ImportanceAnthracycline-containing regimens are highly effective for diffuse large B-cell lymphoma (DLBCL); however, patients with preexisting heart failure (HF) may be less likely to receive anthracyclines and may be at higher risk of lymphoma mortality.ObjectiveTo assess the prevalence of preexisting HF in older patients with DLBCL and its association with treatment patterns and outcomes.Design, Setting, and ParticipantsThis longitudinal cohort study used data from the Surveillance, Epidemiology, and End Results (SEER)–Medicare registry from 1999 to 2016. The SEER registry is a system of population-based cancer registries, capturing more than 25% of the US population. Linkage to Medicare offers additional information from billing claims. This study included individuals 65 years and older with newly diagnosed DLBCL from 2000 to 2015 with Medicare Part A or B continuously in the year prior to lymphoma diagnosis. Data were analyzed from September 2020 to December 2022.ExposuresPreexisting HF in the year prior to DLBCL diagnosis ascertained from billing codes required one of the following: (1) 1 primary inpatient discharge diagnosis, (2) 2 outpatient diagnoses, (3) 3 secondary inpatient discharge diagnoses, (4) 3 emergency department diagnoses, or (5) 2 secondary inpatient discharge diagnoses plus 1 outpatient diagnosis.Main Outcomes and MeasuresThe primary outcome was anthracycline-based treatment. The secondary outcomes were (1) cardioprotective medications and (2) cause-specific mortality. The associations between preexisting HF and cancer treatment were estimated using multivariable logistic regression. The associations between preexisting HF and cause-specific mortality were evaluated using cause-specific Cox proportional hazards models with adjustment for comorbidities and cancer treatment.ResultsOf 30 728 included patients with DLBCL, 15 474 (50.4%) were female, and the mean (SD) age was 77.8 (7.2) years. Preexisting HF at lymphoma diagnosis was present in 4266 patients (13.9%). Patients with preexisting HF were less likely to be treated with an anthracycline (odds ratio, 0.55; 95% CI, 0.49-0.61). Among patients with preexisting HF who received an anthracycline, dexrazoxane or liposomal doxorubicin were used in 78 of 1119 patients (7.0%). One-year lymphoma mortality was 41.8% (95% CI, 40.5-43.2) with preexisting HF and 29.6% (95% CI, 29.0%-30.1%) without preexisting HF. Preexisting HF was associated with higher lymphoma mortality in models adjusting for baseline and time-varying treatment factors (hazard ratio, 1.24; 95% CI, 1.18-1.31).Conclusions and RelevanceIn this study, preexisting HF in patients with newly diagnosed DLBCL was common and was associated with lower use of anthracyclines and lower use of any chemotherapy. Trials are needed for this high-risk population.
Association of Incidentally Discovered Covert Cerebrovascular Disease Identified Using Natural Language Processing and Future Dementia
Background Covert cerebrovascular disease (CCD) has been shown to be associated with dementia in population‐based studies with magnetic resonance imaging (MRI) screening, but dementia risk associated with incidentally discovered CCD is not known. Methods and Results Individuals aged ≥50 years enrolled in the Kaiser Permanente Southern California health system receiving head computed tomography (CT) or MRI for nonstroke indications from 2009 to 2019, without prior ischemic stroke/transient ischemic attack, dementia/Alzheimer disease, or visit reason/scan indication suggestive of cognitive decline or stroke were included. Natural language processing identified incidentally discovered covert brain infarction (id‐CBI) and white matter disease (id‐WMD) on the neuroimage report; white matter disease was characterized as mild, moderate, severe, or undetermined. We estimated risk of dementia associated with id‐CBI and id‐WMD. Among 241 050 qualified individuals, natural language processing identified 69 931 (29.0%) with id‐WMD and 11 328 (4.7%) with id‐CBI. Dementia incidence rates (per 1000 person‐years) were 23.5 (95% CI, 22.9–24.0) for patients with id‐WMD, 29.4 (95% CI, 27.9–31.0) with id‐CBI, and 6.0 (95% CI, 5.8–6.2) without id‐CCD. The association of id‐WMD with future dementia was stronger in younger (aged <70 years) versus older (aged ≥70 years) patients and for CT‐ versus MRI‐discovered lesions. For patients with versus without id‐WMD on CT, the adjusted HR was 2.87 (95% CI, 2.58–3.19) for younger and 1.87 (95% CI, 1.79–1.95) for older patients. For patients with versus without id‐WMD on MRI, the adjusted HR for dementia risk was 2.28 (95% CI, 1.99–2.62) for younger and 1.48 (95% CI, 1.32–1.66) for older patients. The adjusted HR for id‐CBI was 2.02 (95% CI, 1.70–2.41) for younger and 1.22 (95% CI, 1.15–1.30) for older patients for either modality. Dementia risk was strongly correlated with id‐WMD severity; adjusted HRs compared with patients who were negative for id‐WMD by MRI ranged from 1.41 (95% CI, 1.25–1.60) for those with mild disease on MRI to 4.11 (95% CI, 3.58–4.72) for those with severe disease on CT. Conclusions Incidentally discovered CCD is common and associated with a high risk of dementia, representing an opportunity for prevention. The association is strengthened when discovered at younger age, by increasing id‐WMD severity, and when id‐WMD is detected by CT scan rather than MRI.
