Paroxysmal nocturnal hemoglobinuria-related thrombosis in the era of novel therapies: a 2043 patient/years analysis.

Thrombophilia is one of the principal features of paroxysmal nocturnal hemoglobinuria (PNH) and constitutes the main cause of disease morbidity/mortality. Anti-complement treatment has revolutionized the natural history of PNH with control of the hemolytic process and abolition of thrombotic events (TE). However, no guidelines exist for the management of thromboembolic complications in this setting, with type and duration of anti-coagulation depending on individual practices. Besides, a scarcity of data is present on the efficacy of direct oral anti-coagulants (DOACs). Herein, we accrued a large real-world cohort of PNH patients from four US centers to explore features, predictors of TE and anti-coagulation strategies. Among 267 patients followed-up for a total of 2043 patient/years, 56 (21%) developed TE. This occurred at disease onset in 43% of cases, involving more frequently the venous system, typically as Budd-Chiari syndrome. Rate of TE was halved in patients receiving complement inhibitors (21 vs 40 TE per 1000 patient/years in untreated cases, with a 2-year cumulative incidence of thrombosis of 3.9% vs 18.3% respectively), and varied according to PNH granulocytes and erythrocytes clone size, type, disease activity parameters, as well as number (>2 mutations or less) and variant allelic frequency of PIGA mutations. Anti-coagulation with warfarin (39%), DOACs (37%), and low-molecular-weight heparin (16%) was administered for a median of 29 months (9-61.8). No thrombotic recurrence was observed in 19 patients treated with DOACs at a median observation of 17.1 months (8.9-45) while 14 cases discontinued anti-coagulation without TE recurrence at a median time of 51.4 months (29.9-86.8).

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia.

BACKGROUND: TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. METHODS: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. RESULTS: Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. CONCLUSION: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

SARS-CoV-2 Vaccination IgG Antibody Responses in Patients with Hematologic Malignancies in a Myeloid Enriched Cohort: A Single Center Observation.

OBJECTIVE: Patients diagnosed with hematologic malignancies are at increased risk for severe SARS-CoV-2 infection. We evaluated the serological IgG response following two doses of the SARS-CoV-2 vaccine in patients with hematologic malignancies. METHODS: Patients treated at UT Southwestern Medical Center with a diagnosis of a myeloid or lymphoid neoplasm were included. SARS-CoV-2 vaccination response was defined as a positive quantifiable spike IgG antibody titer. RESULTS: Sixty patients were included in the study and 60% were diagnosed with a myeloid neoplasm. The majority (85%) of the patients with a myeloid malignancy and 50% of the patients with a lymphoid malignancy mounted a serological response after receiving two doses of the vaccine. CONCLUSION: Vaccination should be offered irrespective of ongoing treatment or active disease. Findings require validation in a larger cohort of patients.

Molecular patterns identify distinct subclasses of myeloid neoplasia.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia.

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Machine learning prediction for COVID-19 disease severity at hospital admission.

IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. DESIGN, SETTING, AND PARTICIPANTS: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. MAIN OUTCOMES AND MEASURES: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. RESULTS: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html . CONCLUSIONS AND RELEVANCE: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.

Parafoveal acute middle maculopathy (PAMM) in sickle cell disease after discontinuation of hydroxyurea.

Purpose: Paracentral acute middle maculopathy (PAMM) is a rare ophthalmologic emergency involving the intermediate and deep retinal capillary plexus that supply the retina's middle layers. This case report describes an episode of PAMM in a patient with sickle cell disease (SCD) to demonstrate the importance of early diagnosis, review potential pathophysiologic mechanisms, and finally discuss appropriate management in this patient population. Observations: A 33-year-old black female with SCD, who had recently discontinued disease-modifying therapy with hydroxyurea, presented with a central scotoma of the left eye. Examination showed superficial opacification and whitening of the temporal perifoveal macula. After an initial diagnosis of central retinal artery occlusion she was admitted for a stroke workup. MRI was negative for stroke, and the patient was discharged after undergoing a red blood cell exchange (RBCX). Follow-up exam and optical coherence tomography (OCT) findings were more consistent with PAMM. Conclusions and Importance: To our knowledge, this is the first report of PAMM after discontinuation of hydroxyurea in preparation for pregnancy. It highlights the importance of a multidisciplinary approach when treating peripartum patients with SCD and the need for further research regarding vaso-occlusive prophylactic agents and their effects in pregnancy to minimize morbidity during family planning.

