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1.
Blood ; 138:4070, 2021.
Article in English | EMBASE | ID: covidwho-1582214

ABSTRACT

Background: The COVID-19 disease has spread throughout the world in an unprecedented way. France and Brazil confirmed the first cases in the European and South American regions with high incidence rates at the peak of the first wave of contamination along the year 2020. Patients with hematological disorders, especially malignancies, may be more vulnerable to SARS-CoV-2 infection because of the underlying disease and treatment. Since COVID-19 presentation is heterogeneous, from asymptomatic up to severe life-threatening forms and the patients with malignancies and COVID-19 admitted to the hospital show a wide range of clinical manifestations and laboratory abnormalities, it is still unclear for clinicians which patients, blood tests at admission and disease factors are associated with worse outcomes. Getting further insights into patients with specific diseases is of particular interest. We aimed to identify profiles of hematologic patients hospitalized with COVID-19 that would be associated with survival, and to assess the differences between cohorts. Methods: A binational cohort including all consecutive hematological patients aged 18 years or more with moderate or severe COVID-19, requiring hospitalization until December 2020 at two tertiary centers, from Paris, France and São Paulo, Brazil, was studied. Patients with a hospital stay of less than 24 hours were excluded. All patients were followed until the end of hospitalization;then, after discharge, survival data was recovered on medical charts or outpatient consultations, if data were available. Patient profiles were based on age, comorbidities, blood tests at admission, COVID-19 symptoms, and hematological disease characteristics. A semi-supervised learning method was first used to obtain the prognostic driven profiles;then, a classifier was identified to allow the classification of patients using only admission (baseline) data. Results: A total of 263 patients (135 from Brazil and 128 from France) were enrolled. Male patients (58.2%), elderly (≥ 65 years, 46%), with high comorbidities prevalence were frequent. Non-Hodgkin Lymphoma (29.3%), multiple myeloma (19.4%) and chronic myeloid disorders (12.9%) were the most frequent underlying hematological malignancies and 13.3% of patients had benign diseases. Most of the patients (59.7%) had undergone chemotherapy in the last six months before COVID-19 admission. The clinical presentation of COVID-19 was similar in the two countries. Fever (68.4%), dyspnea (60.1%) and cough (55.9%) were the main symptoms at admission. The ICU admission (56% versus 25%) and invasive ventilation (42% versus 19%) rates were notably higher among Brazilian patients due to scarce ICU beds during the peak of transmission in France. The overall in-hospital mortality rate was 115/263 (43.7% [95%CI 37.6-49.9]) and the median follow-up after admission was 63 days (IQR 40-98). There was no evidence of survival difference between countries after adjusting on age, comorbidities, and diagnosis. Two clusters were identified, segregating young patients with few comorbidities, low CRP, D-dimers, LDH and creatinine levels, with a 30-day survival of 77.1%, versus 46.7% in remainders. The profiles (clusters) were strongly associated with survival (p<0.001), even after adjusting on age (p<0.001) (Figure 1). We identified a set of rules to classify patients into the two profiles, using only information available at hospital admission, with a high accuracy rate (97.7% on the training set and 84.9% on the validation set). The baseline predictors consecutively selected by the model were the number of comorbidities, creatinine, CRP, a continuous regimen of chemotherapy, platelets and lymphocytes counts, a symptom of ageusia, dyspnea, hematological malignancy, high blood pressure, and symptom of myalgia. Conclusions: This analysis allowed to identify two profiles of hospitalized hematological patients with COVID-19 that have a different outcome when infected with SARS-CoV-2. The results showed the importance of CRP, LHD, and creatinine in COVID-19 prese tation and prognosis, whatever the geographic origin of the patients. The identification of patterns and clinical manifestations experienced by hematological patients during moderate or severe SARS-CoV-2 infection might be helpful to medical staff in the care management and in the allocation of scarce resources. [Formula presented] Disclosures: No relevant conflicts of interest to declare.

2.
American Journal of Respiratory and Critical Care Medicine ; 203(9):2, 2021.
Article in English | Web of Science | ID: covidwho-1407043
3.
HemaSphere ; 5(SUPPL 2):378-379, 2021.
Article in English | EMBASE | ID: covidwho-1393418

