Why are myelodysplastic syndromes unrecognized and underdiagnosed? A primary care perspective

Screening of Myelodysplastic Syndromes Using Research Parameters of Complete Blood Count: Automated Detection of Dysplasia

Myelodysplastic syndromes (MDS) present with polymorphic and non-specific diagnostic features Research parametersfrom hematology analyzers may be useful to discriminate MDS-related cytopenia.Parameters such as Neu X (related to the cytoplasmic complexity) and Neu Y (related to nucleic acid content) show promise to detect dysplasia of MDS and aid to recognize MDS from cytopenias of other etiologies.

Complete Blood Counts and Research Parameters in the Detection of Myelodysplastic Syndromes

The diagnosis of Myelodysplastic syndromes (MDS) is frequently challenging, especially in terms of the distinction from the other non-neoplastic causes of cytopenia. Currently, it is based on the presence of peripheral blood cytopenias, peripheral blood and bone marrow dysplasia/blasts, and clonal cytogenetic abnormalities, but MDS diagnostic features are polymorphic and non-specific. We investigated the utility of complete blood count (CBC) and research parameters (RUO) from the analyzer BC 6800 Plus (Mindray Diagnostics) to discriminate MDS-related cytopenia.

METHODS

100 samples from healthy individuals were used to establish the values of research parameters in normal subjects. A retrospective study was conducted including 66 patients diagnosed with MDS, 90 cytopenic patients due to other diseases (cancer patients receiving therapy, aplastic anemia, other hematological malignancies) and 50 with macrocytic anemia. The Wilcoxon test was applied to detect statistical differences among the groups of patients, considering p < 0.05 significant. The diagnostic performance of the RUO parameters for discriminating MDS among cytopenias was evaluated using receiver operating characteristic (ROC) curve analysis. Amultivariable logistic regression model was performed to identify the potential predictors for having MDS. The area under curve (AUC) and the Hosmer-Lemeshow test of the model were assessed. The performance of the model was verified in a prospective study including 224 cytopenic patients (validation group).

RESULTS

In the MDS group, the mean cell volume (MCV), percentage of macrocytic red cells (MAC), red cell distribution width (RDW) and immature platelets fraction (IPF) had increased values compared to the cytopenic and normal patients, while platelets, red and white cell counts, Neu X (related to the cytoplasmic complexity of neutrophils), Neu Y (related to nucleic acid content) and Neu Z (related to cell size) were lower (p < 0.001). Neu X, Neu Y, and Neu Z showed higher AUC for detecting MDS > 0.80; MAC, RDW and IPF AUC > 0.76. The multivariable model demonstrated that Neu X and Neu Y could be used in the recognition of MDS, AUC 0.88. In the validation group, 89.0% of the MDS patients were well classified.

CONCLUSION

MDS are common malignant disorders with a poor prognosis, and early diagnosis is warranted for optimal benefit from treatment. RUO gain insights to detect dysplasia of MDS and could be used in the differential diagnosis of MDS from cytopenias of other etiologies.

Disease Perception Is Correlated with Health-Related Quality of Life in Patients Suffering from Myelodysplastic Syndromes: Results of the Belgian Be-QUALMS Study

Patients with myelodysplastic syndromes suffer from an impaired quality of life that is only partially explained by physical symptoms. In an observational study, we aimed to investigate the impact of current MDS treatments and the influence of disease perception on quality of life. Serial measurement of health-related quality of life was performed by 'the QUALMS', a validated MDS-specific patient reported outcome tool. Disease perception was evaluated by means of the Brief Illness Perception Questionnaire (B-IPQ). We prospectively collected data on 75 patients that started on a new treatment and could not demonstrate a significant change in QUALMS score or B-IPQ score during treatment. Six out of eight items evaluated in the B-IPQ correlated significantly with QUALMS score. In this small sample, no significant difference in QUALMS score was found between lower vs. higher risk MDS patients or other studied variables, e.g., targeted hemoglobin showed no correlation with QUALMS score. In daily practice attention must be paid to initial formation of disease perception as it correlates independently with health-related quality of life and does not change during treatment (clinicaltrials.gov identifier: NCT04053933).

A machine learning approach to predicting risk of myelodysplastic syndrome

BACKGROUND

Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease.

METHODS

Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC).

RESULTS

On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively.

CONCLUSIONS

Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

Targeting health-related quality of life in patients with myelodysplastic syndromes - Current knowledge and lessons to be learned

Using a range of health-related quality of life (HRQoL) instruments, most - but not all - studies of myelodysplastic syndromes (MDS) have reported that lower hemoglobin levels and red blood cell transfusion dependency are associated with worse HRQoL. In addition, some MDS treatments may significantly improve HRQoL, particularly among those patients who respond to therapy; however, the majority of these studies were underpowered for this secondary endpoint. Furthermore, decreased HRQoL has been associated with worse survival outcomes, and HRQoL scores can be used to refine classical prognostic systems. Despite the subjective nature of HRQoL, the importance and validity of measuring it in trials and clinical practice are increasingly being recognized, but properly validated MDS-specific instruments are required. We describe what is currently known about HRQoL in patients with MDS, and the limitations of measuring HRQoL, and we provide some recommendations to improve the measurement of this outcome in future trials.

