The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic.

Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.

Comparison of RhD Typing Results by Serology and Molecular Methods.

OBJECTIVE: Molecular testing determines D antigen status when abnormal serologic results are observed. Molecular testing is routinely batched, resulting in longer turnaround time for abnormal D status resolution. During the interim, obstetric patients with questionable/uninterpretable and weak D typing results by serology, per the immunohematology reference laboratory (IRL) policy, will receive RhD negative blood. This study aimed to determine whether serology results achieved a concordance. METHODS: Six hospitals provided samples to the IRL (first IRL) for RhD status by DNA. De-identified samples were sent for serology RhD (second IRL). A concordance of ≥80% was acceptable. RESULTS: Forty-nine samples were evaluated. Results were concordant (65.3% [32/49]) and discordant (34.7% [17/49]). This is significantly lower than clinically acceptable 80% (z = 2.57, P < .05). The turnaround-time was 3.0 hours for serology and 4.4 days for molecular evaluation. CONCLUSION: Due to a low concordance, serology could not be used in place of molecular testing.