Individualized prediction of critical illness in older adults: Validation of an elders risk assessment model.

BACKGROUND: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR-based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. METHODS: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. RESULTS: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83-0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98-12.87). CONCLUSIONS: This simple EHR-based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well-being of older adults at risk of critical illness and its consequences.

Early Prevention of Critical Illness in Older Adults: Adaptation and Pilot Testing of an Electronic Risk Score and Checklist.

OBJECTIVE: Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment-ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting. METHODS: The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction. RESULTS: Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients. CONCLUSIONS: This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use. RECOMMENDATIONS: We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Tracking activities and adaptations in a multi-site stepped wedge pragmatic trial of a cancer symptom management intervention.

Background: Pragmatic trials may need to adapt interventions to enhance local fit, and adaptation tracking is critical to evaluation. This study describes the tracking approach for a multisite, stepped-wedge hybrid pragmatic trial testing implementation and effectiveness of a cancer symptom management intervention. Methods: Study activities were documented in a spreadsheet by date and category. Intervention adaptations were tracked across multiple workgroups in a database structured around the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) domains, e.g., reasons for change. Implementation strategies were tracked longitudinally and by cluster in a database using the Longitudinal Implementation Strategy Tracking System (LISTS) method. A logic model was created at the end of the study to describe core intervention components and implementation strategies with dates of adaptations. Results: Between January 2019 and January 2023, 187 study activities were documented. Most intervention activities took place early, but there were important intervention refinements during the course of the trial, including the expansion of interventionist roles to add two new disciplines. Eleven intervention adaptations were documented. Most were unplanned and aimed at improving fit or increasing engagement. Thirty-three implementation strategies were documented, the largest number of which were related to educating stakeholders. Most (but not all) component and strategy additions were consistent with the mechanisms of change as hypothesized at trial launch. Conclusions: A multifaceted approach to adaptation tracking, combined with a logic model, supported identification of meaningful changes for use in evaluation, but further work is needed to minimize burden and ensure robust and practical systems that inform both evaluation and timely decision-making. Trial: Registration: ClinicalTrials.gov, NCT03892967. Registered on March 25, 2019. https://www.clinicaltrials.gov/.

Implementing cancer symptom management interventions utilizing patient-reported outcomes: a pre-implementation evaluation of barriers and facilitators.

PURPOSE: Symptoms can negatively impact quality of life for patients with a history of cancer. Digital, electronic health record (EHR)-integrated approaches to routine symptom monitoring accompanied by evidence-based interventions for symptom management have been explored as a scalable way to improve symptom management, particularly between clinic visits. However, little research has evaluated barriers and facilitators to implementing these approaches in real-world settings, particularly during the pre-implementation phase. Pre-implementation assessment is critical for informing the selection and sequencing of implementation strategies and intervention adaptation. Thus, this study sought to understand pre-implementation perceptions of a remote cancer symptom monitoring and management intervention that uses electronic patient-reported outcome measures for symptom assessment. METHODS: We interviewed 20 clinical and administrative stakeholders from 4 geographic regions within an academic medical center and its affiliated health system during the months prior to initiation of a stepped-wedge, cluster randomized pragmatic trial. Transcripts were coded using the Consolidated Framework for Implementation Research [CFIR] 2.0. Two study team members reviewed coded transcripts to understand how determinants were relevant in the pre-implementation phase of the trial and prepared analytic memos to identify themes. RESULTS: Findings are summarized in four themes: (1) ability of the intervention to meet patient needs [recipient characteristics], (2) designing with care team needs in mind [innovation design and adaptability], (3) fit of the intervention with existing practice workflows [compatibility], and (4) engaging care teams early [engaging deliverers]. CONCLUSION: Attention to these aspects when planning intervention protocols can promote intervention compatibility with patients, providers, and practices thereby increasing implementation success.