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Zhao P, Dong D, Dong R, Zhou Y, Hong Y, Xiao G, Li Z, Su X, Zheng X, Liu X, Zhang D, Li L, Liu Z. Development and validation of a nomogram for predicting the risk of vasovagal reactions after plasma donation. J Clin Apher 2023; 38:622-631. [PMID: 37466252 DOI: 10.1002/jca.22074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/21/2023] [Indexed: 07/20/2023]
Abstract
BACKGROUND AND OBJECTIVES Vasovagal reactions (VVRs) are the most common adverse reactions and are frequently associated with serious donor adverse events. Even mild VVRs can lead to a significant reduction in the likelihood of subsequent donations. The purpose of this study is to explore the factors related to the occurrence of VVRs after plasma donation and to construct a nomogram to identify individuals at risk for VVRs to improve the safety of plasma donors. MATERIALS AND METHODS We collected the donation data from July 2019 to June 2020 from a plasma center in Sichuan, China, to explore the independent risk factors for vasovagal reactions. From these data, we constructed and validated a predictive model for vasovagal reactions. RESULTS VVRs after plasma donation occurred 737 times in 120 448 plasma donations (0.66%). Gender, season, donor status, weight, pulse, duration of donation, and cycle were independent risk factors for VVRs (P< 0.05). The concordance index (C-index) of a logistic model in the derivation cohort was 0.916, with a Hosmer-Lemeshow goodness-of-fit probability of 0.795. The C-index of a logistic model in the validation cohort was 0.916, with a Hosmer-Lemeshow goodness-of-fit probability of 0.224. The calibration curve showed that the predicted results were in good agreement with the actual observed results. CONCLUSION This study preliminarily constructed and verified a prediction model for VVRs after plasma donation. The model nomogram is practical and can identify high-risk donors.
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Affiliation(s)
- Peizhe Zhao
- Department of Blood Transfusion, Taiyuan Blood Center, Taiyuan, Shanxi Province, People's Republic of China
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, Sichuan Province, People's Republic of China
- Key Laboratory of Transfusion Adverse Reactions, CAMS, Chengdu, Sichuan Province, People's Republic of China
| | - Demei Dong
- Department of Quality Control, Beijing Tiantan Biological Products Co., Ltd, Beijing, People's Republic of China
| | - Rong Dong
- Department of Plasma Apheresis, Jianyang Rongsheng Apheresis Plasma Co., Ltd, Jianyang, Sichuan Province, People's Republic of China
| | - Yuan Zhou
- Department of Blood Transfusion, Taiyuan Blood Center, Taiyuan, Shanxi Province, People's Republic of China
| | - Yan Hong
- Department of Plasma Apheresis, Shifang Rongsheng Apheresis Plasma Co., Ltd, Shifang, Sichuan Province, People's Republic of China
| | - Guanglin Xiao
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, Sichuan Province, People's Republic of China
| | - Zhiye Li
- Department of Blood Transfusion, Taiyuan Blood Center, Taiyuan, Shanxi Province, People's Republic of China
| | - Xuelin Su
- Department of Plasma Apheresis, Jianyang Rongsheng Apheresis Plasma Co., Ltd, Jianyang, Sichuan Province, People's Republic of China
| | - Xingyou Zheng
- Department of Plasma Apheresis, Jianyang Rongsheng Apheresis Plasma Co., Ltd, Jianyang, Sichuan Province, People's Republic of China
| | - Xia Liu
- Department of Plasma Apheresis, Jianyang Rongsheng Apheresis Plasma Co., Ltd, Jianyang, Sichuan Province, People's Republic of China
| | - Demei Zhang
- Department of Blood Transfusion, Taiyuan Blood Center, Taiyuan, Shanxi Province, People's Republic of China
| | - Ling Li
- Department of Blood Transfusion, Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, Sichuan Province, People's Republic of China
| | - Zhong Liu
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, Sichuan Province, People's Republic of China
- Key Laboratory of Transfusion Adverse Reactions, CAMS, Chengdu, Sichuan Province, People's Republic of China
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Dietrich G. Optimierung der Depotführung für Erythrozytenkonzentrate in Krankenhäusern. TRANSFUSIONSMEDIZIN 2022. [DOI: 10.1055/a-1034-8719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
ZusammenfassungBlutdepots ohne eigene Herstellung müssen Versorgungssicherheit garantieren, dabei aber auch eine möglichst geringe Verfallsrate sicherstellen. Für die Anzahl der zu bevorratenden
Erythrozytenkonzentrate (EK) der verschiedenen Blutgruppen wird folgendes Berechnungsmodell gefunden: 1. Die örtliche Verteilung der AB0-Blutgruppen im Patientenkollektiv kann mit dem
Hardy-Weinberg-Gesetz auf Plausibilität überprüft werden. 2. Der Beobachtungszeitraum ist die durchschnittliche Restlaufzeit von der Lieferung bis zum Ende der Haltbarkeit. 3. Der
