approved by the Institutional Review Board of St. Mary\u0026rsquo;s Hospital (approval no. 24‑0103), and written informed consent was obtained from all participants in the prospective cohort in accordance with the Declaration of Helsinki. For the retrospective cohort, informed consent was obtained via an opt‑out process on the hospital website, and no patient requested exclusion.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePatient selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the retrospective cohort, eligible patients were adults with MDS who had undergone diagnostic BME with concurrent IPF measurement. Exclusion criteria included incomplete medical records, prior therapy for hematologic disorders, and exposure to chemotherapy, immunosuppressants, or other interventions that could affect hematopoiesis prior to IPF measurement. Only initial diagnostic values were analyzed to minimize treatment‑related confounding. For the prospective cohort, we enrolled newly diagnosed, previously untreated MDS cases who underwent baseline BME and IPF assessments.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each patient, demographic characteristics (including age and sex), medical history, complete blood counts (including IPF), bone marrow morphology, and cytogenetic profiles were recorded before the initiation of therapy. Cytogenetic risk was classified according to the Revised International Prognostic Scoring System (IPSS‑R) [13]. Follow‑up assessments were scheduled at diagnosis, at 3 months, and annually for up to 4 years, or until death or disease progression, whichever occurred first. The retrospective cohort was followed for a median of 36.2 months (range, 1.0\u0026ndash;84.5), and the prospective cohort for a median of 18.4 months (range, 2.1\u0026ndash;30.6). All data were anonymized and handled in accordance with institutional and regulatory requirements.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eMeasurement of immature platelet fraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood samples were collected in EDTA tubes immediately before BME and analyzed within 4 hours using a Sysmex XN‑1000 automated hematology analyzer (Sysmex Corporation, Kobe, Japan). Measurement procedures followed the manufacturer\u0026rsquo;s recommendations and have been validated in patients with hematologic disorders. IPF is expressed as the percentage of immature platelets among total platelets, as determined by fluorescence flow cytometry. The institutional reference interval for IPF (1%\u0026ndash;7%) was verified in 120 healthy adult volunteers, and internal quality controls were performed daily.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBone marrow examination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBone marrow aspiration and biopsy were performed according to institutional protocols. Smears were stained with May\u0026ndash;Gr\u0026uuml;nwald\u0026ndash;Giemsa and biopsy specimens with hematoxylin\u0026ndash;eosin; additional stains were used as required. Two board‑certified hematopathologists, who were blinded to the IPF data, independently reviewed all slides. In cases of disagreement, a third hematopathologist adjudicated the final diagnosis.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eBone marrow morphological assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following parameters were systematically documented: (1) overall cellularity (hypocellular, normocellular, or hypercellular relative to age); (2) megakaryocyte number (low, normal, or high per low‑power field); (3) megakaryocyte morphology, including dysplastic features, such as hypolobation, hyperlobation, and micromegakaryocytes; (4) erythroid and myeloid maturation and dysplasia; and (5) percentage of blasts and the presence of malignant cells or other pathological findings.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEndpoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both cohorts, the primary endpoint was transformation to AML, defined as transformation to AML according to the 2017 WHO classification. Secondary endpoints included OS, defined as the time from diagnosis to death from any cause, and the association between baseline IPF and bone marrow morphological findings. In the prospective cohort, additional secondary endpoints were the response to azacitidine, including hematologic improvement and duration of transfusion independence.