Detection of Seasonal Trends in National Donor Heart Availability Using the UNOS Dataset

Kamalia, Mohammed A.; Ramamurthi, Adhitya; Rein, Lisa; Mohammed, Asim; Joyce, David L.

doi:10.1016/j.cardfail.2019.07.495

Cited by 3 publications

(4 citation statements)

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“…For the primary analysis, we selected recipients in two seasonally matched 1‐year cohorts, those transplanted from November 1, 2016, to October 31, 2017 (pre‐policy) and November 1, 2018, to October 31, 2019 (post‐policy). Cohorts were seasonally matched to control for known seasonal trends in deceased donor heart donation 12,13 . We selected a pre‐policy cohort that ended 1 year before policy implementation to avoid contamination from any anticipatory practice changes in the final year of the pre‐policy era 14 .…”

Section: Methodsmentioning

confidence: 99%

“…Cohorts were seasonally matched to control for known seasonal trends in deceased donor heart donation. 12,13 We selected a pre-policy cohort that ended 1 year before policy implementation to avoid contamination from any anticipatory practice changes in the final year of the pre-policy era. 14 We estimated survival for each cohort in the first year posttransplant using the Kaplan-Meier method with data through March 2, 2021.…”

Section: Primary Survival Analysismentioning

confidence: 99%

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An updated estimate of posttransplant survival after implementation of the new donor heart allocation policy

Lazenby

Narang

Pelzer

et al. 2022

American Journal of Transplantation

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The Organ Procurement and Transplantation Network (OPTN) implemented a new donor heart allocation policy on October 18, 2018.Studies evaluating the impact of this new policy on posttransplant survival contain discrepant findings. 1 Five reports found decreased posttransplant survival under the new policy, 2-6 and two reports found no difference in posttransplant survival. 7,8 Notably, the studies with lower estimates of posttransplant survival in the post-policy era have significantly fewer follow-up observations of post-policy recipients compared with the studies finding unchanged survival. One proposed explanation for the conflicting results is informative censoring bias. 9 A fundamental assumption of the Kaplan-Meier survival estimator is that censoring is statistically independent of

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Section: Methodsmentioning

confidence: 99%

Section: Primary Survival Analysismentioning

confidence: 99%

An updated estimate of posttransplant survival after implementation of the new donor heart allocation policy

Lazenby

Narang

Pelzer

et al. 2022

American Journal of Transplantation

View full text Add to dashboard Cite

show abstract

“…We also prepared a seasonally matched pre-policy cohort of patients transplants from November 1, 2014 with follow-up time to June 3, 2017 as a sensitivity analysis to account for known seasonal trends in decreased donor heart donation. (12) We also selected a pre-policy cohor thtat ended 1 year before policy implementation to avoid conoufnding from any anticipatory practice changes before the policy implementation in October 2018.…”

Section: Data Preparationmentioning

confidence: 99%

Survival Machine Learning Methods for Mortality Prediction After Heart Transplantation

Liou

Mostofsky

Lehman

et al. 2023

Preprint

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Although prognostic models for heart transplantation (HTx) have been developed, a comprehensive benchmarking of survival machine learning methods for mortality prognosis has not been performed. Futhermore, assessing mortality in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study included 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients database who received their first transplant on or after October 18, 2018, with at least one recorded follow-up visit until June 3, 2021. Each model was trained using a nested 5-repeat, 5-fold cross-validation. Seven statistical and machine learning algorithms – Lasso, Ridge, Elastic Net, Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests were benchmarked against Cox PH in both post-policy and pre-policy eras. In the post-policy era, there was higher discriminatory power of machine learning models for one-year all-cause mortality compared to Cox. Bilirubin, age, BMI, total ischemic time, donor age, and history of ischemic cardiomyopathy were some of the most predictive selected variables. ECMO was highly predictive in the pre-policy cohort, but less so in the post-policy cohort. Machine learning (particularly tree-based ensemble) survival models can be used to capture complex short-term posttransplant mortality outcomes.

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“…Finally, naturally occurring conditions and situational factors can also shape how people respond to interventions. New Year's resolutions supply a familiar example of attaching habit formation to an otherwise arbitrary day of the year, but it's also been shown that organ donation peaks in the summer due to a heightened level of risky activities that lead to accidents and brain death (Kamalia et al, 2019). When cyclical forces are known and predictable, we can craft solutions aimed at preempting nonadherent behaviors, rather than reacting to them once they have occurred.…”

Section: What Situational Factors Might Impact How Interventions Are Acted Upon?mentioning

confidence: 99%

Behavioral planning: Improving behavioral design with “roughly right” foresight

Schmidt

Stenger

2021

SDRJ

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Many challenges emerging from the current COVID-19 pandemic are behavioral in nature, which has prompted the field of behavioral design to propose solutions for issues as wide-ranging as hand-washing, wearing masks, and the adoption of new norms for staying and working from home. On the whole, however, these behavioral interventions have been somewhat underwhelming, exposing an inherent brittleness that comes from three common “errors of projection” in current behavioral design methodology: projected stability, which insufficiently plans for the fact that interventions often function within inherently unstable systems; projected persistence, which neglects to account for changes in those system conditions over time; and projected value, which assumes that definitions of success are universally shared across contexts. Borrowing from strategic design and futures thinking, a new proposed strategic foresight model—behavioral planning—can help practitioners better address these system-level, anticipatory, and contextual weaknesses by more systematically identifying potential forces that may impact behavioral interventions before they have been implemented. Behavioral planning will help designers more effectively elicit signals indicating the emergence of forces that may deform behavioral interventions in emergent COVID-19 contexts, and promote “roughly right” directional solutions at earlier stages in solution development to better address system shifts.

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Detection of Seasonal Trends in National Donor Heart Availability Using the UNOS Dataset

Cited by 3 publications

References 0 publications

An updated estimate of posttransplant survival after implementation of the new donor heart allocation policy

An updated estimate of posttransplant survival after implementation of the new donor heart allocation policy

Survival Machine Learning Methods for Mortality Prediction After Heart Transplantation

Behavioral planning: Improving behavioral design with “roughly right” foresight

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