Kurt Kroenke

Purpose The longitudinal course (trajectory) of symptoms in patients with cancer has been understudied. Sleep disturbance, pain, physical function impairment, anxiety, depression, and energy deficit/fatigue (SPPADE) are particularly common in patients with cancer. This study characterizes the trajectory patterns of SPPADE symptoms in a large and diverse sample of patients with cancer and identifies factors associated with distinct trajectories. Methods To identify the optimal number and functional forms of the symptom trajectories, latent class growth analysis was conducted. Electronic health record (EHR) data were analyzed from 14,590 patients in the Enhanced EHR-facilitated Cancer Symptom Control (E2C2) trial cohort with at least 3 symptom assessments within a 12-month period. Each SPPADE symptom was rated on a 0–10 numeric rating scale. Multivariable multinomial logistic regression was used to determine factors associated with membership in the symptom trajectory subgroups. Results Four distinct trajectory patterns were similar across symptoms. The average sample distribution of membership in a trajectory class across the 6 symptoms was 65.5% for low severity stable, 15.5% for high severity stable, 10.7% for improving, and 8.4% for worsening. Being employed, married/partnered, better educated, older, and not having metastatic disease were independently associated with membership in the low stable trajectory of SPPADE symptoms. Cancer site generally did not influence symptom trajectory class membership. Conclusion For oncology practices that routinely use patient-reported outcomes to assess symptoms, EHR data can be a source for characterizing symptom trajectories. Knowledge of factors associated with trajectory class membership could be useful in clinical research and for tailoring evidence-based symptom management strategies.

Objective: To explore and visually display differences in the distribution of the Glasgow Outcome Scale – Extended (GOS-E) over time after traumatic brain injury (TBI), focusing on variations in outcome distributions based on GOS-E at Year 1 postinjury and age at injury. Setting: Community. Participants: 14 010 individuals who received inpatient rehabilitation in the TBI Model Systems. Design: Cross-sectional analysis of a prospectively collected longitudinal database. Main Measures: GOS-E scores at 1, 2, 5, 10, 15, and 20 years postinjury, and age at injury. Results: The proportions of cases in each GOS-E category are displayed using 100% stacked bar graphs for each follow-up period. These graphs reveal that GOS-E at Year 1 and age at injury clearly influence outcomes over time. Trends include decreasing good recovery and increasing severe disability as Year 1 GOS-E worsens, along with rising mortality rates as age at injury increases. Conclusion: The study introduces a novel approach for visually representing patterns of change in GOS-E outcomes, emphasizing differences across strata defined by GOS-E at Year 1 and age at injury. The figures provide a valuable tool for communicating potential outcomes, particularly when GOS-E at Year 1 and age are known. Evaluating the visual interpretability of these graphs among persons with brain injury, family members, healthcare providers, and other stakeholders will help determine their broader usability and value.

INTRODUCTION: We examined whether the symptom expression of depression as assessed using the Patient Health Questionnaire-9 (PHQ-9) depression screening tool differs between patients with decompensated cirrhosis (DC) compared with primary care patients. METHODS: Study included 218 patients with DC (91% Child-Pugh Class B/C) recruited from a liver transplant center and a real-world cohort of 436 outpatients from 4 primary care clinics in a large tertiary academic health system who completed the PHQ-9. We calculated positive screening rates for depression (PHQ-9 cutoff score of 10) for both cohorts. We evaluated PHQ-9 items for differential item functioning (DIF) in both cohorts within an Item Response Theory framework. We compared DIF-adjusted and unadjusted Item Response Theory scores to characterize the impact of DIF on PHQ-9 total scores. RESULTS: Positive screening rates using a PHQ-9 cutoff score of 10 were 39% and 29% for DC and primary care patients, respectively. Three PHQ-9 somatic symptom items (sleep problems, low energy, psychomotor agitation, or retardation) showed significant DIF, with DC more likely than primary care patients with similar levels of depression severity to endorse these symptoms. DIF-adjusted scores suggested a 1-point increase (PHQ-9 cutoff score of 11) in the screening threshold for patients with DC. DISCUSSION: Equating for depression severity, we found differences in the symptom expression of depression for patients with DC relative to primary care patients. Our findings highlight the need for future clinical and basic research into the diagnostic performance of depression screening tests and the phenomenology of depression in patients with DC.

Objective: Low‐risk chest pain (LRCP) is one of the most common conditions presenting in the emergency department (ED) and is strongly associated with anxiety. The purpose of this study is to determine the prevalence of other psychological comorbidities and clinical factors associated with severe anxiety in LRCP. Methods: Baseline data is analyzed from the PACER trial comparing the effectiveness of two telehealth interventions for LRCP patients with anxiety. Key eligibility criteria are a HEART score < 7 and either a GAD‐7 anxiety score ≥ 8 or a positive PHQ screener for panic disorder. Psychological comorbidity measures included the Patient Health Questionnaire 8‐item (PHQ‐8) depression scale, the PHQ‐14 somatization scale, the Primary Care Posttraumatic Stress Disorder Screen, the Sheehan Disability Scale, and the General Self‐Efficacy Scale. Multivariable modeling is used to determine factors associated with severe anxiety. Results: The 375 patients had a mean age of 39.9; 70.9% were women; 62.9% were White, 32.6% Black, and 4.5% other race. The majority (75%) screened positive for panic disorder, and 42% of participants had severe anxiety (GAD‐7 ≥ 15). Non‐anxiety psychological comorbidity was very high; the proportion of patients exceeding scale cut points was 58% for depression, 57% for PTSD, 52% for somatization, 59% for high disability, and 31% for low self‐efficacy; each was significantly associated with severe anxiety on univariable analysis. Four patient characteristics were independently associated with severe anxiety in multivariable models: odds ratios (95% CI) were 2.7 (1.5–4.9) for depression, 2.3 (1.4–3.9) for low self‐efficacy, 2.1 (1.2–3.6) for low education (high school or less), and 1.8 (1.0 to 3.3) for female sex. Conclusions: LRCP is accompanied not only by anxiety but also by other potentially treatable psychological comorbidities Severe anxiety is more common in individuals with depression, low self‐efficacy, lower education, and possibly women. Trail Registration: PACER is registered in clinicaltrials.gov identifier: NCT04811521

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.