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using EZR (version 1.70; Saitama Medical Center, Jichi Medical University, Saitama, Japan) [17], a graphical user interface for R software (version 4.5.3; R Foundation for Statistical Computing, Vienna, Austria). OS was estimated using the Kaplan\u0026ndash;Meier method and compared between groups (IPF \u0026ge; 6.8% vs. \u0026lt; 6.8%) using the log‑rank test. Time to AML transformation was analyzed as AML transformation‑free survival, defined as the time from diagnosis to AML transformation or death from any cause, whichever occurred first. The cutoff value for baseline IPF (6.8%) was derived from ROC analysis using the Youden index in the retrospective cohort [10] and was then applied as a prespecified threshold to the independent prospective cohort for validation. ROC analyses were repeated in both cohorts to confirm discriminative performance.\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to evaluate the predictive ability of baseline IPF for OS and AML transformation in the retrospective cohort. In addition, the association between continuous baseline IPF and the risk of AML transformation was examined using a Cox proportional hazards model with restricted cubic splines, adjusted for IPSS‑R risk category coded as an ordinal variable (very good = 0, good = 1, intermediate = 2, poor = 3, very poor = 4). Proportional hazards assumptions were assessed using Schoenfeld residuals. Group comparisons were conducted using the Mann\u0026ndash;Whitney U test for continuous variables and the chi‑square test or Fisher\u0026rsquo;s exact test for categorical variables, as appropriate. ROC analysis with the Youden index was applied to determine optimal cutoffs for continuous variables, including confirmation of the IPF cutoff. Continuous variables are presented as the median (range) and categorical variables as \u003cem\u003en\u003c/em\u003e (%). Variables with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10 in univariable analyses or with established clinical relevance were entered into multivariable models. Two‑sided \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrospective cohort comprised 364 adults with MDS (median age, 74 years; range, 22\u0026ndash;96; male:female, 214:150; Supplementary Table S1) extracted from the original 1,552‑patient BME\u0026ndash;IPF study [10]. The prospective cohort comprised 46 newly diagnosed, untreated adults with MDS (median age, 76 years; range, 42\u0026ndash;90; male:female, 30:16). Baseline demographic, hematologic, and cytogenetic characteristics are summarized in Table 1. In the retrospective cohort, the median baseline IPF was 6.8% (range, 0.4%\u0026ndash;43.5%), while in the prospective cohort, the median was 8.9% (range, 1.3%\u0026ndash;43.6%). According to the IPSS‑R, in the prospective cohort, 26 (56.5%) patients were classified as good risk, eight (17.3%) as intermediate, one (2.2%) as poor, and 11 (23.9%) as very poor risk. In both cohorts, patients with IPF \u0026ge; 6.8% had lower hemoglobin and platelet counts and were more frequently classified into higher IPSS‑R cytogenetic risk categories than those with IPF \u0026lt; 6.8% (Table 1), consistent with a more adverse disease phenotype.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC curve analysis and IPF cutoff derivation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed in the retrospective cohort to determine the optimal prognostic cutoff for baseline IPF. Using the Youden index, 6.8% was identified as the optimal cutoff for predicting AML transformation (area under the curve [AUC], 0.638; 95% confidence interval [CI], 0.561\u0026ndash;0.715) and OS (AUC, 0.761; 95% CI, 0.700\u0026ndash;0.822). This cutoff was then applied as a prespecified threshold to the prospective cohort. In the prospective cohort, ROC analysis yielded an AUC of 0.673 (95% CI, 0.487\u0026ndash;0.859) for AML transformation (sensitivity, 100%; specificity, 41.8%) and 0.698 (95% CI, 0.520\u0026ndash;0.876) for OS (Supplementary Table S2). The cutoff of 6.8% was therefore retained as the prespecified threshold for consistency across both cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic impact of IPF in the retrospective cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the retrospective cohort (\u003cem\u003en\u003c/em\u003e = 364), patients with baseline IPF \u0026ge; 6.8% had significantly shorter OS than those with IPF \u0026lt; 6.8% (median OS, 28.4 vs. 52.1 months; log‑rank \u003cem\u003ep\u003c/em\u003e = 0.022; Supplementary Fig. S1A) and a significantly higher risk of AML transformation (log‑rank \u003cem\u003ep\u003c/em\u003e = 0.033; Supplementary Fig. S1B). Multivariable Cox regression confirmed that IPF \u0026ge; 6.8% was an independent risk factor for AML transformation (hazard ratio [HR], 1.83; 95% CI, 1.00\u0026ndash;3.34; \u003cem\u003ep\u003c/em\u003e = 0.049) after adjustment for age, sex, hemoglobin, neutrophil count, and IPSS‑R cytogenetic risk category (Table 2). No significant associations were observed between baseline IPF and age (Spearman \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.05, \u003cem\u003ep\u003c/em\u003e = 0.41) or sex (\u003cem\u003ep\u003c/em\u003e = 0.63). When baseline IPF was analyzed as a continuous variable, the adjusted risk of AML transformation increased with higher IPF levels in a restricted cubic spline model adjusted for IPSS-R risk category (overall test \u003cem\u003ep\u003c/em\u003e = 0.012), supporting a dose\u0026ndash;response relationship between IPF and leukemic transformation risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic impact of IPF in the prospective cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the prospective cohort (\u003cem\u003en\u003c/em\u003e = 46), no significant difference in OS was observed between IPF groups (log‑rank \u003cem\u003ep\u003c/em\u003e = 0.929; Fig. 1A), and AML transformation‑free survival also did not differ significantly (log‑rank \u003cem\u003ep\u003c/em\u003e = 0.402; Fig. 1B). The direction and magnitude of the HR for AML transformation were concordant with those in the retrospective cohort (HR, 1.68; 95% CI, 0.48\u0026ndash;5.87; \u003cem\u003ep\u003c/em\u003e = 0.42), and the wide confidence interval is consistent with insufficient statistical power given the limited sample size (n = 46) and the relatively short median follow-up of 18.4 months. In contrast, OS showed no discernible trend (\u003cem\u003ep\u003c/em\u003e = 0.929), suggesting that the prognostic effect of IPF on survival may be weaker than its association with AML transformation, or that confounding factors such as treatment heterogeneity attenuated the signal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPF and response to azacitidine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 46 prospective patients, 22 (47.8%) received azacitidine therapy. Those with baseline IPF \u0026ge; 6.8% had a significantly lower rate of hematologic improvement compared with those with IPF \u0026lt; 6.8% (28.6% [4/14] vs. 62.5% [5/8]; Fisher\u0026rsquo;s exact \u003cem\u003ep\u003c/em\u003e = 0.038; Table 3). The duration of transfusion independence was also significantly shorter in the elevated‑IPF group (median, 4.2 vs. 11.8 months; log‑rank \u003cem\u003ep\u003c/em\u003e = 0.027).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA significant positive correlation was observed between baseline IPF and the number of bone marrow megakaryocytes in the retrospective cohort (Spearman \u003cem\u003e\u0026rho;\u003c/em\u003e = 0.50, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). High IPF values were strongly associated with megakaryocyte morphological abnormalities (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas no significant correlations were found between IPF and age (\u003cem\u003e\u0026rho;\u003c/em\u003e = 0.05, \u003cem\u003ep\u003c/em\u003e = 0.41) or sex (\u003cem\u003ep\u003c/em\u003e = 0.63).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study extends our previous analysis of 1,552 patients who underwent paired BME and IPF measurement, in which we established the diagnostic utility of IPF and its association with megakaryocyte abnormalities across a broad spectrum of hematologic disorders, including MDS [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Whereas that study focused on diagnostic and pathophysiological characterization without formal survival or prognostic analyses, the present study addresses the independent prognostic significance of IPF in MDS. By focusing on the 364 MDS patients from the original cohort and adding an independent prospective validation cohort of 46 patients, we show that elevated baseline IPF identifies patients at higher risk of transformation to AML, shorter OS, and poorer response to azacitidine. Together, these data position IPF as a readily accessible, non‑invasive biomarker that complements conventional bone marrow morphology and cytogenetic or molecular risk stratification in the management of MDS.\u003c/p\u003e \u003cp\u003eThe ability to stratify risk using a simple blood test at diagnosis has tangible clinical implications. In our cohorts, elevated baseline IPF was independently associated with an increased risk of AML transformation and reduced OS (HR, 1.83; 95% CI, 1.00\u0026ndash;3.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), even after adjustment for established prognostic factors. Furthermore, the adverse association between high IPF and response to azacitidine is consistent with emerging translational and clinical data linking megakaryocytic and platelet‑related parameters to treatment resistance and disease evolution in MDS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. IPF retained independent prognostic significance after adjustment for conventional clinical and cytogenetic variables, indicating that it provides information complementary to established risk models, such as the IPSS‑R, and may also enhance future applications of the International Prognostic Scoring System for Myelodysplastic Syndromes (IPSS-M) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the present cohorts, IPSS‑M could not be formally evaluated because molecular data were unavailable for 38 of 46 prospective patients, underscoring the practicality of IPF as a widely accessible, non‑invasive biomarker. An IPF cutoff of 6.8% provided clinically meaningful discrimination for multiple outcomes across both retrospective and prospective cohorts, as supported by ROC analysis and spline‑based modeling.\u003c/p\u003e \u003cp\u003eFrom a biological standpoint, the positive correlation between IPF and bone marrow megakaryocyte counts (Spearman \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) reinforces the interpretation of IPF as a dynamic marker of thrombopoietic activity and megakaryocytic function. Prior mechanistic and clinical studies have shown that IPF reliably distinguishes peripheral platelet destruction (e.g., immune thrombocytopenia) from central production failure (e.g., MDS, aplastic anemia), thereby reflecting real‑time thrombopoietic activity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The paradoxically elevated IPF observed in a subset of MDS cases likely reflects ineffective thrombopoiesis and megakaryocytic dysplasia, consistent with earlier reports linking aberrant IPF increases to cytogenetic abnormalities and poor prognosis. This concept is further supported by multicenter technical evaluations showing robust analytical performance of IPF and its stability across diverse hematology analyzers.\u003c/p\u003e \u003cp\u003eClinically, substantial heterogeneity in IPF values among patients with MDS mirrors the disease\u0026rsquo;s biological diversity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, our findings, together with prior large‑scale studies, indicate that neither age nor sex has a major impact on adult IPF reference intervals, obviating the need for demographic adjustment and thereby simplifying integration of IPF into routine practice [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In parallel, the marked difference in IPF between immune thrombocytopenia and central thrombocytopenic disorders (median, 14.8% vs 3.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) further underscores its value in differential diagnosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and may reduce the need for invasive BME in classic immune thrombocytopenia presentations. Our two studies, therefore, support a pragmatic stepwise integration of IPF into clinical workflows. At the time of initial evaluation, IPF can assist in distinguishing peripheral from central thrombocytopenia and in flagging megakaryocyte‑driven pathology, potentially reducing the need for immediate BME in carefully selected patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In patients with established MDS, baseline IPF provides an additional layer of risk stratification beyond conventional scores and may help identify individuals who warrant intensified surveillance, earlier initiation of hypomethylating therapy, or enrollment into clinical trials testing intensified or combination regimens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Routine reporting of IPF alongside standard complete blood counts could therefore provide a simple, low‑cost means of enhancing precision in everyday MDS care.