Citations

64 Background: The incidence of eoCRC (diagnosed at age <50) is increasing. We have previously shown higher rates of severe anxiety and depression in this group compared to adults diagnosed at age ≥50 (average-onset CRC, aoCRC). We assessed the impact of ePROM collection coupled with a CC-based supportive care intervention on symptom burden in patients with eoCRC. Methods: The Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) trial was a cluster-randomized, pragmatic trial conducted at Mayo Clinic Rochester and within the Mayo Clinic Health System (in Minnesota and Wisconsin) between 2019 and 2023. Enrolled patients serially reported the severity of six SPPADE symptoms (Sleep deficit, Pain, Physical function impairment, Anxiety, Depression, and Energy deficit/fatigue) on 11-point numerical rating scales. Symptom scores were interpreted as none to mild (0-3), moderate (4-6), or severe (7-10). Those with moderate symptoms were offered symptom management education and self-management materials, while those with severe symptoms were offered the option to work with a symptom care manager. In this post hoc analysis we examined the effects of the intervention on change in symptom scores among those with eoCRC, and using the Kaplan-Meier method, the median time to improvement to a mild/moderate level in those with a severe symptom, comparing eoCRC with aoCRC. Results: Among 4,422 patients with CRC, 887 had eoCRC. Forty-six percent of patients were female ( p =0.61), and a greater proportion of eoCRC patients were of a non-white race (11.6% vs 4.7%), lived in an urban setting (65.6% vs 50.5%), had a Bachelor’s degree or higher (36.8% vs 24.4%), were employed (78.5% vs 36.7%), on a non-government insurance plan (83.4% vs 35.3%), single (19.7% vs 11.2%) or married (74.2% vs 67.9%), and had metastatic disease (54.8% vs 46.1%, p <0.0001 for all). Compared to a pre-intervention baseline, the mean change in symptom scores for eoCRC patients following the intervention demonstrated improvements in anxiety (-0.238), depression (-0.134), and fatigue (-0.146, p <0.05 for all). Time to symptom improvement from severe to mild/moderate was longer for anxiety and depression in eoCRC patients compared to aoCRC patients (Table 1). Conclusions: Collection of ePROMs coupled with a CC-based intervention led to improvements in anxiety, depression, and fatigue among eoCRC patients, although time to symptom improvement was longer among eoCRC patients compared to aoCRC patients. Time to improvement of severe symptom (months), median (95% CI). eoCRC aoCRC p -value Sleep 4.2 (3.1-5.8) 3.8 (2.7-5.4) 0.4 Pain 4.1 (3.0-7.5) 3.2 (2.3-5.0) 0.5 Physical function impairment 3.1 (1.9-6.3) 2.8 (2.2-4.2) 0.5 Anxiety 4.3 (3.4-9.1) 2.6 (2.0-3.5) 0.009 Depression 4.3 (2.6-7.5) 2.6 (2.1-3.5) 0.04 Fatigue 4.3 (3.7-9.5) 3.4 (2.7-4.2) 0.06

AbstractElectronic patient-reported outcome measures (ePROMs) can improve care for people with cancer, but effectiveness hinges on well-supported integration in clinical settings.We evaluated clinician use of specific clinical decision support (CDS) tools in the electronic health record (EHR) designed to facilitate timely, clinically appropriate responses to ePROM scores for six symptoms commonly experienced by cancer patients.The parent pragmatic trial, which took place at Mayo Clinic (Rochester, Minnesota, United States) and its affiliated community health care system between March 2019 and January 2023, evaluated the population-level effectiveness and implementation of an ePROM surveillance and EHR-facilitated collaborative care symptom management intervention. The present evaluation used a case study approach with four data sources: (1) clinician interactions with CDS tools abstracted from the EHR; (2) clinician notes identified with an institution-specific textual search tool; (3) qualitative interviews and group discussions with care teams; and (4) administrative records reviewed to identify training and outreach to care teams.EHR metrics showed very low adoption of CDS tools including alerts and symptom-specific order sets, despite educational outreach and information technology support provided to clinical care teams. Qualitative findings revealed that CDS use was not easy to integrate into busy clinical workflows and highlighted clinician perceptions that the collaborative care intervention provided additional patient support that reduced clinicians' need to utilize CDS tools. They also highlight the importance of contextual factors, including institutional priorities and EHR changes.This pragmatic clinical trial case study found limited adoption of EHR CDS tools that had been developed to increase clinicians' awareness of and responses to ePROM data. Findings suggest the need to align clinician and organizational implementation strategies, simplify CDS tools to fit practice expectations, and identify and address contextual factors that could undercut strategies like education and peer support. This may be especially important for teams who aim to iteratively evaluate and refine CDS and implementation strategies for multicomponent interventions or introduce new strategies that are responsive to barriers while maintaining scalability.