Immature Platelet Fraction as a Biomarker for Disease Severity in Pediatric Respiratory Coronavirus Disease 2019.

In this retrospective single-institution cohort study of 113 hospitalized pediatric patients with respiratory coronavirus disease 2019, those admitted to the intensive care unit or requiring mechanical ventilation had significantly higher immature platelet fractions than those who did not require intensive care unit-level care or ventilation. Immature platelet fraction may be an accessible biomarker for disease severity in pediatric respiratory coronavirus disease 2019.

"Ideal" parathyroid hormone in erythropoietin-stimulating agents-resistant anemia.

Erythropoietin-stimulating agents (ESAs) have revolutionized anemia treatment in end-stage renal disease (ESRD), but ESA resistance is increasingly identified. Secondary hyperparathyroidism (SHP) is one cause of ESA resistance. We describe a patient with ESA-resistant, transfusion-dependent anemia and mild SHP with remodeling and reticulin fibrosis on bone marrow biopsy, all of which resolved with stricter SHP management. We identified 64 patients with anemia, ESRD, and bone marrow biopsy. The parathyroid hormone (PTH) range for bony remodeling was 183-16,161.9 pg/ml versus 90.8-3283 pg/ml. The PTH range for fibrotic changes was 183-2487 pg/ml versus 90.8-16,161.9 pg/ml. We found no clear PTH range predictive for bone marrow changes.

Hepatitis C Infection Associated with Acquired Pure Red Cell Aplasia.

Acquired pure red cell aplasia is a rare bone marrow failure disorder characterized by many underlying etiologies. The hallmark bone marrow feature is the near absence of erythroid precursors that otherwise exhibit normal cellularity, which has been attributed to both immune- and cellular-mediated mechanisms. Besides being merely speculative and considering the rarity of the disorder, the description of acquired pure red cell aplasia clinical associations represents a unique occasion to improve our current clinical knowledge of the disease, reveal clues on its pathogenesis, and guide therapeutic decisions. The varied clinical scenarios and common acquired pure red cell aplasia associated conditions (i.e., thymoma, T cell/NK-cell large granular lymphocyte leukemia, B cell dyscrasia) suggest a heterogeneity of pathogenic routes. Viral etiologies must always be considered and worked up in the initial assessment of newly diagnosed acquired pure red cell aplasia patients. In this report, we present two cases of hepatitis-C-related acquired pure red cell aplasia and successful use of anti-viral strategies in the achievement of a complete response.

Immature platelets as a biomarker for disease severity and mortality in COVID-19 patients.

COVID-19, caused by SARS-CoV-2, is a contagious life-threatening viral disease that has killed more than three million people worldwide to date. Attempts have been made to identify biomarker(s) to stratify disease severity and improve treatment and resource allocation. Patients with SARS-COV-2 infection manifest with a higher inflammatory response and platelet hyperreactivity; this raises the question of the role of thrombopoiesis in COVID-19 infection. Immature platelet fraction (IPF, %) and immature platelet counts (IPC, ×109 /l) can be used to assess thrombopoiesis. This study investigates whether the level of thrombopoiesis correlates with COVID-19 severity. A large cohort of 678 well-characterized COVID-19 patients was analyzed, including 658 (97%) hospitalized and 139 (21%) admitted to the intensive care unit (ICU). Elevated percentage IPF at presentation was predictive of length of hospitalization (P < 0·01) and ICU admission (P < 0·05). Additionally, percentage IPF at the peak was significantly higher among ICU patients than non-ICU patients (6·9 ± 5·1 vs 5·3 ± 8·4, P < 0·01) and among deceased patients than recovered patients (7·9 ± 6·3 vs 5·4 ± 7·8, P < 0·01). Furthermore, IPC at the peak was significantly higher among ICU patients than non-ICU patients (18·5 ± 16·2 vs. 13·2 ± 8·3, P < 0·05) and among patients on a ventilator than those not (22·1 ± 20·1 vs.13·4 ± 8·4, P < 0·05). Our study demonstrated that elevated initial and peak values of percentage IPF and IPC might serve as prognostic biomarkers for COVID-19 progression to severe conditions.