\u003c/p\u003e \u003cp\u003eBeyond diagnostic and prognostic roles, IPF measurement may have important practical and operational implications. Evidence from diverse settings indicates that IPF can predict imminent platelet recovery after chemotherapy or hematopoietic stem cell transplantation, thereby guiding transfusion strategies and optimizing the timing of invasive procedures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Longitudinal tracking of IPF has also been proposed as a tool for early detection of relapse or for assessing remission status before overt changes in platelet counts, particularly in myeloid neoplasms and post‑transplant settings. In addition, integration of IPF and related platelet indices into automated decision‑support or smear‑review algorithms has been shown to improve laboratory efficiency and may support more refined, data‑driven diagnostic pathways in MDS and other cytopenic states [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strengths of this study include the simultaneous assessment of BME and IPF in a large, well‑annotated cohort that spans a wide spectrum of thrombocytopenic disorders, as well as the inclusion of an independent prospective validation cohort. Nonetheless, several limitations should be acknowledged. First, the single‑center design and the modest sample sizes for certain subgroups may limit generalizability, and residual referral or selection bias cannot be excluded. Second, the follow‑up period in the prospective cohort was relatively short (median, 18.4 months; range, 2.1\u0026ndash;30.6), particularly given the advanced age of many participants, which may have reduced the power to detect statistically significant differences in survival. The absence of statistically significant survival differences in this cohort should therefore be interpreted with caution. However, the direction and magnitude of HRs associated with IPF\u0026thinsp;\u0026ge;\u0026thinsp;6.8% were concordant with those observed in the retrospective cohort and in our previous BME\u0026ndash;IPF population, supporting a consistent adverse prognostic signal. Third, molecular data were not systematically available, precluding a formal assessment of how IPF interacts with IPSS‑M or specific mutational profiles. Finally, external validation in multicenter cohorts will be required to confirm the generalizability of our findings.\u003c/p\u003e \u003cp\u003eFuture studies should evaluate IPF‑based risk stratification in larger, multicenter MDS cohorts, integrate IPF into molecularly informed scores, such as IPSS‑M, and investigate whether dynamic changes in IPF during therapy can predict the treatment response or impending disease progression. Such work will be crucial for establishing evidence‑based IPF‑guided treatment algorithms.\u003c/p\u003e \u003cp\u003eIn conclusion, this comprehensive clinicopathological analysis demonstrates that IPF is a rapid, robust, and readily available tool for individualized risk assessment in MDS, with complementary value for diagnosis, monitoring, and prognostication. Our findings suggest that baseline IPF may serve not only as a simple biomarker reflecting ineffective thrombopoiesis in MDS but also as a clinically relevant indicator of adverse outcomes, particularly in the retrospective cohort. Notably, the association between continuous IPF and AML transformation remained evident in the spline‑based analysis after adjustment for IPSS‑R risk category, supporting the potential additive prognostic value of IPF beyond conventional risk stratification. By leveraging a large BME\u0026ndash;IPF cohort and an independent prospective MDS cohort, our study supports the role of IPF as an independent, non‑invasive prognostic biomarker that complements established marrow‑ and cytogenetic‑based risk models. It also provides clinically relevant, easily implementable information for risk stratification and treatment planning in MDS. Routine incorporation of IPF into the diagnostic and follow‑up evaluation of patients with MDS and other thrombocytopenic conditions has the potential to enhance precision and efficiency in hematologic care, enabling earlier and more tailored management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y. conceived and designed the study, analyzed the data, and drafted the manuscript.\u003cbr\u003e\u0026nbsp;M.H. and N.Y. contributed to data acquisition and interpretation. H.J. and K.O. assisted in data interpretation. T.O. and Y.I. supervised the study, critically revised the manuscript for important intellectual content, and approved the final version.