AbstractThe Enhanced EHR-facilitated Cancer Symptom Control (E2C2) Trial is a pragmatic trial testing a collaborative care approach for managing common cancer symptoms. There were challenges in identifying cancer site and metastatic status.This study compares three different approaches to determine cancer site and six strategies for identifying the presence of metastasis using EHR and cancer registry data.The E2C2 cohort included 50,559 patients seen in the medical oncology clinics of a large health system. SPPADE symptoms were assessed with 0 to 10 numeric rating scales (NRS). A multistep process was used to develop three approaches for representing cancer site: the single most prevalent International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) code, the two most prevalent codes, and any diagnostic code. Six approaches for identifying metastatic disease were compared: ICD-10 codes, natural language processing (NLP), cancer registry, medications typically prescribed for incurable disease, treatment plan, and evaluation for phase 1 trials.The approach counting the two most prevalent ICD-10 cancer site diagnoses per patient detected a median of 92% of the cases identified by counting all cancer site diagnoses, whereas the approach counting only the single most prevalent cancer site diagnosis identified a median of 65%. However, agreement among the three approaches was very good (kappa > 0.80) for most cancer sites. ICD and NLP methods could be applied to the entire cohort and had the highest agreement (kappa = 0.53) for identifying metastasis. Cancer registry data was available for less than half of the patients.Identification of cancer site and metastatic disease using EHR data was feasible in this large and diverse cohort of patients with common cancer symptoms. The methods were pragmatic and may be acceptable for covariates, but likely require refinement for key dependent and independent variables.

Abstract Objectives In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards. Materials and Methods We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation. The distributions of the First Score 3 hours after admission, the Highest Score at any time during the hospitalization, and the Last Score just before an AE or dismissal without an AE were calculated. The Last Score was used to calculate the area under the receiver operating characteristic curve (ROC-AUC) and the precision-recall curve (PRC-AUC). Results From August 23, 2021 to March 31, 2022, 35 937 medical admissions had 2173 (6.05%) AE compared to 25 214 surgical admissions with 4984 (19.77%) AE. Medical and surgical admissions had significant different (P &lt;.001) distributions of the First Score, Highest Score, and Last Score among those with an AE and without an AE. The model performed better in the medical group when compared to the surgical group, ROC-AUC 0.869 versus 0.677, and RPC-AUC 0.988 versus 0.878, respectively. Discussion Heterogeneity of medical and surgical patients can significantly impact the performance of a ML-based EWS, changing the model validity and clinical discernment. Conclusions Characterization of the target patient subpopulations has clinical implications and should be considered when developing models to be used in general hospital wards.