Erwartungswert für die Fallzahl transfundierter Patienten einer bestimmten Blutgruppe gehorcht einer Binomialverteilung. 4. Die Anzahl transfundierter Erythrozytenkonzentrate pro Patient ist
geometrisch verteilt. 5. Eine Matrix wird gebildet, deren Zellen das Produkt aus Fallzahl (3.) und EK pro Fall (4.) und somit die Anzahl der benötigten EK enthalten. Nur letztgenannte sind
für die Führung des Blutdepots interessant. Subtrahiert man die Zahl der im Beobachtungszeitraum (2.) benötigten EK von der Depotgröße, erhält man den Verfall. Der Vorrat von EK der
Blutgruppe 0 bemisst sich an der maximal zu erwartenden Zahl benötigter Konserven für einen Fall bis zur nächstmöglichen Lieferung, wenn der durchschnittliche Depotumsatz diesen Wert nicht
übersteigt. Er kann somit insbesondere in peripheren Krankenhäusern mit Akutversorgung die oben beschriebene Kalkulation deutlich überschreiten. Für Blutgruppe A gilt dieser Grundsatz nicht,
wenn auch kompatible (0 auf A) Transfusionen stattfinden sollen. Binomial ist bei kleiner Anzahl nach links – das heißt gegen Null – schief verteilt. Dies betrifft in jedem Fall AB, bei
jährlichem Depotumsatz unter ungefähr 1000 EK aber auch Blutgruppe B. Möchte man den Verfall vermeiden, darf man die betroffenen Blutgruppen nicht – zumindest nicht in dem notwendigen Umfang
– bevorraten und muss dann majorkompatibel transfundieren. Die Unterschiede der einzelnen AB0-Blutgruppen in Herstellung (Blutgruppenverteilung in der Spenderpopulation) und der Bevorratung
in Krankenhaus-Blutdepots sind hierin begründet. Entsprechende Überlegungen gelten gleichermaßen für den Rhesusfaktor D. Die oft emotional geführte Debatte über die zu bevorratende Anzahl an
EK kann mittels dieses mathematischen Modells auf eine rationale Grundlage zurückgeführt werden. Das Rechenmodell ist mit einem einfachen Tabellenkalkulationsprogramm möglich. Das Blutdepot
bestimmt damit den optimalen Bestand. Für die Blutspendedienste ergibt sich hieraus die Möglichkeit, die Kunden individuell, das heißt umsatzabhängig, zu beraten und gegebenenfalls den Preis
entsprechend zu gestalten. Im Rahmen der Qualitätssicherung können abteilungs- oder krankenhausspezifische Besonderheiten im Transfusionsverhalten dargestellt werden.
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Goldman M, Uzicanin S, Marquis-Boyle L, O'Brien SF. Implementation of measures to reduce vasovagal reactions: Donor participation and results. Transfusion 2021; 61:1764-1771. [PMID: 33880796 DOI: 10.1111/trf.16375] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/26/2021] [Accepted: 02/26/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND There are several strategies to reduce donor reactions. We report donor participation and reaction rates before and after implementing multiple measures at Canadian Blood Services. STUDY DESIGN AND METHODS We introduced a structured program of 500 mL of water and a salty snack pre-donation and applied muscle tension (AMT) during donation. Donors were not deferred for out of range blood pressure (BP); however, BP was measured in first time donors. Time on the donation chair post-donation was decreased from 5 to 2 min for repeat donors. We assessed participation rates using our quarterly survey of 10,000 recent donors. We extracted vasovagal reactions with loss of consciousness (LOC) from our operational database and compared pre-implementation (Oct 12,018-March 31,2019) and post-implementation (Oct 12,019-March 31,2020) periods. RESULTS Survey response rates varied from 11% to 16%. The percentage of donors who drank the water and ate the salty snack increased from 58% to 82% and 44% to 70% over 4 quarters; those performing AMT increased from 24% to 41%. Reactions decreased from 19.07 per 10,000 (744 reactions in 390,123 donations) to 14.04 per 10,000 (537 in 382,382 donations) (p < .0001). No first-time donors with high BP (n = 684) but 5 with low BP (n = 718) had reactions, CI were very large. CONCLUSIONS Achieving optimal participation was challenging. After implementation of a donor wellness initiative based on best practice, rates of vasovagal reactions with LOC decreased by 25%. A larger dataset is necessary to assess the safety contribution of BP deferrals when other mitigation measures are in place.
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Affiliation(s)
- Mindy Goldman
- Medical Affairs and Innovation, Canadian Blood Services, Ottawa, Ontario, Canada.,Department of Pathology and Laboratory Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Samra Uzicanin
- Medical Affairs and Innovation, Canadian Blood Services, Ottawa, Ontario, Canada
| | - Lynne Marquis-Boyle
- Supply Chain Process Management, Canadian Blood Services, Saint John, New Brunswick, Canada
| | - Sheila F O'Brien
- Medical Affairs and Innovation, Canadian Blood Services, Ottawa, Ontario, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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