Determinants of low health-related quality of life in patients with myelodysplastic syndromes: EUMDS Registry study.

Patients with myelodysplastic syndromes (MDS) frequently experience a significant symptom burden, which reduces health-related quality of life (HRQoL). We aimed to identify determinants of low HRQoL in patients recently diagnosed with MDS, for guiding early intervention strategies. We evaluated longitudinal data in 2205 patients with MDS during their first year after diagnosis. Median values of EQ-5D 3-level (EQ-5D-3L) index (0.78) and visual analog scale (VAS) score (0.70) were used as thresholds for low HRQoL. In addition, the 5 dimensions of EQ-5D-3L were analyzed for impairments (any level vs "no problem" category). After multiple imputation of missing values, we used generalized estimating equations (GEE) to estimate odds ratios (OR) for univariable determinant screening (P < .15), and to subsequently derive multivariable models for low HRQoL with 95% confidence intervals (CI). Multivariable GEE analysis showed the following independent determinants (OR, 95% CI) for low EQ-5D index: increased age (60-75 years: 1.33, 1.01-1.75; >75: 1.84, 1.39-2.45), female sex (1.70, 1.43-2.03), high serum ferritin level (≥1000 vs ≤300 µg/L: 1.41, 1.06-1.87), comorbidity burden (per unit: 1.11, 1.02-1.20), and reduced Karnofsky performance status (KPS, per 10 units: 0.62, 0.58-0.67). For low VAS score, additional determinants were transfusion dependence (1.53, 1.03-2.29), low hemoglobin <10 g/dL (1.34, 1.12-1.61), and high body mass index (≥30 vs 23-29.9 kg/m2: 1.26, 1.02-1.57). Sex, KPS, comorbidity burden, hemoglobin count, and transfusion burden were determinants for all EQ-5D dimensions. Low HRQoL is determined by multiple factors, which should be considered in the management and shared decision making of patients with MDS. This trial was registered at www.clinicaltrials.gov as #NCT00600860.

Adapting to heatwave-induced seagrass loss: Prioritizing management areas through environmental sensitivity mapping.

Seagrass meadows support complex species assemblages and provide ecosystem services with a multitude of socio-economic benefits. However, they are sensitive to anthropogenic pressures such as coastal development, agricultural run-off, and overfishing. The increasing prevalence of marine heatwaves (MHWs) due to climate change poses an additional and growing threat. In this study, we apply the environmental sensitivity mapping approach MESA (Mapping Environmentally Sensitive Assets) to explore the potential consequences of MHWs on the ecosystem services that Posidonia oceanica provides to coastal communities. Under the intermediate climate change scenario Representative Concentration Pathway 4.5, Mediterranean marine heatwaves will be severe by 2050, and will very likely increase mortality of P. oceanica. However, the societal risk of seagrass loss is not evenly distributed across the Mediterranean. The spatial distribution of socio-economic implications of seagrass loss is highlighted through two case studies on seagrass-dependent fisheries and coastal hazards. Coastal communities in Tunisia and Libya show very high sensitivity to a loss of fisheries due to a combination of increasingly intense and frequent MHWs, coupled with high proportions of regional seagrass-dependent fisheries catch. The coastlines of Italy, Tunisia, and Cyprus are shown to potentially be highly sensitive to loss of seagrass due to high levels of coastal hazards, and seagrass meadows susceptible to MHW-induced degradation. These coastlines are likely to suffer from reduced coastal protection services provided by intact seagrass meadows. We demonstrate the implications of MHWs for ecosystem service provision to coastal communities in the Mediterranean and the need for policy instruments to help mitigate and adapt to its effect. We also highlight the potential for environmental sensitivity mapping to help support policymakers with rapid screening tools to prioritize resources more effectively to areas where in-depth local planning is needed.

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS.

We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.