ABSTRACT
Background: Haploidentical HLA stem cell transplantation (haplo- HSCT) is increasingly being used as part of the treatment of hematological malignancies lacking a suitable HLA-matched donor. Cytokine release syndrome (CRS) is a systemic inflammatory response with aberrant immune activation and hyperstimulation which has been described in a number of clinical settings, including haplo-HSCT. However, clinical impact of CRS on these patients is controversial. During COVID-19 pandemic, graft cryopreservation in haplo-HSCT has increased in our centre. As a result, we clinically noticed a change in CRS onset in these patients. Aims: To identify novel CRS features that might have a prognostic impact on haplo-HSCT, as well as to determine if graft cryopreservation might influence any of those features. Methods: A cohort of154 patients undergoing allogeneic stem cell transplantation in2019 and2020 in our centre, from which 51 patients receiving peripheral blood haplo-HSCT from1st February2019 to31st December2020 were selected. Two of them were excluded for the CRS analysis due to relevant microbiological isolation close to the infusion date and impossibility to distinguish between infectious and CRS fever. CRS was graded according to the ASBMT consensus. Results: The median age was 51 years (range17-72), 59% were male. Median follow-up was359 days for alive patients. Hematopoietic Cell Transplantation Comorbidity Index (HCT-CI) was low risk in 45%, intermediate risk in22% and high risk in33%. Most patients (71%) had an intermediate Disease Risk Index (DRI). Conditioning regimen was myeloablative in12 (24%). 84% of the patients presented with CRS after haplo-HSCT (71% of them occurred in the first 48 hours after infusion). CRS was mild in most patients (grade1 in29 (71%) and grade2 in12 (29%);no grade3 CRS or further occurred. Graft cryopreservation accounted for13 (25%) patients. Early-onset CRS (<48h since stem cell infusion) was associated with lower overall survival (Figure1, p=0,01), as well as lower event free survival (relapse or death, p=0,03) when compared with the remaining patients in a univariate analysis. Non-myeloablative conditioning was associated with a higher proportion of early-onset CRS (74% vs 9%, p<0,001) as well as age >60 years (80% vs 45% p=0,014). It should be noted that age >60 years did not impact on overall survival in the first year following haplo-HSCT in our patients (p=0,485). We also found an interesting trend between graft cryopreservation and early- onset CRS (only38% of patients with graft cryopreservation presented early-onset CRS, whereas 67% of patients with fresh graft did so, p=0,075). Cryopreservation was related to less proportion of CRS grade ≥2 (0% vs33%, p=0,014) and it did not impact on engraftment, overall survival nor graft versus receptor disease rate or severity in our patients. Patients with refractory disease prior to transplantation presented higher rates of CRS grade ≥2 (75% vs20%, p=0,041). Moreover, a trend was observed for mononucleated cell count >8 x108 (37% vs14%, p=0,067). However, CRS grade ≥2 did not impact on overall survival in our patients (p=0,251) Summary/Conclusion: Early-onset CRS is associated with less overall survival and less event-free survival following the first year after haplo- HSCT. Cryopreservation is associated with less CRS grade =2 probability and might also reduce the chance of developing early-onset CRS.
ABSTRACT
Context: Data collection involving a large number of patients is usually known as a tedious and time-consuming task by healthcare professionals. Current patient load makes collecting clinical data almost impossible even though we need that information more than ever. Objective: We wanted to deploy a system that automatically and autonomously retrieves clinical data from our patients suffering from SARS-CoV2 that arrive at hospital admission to collect that information for further analysis. Design: We designed a daemon in PHP programming language connected to a MySQL MariaDB database that continuously searches for new patients consulting at hospital. We collected medical history, disease records, regular medication, physical exploration, vital signs, blood chemistry and count, and finally, microbiology testing of SARS-CoV2 (both PCR and ELISA antibody testing). As we don't have access to any API service (out-of-the-box connection to the data mainframe), we took advantage of web-scraping (brute-force data extraction from webpages using HTTP protocol) applied to our hospital web interface. Setting: Monitoring was made between 1st March, 2020 and 15th April, 2020 (during worst Coronavirus outbreak phase of the country), using only one computer connected to the hospital network. The number of patients identified was 259, each one with 344 clinical and testing variables. Results: Using this technique, we collected data of 259 hematologic patients without human intervention and more than 300 variables have been analyzed. Nowadays, manual revision of certain aspects of the database (e.g., comorbidities) is needed and some data needs to be manually entered due to the lack of proper codification. In the future, with the development of semantic-matching technologies, fully autonomous building of the databases will be possible. In the meantime, our technique can solve the capture of enormous amount of clinical information without effort. With that information, observational studies, even a prognosis score using machine learning, have been developed in our center. Conclusions: Data collection for further analysis is usually a vital, but time-consuming, task in order to answer clinical questions. We developed a technique that helped our center retrieve patients' clinical information autonomously during the SARS-Cov-2 pandemic.