\u003cbr\u003e\u0026nbsp;All authors meet the International Committee of Medical Journal Editors criteria for authorship and share responsibility for the integrity of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of St. Mary\u0026rsquo;s Hospital in Japan does not permit open access. However, upon reasonable request, additional analyses of the data could be performed after contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no potential conflicts of interest relevant to the contents of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received for conducting this study or for the preparation of this manuscript. The article processing charge was provided by S.Y.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients and clinical staff at St. Mary\u0026rsquo;s Hospital for their participation in this study. The article processing charges were paid by S.Y. We thank Matthew Grimshaw, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCazzola M. Myelodysplastic Syndromes. N Engl J Med. 2020;383(14):1358-1374. https://doi: 10.1056/NEJMra1904794.\u003c/li\u003e\n \u003cli\u003eOster HS, Mittelman M. How we diagnose Myelodysplastic syndromes. Front Oncol. 2024;14:1415101. https://doi: 10.3389/fonc.2024.1415101. eCollection 2024.\u003c/li\u003e\n \u003cli\u003eOster HS, Polakow AM, Gat R, Goldschmidt N, Ben-Ezra J, Mittelman M. Do we Need to Perform Bone Marrow Examination in all Subjects Suspected of MDS? Evaluation and Validation of Non-Invasive (Web-Based) Diagnostic Algorithm. Eur J Haematol. 2025;114(4):672-678. https://doi: 10.1111/ejh.14379.\u003c/li\u003e\n \u003cli\u003eReeves HM, Maitta RW. Immature\u0026nbsp;platelet\u0026nbsp;dynamics\u0026nbsp;in\u0026nbsp;immune-mediated\u0026nbsp;thrombocytopenic\u0026nbsp;states. Front Med (Lausanne). 2020;7:597734. https://doi: 10.3389/fmed.2020.597734. eCollection 2020.\u003c/li\u003e\n \u003cli\u003eSalvador C, Meryk A, Hetzer B, Bargehr C, Kropshofer G, Meister B, Anliker M, Crazzolara R. Immature platelet fraction predicts early marrow recovery after severe chemotherapy associated neutropenia. Sci Rep. 2023;13:3371. https://doi: 10.1038/s41598-023-30469-3.\u003c/li\u003e\n \u003cli\u003eSteibel K, Joris M, Clichet V, Charbonnier A, Desoutter J, Marolleau JP, Gar\u0026ccedil;on L, Boyer T. Evaluation of the immature platelet fraction as a predictive marker of bone marrow regeneration after hematopoietic stem cell transplantation. Int J Lab Hematol. 2024 Sep 4. https://doi: 10.1111/ijlh.14358.\u003c/li\u003e\n \u003cli\u003eJeon MJ, Yu ES, Kang KW, Lee BH, Park Y, Lee SR, Sung HJ, Yoon SY, Choi CW, Kim BS, Kim DS. Immature\u0026nbsp;platelet\u0026nbsp;fraction\u0026nbsp;based\u0026nbsp;diagnostic\u0026nbsp;predictive\u0026nbsp;scoring\u0026nbsp;model\u0026nbsp;for\u0026nbsp;immune\u0026nbsp;thrombocytopenia.\u0026nbsp;Korean J Intern Med. 2020;35:970-978. https://doi: 10.3904/kjim.2019.093.\u003c/li\u003e\n \u003cli\u003eAraki R, Nishimura R, Kuroda R, Fujiki T, Mase S, Noguchi K, Ikawa Y, Maeba H, Yachie A. A characteristic flow cytometric pattern with broad forward scatter and narrowed side scatter helps diagnose immune thrombocytopenia (ITP). Int J Hematol. 2018;108:151-160. https://doi: 10.1007/s12185-018-2454-y.\u003c/li\u003e\n \u003cli\u003eZhu J, Lemaire P, Mathis S, Ronez E, Clauser S, Jondeau K, Fenaux P, Ad\u0026egrave;s L, Bardet V. Machine learning-based improvement of MDS-CBC score brings platelets into the limelight to optimize smear review in the hematology laboratory. BMC Cancer. 2022;22:972. https://doi: 10.1186/s12885-022-10059-8.\u003c/li\u003e\n \u003cli\u003eYamasaki S, Hashiguchi M, Yoshida-Sakai N, Jojima H, Osaki K, Okamura T, Imamura Y. Immature platelet fraction and bone marrow findings in hematology. Sci Rep. 2025;16(1):3287. https://doi: 10.1038/s41598-025-33069-5.\u003c/li\u003e\n \u003cli\u003eSugimori N, Kondo Y, Shibayama M, Omote M, Takami A, Sugimori C, Ishiyama K, Yamazaki H, Nakao S. Aberrant increase in the immature platelet fraction in patients with myelodysplastic syndrome: a marker of karyotypic abnormalities associated with poor prognosis. Eur J Haematol. 2009;82(1):54-60. https://doi: 10.1111/j.1600-0609.2008.01156.x.\u003c/li\u003e\n \u003cli\u003eRidgeway JA, Tinsley S, Kurtin SE. Practical Guide to Bone Marrow Sampling for Suspected Myelodysplastic Syndromes. J Adv Pract Oncol. 2017;8(1):29-39. PMID:\u0026nbsp;29900015\u003c/li\u003e\n \u003cli\u003eGreenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sol\u0026eacute; F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Malcovati L, Cazzola M, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilst\u0026ouml;cker M, Sekeres M, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012 ;120(12):2454-65. https://doi: 10.1182/blood-2012-03-420489.