Hui Zhao

Association Between Systemic Anticancer Therapy Administration Near the End of Life With Health Care and Hospice Utilization in Older Adults: A SEER Medicare Analysis of End-of-Life Care Quality

PURPOSE Use of cytotoxic chemotherapy at end-of-life (EOL) is associated with adverse quality of life, increased health care utilization, and lower hospice rates. Although EOL cytotoxic chemotherapy use has declined in recent years, EOL novel (immunotherapy and targeted therapy) use has increased. The association between use of novel therapies at EOL and health care utilization has not been widely studied. METHODS We identified patients within SEER-Medicare who had part D coverage (excluding those with Medicare Advantage) age 66 years and older, and breast, colorectal, lung, prostate, bladder, cervical, kidney, liver, ovarian, pancreatic, melanoma, or uterine cancer. Patients were diagnosed between 2005 and 2019 and died between 2015 and 2020. We analyzed associations between EOL systemic anticancer therapy (SACT) use (overall and by subtype), and health care utilization in the last 30 days of life (emergency department [ED], hospitalization, intensive care unit [ICU], and inpatient death), and hospice with multivariable regression, controlling for sociodemographic and cancer covariates. RESULTS Of 315,089 beneficiaries, 23,970 (7.6%) received SACT within 30 days of death. The breakdown by type was cytotoxic therapy 50.6%, immunotherapy 20.8%, targeted therapy 18%, and combination therapies 10.6%. After adjusting for covariates, any SACT use at EOL was associated with higher ED use (odds ratio [OR], 3.05 [95% CI, 2.95 to 3.15]), hospital admissions (OR, 2.64 [95% CI, 2.56 to 2.72]), ICU admission (OR, 1.78 [95% CI, 1.72 to 1.83]), hospital death (OR, 2.02 [95% CI, 1.96 to 2.08]), and lower hospice use (OR, 0.51 [95% CI, 0.50 to 0.53]) compared with no SACT. All subtypes of SACT were individually associated with higher health care utilization and lower hospice use ( P < .001). CONCLUSION All subtypes of SACT use were associated with markers of worse-quality EOL care. These data can inform decisions for current care guidelines and efforts to reduce overutilization.

Trends in Medicare payments within the first year of cervical cancer diagnosis, 2010-2019

Abstract Assessing Medicare payment trends for cervical cancer care is important to mitigate the financial impact on Medicare. This multiyear cross-sectional study included 65 years and older cervical cancer patients in SEER registries diagnosed between 2010 and 2019 who had continuous Part A and B Medicare coverage at least 6 months before diagnosis and at least within the first year of diagnosis and were not enrolled in any Health Maintenance Organization (HMO) in this duration. The main outcomes were trends in total and service-specific mean monthly Medicare payments within the first year of a cervical cancer diagnosis. This study included 2147 cervical cancer patients. The mean Medicare payments increased from $8300 in 2010 to $8520 in 2019, largely driven by a statistically significant increase in outpatient services costs, from $1361 to $2056 (AAPC = 5.45, 95% CI = 1.38 to 9.67, P = .008). These findings highlight the need for policy actions to mitigate cervical-cancer-related financial impact on Medicare.

Factors impacting the time to ovarian cancer diagnosis based on classic symptom presentation in the United States