T083: The Effect of Heart Failure on Management and Outcomes of Older Patients with Hodgkin Lymphoma
Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real‐World Cohort
Objective This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. Methods Patients were ≥50 years and received neuroimaging for non‐stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. Results 230,062 patients were included (median follow‐up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time‐to‐event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person‐years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). Interpretation Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620–630
Association of Silent Cerebrovascular Disease Identified Using Natural Language Processing and Future Ischemic Stroke
Abstract 135: Examining the Information Loss Between Neuroimages and Neuroimaging Reports for Detection of Silent Brain Infarcts and White Matter Disease Using Artificial Intelligence Technologies
Background: Real world studies of silent brain infarction (SBI) and white matter disease (WMD) are impeded by challenges in cohort identification. Natural language processing (NLP) from imaging reports may facilitate future studies. However, electronic health records can be heterogeneous and the process of interpreting neuroimages and generating reports can vary. Understanding knowledge representation and relationships between neuroimages and imaging reports is crucial for using NLP to facilitate disease management and cohort identification. Methods: A balanced sample of head neuroimages (CT, MRI) of patients >50 years without clinical histories of symptomatic stroke, TIA, or dementia were obtained at Mayo Clinic and Tufts Medical Center. A team of 4 radiology residents performed report interpretation (RI) on 1000 reports according to a standardized protocol for the presence of SBIs, the presence of WMD, and WMD grade. A random subsample of 400 was doubly read for interrater reliability. For benchmarking, a team of 4 neuroradiologists directly reviewed and described findings on a subsample of 182 images, each doubly read. We assessed interrater reliability for direct review (DR) and RI, and agreement between these 2 information sources. An NLP algorithm was developed to review and extract findings from 1000 imaging reports. Results: For DR, interrater reliability was moderate for SBIs and WMD (k = 0.53, 95% CI 0.43-0.64 and k = 0.47, 95% CI 0.33-0.61) and good for WMD grade (Spearman 0.71, p<0.001). For RI, interrater reliability for SBIs, WMD and WMD grade was good (k = 0.88, 95% CI 0.80-0.97; k = 0.98, 95% CI 0.97-1.00; and Spearman = 0.985, p<0.001, respectively). Agreement between DR and RI was good for SBIs (k = 0.77, 95% CI 0.67-0.86) and WMD (k = 0.65, 95% CI 0.54-0.77). Spearman rank correlations comparing WMD grade interpretation DR to RI was 0.60 (p<0.001). In identifying the presence of SBIs and WMDs, the accuracy of the NLP algorithm was 0.991 and 0.994, respectively. Conclusion: For the presence of SBI and WMD, and WMD grade, agreement between RI and DR was similar to agreement between 2 neuroradiologists directly reviewing neuroimages. It is highly feasible to use NLP to identify patients with SBIs and WMDs for clinical studies.
Lymphedema Signs, Symptoms, and Diagnosis in Women Who Are in Minority and Low-Income Groups and Have Survived Breast Cancer
Abstract Background Breast cancer–related lymphedema (BCRL) is a well-known side effect of cancer and its treatment with wide-ranging prevalence estimates. Objective This study describes associations between breast cancer–related lymphedema (BCRL) signs, symptoms, and diagnosis for women who were African American, white, or had a low income and survived breast cancer. Design This is a cross-sectional, observational study that used a computer-assisted telephone interview. Methods Women who had survived breast cancer were queried on the presence of 5 lymphedema signs and symptoms (edema in the breast, axilla, arm, and/or hand; tissue fibrosis; pitting; hemosiderin staining; heaviness) and whether they had a diagnosis of BCRL. Relationships between signs/symptoms and diagnosis for each group were evaluated with kappa and chi-square statistics. Results The study sample included 528 women who had survived breast cancer (266 white and 262 African American), with 514 reporting complete data on household income; 45% of the latter reported an annual household income of ≤$20,000. Women who were African American or had a low income were nearly twice as likely as women who were white to have any of 8 signs/symptoms of BCRL. Regardless of race and income, >50% of women with all BCRL signs and symptoms reported that they were not diagnosed with BCRL. Limitations The main limitations of our study are the lack of medical chart data and longitudinal design. Conclusions Women who were African American or had a low income and had survived breast cancer had a greater burden of BCRL signs and symptoms than women who were white. The lack of a strong association between BCRL signs, symptoms, and diagnosis suggests that BCRL may be underdiagnosed. These findings suggest that more rigorous screening and detection of BCRL—especially for women who are African American or have a low income—may be warranted. Cancer rehabilitation programs may be able to fill this gap.