Dislipemia y obesidad en la enfermedad tromboembólica venosa: factores de riesgo y complicaciones trombóticas

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina. Fecha de lectura: 25 de Julio de 2013

Cut-Off Values of Hematologic Parameters to Predict the Number of Alpha Genes Deleted in Subjects with Deletional Alpha Thalassemia

Most α-thalassemia cases are caused by deletions of the structural α-globin genes. The degree of microcytosis and hypochromia has been correlated with the number of affected α-globin genes, suggesting a promising role of hematologic parameters as predictive diagnostic tools. However, cut-off points for these parameters to discriminate between the different subtypes of α-thalassemia are yet to be clearly defined. Six hematologic parameters (RBC, Hb, MCV, MCH, MCHC and RDW) were evaluated in 129 cases of deletional α-thalassemia (56 heterozygous α+ thalassemia, 36 homozygous α+ thalassemia, 29 heterozygous α0 thalassemia and 8 cases of Hb H disease). A good correlation between the number of deleted alpha genes and MCV (r = −0.672, p < 0.001), MCH (r = −0.788, p < 0.001) and RDW (r = 0.633, p < 0.001) was observed. The presence of an α0 allele should be discarded in individuals with microcytosis without iron deficiency and normal values of Hb A2 and Hb F with MCH < 23.40 pg. Furthermore, MCH < 21.90 pg and/or MCV < 70.80 fL are strongly suggestive of the presence of one α0 allele. Finally, an accurate presumptive diagnosis of Hb H disease can be made if both RDW ≥ 20% and MCH < 19 pg are seen

Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.

We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p80 years (p2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome