Joint effects of risk factors on adverse events associated with adult blood donations

Adverse effects of therapeutic phlebotomies: Prospective study of 587 procedures

BACKGROUND

Therapeutic phlebotomy (TP), a widely used medical procedure, can be performed on diverse patients with iron overload or polyglobulia. However, its adverse events are not well known as most of the information on phlebotomy is derived from healthy blood donors (0.1%-5.3%). In contrast, TP is applicable to a broader, more complex population with comorbidities and old age. To ascertain the incidence of adverse events in phlebotomies, we conducted a prospective study on patients who attended our Unit.

STUDY DESIGN AND METHODS

We prospectively gathered data from patients referred to our Unit for TP. Data regarding demographics, health status, and adverse events within at least 24 h of phlebotomy were gathered via a structured questionnaire during each visit.

RESULTS

Between August 2021 and September 2022, 189 patients underwent 587 procedures. Most patients were men, over 60 (57.3%) had comorbidities, and 93% underwent at least two procedures during the study period. Twenty patients (10.8%) presented 25 adverse events (4.3% of phlebotomies), usually vasovagal reactions, none of which were clinically relevant, and all were managed by nursing staff on site, with full patient recovery.

DISCUSSION

The rate of adverse events (<5%) in patients undergoing TP was low and comparable to that seen in healthy blood donors. Consequently, even old patients and those with some comorbidities can safely undergo TP when the process is carefully managed.

Development and validation of a nomogram for predicting the risk of vasovagal reactions after plasma donation

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.

Novel risk patterns of vasovagal reactions in NZ blood donations complicated by COVID-19 restrictions

Introduction

Vasovagal reactions (VVRs) are common but complex donor adverse reactions (DAEs) in blood donations. VVRs have been extensively studied with a multitude of risk factors identified including young age, female gender and first-time donor status. How they may interplay remains obscure.

Methods

A total of 1,984,116 blood donations and 27,952 immediate VVRs (iVVRs) and 1,365 delayed VVRs (dVVRs) reported between 2011 and 2021 in NZ were used in multivariate logistic regression analyses each concerning donations with iVVRs as cases and those free of DAEs as controls. For each analysis stepwise selection was used to identify the best model and risk factors carrying significant main effects and/or interactions. Identified interactions informed further in-depth regression analyses to dissect iVVR risk patterns.

Results

Over 95% of VVRs were iVVRs that had lower female preponderance and deferrals than dVVRs. iVVRs had a school seasonal pattern in whole blood donations driven by first-time donors from schools/colleges, and interactions between gender and age group differentiating the first-time from repeat donations. Subsequent regression analyses identified the known and novel risk factors of year and mobile collection sites and their interactions. iVVR rates were roundly elevated in 2020 and 2021 probably because of COVID-19 restrictions like facemask wearing. Exclusion of the 2020 and 2021 data removed the interactions with year, but confirmed interactions of gender with mobile collection sites (p = 6.2e-07) in first-time donations only and with age group in repeat donations only (p < 2.2e-16), together indicating young female donors at the highest risk of iVVRs. Our results also revealed that donation policy changes contributed to the year effects; donors had a lower iVVR risk at mobile sites than well-medicalized donation centers probably because of under-reporting.

Conclusion

Modeling statistical interactions is valuable in identifying odds and revealing novel iVVR risk patterns and insights into blood donations.

Machine learning-based prediction of fainting during blood donations using donor properties and weather data as features

Background and objectives

Fainting is a well-known side effect of blood donation. Such adverse experiences can diminish the return rate for further blood donations. Identifying factors associated with fainting could help prevent adverse incidents during blood donation.

Materials and methods

Data of 85,040 blood donations from whole blood and apheresis donors within four consecutive years were included in this retrospective study. Seven different machine learning models (random forests, artificial neural networks, XGradient Boosting, AdaBoost, logistic regression, K nearest neighbors, and support vector machines) for predicting fainting during blood donation were established. The used features derived from the data obtained from the questionnaire every donor has to fill in before the donation and weather data of the day of the donation.

