Browsing by Author "Mitchell, Sandra A."

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    A Systematic Review of Functional Outcomes in Cancer Rehabilitation Research
    (Elsevier, 2022) Sleight, Alix G.; Gerber, Lynn H.; Marshall, Timothy F.; Livinski, Alicia; Alfano, Catherine M.; Harrington, Shana; Flores, Ann Marie; Virani, Aneesha; Hu, Xiaorong; Mitchell, Sandra A.; Varedi, Mitra; Eden, Melissa; Hayek, Samah; Reigle, Beverly; Kerkman, Anya; Neves, Raquel; Jablonoski, Kathleen; Hacker, Eileen; Sun, Virginia; Newman, Robin; McDonnell, Karen Kane; L’Hotta, Allison; Schoenhals, Alana; Stout, Nicole L.; School of Nursing
    Objective: To systematically review the evidence regarding rehabilitation interventions targeting optimal physical or cognitive function in adults with a history of cancer and describe the breadth of evidence as well as strengths and limitations across a range of functional domains. Data sources: PubMed, Cumulative Index to Nursing and Allied Health Plus, Scopus, Web of Science, and Embase. The time scope was January 2008 to April 2019. Study selection: Prospective, controlled trials including single- and multiarm cohorts investigating rehabilitative interventions for cancer survivors at any point in the continuum of care were included, if studies included a primary functional outcome measure. Secondary data analyses and pilot/feasibility studies were excluded. Full-text review identified 362 studies for inclusion. Data extraction: Extraction was performed by coauthor teams and quality and bias assessed using the American Academy of Neurology (AAN) Classification of Evidence Scheme (class I-IV). Data synthesis: Studies for which the functional primary endpoint achieved significance were categorized into 9 functional areas foundational to cancer rehabilitation: (1) quality of life (109 studies), (2) activities of daily living (61 studies), (3) fatigue (59 studies), (4) functional mobility (55 studies), (5) exercise behavior (37 studies), (6) cognition (20 studies), (7) communication (10 studies), (8) sexual function (6 studies), and (9) return to work (5 studies). Most studies were categorized as class III in quality/bias. Averaging results found within each of the functional domains, 71% of studies reported statistically significant results after cancer rehabilitation intervention(s) for at least 1 functional outcome. Conclusions: These findings provide evidence supporting the efficacy of rehabilitative interventions for individuals with a cancer history. The findings should be balanced with the understanding that many studies had moderate risk of bias and/or limitations in study quality by AAN criteria. These results may provide a foundation for future work to establish clinical practice guidelines for rehabilitative interventions across cancer disease types.
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    Using Electronic Health Records to Classify Cancer Site and Metastasis
    (Thieme, 2025) Kroenke, Kurt; Ruddy, Kathryn J.; Pachman, Deirdre R.; Grzegorczyk, Veronica; Herrin, Jeph; Rahman, Parvez A.; Tobin, Kyle A.; Griffin, Joan M.; Chlan, Linda L.; Austin, Jessica D.; Ridgeway, Jennifer L.; Mitchell, Sandra A.; Marsolo, Keith A.; Cheville, Andrea L.; Medicine, School of Medicine
    The 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.