Visual assessment of the similarity between a patient and trial population

Cahan, Amos; Cimino, James J.

doi:10.4338/aci-2015-12-ra-0178

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…It can potentially be employed to manage a real-world patient with a complex health status and comorbidity profile. Patient similarity analytics also has the potential to assess the similarity between an index patient and trial population in conventional studies and help clinicians choose the most appropriate clinical trial [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Sharafoddini¹,

Dubin²,

Lee³

2017

JMIR Med Inform

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BackgroundPhysicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions..ObjectiveThe aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research.MethodsThe method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results.ResultsAfter duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods.ConclusionsInterest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes.

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Section: Introductionmentioning

confidence: 99%

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Sharafoddini¹,

Dubin²,

Lee³

2017

JMIR Med Inform

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“…The reference population can be the initial study cohort obtained after running the buildcohort() function or a subsequent selection of so-called nearest patients. The Draw_Scale_Chart() function adapts the scale chart as introduced by Cahan and Cimino [17]. The function arguments are described in more detail in S2 Table . An example on how to use this function can be found in the S5 Appendix.…”

Section: Data Subsets and Similarity Visualizationmentioning

confidence: 99%

A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

2020

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Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient's characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients.

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“…5 While researchers are increasingly utilizing the EHR and digital tools to screen and contact patients for recruitment into clinical trials, a key challenge is ensuring that the sampled cohort accurately represents the eligible population. [6][7][8] Equitable representation of the sampled cohort is crucial to ensuring validity and generalizability of the study results. Vulnerable and underserved populations are underrepresented in clinical trials due to numerous factors, including investigator bias, medical mistrust, barriers due to differences in health or research literacy, and lack of access to transportation.…”

Section: Introductionmentioning

confidence: 99%

Assessing Equitable Recruitment in a Digital Health Trial for Asthma

Plombon,

S. Rudin,

Sulca Flores

et al. 2023

Appl Clin Inform

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Objective: To assess a multi-pronged strategy using primarily digital methods to equitably recruit asthma patients into a clinical trial of a digital health intervention. Methods: We approached eligible patients using at least one of eight recruitment strategies. We recorded approach dates and the strategy that led to completion of a web-based eligibility questionnaire that was reported during the verbal consent phone call. Study team members conducted monthly sessions using a structured guide to identify recruitment barriers and facilitators. The proportion of participants who reported being recruited by a portal or non-portal strategy was measured as our outcomes. We used Fisher’s Exact test to compare outcomes by equity variable, and multivariable logistic regression to control for each covariate and adjust effect size estimates. Using grounded theory, we coded and extracted themes regarding recruitment barriers and facilitators. Results: The majority (84.4%) of patients who met study inclusion criteria were patient portal enrollees. Of 6366 eligible patients who were approached, 627 completed the eligibility questionnaire and were less frequently Hispanic, less frequently Spanish-speaking, and more frequently patient portal enrollees. Of 445 patients who consented to participate, 241 (54.2%) reported completing the eligibility questionnaire after being contacted by a patient portal message. In adjusted analysis, only race (OR 0.46 [(0.28, 0.77)], p=0.003) and college education (OR 0.60, 95% CI [0.39, 0.91], p = 0.016) remained significant. Key recruitment barriers included technology issues (e.g., lack of email access) and facilitators included bilingual study staff, Spanish-language recruitment materials, targeted phone calls, and clinician-initiated “1-click” referrals. Discussion/Conclusion: A primarily digital strategy to recruit patients into a digital health trial is unlikely to achieve equitable participation, even in a population overrepresented by patient portal enrollees. Non-digital recruitment methods that address racial and educational disparities and less active portal enrollees are necessary to ensure equity in clinical trial enrollment.

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Visual assessment of the similarity between a patient and trial population

Abstract: We suggest that further research is warranted to explore visual tools facilitating the choice of the most applicable clinical trial to a specific patient. Automating patient and trial population characteristics extraction is key to support this effort.

Cited by 7 publications

References 21 publications

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

Assessing Equitable Recruitment in a Digital Health Trial for Asthma

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