BackgroundPatients with ovarian cancer often present with late‐stage disease and nonspecific symptoms, but little is known about factors affecting the time to diagnosis (TTD) in the United States.MethodsA retrospective, population‐based study of the Surveillance, Epidemiology, and End Results–Medicare database was conducted. It included women 66 years old or older with stage II to IV epithelial ovarian cancer with at least 1 code for abdominal/pelvic pain, bloating, difficulty eating, or urinary symptoms within 1 year of the cancer diagnosis. TTD was defined from the first claim with a prespecified symptom to the ovarian cancer diagnosis. Kruskal‐Wallis tests were used to assess for differences in TTD by group medians. Univariate and generalized linear models with a log‐link function evaluated TTD by covariables.ResultsFor the 13,872 women analyzed, the mean and median times to diagnosis were 2.9 and 1.1 months, respectively. The median TTD differed significantly by first symptom (P < .001), number of symptoms (P < .001), and first physician specialty seen (P < .001). In a multivariable analysis, TTD differed significantly according to race/ethnicity (P < .001), geographic region (P = .001), urban‐rural location (P = .031), emergency room presentation (P < .001), and number of specialties seen (P < .001). A shorter TTD was associated with a diagnosis in 2006‐2010 (relative risk [RR], 0.92; 95% confidence interval [CI], 0.87‐0.98) or 2011‐2015 (RR, 0.87; 95% CI, 0.81‐0.93) in comparison with 1992‐1999.ConclusionsThe time from a symptomatic presentation to care to a diagnosis of ovarian cancer is influenced by clinical and demographic variables. This study's findings reinforce the importance of educating all physicians on ovarian cancer symptoms to aid in diagnosis.Lay Summary Ovarian cancer is often diagnosed once disease has spread because the classic symptoms of ovarian cancer—abdominal or pelvic pain, bloating, difficulty eating, and urinary issues—can be mistaken for other problems. This study examined the time between when women with classic ovarian cancer symptoms went to a physician and when they received a cancer diagnosis in a large database population. The authors found that the time to diagnosis differed according to the type and number of symptoms and what type of physician a woman saw as well as factors such as race, geographic location, and year of diagnosis.

Algorithm to Identify Incident Epithelial Ovarian Cancer Cases Using Claims Data

PURPOSE To create an algorithm to identify incident epithelial ovarian cancer cases in claims-based data sets and evaluate performance of the algorithm using SEER-Medicare claims data. METHODS We created a five-step algorithm on the basis of clinical expertise to identify incident epithelial ovarian cancer cases using claims data for (1) ovarian cancer diagnosis, (2) receipt of platinum-based chemotherapy, (3) no claim for platinum-based chemotherapy but claim for tumor debulking surgery, (4) removed cases with nonplatinum chemotherapy, and (5) removed patients with prior claims with personal history of ovarian cancer code to exclude prevalent cases. We evaluated algorithm performance using SEER-Medicare claims data by creating four cohorts: incident epithelial ovarian cancer, a 5% random sample of cancer-free Medicare beneficiaries, a 5% random sample of incident nonovarian cancer, and prevalent ovarian cancer cases. RESULTS Using SEER tumor registry data as the gold standard, our algorithm correctly classified 89.9% of incident epithelial ovarian cancer cases (cohort n = 572) and almost 100% of cancer-free controls (n = 97,127), nonovarian cancer (n = 714), and prevalent ovarian cancer cases (n = 3,712). The overall algorithm sensitivity was 89.9%, the positive predictive value was 93.8%, and the specificity and negative predictive value were > 99.9%. Patients were more likely to be correctly classified as incident ovarian cancer if they had stage III or IV disease compared with early stage I or II disease (93.5% v 83.7%, P < .01), and grade 1-4 compared with unknown grade tumors (93.8% v 81.4%, P < .01). CONCLUSION Our algorithm correctly identified most incident epithelial ovarian cancer cases, especially those with advanced disease. This algorithm will facilitate research in other claims-based data sets where cancer registry data are unavailable.

ctDNA monitoring using tumor-informed copy number analysis

Abstract Methods to detect circulating tumor DNA (ctDNA) enable minimally invasive responsive monitoring of cancer dynamics. However, sensitive and cost-effective methods are still lacking. Current methods for detecting cancer signals in shallow whole-genome sequencing (sWGS) data from cell-free DNA (cfDNA) via copy number aberration (CNA) analysis typically have a limit of detection of approximately 3% tumor fraction (TF). We developed informCNA, a bioinformatics method that leverages CNA information from sWGS of tumor or pre-treatment plasma samples with high TF as references, enabling ctDNA detection down to 0.2% TF across multiple cancer types. In 177 serial plasma samples from 18 patients with ovarian cancer, informCNA showed high concordance with the standard serum protein marker CA-125 and identified recurrence a median of 3.7 months earlier than CA-125 test. These results demonstrate the potential of personalized CNA analysis through sWGS for estimating ctDNA burden, enabling precise and cost-effective disease monitoring and early detection of relapse.