An Electronic Health Record–Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

Kent, David M.; Nelson, Jason; Pittas, Anastassios G.; Colangelo, Francis R.; Koenig, Carolyn; Klaveren, David van; Ciemins, Elizabeth L.; Cuddeback, John K.

doi:10.1016/j.mayocp.2021.09.012

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…60 Importantly, the use of transactional data for causal inference has facilitated the discovery of disease subgroups and analysis of treatment effect heterogeneity among these groups, a feature which is invaluable to the concept of precision medicine. [61][62][63][64][65] Furthermore, EHR provide a multifaceted trove of longitudinal and temporally rich data. 60 This temporal granularity is of paramount importance for causal inference, as it enables the tracking of individual patients over time, capturing dynamic changes in exposures, interventions, and outcomes.…”

Section: Opportunitiesmentioning

confidence: 99%

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Esteban,

Szmulewicz

2024

J Int Med Res

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The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework’s application.

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Section: Opportunitiesmentioning

confidence: 99%

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Esteban,

Szmulewicz

2024

J Int Med Res

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Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor

Ladin,

Cuddeback,

Duru

et al. 2024

npj Digit. Med.

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Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.

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Role of personalized predictions for people with prediabetes: disseminating patient-centered estimates of benefit

Olchanski,

Ciemins,

Cuddeback

et al. 2024

Preprint

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Background:To assess acceptability and feasibility of incorporating individualized risk prediction into clinical assessment, decision making, and communication of risk of type 2 diabetes and prevention recommendations.Methods:We integrated a prediction model into the clinical workflow at a US health care organization. We conducted patient and provider focus groups and pre- and post-dissemination surveys and assessed the effect on referrals to and enrollment in the Diabetes Prevention Program, among 2,775 patients with prediabetes who had primary care visits between May 2018 and December 2020.Results:Among patients with prediabetes seen in primary care during the study period, 79% had a calculation with the risk prediction model completed. After implementation of the risk prediction model, prevention intervention rates increased, with 62.3% of high-risk patients receiving an intervention within 1 year.Conclusions:Used at the point of care during a shared decision-making discussion between the patient and provider, the diabetes risk calculator helped providers prioritize patients for diabetes prevention interventions, facilitated communication, and successfully improved rates of engagement in their care among patients with prediabetes.

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An Electronic Health Record–Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

Cited by 3 publications

References 41 publications

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor

Role of personalized predictions for people with prediabetes: disseminating patient-centered estimates of benefit

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