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- Waszczuk-Gajda, A, et al.
(författare)
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Complications of Autologous Stem Cell Transplantation in Multiple Myeloma: Results from the CALM Study
- 2022
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Ingår i: Journal of clinical medicine. - : MDPI AG. - 2077-0383. ; 11:12
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Tidskriftsartikel (refereegranskat)abstract
- Background: The main goal of this post hoc analysis of the Collaboration to Collect Autologous Transplant Outcomes in Lymphoma and Myeloma (CALM) study was to evaluate the rate of short- and long-term infectious and non-infectious complications occurring after ASCT in patients with multiple myeloma (MM). Methods: The analysis included all patients with MM from the CALM study who underwent ≥1 ASCT. The primary endpoint of the analysis was to determine the rate of infectious and non-infectious complications after ASCT and to compare them in three time periods: 0–100 days, 101 days–1 year, and >1 year after the first transplant. Results: The analysis included a total of 3552 patients followed up for a median of 56.7 months (range 0.4–108.1). Complication rates decreased with the time from ASCT with 24.85 cases per 100 patient-years from day 0 to 100 days after the transplant, and <2.31 cases per 100 patient-years from the 101st day. At 100 days after ASC T, 45.7% of patients had complications, with infectious events being twice as frequent as non-infectious complications. Bacterial infections (6.5 cases per 100 patient-years, 95% CI: 6.1–7.0) and gastrointestinal complications (4.7 cases per 100 patient-years, 95% CI: 4.3–5.1) were the most common early events. The pattern of complications changed with time from ASCT. The presence of complications after ASCT was not associated with overall survival. Conclusions: Our data provide a solid basis for comparing ASCT-related complications to those caused by emerging treatments in multiple myeloma, such as CAR T-cell therapy and other immunotherapies.
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- Jantunen, E., et al.
(författare)
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Open Source Analytics Solutions for Maintenance
- 2018
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Ingår i: 2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018. - : IEEE. - 9781538650653 ; , s. 688-693
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Konferensbidrag (refereegranskat)abstract
- The current paper reviews existent data mining and big data analytics open source solutions. In the area of industrial maintenance engineering, the algorithms, which are part of these solutions, have started to be studied and introduced into the domain. In addition, the interest in big data and analytics have increased in several areas because of the increased amount of data produced as well as a remarkable speed attained and its variation, i.e.The so-called 3 V's (Volume, Velocity, and Variety). The companies and organizations have seen the need to optimize their decision-making processes with the support of data mining and big data analytics. The development of this kind of solutions might be a long process and for some companies something that is not within their reach for many reasons. It is, therefore, important to understand the characteristics of the open source solutions. Consequently, the authors use a framework to organize their findings. Thus, the framework used is called the knowledge discovery in databases (KDD) process for extracting useful knowledge from volumes of data. The authors suggest a modified KDD framework to be able to understand if the respective data mining/big data solutions are adequate and suitable to use in the domain of industrial maintenance engineering. © 2018 IEEE.
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