Automated Early Detection of Myelodysplastic Syndrome within the General Population Using the Research Parameters of Beckman-Coulter DxH 800 Hematology Analyzer

Simple Summary

A substantial fraction of the elderly population suffers from moderate anemia, and blood smear analysis can guide towards a diagnosis of myelodysplastic syndrome (MDS). Nevertheless, in medical laboratories, blood smear review is only performed when quantitative or qualitative flags occur upon complete blood count (CBC). Consequently, the suspicion of MDS can be delayed in the absence of systematic blood smear observation, which is crucial to initiate a full diagnosis process by cytological analysis of bone marrow aspiration. The Beckman Coulter DxH 800 hematology analyzer (Beckman-Coulter, Brea, CA) is widely used over the world. We propose in this study the clinical use of 10 unexploited “research parameters” for early detection of subclinical MDS by selective triggering of blood smear examination.

Abstract

The incidence of myelodysplastic syndrome increases with aging and the early diagnosis enables optimal care of these diseases. The DxH 800 hematology analyzer measures and calculates 126 cytological parameters, but only 23 are used for routine CBC assessment. The goal of this study was to use the 103 unexploited “research parameters” to develop an algorithm allowing for an early detection of subclinical MDS patients by triggering morphological analysis. Blood sample parameters from 101 MDS patients and 88 healthy volunteers were analyzed to identify the critical “research parameters” with: (i) the most significant differences between MDS patients and healthy volunteers, (ii) the best contributions to principal component analysis (PCA), first axis, and (iii) the best correlations with PCA, first two axes (cos² > 0.6). Ten critical “research parameters” of white blood cells were identified, allowing for the calculation of an MDS-likelihood score (MDS-LS), based on logistic regression. Automatic calculation of the MDS-LS is easily implementable on the middleware system of the DxH 800 to generate a flag for blood smear review, and possibly early detection of MDS patients in the general population.

Prior cytopenia predicts worse clinical outcome in acute myeloid leukemia

The prognosis of acute myeloid leukemia (AML) is influenced by both disease-intrinsic and patient-related factors. In particular, AML following myelodysplastic syndrome (MDS) (AML with myelodysplasia-related changes, AML-MRC) has a poor prognosis. We hypothesized that patients with cytopenias prior to AML, but no known prior MDS, may share biologic features with AML-MRC. We evaluated 140 AML patients without prior MDS who had complete blood count (CBC) data available 6-36 months prior to their diagnosis. Cytopenia, defined as clinically unexplained thrombocytopenia or macrocytic anemia, was present in 29/140 (21%) patients. Compared to non-cytopenic patients, AML patients with prior cytopenia were older and more often met morphologic or cytogenetic criteria for AML-MRC. Prior cytopenia was associated with shorter survival in patients with intermediate-risk cytogenetics (median OS 4.2 versus 24.1 months, p<0.0001), but not in patients with adverse-risk cytogenetics (median OS 4.4 versus 6.0 months, p=0.57). Prior thrombocytopenia, but not macrocytic anemia, was significantly associated with shorter overall survival (p=0.01) independent of treatment approach, karyotype risk, and age on multivariable analysis. Our data suggest that AML patients with prior cytopenias have features similar to AML-MRC, and in particular support the use of prior unexplained thrombocytopenia as an independent marker of high-risk disease.

Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters

The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.

Review of therapeutic options and the management of patients with myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are a poorly understood group of disorders caused by one or more genetic aberrations in the bone marrow-derived cell line responsible for hematopoiesis. Recent advances in genetic medicine have offered new insights into the epigenesis as well as the prognosis of MDS, but have not resulted in new or improved curative treatment options. Bone marrow transplantation, introduced before the advent of genetic medicine, is still the only potential cure. Advances in other medical and pharmaceutical areas have broadened the scope of supportive care and disease-modifying therapies, and treating physicians now have a broad range of disease management options depending on a patient's likely prognosis. There is now clear evidence that appropriate supportive care and therapeutic intervention can improve progression-free and overall survival of MDS patients.

Myelodysplastic syndromes: what do hospitalists need to know?

Myelodysplastic syndromes (MDS) comprise a heterogeneous group of clonal hematopoietic stem cell disorders characterized by dysplasia, ineffective hematopoiesis leading to peripheral blood cytopenias, and a variable risk of leukemic progression. MDS primarily affects the elderly, and although its prevalence is increasing, MDS is frequently overlooked and underdiagnosed. MDS should be suspected in any patient with unexplained cytopenias, and a bone marrow evaluation is ultimately needed to make the diagnosis and exclude other causes of bone marrow failure. The last 15 years have witnessed significant advances in our understanding of the complex pathogenesis, classification and prognostication, and therapeutic approaches to MDS. Several prognostic models facilitate outcome prediction and risk-adapted therapy. The addition of azacitidine, decitabine, and lenalidomide to erythropoiesis-stimulating agents in our armamentarium offered new effective therapeutic options for many patients who are not candidates for intensive interventions. Improved understanding of the genetic, epigenetic, and immunologic mechanisms that operate in MDS will help develop better prognostication tools and rationally design more effective therapies. Hospitalists are likely to encounter both patients with MDS and patients in whom MDS should be suspected. In this review of MDS, we focus on the epidemiology, diagnosis, pathogenesis, classification and prognostic tools, and treatment options.