Optimal individualized decision rules from a multi-arm trial: A comparison of methods and an application to tailoring inter-donation intervals among blood donors in the UK

Iron status of blood donors

PURPOSE OF REVIEW

This review examines recent research on the prevalence and importance of iron deficiency in blood donors, and on efforts to mitigate it.

RECENT FINDINGS

Premenopausal females, teenagers, and high-frequency donors are at the highest risk for donation-induced iron deficiency, in both high-resource and low-resource settings. The physiology relating iron stores to hemoglobin levels and low hemoglobin deferral is well elucidated in blood donor populations, yet the clinical effects attributable to iron loss in the absence of anemia are challenging to identify. Expanded adoption of ferritin testing is improving donor management but may cause decreases in the blood supply from temporary donor loss. The potential for personalized donor management is emerging with development of computational models that predict individual interdonation intervals that aim to optimize blood collected from each donor while minimizing low hemoglobin deferrals.

SUMMARY

Measures to reduce iron deficiency are available that can be deployed on a standardized or, increasingly, personalized basis. Blood centers, regulators, and donors should continue to evaluate different tactics for addressing this problem, to obtain a balanced approach that is optimal for maintaining adequate collections while safeguarding donor health.

Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning

PURPOSE

Donor selection practices for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) vary, and the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models has not been studied.

METHODS

We trained a Bayesian ML model in 10,318 patients who underwent MUD HCT from 1999 to 2014 to provide patient- and donor-specific predictions of clinically severe (grade 3 or 4) acute graft-versus-host disease or death by day 180. The model was validated in 3,501 patients from 2015 to 2016 with archived records of potential donors at search. Donor selection optimizing predicted outcomes was implemented over either an unlimited donor pool or the donors in the search archives. Posterior mean differences in outcomes from optimal donor selection versus actual practice were summarized per patient and across the population with 95% intervals.

RESULTS

Event rates were 33% (training) and 37% (validation). Among donor features, only age affected outcomes, with the effect consistent regardless of patient features. The median (interquartile range) difference in age between the youngest donor at search and the selected donor was 6 (1-10) years, whereas the number of donors per patient younger than the selected donor was 6 (1-36). Fourteen percent of the validation data set had an approximate 5% absolute reduction in event rates from selecting the youngest donor at search versus the actual donor used, leading to an absolute population reduction of 1% (95% interval, 0 to 3).

CONCLUSION

We confirmed the singular importance of selecting the youngest available MUD, irrespective of patient features, identified potential for improved HCT outcomes by selecting a younger MUD, and demonstrated use of novel ML models transferable to optimize other complex treatment decisions in a patient-specific way.

Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data

When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.