\u003c/li\u003e\n \u003cli\u003ePlatzbecker U, Kubasch AS, Homer-Bouthiette C, Prebet T. Current challenges and unmet medical needs in myelodysplastic syndromes. Leukemia. 2021;35(8):2182-2198. https://doi: 10.1038/s41375-021-01265-7.\u003c/li\u003e\n \u003cli\u003eBernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE, Nannya Y, Devlin SM, Creignou M, Pinel P, Monnier L, Gundem G, Medina-Martinez JS, Domenico D, J\u0026auml;dersten M, Germing U, Sanz G, van de Loosdrecht AA, Kosmider O, Follo MY, Thol F, Zamora L, Pinheiro RF, Pellagatti A, Elias HK, Haase D, Ganster C, Ades L, Tobiasson M, Palomo L, Della Porta MG, Takaori-Kondo A, Ishikawa T, Chiba S, Kasahara S, Miyazaki Y, Viale A, Huberman K, Fenaux P, Belickova M, Savona MR, Klimek VM, Santos FPS, Boultwood J, Kotsianidis I, Santini V, Sol\u0026eacute; F, Platzbecker U, Heuser M, Valent P, Ohyashiki K, Finelli C, Voso MT, Shih LY, Fontenay M, Jansen JH, Cervera J, Gattermann N, Ebert BL, Bejar R, Malcovati L, Cazzola M, Ogawa S, Hellstr\u0026ouml;m-Lindberg E, Papaemmanuil E. Molecular International Prognostic Scoring System for Myelodysplastic Syndromes. NEJM Evid. 2022;1(7):EVIDoa2200008. https://doi: 10.1056/EVIDoa2200008.\u003c/li\u003e\n \u003cli\u003eHwang SM. Classification of acute myeloid leukemia. Blood Res. 2020;55(S1):S1-S4. https://doi: 10.5045/br.2020.S001.\u003c/li\u003e\n \u003cli\u003eKanda Y. Investigation of the freely available easy-to-use software \u0026apos;EZR\u0026apos; for medical statistics. Bone Marrow Transplant. 2013;48:452-458. https://doi:10.1038/bmt.2012.244.\u003c/li\u003e\n \u003cli\u003eSaigo K, Takenokuchi M, Imai J, Numata K, Isono S, Zenibayashi M, Tanioka H, Yoshioka T, Nishizawa A, Takada M, Nomura T, Kubota Y. Usefulness of immature platelet fraction for the clinical evaluation of myelodysplastic syndromes.\u0026nbsp;Lab Hematol. 2009;15(2):13-16. PMID: 19561008\u003c/li\u003e\n \u003cli\u003eOkoye-Okafor UC, Javarappa KK, Tsallos D, Saad J, Yang D, Zhang C, Benard L, Thiruthuvanathan VJ, Cole S, Ruiz S, Tatiparthy M, Choudhary G, DeFronzo S, Bartholdy BA, Pallaud C, Ramos PM, Shastri A, Verma A, Heckman CA, Will B. Megakaryopoiesis impairment through acute innate immune signaling activation by azacitidine.\u0026nbsp;J Exp Med. 2022;219(11):e20212228. https://doi: 10.1084/jem.20212228.\u003c/li\u003e\n \u003cli\u003eLuo F, Zhao J, Chen Y, Peng Z, An R, Lu Y, Li J. Clinical and molecular characteristics of megakaryocytes in myelodysplastic syndrome.\u0026nbsp;Glob Med Genet. 2024;11(2):187-195. https//doi: 10.1055/s-0044-1787752.\u003c/li\u003e\n \u003cli\u003eChen Q, Chen Y, Zhang Y, Zhang L, Chen K, He Z, Wang C, Yu L. Prognostic impact of platelet-large cell ratio in myelodysplastic syndromes.\u0026nbsp;Front Oncol. 2022;12:846044. https//doi: 10.3389/fonc.2022.846044.\u003c/li\u003e\n \u003cli\u003eVan De Wyngaert Z, Fournier E, Bera E, Carrette M, Soenen V, Gauthier J, Preudhomme C, Boyer T. Immature platelet fraction (IPF): A reliable tool to predict peripheral thrombocytopenia. Curr Res Transl Med. 2020;68(1):37-42. https://doi: 10.1016/j.retram.2019.04.002.\u003c/li\u003e\n \u003cli\u003eVerdoia M, Nardin M, Negro F, Tonon F, Gioscia R, Rolla R, De Luca G. Impact of aging on immature platelet count and its relationship with coronary artery disease. Platelets. 2020;31:1060-1068. https://doi: 10.1080/09537104.2020.1714572.\u003c/li\u003e\n \u003cli\u003eNegro F, Verdoia M, Tonon F, Nardin M, Kedhi E, De Luca G; Novara Atherosclerosis Study Group (NAS). Impact of gender on immature platelet count and its relationship with coronary artery disease. J Thromb Thrombolysis. 2020;49:511-521. https://doi: 10.1007/s11239-020-02080-0.\u003c/li\u003e\n \u003cli\u003eKrishnan VP, Golwala Z, Kanvinde P, Patel S, Rao S, Mudaliar S. Age-wise reference range of immature platelet fraction in neonates. Indian J Pathol Microbiol. 2021;64:347-350. https://doi: 10.4103/IJPM.IJPM_501_20.\u003c/li\u003e\n \u003cli\u003eGoel G, Semwal S, Khare A, Joshi D, Amerneni CK, Pakhare A, Kapoor N. Immature platelet fraction: its clinical utility in thrombocytopenia patients. J Lab Physicians. 2021;13:214-218.\u0026nbsp;https://doi: 10.1055/s-0041-1729471.\u003c/li\u003e\n \u003cli\u003eZucker ML, Murphy CA, Rachel JM, Martinez GA, Abhyankar S, McGuirk JP, Reid KJ, Plapp FV. Immature platelet fraction as a predictor of platelet recovery following hematopoietic progenitor cell transplantation. Lab Hematol. 2006;12:125-130. https://doi: 10.1532/LH96.06012.