COVID19-hematological Malignancies: the Italian Hematology Alliance

Recruiting

• Conditions: Hematological Malignancies; SARS-CoV-2 Infection

• Registration Number: NCT04352556
• Lead Sponsor: Ospedale di Circolo - Fondazione Macchi

Brief Summary

This is a retrospective/prospective, cohort, non-interventional observational study. This means that all patients with documented COVID and HM diagnosed between February 2020 and study initiation will compose the retrospective part, while those diagnosed after study approval will enter prospective part.

The total duration of the study will be 12 months.

The study population will must be older than 18 years of age with HM and SARS-CoV-2 infection. All patients with documented SARS-CoV-2 infection (COVID) and history or active hematological malignancies, who refer to any Hematological Unit will be included.

Detailed Description

This is a retrospective/prospective, cohort, non-interventional observational study. An informed consensus for the participation is available. In this section we provide informations on sample size and statistical analysis.

In Italy, the projected estimate of complete HM prevalence at Jan 1, 2020 has been established as 48,254 cases for Hodgkin lymphoma, 110.715 cases for non Hodgkin Lymphomas, 67,301 for leukemias, and 25,066 for multiple myeloma (Guzzinati et al, BMC Cancer 2018). The Italian Dipartimento della Protezione Civile website reported (March 23, 2020) that 63,927 cases are currently infected with SARS-CoV-2. No formal sample size calculation was made for this project but, on the basis of data available to date, considering the prevalence of hematological patients in Italy (0.4%) and assuming that these patients have the same risk of contracting COVID-19 as the general population, we supposed to enroll at least 250 patients (at March 24, 2020).

Statistical analyses All data collected will be summarized using appropriate descriptive statistics: absolute and relative frequencies for discrete variables; mean, standard deviation, median and interquartile range for continuous ones. To identify factors significantly associated with composite endpoint, log-binomial regression will be used for modelling risk ratio together with 95% confidence interval estimated.

The least absolute shrinkage and selection operator (LASSO) method will be applied for selecting the factors able to independently predict primary end-point. LASSO selects variables correlates to the measured outcome by shrinking coefficients weights, down to zero for the ones not correlated to outcome. In addition, machine learning techniques will be used for validating results from LASSO. A weight will be assigned to each coefficient of the selected predictors and weights will be summed to produce a total aggregate score. Predictive performance will be assessed through discrimination and calibration. Discrimination indicates how well the model can distinguish individuals with the outcome from those without the outcome. Two, the net reclassification improvement (NRI) will be calculated for assessing the 'net' number of individuals correctly reclassified using "the new model" over a comparator index \[i.e., CCI (Charlson Comorbidity Score) or MCS (Multisource Comorbidity Score), or HM-disease specific\]. Calibration ascertains the concordance between the model's predictions and observed outcomes, which we evaluated using a calibration plot. Cartographic and geostatistical methods will be used to exploring the spatial patterns of disease. An Exploratory Spatial Data Analysis (ESDA) and the Kriging method will be also applied to describe and model spatial (geographical) pattern.

Study Timeline

• Study Start: 2020-04-07
• Study First Submitted: 2020-04-09
• Study First Submitted QC: 2020-04-16
• Study First Posted: 2020-04-20
• Status Verified: 2022-09
• Last Update Submitted: 2022-09-22
• Last Update Posted: 2022-09-23
• Primary Completion: 2022-10-01
• Study Completion: 2022-10-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 250

Inclusion Criteria

• Age equal to or greater than 18 years of age.
• History of hematological malignancies (acute leukemias, myelodysplastic syndromes, myeloproliferative neoplasms, lymphomas, myeloma).
• Active hematological malignancies (acute leukemias, myelodysplastic syndromes, myeloproliferative neoplasms, lymphomas, myeloma) at any stage/status.
• SARS-CoV-2 positive test (nasopharyngeal, BAL, fecal), documented by Real-Time Reverse Transcriptase (RT)-PCR Diagnostic Panels.