AbstractObjectivesTo report lessons from integrating the methods and perspectives of clinical informatics (CI) and implementation science (IS) in the context of Improving the Management of symPtoms during and following Cancer Treatment (IMPACT) Consortium pragmatic trials.Materials and MethodsIMPACT informaticists, trialists, and implementation scientists met to identify challenges and solutions by examining robust case examples from 3 Research Centers that are deploying systematic symptom assessment and management interventions via electronic health records (EHRs). Investigators discussed data collection and CI challenges, implementation strategies, and lessons learned.ResultsCI implementation strategies and EHRs systems were utilized to collect and act upon symptoms and impairments in functioning via electronic patient-reported outcomes (ePRO) captured in ambulatory oncology settings. Limited EHR functionality and data collection capabilities constrained the ability to address IS questions. Collecting ePRO data required significant planning and organizational champions adept at navigating ambiguity.DiscussionBringing together CI and IS perspectives offers critical opportunities for monitoring and managing cancer symptoms via ePROs. Discussions between CI and IS researchers identified and addressed gaps between applied informatics implementation and theory-based IS trial and evaluation methods. The use of common terminology may foster shared mental models between CI and IS communities to enhance EHR design to more effectively facilitate ePRO implementation and clinical responses.ConclusionImplementation of ePROs in ambulatory oncology clinics benefits from common understanding of the concepts, lexicon, and incentives between CI implementers and IS researchers to facilitate and measure the results of implementation efforts.

Abstract Background: SPPADE symptoms (Sleep disturbance, Pain, Physical function impairment, Anxiety, Depression/distress, and Energy deficits/fatigue) may result from cancer and its treatment. The aim of this analysis was to characterize SPPADE symptoms in patients with breast cancer. Methods. SPPADE data were drawn from the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) study, a stepped-wedge pragmatic cluster-randomized trial designed to evaluate a symptom management intervention in routine practice (NCT03892967). Symptom severity was measured on a 0-10 scale via a survey administered by patient portal, in-clinic tablet, or interactive voice response system (by phone) prior to, during, or soon after an outpatient oncology encounter. Scores of 4-6 were categorized as “moderate” and ≥7 as “severe.” In this analysis, only the data from each patient’s initial survey were included. Results. A total of 9,651 patients with breast cancer completed an initial E2C2 survey between March 2019 and January 2023. The proportion of patients with moderate or severe SPPADE symptoms were as follows: 22% moderate and 10% severe for insomnia; 18% moderate and 7% severe for pain; 17% moderate and 7% severe for anxiety; 15% moderate and 5% severe for emotional distress; 25% moderate and 12% severe for fatigue; and 22% moderate and 8% severe for impaired physical function. Table 1 presents score means and standard deviations by demographic subgroup. Conclusions. SPPADE symptoms are common in patients with breast cancer. These observations warrant routine implementation of symptom surveillance and management in breast oncology. Funding acknowledgement: The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium is a Cancer Moonshot Research Initiative under the authorization of the 2016 United States 21st Century Cures Act. This research was supported by the National Cancer Institute of the NIH, UM1CA233033 (Mayo Clinic, Rochester, MN). Insomnia, Pain, Anxiety, Distress, Fatigue, and Functional Impairment in Patients with Breast Cancer Citation Format: Kathryn Ruddy, Veronica Grzegorczyk, Deirdre Pachman, Sandra Mitchell, Bolun Liu, Deanne Smith, Ciara O'Sullivan, Shawna Ehlers, Jeph Herrin, Parvez Rahman, Andrea Cheville. Insomnia, Pain, Anxiety, Distress, Fatigue, and Functional Impairment in Patients with Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-12-09.

The Deterioration Index (DI) is an automatic early warning system that utilizes a machine learning algorithm integrated into the electronic health record and was implemented to improve risk stratification of inpatients. Our pilot implementation showed superior diagnostic accuracy than standard care. A score >60 had a specificity of 88.5% and a sensitivity of 59.8% (PPV 0.1758, NPP 0.9817). However, acceptance in the clinical workflow was divided; nurses preferred standard care, while providers found it helpful.

