Browsing by Author "Griffin, Joan M."

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    Prevalence, Severity, and Co-Occurrence of SPPADE Symptoms in 31,866 Patients with Cancer
    (Elsevier, 2023) Kroenke, Kurt; Lam, Veronica; Ruddy, Kathryn J.; Pachman, Deirdre R.; Herrin, Jeph; Rahman, Parvez A.; Griffin, Joan M.; Cheville, Andrea L.; Medicine, School of Medicine
    Objectives: To examine the prevalence, severity, and co-occurrence of SPPADE symptoms as well as their association with cancer type and patient characteristics. Background: The SPPADE symptoms (sleep disturbance, pain, physical function impairment, anxiety, depression, and low energy /fatigue) are prevalent, co-occurring, and undertreated in oncology and other clinical populations. Methods: Baseline SPPADE symptom data were analyzed from the E2C2 study, a stepped wedge pragmatic, population-level, cluster randomized clinical trial designed to evaluate a guideline-informed symptom management model targeting the six SPPADE symptoms. Symptom prevalence and severity were measured with a 0-10 numeric rating (NRS) scale for each of the six symptoms. Prevalence of severe (NRS ≥ 7) and potential clinically relevant (NRS ≥ 5) symptoms as well as co-occurrence of clinical symptoms were determined. Distribution-based methods were used to estimate the minimally important difference (MID). Associations of cancer type and patient characteristics with a SPPADE composite score were analyzed. Results: A total of 31,886 patients were assessed for SPPADE symptoms prior to, during, or soon after an outpatient medical oncology encounter. The proportion of patients with a potential clinically relevant symptom ranged from 17.5% for depression to 33.4% for fatigue. Co-occurrence of symptoms was high, with the proportion of patients with three or more additional clinically relevant symptoms ranging from 45.2% for fatigue to 68.6% for depression. The summed SPPADE composite score demonstrated good internal reliability (Cronbach's alpha of 0.86), with preliminary MID estimates of 4.1-4.3. Symptom burden differed across several types of cancer but was generally similar across most sociodemographic characteristics. Conclusion: The high prevalence and co-occurrence of SPPADE symptoms in patients with all types of cancer warrants clinical approaches that optimize detection and management.
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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.