Plateletpheresis efficiency and mathematical correction of software‐derived platelet yield prediction: A linear regression and ROC modeling approach

Jaime‐Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez‐Ramírez, Nereida; Gómez‐Almaguer, David

doi:10.1002/jca.21518

Cited by 4 publications

(23 citation statements)

References 29 publications

(34 reference statements)

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“…A previous report showed a median yield of 5.9 × 10 11 platelets obtained by a Trima Accel v. 6 automated blood collection system. 9 Another report, assessing the performance of the Trima Accel v. 5 automated blood collection system showed a lower platelet yield of 3.85 × 10 11 platelets. 11 In this study, we observed a higher median platelet yield of 6.5 × 10 11 platelets.…”

Section: Discussionmentioning

confidence: 99%

“…However, studies have reported Pearson's correlation coefficients ranging from 0.496 to 0.802 in univariate models, showing a significant heterogeneity in the reported results across the literature. 9,16,17 It has been reported that the extraction of double platelet products is correlated with slightly longer procedure times, 13 which can be modified by the operator to increase the processed volume of blood and hence increase the platelet yield. For that reason, it is crucial to have good predictive algorithms to accurately adjust the parameters of the apheresis.…”

Section: Discussionmentioning

confidence: 99%

“…18 The results of this study agree with the demographic data reported in the literature. 9,11 On the other hand, blood type distribution is less affected by selection biases, because apheresis donors are not selected based on their blood type. In our study, the distribution of ABO and Rh blood systems was consistent with the reported for the Mexican population.…”

Section: Discussionmentioning

confidence: 99%

“…The median platelet count reported in this study was higher than the previously reported (270 × 10 3 vs 239 × 10 3 platelets/mm 3 ). 9 The efficacy of the predictive algorithms of cell separators has not been widely reviewed in the literature. Jaime-Pérez et al assessed the predictive algorithm for the platelet yield in the Trima Accel v. 6, comparing the calculated with the actual values by a model of linear regression.…”

Section: Discussionmentioning

confidence: 99%

“…However, the evaluation of these algorithms has not been widely reported in the literature. 9,10 The algorithms of several instruments, such as the Amicus Crescendo (Baxter Biotech Corp.), the MCS Plus Version LDL (Haemonetics Corp.), and the Trima Accel v. 5.0 (Gambro BCT) have been evaluated, however, a comprehensive assessment is still lacking, because this report only analyzes the mean of the differences between the prediction and the actual values. 11 Several attempts have been made to predict whether a double platelet product could be obtained from a donor, which could help to optimize the available resources in developing countries.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of software‐derived predictive algorithms for platelet yield and blood cell count after apheresis

Cienfuegos-Pecina

Ayala-de-la-Cruz

Leal-Nava

et al. 2020

J of Clinical Apheresis

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Introduction: Algorithms have been developed to predict the platelet yield after apheresis from the donor's data, as well as the effect on the blood cell count, to extract an acceptable platelet number without affecting the donor. However, the evaluation of these algorithms has not been widely reported. This study aimed to assess the accuracy of the predictive algorithms of the Trima Accel v. 6 blood collection system. Methods: Platelet concentrates (PCs) obtained by apheresis were analyzed. Platelet count and hematocrit were compared pre-and post-apheresis. Calculated post-apheresis platelet count (CPAPC), hematocrit (CPAH), and platelet yield (CPY), and their actual values were correlated. The bias of the algorithms was assessed with Bland-Altman plots, and the prediction of the extraction of single or double platelet products was evaluated. Results: Two hundred and seventy-nine PCs were analyzed. Post-apheresis platelet count (PAPC) and hematocrit were decreased. A moderate correlation was observed between CPY and the actual yield, with a negative bias, and a trend to increase alongside the magnitude of the measurements. CPAPC and CPAH were strongly correlated with their actual values without bias. Prediction of single or double platelet product extraction showed a significant agreement with the actual outcomes. Conclusions: The predictive algorithm for the platelet yield showed bias, and a trend to underestimate the actual platelet yields when they are higher. The algorithms for the prediction of the PAPC and hematocrit did not show bias, proving their accuracy. Prediction of a single or double platelet product extraction has a strong agreement with the APY.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of software‐derived predictive algorithms for platelet yield and blood cell count after apheresis

Cienfuegos-Pecina

Ayala-de-la-Cruz

Leal-Nava

et al. 2020

J of Clinical Apheresis

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Assessing the performance of a plateletpheresis unit at a tertiary‐care academic medical center in Mexico: A 6‐year experience

Jaime‐Pérez

Beltrán-López

Alvarado-Navarro

et al. 2021

J of Clinical Apheresis

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BackgroundOptimization of platelet (PLT) apheresis collection is a priority to satisfy the increasing demand of hemato‐oncology patients. We assessed the performance of a plateletpheresis unit supporting hematology patients.Study design and methodsThis descriptive retrospective study included 561 plateletpheresis collections from 2013 to 2018. For data analysis, descriptive statistics and receiver operating characteristic (ROC) curve were used. A 5‐item satisfaction questionnaire was analyzed.ResultsNinety percent of the donors were males. The median plateletpheresis time was 89 minutes; its success rate was 92.5%; median donor PLT count was 232 × 109/L, women median PLT count was 247 × 109/L vs 231x109/L in men (P = .017). Seventy‐seven percent donors were candidates for a double product and 24.5% were processed; 20.8% of these donors had a weight ≤75 and 79.2% >75 kg, P = .003, and 6.6% were women and 93.4% men, P = .161. Thirty‐six of donors had ≥250 × 109/L and 16.8% was processed as a triple product. ROC analysis showed that with donor PLT counts ≥200 × 109/L the sensitivity for obtaining double products was 0.981 and specificity 0.714, with an area under the curve (AUC) = 0.877. The adverse effect rate was 4.3%. Of the potential donors, 6.3% were rejected. The cost of processing single or double products was 430 USD. Comfort and time spent during plateletpheresis were areas for improvement.ConclusionPlatelet count and donor weight predicted PLT yield and obtaining double products. Women had higher PLT counts, but no significant difference was found between donor gender and processed products. Assessment of the apheresis unit can help to improve its performance.

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Predicting donor-related factors for high platelet yield donations by classification and regression tree analysis

Malodan

Murugesan

Nayanar

2023

Hematology, Transfusion and Cell Therapy

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Plateletpheresis efficiency and mathematical correction of software‐derived platelet yield prediction: A linear regression and ROC modeling approach

Abstract: Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP.

Cited by 4 publications

References 29 publications

Assessment of software‐derived predictive algorithms for platelet yield and blood cell count after apheresis

Assessment of software‐derived predictive algorithms for platelet yield and blood cell count after apheresis

Assessing the performance of a plateletpheresis unit at a tertiary‐care academic medical center in Mexico: A 6‐year experience

Predicting donor-related factors for high platelet yield donations by classification and regression tree analysis

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