ABSTRACT

Background: The COVID-19 pandemic had a high burden in Brazil. To date, data on mortality and prognostic factors of COVID-19 infection in Brazilian patients with hematological disorders are scarce. Aims: To describe the characteristics and outcomes of patients with hematological disorders admitted to the hematological COVID care unit of a reference center in Brazil;to analyze the impact of prognostic factors on in-hospital mortality. Methods: This prospective, single-center study,included 118 patients who have been admitted to the hematological COVID care unit of the Hospital das Clínicas da Faculdade de Medicina da USP, S.o Paulo, Brazil, from March to September 2020.All patients had >18 years,an underlying hematological disease and a moderate or severe COVID- 19 infection.For analyses, patients were grouped in:(1)benign or no oncological treatment(n=43),(2) intensive chemotherapy,including induction protocols for acute leukemia and stem cell transplantation conditioning(n=44) or(3) intermediate chemotherapy,including lymphoma regimens,myeloma triple treatment or continuous treatment( n=31).The primary outcome was in-hospital mortality;secondary outcome was overall survival after admission in the COVID-19 unit.Univariate analysis(UVA) used odds ratio(OR) for baseline characteristics and ROC curve analysis for laboratory tests collected at admission.Multivariate analyses(MVA) were adjusted by age and hematological disease status group.The median follow-up and survival time after COVID-19-unit admission were estimated by Kaplan- Meier method.All statistical tests were two-sided;p-values<0.05 were considered significant. Results: Median age was 58(19-90) years and 55% of patients were male. Most patients(83%)had hematological malignancies,- mainly non-Hodgkin lymphoma(29%) and multiple myeloma(19%). The most frequent benign disease was sickle cell disease(5%).12 patients had undergone hematopoietic stem cell transplantation (HSCT),4 allogeneic and 8 autologous.70% had at least one comorbidity, mostly arterial hypertension and diabetes mellitus. Thromboembolic events occurred in 9%. Median hospital stay in the COVID-19 unit was 12(1-63) days;54% needed intensive care and 41% mechanical ventilation.In-hospital mortality rate was 41%[95%CI 32-50];most deaths occurred in patients with malignancies. Median follow-up was 73(95%CI 61-81) and 54(95%CI 39-66) days after admission and discharge from the COVID-19 unit, respectively.UVA showed a risk of death increased by 25% every 10 years old.The risk of in-hospital death was 3-fold and 5-fold higher in groups 2 and 3 compared with group 1.MVA showed higher risk of death in patients from group 2(OR=11.1,95% CI 2.9- 54.8) or group 3(OR=9.7,95%CI 2.4-47.5]),who had lactate dehydrogenase( LDH)>440 U/L(OR=16.8,95%CI 4.9-71.8),C-reactive protein(CRP)>100 mg/L(OR=4.1,95%CI 1.4-13.6) or platelet count<150x10e9/L(OR=3.7,95%CI 1.3-11.7), regardless of age(OR=1.2,95%CI 1.0-1.5).79% of in-hospital deaths were from COVID-19;others were mainly due to hematological disease.The overall median survival time after admission was 92 days(95% CI 34-NA) and the 75-day survival probability was 51%(95%CI 41-60).25% of patients had hospital readmission,mostly due to other infections. Summary/Conclusion: In line with other reports,patients with hematological diseases are at higher risk of mortality from COVID-19 infection, particularly in low and middle income countries.In our cohort, prognostic factors were status of disease,platelets count,LDH and CRP. These findings might help risk stratification and prioritization of vaccines in this setting.

4.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277590

ABSTRACT

RATIONALE: Numerous data regarding both clinical presentation and prognosis of COVID-19 have been published. Most studies focused on individual predictors for mortality. Although some prognostic factors were consistently identified across the different studies such as older age or cardiovascular comorbidities, other discrepancies reflect geographical location of the studies, characteristics of study population, admission in wards and/or intensive care units, and variables incorporated in the statistical model. We aimed to a priori identify specific patient profiles, then assessing their association with the outcomes in COVID-19 patients with respiratory symptoms admitted specifically in hospital wards. METHODS: We conducted a retrospective single center study from February, 27, 2020 to April, 27, 2020. A non-supervised cluster analysis was first used to detect patient profiles based on characteristics at admission of 220 consecutive patients admitted at our institution. Then, we assessed its prognostic value, using Cox regression analyses to predict survival. RESULTS: Three clusters were identified, with 47 patients in cluster 1, 87 in cluster 2, and 86 in cluster 3, and whose presentation differed. Cluster 1 mostly included sexagenarian patients with active malignancy who were admitted early after COVID-19 onset. Cluster 2 included the oldest, overweight patients with high blood pressure and renal insufficiency, while cluster 3 included the youngest patients with gastrointestinal symptoms and delayed admission. These subgroups of patients were associated with different outcomes, with 60 days survival of 74.3% (cluster1), 50.6% (cluster2) and 96.5% (cluster3) (figure 1). This was confirmed by the multivariable Cox analyses that exhibited the prognostic value of those patterns. CONCLUSION: The cluster approach seems appropriate and pragmatic to early identify patient profiles that could help physicians to segregate patients according to their prognosis. Figure 1: Survival since hospital admission according to the clusters .

5.
Revue d'Épidémiologie et de Santé Publique ; 69:S12-S13, 2021.
Article in English | ScienceDirect | ID: covidwho-1240585

ABSTRACT

Clinical trials’ attempts to improve design and analysis in order to reduce the experimental burden, providing more rapid and valid answers, are common, especially in rare or pediatric diseases, or in the setting of an emerging disease such as COVID-19. One promising though underused approach is to increase the information by borrowing external data on control and/or on treatment effect, which is almost always available. However, the objectives and extent of use of such external data differ widely across studies. They may be dedicated to extrapolate from different populations (from adults to children, across geographic regions, etc.) or treatments. They may adaptively apply during an ongoing trial or be only used at the time of analysis of a trial after study enrollment is closed. They can use only outcome data from a historical control group for either a complete or partial replacement of the current control arm, or focusing on estimated treatment benefit from a whole historical sample. They make various assumptions regarding the exchangeability or commensality of the historical and current populations, with various diagnostics for checking those assumptions. Finally, they use either a frequentist or Bayesian inference, based on aggregated data or requiring individual patient data (IPD). Notably, borrowing of external data often uses Bayesian approaches, by modelling external information into a prior, with increasing complexity to face heterogeneity across populations. Similar frequentist methods based on weighted tests or joint models, have also been proposed, as well as hierarchical models, either Bayesian or frequentist, similar to a meta-analysis setting. Last, methods based on propensity scores and those based on matching-adjusted indirect comparisons could also been used, depending on the availability of IPD. We aim to provide some comparison of the competing approaches for incorporating historical data into a contemporary trial. Data from randomized clinical trials performed in patients with COVID-19 will serve as illustrative examples.

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