open access

Vol 52, No 1 (2021)
Original research article
Submitted: 2021-01-20
Accepted: 2021-01-20
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Comparison of prediction models for two different peripheral stem cell collection protocols in autologous patients. How to avoid errors in calculating total blood volume to process?

Roman Małachowski, Olga Grzybowska-Izydorczyk, Anna Szmigielska-Kapłon, Kamil Brzozowski, Mateusz Nowicki, Kamil Zieliński, Agnieszka Pluta, Magdalena Czemerska, Piotr Stelmach, Agnieszka Wierzbowska
DOI: 10.5603/AHP.2021.0006
·
Acta Haematol Pol 2021;52(1):38-47.

open access

Vol 52, No 1 (2021)
ORIGINAL RESEARCH ARTICLE
Submitted: 2021-01-20
Accepted: 2021-01-20

Abstract

Introduction: Calculating accurate blood volume to process is a critical practice in apheresis planning; therefore, researchers try to develop dedicated prediction models. In this analysis, we have attempted to compare three algorithms for two different apheresis collection protocols.

Methods: In a retrospective study, we have analyzed 137 apheresis procedures performed on 100 autologous patients. Apheresis procedures were performed with the Spectra Optia apheresis device with two protocols: mononuclear cell collection (MNC) and continuous mononuclear cell collection (cMNC). Three algorithms: a model based on mean collection efficiency (CE2), a linear regression model, and a power regression model were validated by plotting collected CD34+ cell dose versus predicted CD34+ cell dose.

Results: All models showed high predictability for MNC procedure, a high correlation of predicted CD34+ yield and actual CD34+ yield (R2 = 0.9547; 0.9487; 0.9474 for CE2-based model, linear and power regression model, respectively). In contrast, alteration between models for the cMNC procedure was greater (R= 0.8049, 0.7970, and 0.8169) with a higher number of overpredictions. Further analysis revealed that for low CD34+ precounts blood volume to process, calculated with the three models, differ significantly up to fivefold times.

Conclusions: Utilizing regression models may lead to calculation errors, which can affect undercollection, repetition of apheresis, or even mobilization failure. Contrary to regression models, the model based on mean CE2 gave the most accurate prediction both for MNC and cMNC procedures. Although new prediction algorithms are created, this simple formula remains a reliable tool that promotes careful planning of apheresis, thus improving patient safety.

Abstract

Introduction: Calculating accurate blood volume to process is a critical practice in apheresis planning; therefore, researchers try to develop dedicated prediction models. In this analysis, we have attempted to compare three algorithms for two different apheresis collection protocols.

Methods: In a retrospective study, we have analyzed 137 apheresis procedures performed on 100 autologous patients. Apheresis procedures were performed with the Spectra Optia apheresis device with two protocols: mononuclear cell collection (MNC) and continuous mononuclear cell collection (cMNC). Three algorithms: a model based on mean collection efficiency (CE2), a linear regression model, and a power regression model were validated by plotting collected CD34+ cell dose versus predicted CD34+ cell dose.

Results: All models showed high predictability for MNC procedure, a high correlation of predicted CD34+ yield and actual CD34+ yield (R2 = 0.9547; 0.9487; 0.9474 for CE2-based model, linear and power regression model, respectively). In contrast, alteration between models for the cMNC procedure was greater (R= 0.8049, 0.7970, and 0.8169) with a higher number of overpredictions. Further analysis revealed that for low CD34+ precounts blood volume to process, calculated with the three models, differ significantly up to fivefold times.

Conclusions: Utilizing regression models may lead to calculation errors, which can affect undercollection, repetition of apheresis, or even mobilization failure. Contrary to regression models, the model based on mean CE2 gave the most accurate prediction both for MNC and cMNC procedures. Although new prediction algorithms are created, this simple formula remains a reliable tool that promotes careful planning of apheresis, thus improving patient safety.

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Keywords

algorithm, apheresis, cMNC, MNC, Optia, peripheral blood progenitor cells

About this article
Title

Comparison of prediction models for two different peripheral stem cell collection protocols in autologous patients. How to avoid errors in calculating total blood volume to process?

Journal

Acta Haematologica Polonica

Issue

Vol 52, No 1 (2021)

Article type

Original research article

Pages

38-47

DOI

10.5603/AHP.2021.0006

Bibliographic record

Acta Haematol Pol 2021;52(1):38-47.

Keywords

algorithm
apheresis
cMNC
MNC
Optia
peripheral blood progenitor cells

Authors

Roman Małachowski
Olga Grzybowska-Izydorczyk
Anna Szmigielska-Kapłon
Kamil Brzozowski
Mateusz Nowicki
Kamil Zieliński
Agnieszka Pluta
Magdalena Czemerska
Piotr Stelmach
Agnieszka Wierzbowska

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