Clinical management of the acute complications of sickle cell anemia: 11 years of experience in a tertiary hospital

Sickle cell disease is an emerging anemia in Europe leading to high morbidity with severe acute complications requiring hospital admission and chronic consequences. The management of these patients is complex and needs interdisciplinary care. The objective is to analyze clinical characteristics and management of patients with sickle cell disease admitted for acute complications. Methods: Retrospective descriptive study of admissions for acute complications of patients with sickle cell disease under 16 years of age in a tertiary hospital between 2010 and 2020. Clinical, laboratory and radiological data were reviewed. Results: We included 71 admissions corresponding to 25 patients, 40% diagnosed by neonatal screening. Admissions increased during this period. The most frequent diagnoses were vaso-occlusive crisis (35.2%), febrile syndrome (33.8%) and acute chest syndrome (32.3%). Nine patients required critical care at PICU. Positive microbiological results were confirmed in 20 cases, bacterial in 60%. Antibiotic therapy was administered in 86% of cases and the vacci-nation schedule of asplenia was adequately fulfilled by 89%. Opioid analgesia was required in 28%. Chronic therapy with hydroxyurea prior to admission was used in 41%. Conclusions: Acute complications requiring hospital admission are frequent in patients with sickle cell disease, being vaso-occlusive crisis and febrile syndrome the most common. These patients need a high use of antibiotics and opioid analgesia. Prior diagnosis facilitates the recog-nition of life-threatening complications such as acute chest syndrome and splenic sequestration. Despite the prophylactic and therapeutic measures currently provided to these patients, many patients suffer acute complications that require hospital management

Combined study of ADAMTS13 and complement genes in the diagnosis of thrombotic microangiopathies using next-generation sequencing

BACKGROUND: The 2 main forms of thrombotic microangiopathy (TMA) are thrombotic thrombocytopenic purpura (TTP) and atypical hemolytic uremic syndrome (aHUS). Deficiency of ADAMTS13 and dysregulation of the complement pathway result in TTP and aHUS, respectively; however, overlap of their clinical characteristics makes differential diagnosis challenging. OBJECTIVES AND METHODS: We aimed to develop a TMA diagnosis workflow based on ADAMTS13 activity and screening of ADAMTS13 and complement genes using a custom next-generation sequencing (NGS) gene panel. PATIENTS: For this, from a cohort of 154 Portuguese patients with acute TMA, the genotype-phenotype correlations were analyzed in 7 hereditary TTP (ADAMTS13 activity <10%, no inhibitor), 36 acquired TTP (ADAMTS13 activity <10%, presence of an inhibitor), and in 34 presumable aHUS. RESULTS: In total, 37 different rare variants, 8 of which novel (in ADAMTS13,CFH, and CD46), were identified across 7 genes. Thirteen TTP patients were homozygous (n=6), compound heterozygous (n=2), and heterozygous (n=5) for 11 ADAMTS13 variants (6 pathogenic mutations). Among the 34 aHUS patients, 17 were heterozygous for 23 variants in the different complement genes with distinct consequences, ranging from single pathogenic mutations associated with complete disease penetrance to benign variants that cause aHUS only when combined with other variants and/or CFH and CD46 risk haplotypes or CFHR1-3 deletion. CONCLUSIONS: Our study provides evidence of the usefulness of the NGS panel as an excellent technology that enables more rapid diagnosis of TMA, and is a valuable asset in clinical practice to discriminate between TTP and aHUS.info:eu-repo/semantics/publishedVersio

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms : An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Combined study of 13 and complement genes in the diagnosis of thrombotic microangiopathies using next-generation sequencing

The 2 main forms of thrombotic microangiopathy () are thrombotic thrombocytopenic purpura () and atypical hemolytic uremic syndrome (). Deficiency of 13 and dysregulation of the complement pathway result in and , respectively; however, overlap of their clinical characteristics makes differential diagnosis challenging. We aimed to develop a diagnosis workflow based on 13 activity and screening of ADAMTS13 and complement genes using a custom next-generation sequencing () gene panel. For this, from a cohort of 154 Portuguese patients with acute , the genotype-phenotype correlations were analyzed in 7 hereditary (13 activity <10%, no inhibitor), 36 acquired (13 activity <10%, presence of an inhibitor), and in 34 presumable . In total, 37 different rare variants, 8 of which novel (in ADAMTS13, , and CD46), were identified across 7 genes. Thirteen patients were homozygous (n=6), compound heterozygous (n=2), and heterozygous (n=5) for 11 ADAMTS13 variants (6 pathogenic mutations). Among the 34 patients, 17 were heterozygous for 23 variants in the different complement genes with distinct consequences, ranging from single pathogenic mutations associated with complete disease penetrance to benign variants that cause only when combined with other variants and/or and CD46 risk haplotypes or CFHR1-3 deletion. Our study provides evidence of the usefulness of the panel as an excellent technology that enables more rapid diagnosis of , and is a valuable asset in clinical practice to discriminate between and

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Real-world analysis of main clinical outcomes in patients with polycythemia vera treated with ruxolitinib or best available therapy after developing resistance/intolerance to hydroxyurea.

Ruxolitinib is approved for patients with polycythemia vera (PV) who are resistant/intolerant to hydroxyurea, but its impact on preventing thrombosis or disease-progression is unknown. A retrospective, real-world analysis was performed on the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to subsequent treatment with ruxolitinib (n = 105) or the best available therapy (BAT; n = 272). Survival probabilities and rates of thrombosis, hemorrhage, acute myeloid leukemia, myelofibrosis, and second primary cancers were calculated according to treatment. To minimize biases in treatment allocation, all results were adjusted by a propensity score for receiving ruxolitinib or BAT. Patients receiving ruxolitinib had a significantly lower rate of arterial thrombosis than those on BAT (0.4% vs 2.3% per year; P = .03), and this persisted as a trend after adjustment for the propensity to have received the drug (incidence rate ratio, 0.18; 95% confidence interval, 0.02-1.3; P = .09). There were no significant differences in the rates of venous thrombosis (0.8% and 1.1% for ruxolitinib and BAT, respectively; P = .7) and major bleeding (0.8% and 0.9%, respectively; P = .9). Ruxolitinib exposure was not associated with a higher rate of second primary cancers, including all types of neoplasia, noncutaneous cancers, and nonmelanoma skin cancers. After a median follow-up of 3.5 years, there were no differences in survival or progression to acute leukemia or myelofibrosis between the 2 groups. The results suggest that ruxolitinib treatment for PV patients with resistance/intolerance to hydroxyurea may reduce the incidence of arterial thrombosis. Ruxolitinib is better than other available therapies in achieving hematocrit control and symptom relief in patients with polycythemia vera who are resistant/intolerant to hydroxyurea, but we still do not know whether ruxolitinib provides an additional benefit in preventing thrombosis or disease progression. We retrospectively studied the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to whether they subsequently received ruxolitinib (n = 105) or the best available therapy (n = 272). Our findings suggest that ruxolitinib could reduce the incidence of arterial thrombosis, but a disease-modifying effect could not be demonstrated for ruxolitinib in this patient population