Results

One thousand seven hundred fifteen fainting reactions were observed in 228 846 blood donations from 88,003 donors over a study period of 48 months. Similar values for all machine learning algorithms investigated for NPV, PPV, AUC, and F1-score were obtained. In general, NPV was above 0.996, whereas PPV was below 0.03. AUC and F1-score were close to 0.9 for all models. Essential features predicting fainting during blood donation were systolic and diastolic blood pressure and ambient temperature, humidity, and barometric pressure.

Conclusion

Machine-learning algorithms can establish prediction models of fainting in blood donors. These new tools can reduce adverse reactions during blood donation and improve donor safety and minimize negative associations relating to blood donation.

Vasovagal reactions in whole blood and apheresis donors: a cross-sectional study on donor haemovigilance data from 2016 to 2019 in Italy

BACKGROUND

Acute and delayed vasovagal reactions (VVR) are the most frequent adverse reactions (AR) associated with donations. The aim of this study was to provide the data of the Italian donor haemovigilance system and contextualise the VVR data within the international framework, as well as evaluating, among first-time donors, the association of gender and age and the prevalence of VVR compared to other AR.

MATERIALS AND METHODS

The prevalence analysis was performed on VVR and other AR notified to the Italian haemovigilance system from 2016 to 2019. The analysis on the association of gender and age group and VVR prevalence was performed on first-time donations. The definitions and severity of AR were as set out in the 2014 ISBT/IHN international standards.

RESULTS

From 2016 to 2019, 34,519 AR were notified, of which 87.1% were VVR. The overall VVR prevalence was 25.0/10,000 donations and the overall prevalence of other AR was 3.7/10,000 donations. All the estimated prevalences of AR were higher for first-time donations than for regular donations and lower for whole blood than for apheresis donations. No difference was noted between whole blood and apheresis donations for VVR with complications or injuries. The prevalence of AR among first-time donors was higher in females than in males. The prevalence of VVR decreased as donor age increased.

DISCUSSION

The prevalence of VVR related to blood donation was very low and similar to those calculated by other haemovigilance systems. Among first-time donors, the prevalence of AR was higher in females than in males. The higher prevalence of VVR in young donors and a significant decreasing trend by age group confirmed the results reported in the literature. Finally, no trend by age group in first-time donors was observed for other AR to donations.

Factors associated with first-time and repeat blood donation: Adverse reactions and effects on donor behavior

BACKGROUND

Blood centers have a dual mission to protect donors and patients; donor safety is paramount to maintaining an adequate blood supply. Elucidating donor factors associated with adverse reactions (AR) is critical to this mission.

STUDY DESIGN/METHODS

A retrospective cohort analysis of whole blood donors from 2003 to 2020 was conducted at a single blood center in northern California. Adjusted odds ratios (AORs) with 95% CIs for ARs were estimated via multivariable logistic regression on demographics, donation history, and physical examination data. Where appropriate, Wilcoxon-Rank Sum and chi-squared tests were used to determine significance.

RESULTS

First-time blood donors (FTD) exhibited a higher AR rate than repeat donors (4.4% vs. 1.9% p < .0001). When compared with FTDs without AR, FTDs with ARs (FT-AR) were less likely to return (30.0% vs. 47.3%, p < .0001), and, of those who returned, had a higher rate of reaction 20.2% versus 2.8% (p < .001). Factors found to be associated with FT-AR (younger age, increased heart rate, and higher diastolic blood pressure) still correlated positively with AR on return donation, but to a lower degree. FTD who potentially witnessed an AR had a lower return rate (44.6% vs. 47.3%, p = <.001) and donated fewer units (2.38 vs. 3.37, p < .001) when compared to FTD who did not witness an AR.

CONCLUSION

The AR on FTD increases the AR likelihood of return donation. Longitudinal analysis shows that a time-based deferral policy targeted at FT-AR young donors can reduce the number of ARs while not dramatically impacting the blood supply.