31 Background: Incidence of early-onset colorectal cancer (eoCRC, <50 years of age at diagnosis) is increasing globally. However, little is known regarding symptom burden in this sub-population. We compared symptoms in patients with eoCRC to those of patients with average-onset CRC (aoCRC, diagnosed at age ≥ 50). Methods: E2C2 is a stepped-wedge, cluster-randomized pragmatic trial evaluating a collaborative care model for the assessment and management of six symptoms [Sleep disturbance, Pain, Physical function impairment, Anxiety, emotional Distress, and Energy deficit (SPPADE)]. Patients were asked to complete assessments rating each of these symptoms on an 11-point numerical rating scale. Symptom severity was classified as mild (0-3), moderate (4-6), or severe (7-10). We conducted a subgroup analysis of patients with CRC, comparing SPPADE symptoms on an initial survey, regardless of timing in treatment course, between patients with eoCRC and aoCRC. Results: Patients with CRC (n=4281) completed a survey between March 2019 and December 2022. Patient demographics and SPPADE symptoms are reported in the table. Patients with eoCRC were more likely to report severe sleep disturbance ( p<0.0001), pain ( p=0.03485), anxiety ( p<0.0001), emotional distress ( p<0.0001), and fatigue ( p=0.01083). In patients with eoCRC, women were more likely to report severe pain (12.6% vs 6.9%, p=0.0069), anxiety (16.1% vs 10.0%, p=0.012), emotional distress (12.1% vs 5.4%, p=0.00066), fatigue (18.9% vs 13.4%, p=0.038), and physical function impairment (10.1% vs 5.8%, p=0.026) compared to men. Conclusions: Patients with eoCRC reported higher symptom severity than patients with aoCRC. This suggests unique needs for symptom management in patients with eoCRC. Additional research is needed to evaluate how time since diagnosis, disease characteristics, cancer-directed treatment patterns, and co-occurring symptoms are associated with specific symptoms in this population. [Table: see text]

AbstractCancer and its treatment produce deleterious symptoms across the phases of care. Poorly controlled symptoms negatively affect quality of life and result in increased health-care needs and hospitalization. The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium was created to develop 3 large-scale, systematic symptom management systems, deployed through electronic health record platforms, and to test them in pragmatic, randomized, hybrid effectiveness and implementation trials. Here, we describe the IMPACT Consortium’s conceptual framework, its organizational components, and plans for evaluation. The study designs and lessons learned are highlighted in the context of disruptions related to the COVID-19 pandemic.

Background: Concerns for side-effects makes the identification of treatment targets of hypertension in the very elderly challenging. We sought to utilize natural language processing (NLP) to evaluate the prevalence of prespecified side-effects in a cohort of very elderly (≥80 years) patients. Methods and Results: We conducted a two-year, cross-sectional study involving 5169 very elderly patients with and without hypertension seen by primary or specialist providers. Hypertension patients had to have a prior hypertension ICD-9 code in the electronic medical records problem list and ≥2 office visits in 2008 and 2009. We used NLP to analyze extracted free text reports from last clinic visits between 2008 and 2009. Of the 10 prespecified side-effects, hypotension, fatigue, and dizziness were the commonest three conditions and hypotension was significantly higher in the HTN patients. Of the 2835 patients with no prior hypertension history and 2334 hypertension patients identified, the prevalence of each prespecified item in both groups were: acute renal failure (0.3% v 0.6%, p=0.13); syncope (2.8% v 2.2%, p = 0.17), dizziness (3.1% v 4.0%, p=0.09), history of fall (1.0% v 0.9%, p=0.65), fatigue (5.4% v 4.5%, p=0.19), femoral neck fracture (0.18% v 0.04%, p=0.16), palpitations (1.2% v 1.5%, p=0.35), hypotension (4.1% v 6.6%, p<0.001), hyperkalemia (1.1% v 1.7%, p=0.04), hypokalemia (0.4% v 0.9%, p=0.01). At least one presumed side-effect was present in 17.5% of all 5169 patients. Among hypertension patients, there was no significant difference in the prevalence of the side-effects among patients with BP above (578) or below 140/90 (1756) except for acute renal failure (1.21% vs 0.40%, p = 0.03). Conclusion: Analysis of free-text outpatient notes using NLP showed that hypotension and potassium abnormalities were significantly more prevalent in very elderly patients with hypertension compared to similarly aged patients with no hypertension history.