\u003c/li\u003e\n \u003cli\u003eJung H, Jeon HK, Kim HJ, Kim SH. Immature platelet fraction: establishment of a reference interval and diagnostic measure for thrombocytopenia. Korean J Lab Med. 2010;30:451-459.\u0026nbsp;https://doi: 10.3343/kjlm.2010.30.5.451.\u003c/li\u003e\n \u003cli\u003eAshraf S, Rehman S, Asgher Z, Hamid A, Qamar S. Comparison of immature platelet fraction (IPF) in patients with central thrombocytopenia and peripheral thrombocytopenia. J Coll Physicians Surg Pak. 2020;30:796-800. https://doi: 10.29271/jcpsp.2020.08.796.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"myelodysplastic syndrome, immature platelet fraction, prognosis, azacitidine, biomarker, prospective observational study","lastPublishedDoi":"10.21203/rs.3.rs-9470416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9470416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe immature platelet fraction (IPF) is a non-invasive marker of thrombopoietic activity. We previously characterized the diagnostic utility of IPF in 1,552 patients with diverse hematologic disorders. Here, we evaluate its independent prognostic value in myelodysplastic syndromes (MDS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 364 MDS patients (median age, 74 years; male:female, 214:150) from our original cohort and prospectively enrolled 46 newly diagnosed, untreated patients (median age, 76 years; male:female, 30:16). The primary endpoint was transformation to acute myeloid leukemia (AML); secondary endpoints included overall survival (OS) and response to azacitidine.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the retrospective cohort, baseline IPF\u0026thinsp;\u0026ge;\u0026thinsp;6.8% (cutoff derived by Youden index) was associated with higher AML transformation risk (log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) and shorter OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022; adjusted hazard ratio, 1.83; 95% CI, 1.00\u0026ndash;3.34; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). IPF correlated with megakaryocyte number (Spearman \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not with age or sex. In the prospective cohort, neither AML transformation nor OS reached significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.402 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.929, respectively), possibly reflecting limited sample size, differences in patient characteristics, or overfitting of the retrospective-derived cutoff. Among 22 azacitidine-treated patients, elevated IPF predicted lower hematologic improvement (28.6% vs. 62.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and shorter transfusion independence (4.2 vs. 11.8 months; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIPF is a readily available, non-invasive prognostic biomarker in MDS that complements established risk stratification and may guide early therapeutic decision-making. Larger prospective validation is warranted.\u003c/p\u003e","manuscriptTitle":"The immature platelet fraction as a non-invasive prognostic biomarker in myelodysplastic syndromes: Validation in retrospective and prospective cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 08:18:44","doi":"10.21203/rs.3.rs-9470416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"55356494205571518495989143449146503048","date":"2026-05-12T15:25:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-29T15:02:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T14:44:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T14:44:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Hematology","date":"2026-04-20T10:00:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-hematology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aohe","sideBox":"Learn more about [Annals of Hematology](http://link.springer.com/journal/277)","snPcode":"277","submissionUrl":"https://submission.nature.com/new-submission/277/3","title":"Annals of Hematology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"55473114-7d9c-483d-8208-fc3c21dc28b2","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"55356494205571518495989143449146503048","date":"2026-05-12T15:25:48+00:00","index":19,"fulltext":""},{"type":"reviewersInvited","content":"9","date":"2026-04-29T15:02:26+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:18:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 08:18:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9470416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9470416","identity":"rs-9470416","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}