Dataset Information

Interactive decision support for esophageal adenocarcinoma screening and surveillance.

ABSTRACT: BACKGROUND:A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making. RESULTS:U.S. incidence and mortality data and published relative risk estimates from observational studies and clinical trials were used to calculate absolute risk of EAC over 10?years adjusting for competing risks. These input parameters varied depending on presence of the key precursor, Barrett's esophagus. The open source application works across common devices to gather risk factor data and graphically illustrate estimated risk on a single page. Changes to input data are immediately reflected in the colored graphs. We used the calculator to compare the risk distribution between EAC cases and controls from six population-based studies to gain insight into the discrimination metrics of current practice guidelines for screening, observing that current guidelines sacrifice a significant amount of specificity to identify 78-86% of eventual cases in the US population. CONCLUSIONS:This educational tool provides a simple and rapid means to graphically communicate risk of EAC in the context of other health risks, facilitates "what-if" scenarios regarding potential preventative actions, and can inform discussions regarding screening, surveillance and treatment options. Its generic architecture lends itself to being easily extended to other cancers with distinct pathways and/or intermediate stages, such as hepatocellular cancer. IC-RISC™ extends current qualitative clinical practice guidelines into a quantitative assessment, which brings the possibility of preventative actions being offered to persons not currently targeted for screening and, conversely, reducing unnecessary procedures in those at low risk. Prospective validation and application to existing well-characterized cohort studies are needed.

SUBMITTER: Vaughan TL

PROVIDER: S-EPMC6598240 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Interactive decision support for esophageal adenocarcinoma screening and surveillance.

Vaughan Thomas L TL Onstad Lynn L Dai James Y JY

BMC gastroenterology 20190627 1

<h4>Background</h4>A key barrier to controlling esophageal adenocarcinoma (EAC) is identifying those most likely to benefit from screening and surveillance. We aimed to develop an online educational tool, termed IC-RISC™, for providers and patients to estimate more precisely their absolute risk of developing EAC, interpret this estimate in the context of risk of dying from other causes, and aid in decision-making.<h4>Results</h4>U.S. incidence and mortality data and published relative risk estim ...[more]

PMID: 31248371

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Project description:BackgroundDuring the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs' underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree's nodes step-by-step in an interactive manner.ObjectiveThis study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts.MethodsWe developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention's guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants' accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants' ratings of the DSTs' usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants' perception of the tools. We used ANOVAs and t tests to assess statistical significance.ResultsOur survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants' perception of the DSTs.ConclusionsWhen the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other.Trial registrationDeutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered).

Dataset Information

Interactive decision support for esophageal adenocarcinoma screening and surveillance.

Publications

Interactive decision support for esophageal adenocarcinoma screening and surveillance.

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