Exclusion Criteria

• Hematological diseases, other than hematological malignancies.
• SARS-CoV-2 negative test.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
To evaluate mortality.	At 2 months from study initiation	The percentage of HM patients with COVID-19 who died.
To evaluate potential predictive biochemical parameters of mortality.	At 2 months from study initiation	We will assess the correlation between some biochemical parameters at diagnosis of COVID (i.e. hemoglobin, platelets, lymphocytes, clotting tests, CRP), each on the basis of its specific unit of measure, and mortality.
To evaluate COVID severity as predictive parameter of mortality.	At 2 months from study initiation	We will assess the correlation between COVID severity \[mild (non-pneumonia and mild pneumonia), severe (dyspnea, respiratory frequency ≥ 30/min, SpO2 ≤ 93%, PaO2/FiO2 \< 300 and/or lung infiltrates \> 50%) and critical (respiratory failure, septic shock, and/or multiple organ disfunction or failure)\] and mortality
To evaluate potential predictive HM-related parameters of mortality.	At 2 months from study initiation	We will assess the correlation between HM-related parameters at diagnosis of COVID \[i.e. disease type (leukemia, lymphomas, myeloma), disease status (remission / stable / progression), therapy status (on / off therapy)\] and mortality.

Secondary Outcome Measures

Name	Time	Method
Evolution of HM	At 2 months from study initiation	Assessment of HM status post SARS-CoV-2 infection stratified as no implication, loss of response, progression of the hematological disease.
To evaluate admission to ICU requiring mechanical ventilation or death per characteristics	At 2 months from study initiation	Percentage of HM patients being admitted to ICU requiring mechanical ventilation, or death stratified per disease type, status, per off-therapy/on-therapy, per type of therapy (chemo, immunotherapy, cell therapy, stem cell transplant).
Epidemiology of patients with HM infected by SARS-CoV-2with any spectrum of illness severity	At 6 months from study initiation	Description of the different types of hematological malignancies (WHO criteria) in patients with SARS-CoV-2 infection. All aggregated data will be stratified on the basis of COVID severity: mild (non-pneumonia and mild pneumonia), severe (dyspnea, respiratory frequency ≥ 30/min, SpO2 ≤ 93%, PaO2/FiO2 \< 300 and/or lung infiltrates \> 50%) and critical disease (respiratory failure, septic shock, and/or multiple organ disfunction or failure)
Definition of complete clinical picture of COVID-19 in HM	At 2 months from study initiation	Characterization of clinical and biochemical profile of patients with SARS-CoV-2 positivity.
Viral dynamics in infected HM patients	At 12 months from study initiation

Trial Locations

Locations (78)

SC Ematologia Ospedale SS Antonio e Biagio e Cesare Arrigo; 🇮🇹 Alessandria, Italy

UOC Ematologia, Ospedali Riuniti; 🇮🇹 Ancona, Italy

UOC Ematologia e Terapia Cellulare, Ospedale Mazzoni; 🇮🇹 Ascoli Piceno, Italy

SC Oncologia Medica, CRO; 🇮🇹 Aviano, Italy

SC Ematologia, Policlinico Bari; 🇮🇹 Bari, Italy

UOC Ematologia, I.R.C.C.S Istituto Tumori Giovanni Paolo II; 🇮🇹 Bari, Italy

SSD Ematologia, Ospedale degli Infermi; 🇮🇹 Biella, Italy

UOC Ematologia, Azienda Ospedaliero-Universitaria Policlinico S.Orsola-Malpighi,; 🇮🇹 Bologna, Italy

Ematologia e Centro Trapianto Midollo Osseo, Ospedale di Bolzano; 🇮🇹 Bolzano, Italy

UO Ematologia e CTMO, ASST Spedali Civili; 🇮🇹 Brescia, Italy

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